• Research article
  • Open access
  • Published: 15 April 2021

What does the patient have to say? Valuing the patient experience to improve the patient journey

  • Raffaella Gualandi   ORCID: orcid.org/0000-0001-8602-5249 1 ,
  • Cristina Masella 2 ,
  • Michela Piredda 3 ,
  • Matteo Ercoli 1 &
  • Daniela Tartaglini 1  

BMC Health Services Research volume  21 , Article number:  347 ( 2021 ) Cite this article

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Patient-reported data—satisfaction, preferences, outcomes and experience—are increasingly studied to provide excellent patient-centred care. In particular, healthcare professionals need to understand whether and how patient experience data can more pertinently inform the design of service delivery from a patient-centred perspective when compared with other indicators. This study aims to explore whether timely patient-reported data could capture relevant issues to improve the hospital patient journey.

Between January and February 2019, a longitudinal survey was conducted in the orthopaedics department of a 250-bed Italian university hospital with patients admitted for surgery; the aim was to analyse the patient journey from the first outpatient visit to discharge. The same patients completed a paper-and-pencil questionnaire, which was created to collect timely preference, experience and main outcomes data, and the hospital patient satisfaction questionnaire. The first was completed at the time of admission to the hospital and at the end of hospitalisation, and the second questionnaire was completed at the end of hospitalisation.

A total of 254 patients completed the three questionnaires. The results show the specific value of patient-reported data. Greater or less negative satisfaction may not reveal pathology-related needs, but patient experience data can detect important areas of improvement along the hospital journey. As clinical conditions and the context of care change rapidly within a single hospital stay for surgery, collecting data at two different moments of the patient journey enables researchers to capture areas of potential improvement in the patient journey that are linked to the context, clinical conditions and emotions experienced by the patient.

By contributing to the literature on how patient-reported data could be collected and used in hospital quality improvement, this study opens the debate about the use of real-time focused data. Further studies should explore how to use patient-reported data effectively (including what the patient reports are working well) and how to improve hospital processes by profiling patients’ needs and defining the appropriate methodologies to capture the experiences of vulnerable patients. These topics may offer new frontiers of research to achieve a patient-centred healthcare system.

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Patient-reported data (satisfaction, preferences, outcomes and experience) have been increasingly studied with the aim of providing excellent patient-centred care [ 1 , 2 ]. In particular, the collection of patient experience data is emerging as an increasingly key component in assessing the quality of delivered health services [ 3 , 4 ]. Some authors have emphasised that understanding the patient experience represents an opportunity to design healthcare service delivery [ 5 , 6 ]. However, healthcare professionals need to understand whether and how patient experience data can inform the design of service delivery from a patient-centred perspective more pertinently than other indicators [ 7 , 8 , 9 , 10 ].

Studies in the service management literature have shown that it is possible to understand the experience starting from the customer journey. The term ‘customer journey’ refers to ‘the processual and experiential aspects of service processes as seen from the customer viewpoint’ [ 11 ]. Kankainen et al. [ 12 ] describe it as ‘the process of experiencing service through different touchpoints from the customer’s point of view’. Customer experience is shaped before, during and after interactions with the service provider. Moving from services to healthcare, the experience of care is not only a matter of interaction but a multifaceted and complex phenomenon in which the health status, the context of care and presence of different health staff play an important role in achieving clinical outcomes [ 9 , 13 ].

In the hospital context, the requirements of responding rapidly to the acute needs of patients through the integration of multiple actors and services increases this complexity. Timely movement of patients from one service to another is a necessary condition both for managing the volume of patients with different pathologies and for obtaining better clinical outcomes. Consequently, the patient experience of care and service delivery is the result of many successive touchpoints across services to receive care from different units, the totality of which constitutes the patient journey. Because on an individual level any experience is subjective, dynamic and context dependent [ 14 ], patient experience data collected at different points of the journey should make it possible to evaluate if there are discontinuities within the hospital units (e.g., inpatient ward) and between the different units (e.g., between hospital wards and operating rooms) crossed by the patient journey. Inter- and cross-organisational gaps such as obstructed data flow, unavailability of relevant information at points of intervention and a lack of services synchronisation may occur when a complete and consequential view of the whole process is missing. However, few studies have analysed how to improve the patient journey by starting from the patient experience of the service provided [ 15 ]. In particular, most of them focus only on a single step of the hospital journey without identifying which are the meaningful touchpoints for the patient [ 16 , 17 , 18 ]. Indeed, if on the one hand, the patient’s stay is itself composed of multiple steps within the hospital, the hospital journey is part of a larger patient journey, which extends further in time before and after hospitalisation. This is particularly the case for patients who have to undergo surgery, for which clinical examinations are required before admission and a follow-up is scheduled after discharge.

Furthermore, it is not yet clear what the best method is for collecting patient experience data throughout the patient journey [ 19 ]. A recent study analysed the hospital stay experience through the use of unstructured diaries completed in a patient’s own words. However, if, on the one hand, the authors confirm that it is possible to collect valuable data for the improvement of the service directly from the patient, then, on the other hand, the education level, age and clinical conditions could be a limit in understanding the experience of fragile patients [ 20 ].

The goal of the current study is twofold: 1) explore which data collected directly from the patient could be useful in improving the patient journey and 2) to analyse whether gathering timely patient experience data at different points of the patient journey within the hospital can capture areas for improvement in the patient journey.

Design and setting

A longitudinal survey was conducted in the orthopaedics department of a 250-bed Italian university hospital between January and February 2019. The unit of analysis was the journey of the orthopaedic patient from the first outpatient visit to hospital discharge. Accordingly, all patients who underwent major or minor orthopaedic surgery during the time period were considered for inclusion. The type of surgery and stage of the patient journey formed the analysis groups. The study was part of a larger hospital project to redesign the orthopaedic patient journey for hip or knee replacement surgery, here starting with the patient experience [ 19 , 21 ]. In particular, the data collected by the hospital management to assess the quality of the service and that are presented in this work were integrated with interviews and the shadowing of patients, whose results are reported in other papers. The entire project received ethical approval from the organisation’s Ethics Committee (Protocol n.: 25/16 OSS ComEt CBM).

The orthopaedics unit has 34 beds for ordinary hospitalisation or day surgery and is divided into two multispecialty wards: one for ordinary admissions and one mainly for day surgery recovery. Some of the healthcare staff working within the various services are specialised, and a large part is composed of staff in training (residents and degree course students of medicine, nursing and physiotherapy). A centralised team that includes administrative staff and bed manager nurses handles the admissions calls and reception procedures.

Reference terminology

To conduct the present research, the authors employed the following terms with corresponding meanings:

Patient-reported data: views and opinions of patients on the care and on the service they have experienced.

Patient satisfaction: ‘the extent to which the patient’s expectations were fulfilled’ [ 22 ].

Patient experience: ‘the sum of all interactions, shaped by an organisation’s culture, that influences patient perceptions, across the continuum of care’ [ 23 ].

Patient preference: ‘statements made by the patients regarding the relative desirability of a range of health experiences, treatment options and health states’ [ 24 ].

Patient outcomes: ‘any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else’ [ 25 ].

Instruments

The current study was carried out with two different paper-and-pencil questionnaires delivered to the same patients at two different stages of the hospital patient journey. The first questionnaire was developed on purpose by the authors and completed by the patients at the time of admission to the hospital and at the end of hospitalisation. An English language version of the questionnaire is available as a supplementary file to this paper (see Additional file  1 ). The second questionnaire was the patient satisfaction questionnaire adopted by the hospital and completed by the patients at the end of hospitalisation. In this way, patient-reported outcomes (PRO), patient-reported experience (PRE), patient-reported satisfaction (PRS) and patient-reported preferences (PRP) were collected and analysed.

Figure  1 summarises the points of the patient journey where the data were collected and the focus of each questionnaire.

figure 1

Patient journey and data collection

Consistent with the need to capture patient-reported data during a relatively rapid surgical pathway, the researchers chose to develop a questionnaire focused on the key themes that emerged from the results of the qualitative study previously conducted [ 19 ]. The questionnaire was developed by the first three authors to capture data on the following patient journey touchpoints: preadmission, admission to hospital, preparation for surgery, the postsurgery period and discharge. The purposes were the following: to create a questionnaire that is easy to read and fill in by the patient and to be administered at two key moments of the journey (at the time of admission before surgery and at discharge); to make the data more easily comparable between the different types of patient-reported data; to minimise the risk of patients not completing the questionnaire because of the high number of questions [ 26 ]; and to avoid less data being recorded in the case of elderly or low-educated patients [ 20 ].

The questionnaire items were identified to cover all the service quality dimensions indicated by Dagger [ 27 ] and Gustavsson [ 28 ]: interpersonal quality, technical quality, environmental quality, administrative quality, family quality and involvement quality. In addition, the international literature was consulted by the first and the second authors to develop a set of items evaluating the patient perspective on the level of importance of the different issues related to the hospital journey.

After a discussion between the authors, the questionnaire resulted in 37 closed items and one open question to be administered when the patient entered the hospital ward (Part A) and 15 closed items and one open question when the patient was discharged (Part B). The answers to the closed questions were possible within a 5- or 10-point Likert scale (depending on the items), on outcomes, preferences, experience and satisfaction.

Part A, which was administered upon arrival in the patient ward, included the following sections:

Pain assessment scale (0 = absent; 10 = the strongest pain) and perceived health state (0 = not satisfied at all; 5 = very satisfied);

Patient preferences: evaluation of the self-perceived impact of the different issues related to the hospital journey on the patient’s life (e.g., instructions on how to get to the hospital or in case of waiting; not feeling pain; trusting professionals, etc.) – 13 items (0 = not at all important; 5 = very important);

Positive or negative emotions experienced at the moment of completing the questionnaire by choosing the main ones from the Plutchik’s wheel: serenity, trust, anticipation, apprehension, fear and anger.

A final open question: ‘What can we do better?’

Part B, administered upon discharge from the hospital ward, included the following sections:

The analysis of the internal consistency of the questionnaire through an analysis of the closed-ended items showed a high level of reliability (Cronbach’s alpha: patient perspective and preference 13 items > 0.7; patient experience before surgery 20 items > 0.8; patient experience after surgery 11 items > 0.8).

The patient satisfaction questionnaire included demographic data (age, gender, education and region of origin) and assessed patient satisfaction. The 28 items included a first question on overall satisfaction; the items were divided into seven macro-areas: admission and organisation; medical assistance; nursing and other healthcare personnel; services and comfort; religious assistance (if requested); posthospitalisation; and other information. A final question was ‘Would you recommend the hospital to others?’ with a 10-point Likert scale (from ‘Absolutely not” to ‘Absolutely yes’).

Data collection

An exploratory sample was used, including all orthopaedic patients admitted for surgery during the study period. The patients were recruited at the time of administrative admission for hospitalisation from among those who could understand and consent, speak Italian fluently and write. The data collected were part of the quality of service survey approved by the hospital management and included in the quality surveys in which the patient agreed to participate by signing the consent form at the time of hospital admission. In addition, a trained research assistant asked them for oral consent to participate by explaining the study’s purpose, discussing how participation was voluntary and about the anonymity of data collection.

The fourth author delivered the paper-and-pencil questionnaire to the patient to be filled out on the spot upon arrival in the hospital room (10-min duration) and upon discharge (15-min duration, including the patient satisfaction questionnaire). The same author collected the questionnaire after the patient had filled it in, monitored the completeness of the data and reported all the data on an Excel worksheet for subsequent analysis.

Data analysis

A score was created for each quantitative item of the questionnaire by coding the item response from ‘1’ if the experience was considered completely negative to ‘5’ if it was considered completely positive. A higher score indicates a positive experience and satisfaction with the hospital patient journey. Quantitative data were analysed with descriptive statistics, including the mean and standard deviation and by analysis of a significant difference between the following a priori established groups: type of surgery (major surgery or minor surgery) and time of the patient journey (at the entrance to the hospital and at discharge). Statistical analyses were performed using SPSS 21.0 (IBM Corp., Armonk, NY, USA). Qualitative data were analysed by the first author by reporting and classifying patient responses to the open question ‘What can we do better?’ Specifically, the content of the responses was classified according to the service quality dimensions of Dagger [ 27 ] and Gustavsson [ 28 ].

Sample characteristics

A total of 255 patients were included in the study; of them, only one patient refused to participate because of the limited time available to prepare for surgery upon entering the hospital. Table  1 shows the main characteristics of the participants. The participants had a mean age of 62 years (SD: 14; range: 18–96), and 80% were over the age of 50. The sample was equally distributed between men and women. The most frequent major surgical operations were knee replacement (53% of major surgery) and hip replacement (29%). The most frequent minor surgical procedures were knee arthroscopy (39% of minor surgery) and shoulder arthroscopy (36%). Of the patients admitted for major surgery, 49% had been admitted to the same hospital in the past, while 70% of the patients who had to undergo minor surgery were being admitted to the hospital for the first time.

The evaluation of the patient preferences on the different issues related to the hospital journey that were collected at the beginning of hospitalisation show that the five aspects considered most important for a good hospital journey experience are as follows: ‘Receive the best treatment for the related health conditions’ (Mean: 4.8, SD: 0.4); ‘Have clear instructions on how to prepare for surgery (therapy, fasting, surgery aids)’ (Mean: 4.8, SD: 0.4); ‘Have clear instructions on how to check in at the hospital’ (Mean: 4.7, SD: 0.5); ‘Have clear indications on the treatment pathway I will also have to take’ (Mean: 4.7, SD: 0.5); and ‘Receive explanations from staff in case of waiting’ (Mean: 4.7, SD: 0.5). The least important aspects among those listed are as follows: ‘Have explanations and understand everything that happens to me’ (Mean: 4.0, SD: 0.7); ‘Be involved in all decisions concerning my care’ (Mean: 3.9, SD: 0.8); ‘Feel comfortable in the environments where I have to be’ (Mean: 3.9, SD: 0.9); ‘Wait as little time as possible for a visit or for assistance’ (Mean: 3.9, SD: 0.9); and ‘Have a room where I am not disturbed and with hotel services (TV, landline, etc.)’ (Mean: 3.8, SD: 0.9). When asked if other aspects were important, one participant added ‘Empathic relationship with all the staff’, while another added ‘Admission in a clean facility like this’. No significant differences were found between the major and minor surgery patients.

Evaluating the patient journey at two different points

All patients completed the quantitative items of the experience questionnaire, which was administered on arrival and on discharge, and the satisfaction questionnaire, which was administered on discharge. On admission, 58% of patients answered the open question ‘What can we do better?’ and 68% answered the same question administered on discharge.

Table  2 reports the answers to the overall questions on patient-reported data, referring to the two moments in which the patients were interviewed.

PRO changed between the time of entry and time of discharge, with a different trend between major and minor surgery patients. Upon arrival at the hospital, orthopaedic patients who needed major surgery reported significant pain, here rated on a scale of 0 (absent) to 10 (the strongest pain); this decreased after surgery (Mean: 5.5, SD: 2.7 vs. Mean: 3.8, SD: 2.6). Pain remained constant and not particularly high in minor surgery patients (Mean: 2.8, SD: 2.4 vs. Mean: 2.6, SD: 2.7). The self-reported state of health assessed on a scale of values between 1 (not at all satisfied) to 5 (very satisfied) showed a more evident improvement in patients with major surgery between the time of arrival in the hospital and time of discharge (Mean: 3.7, SD: 0.8 vs., Mean: 4.0, SD: 0.6). Minor surgery patients reported a generally higher level of health than major surgery patients (Mean: 4.0 SD: 0.7 vs. Mean: 4.3, SD: 0.6). In these items, the age group does not seem to be significant.

Regarding the closed-answer items on the overall patient experience, an average of high scores, with a slight difference between the time of entry into the ward and time of discharge, was reported. On discharge, the hospital experience was rated with lower average scores than patient satisfaction. The patient satisfaction relating to hospitalisation showed significant high scores: on a score from 1 to 5, 97% of patients rated 4 (22.8%) or 5 (74.4%). Additionally, 95% of patients would recommend the hospital to other patients.

Table  3 reports how patients’ emotional status changed along the hospital journey. Trust and apprehension were the prevailing emotions at the time of arriving in the ward (respectively 37.8 and 20.5% of patients). Apprehension decreased noticeably among patients after surgery (6.3%), and serenity increased (from 21.7% before surgery to 46.1% at the time of discharge). The change is more evident in major surgery patients: 32.7% of them experienced apprehension or fear before surgery, decreasing to 13.1% at the time of discharge, with an increase in patient serenity from 5.8 to 14.8%.

Detecting areas of improvement by following the patient journey

When analysing the specific items in relation to the time of the journey, the data on experience and satisfaction showed differing information around some key topics. Table  4 shows the experience and satisfaction items that are the most related to the patient’s journey.

At the time of discharge, the patient satisfaction items reported high scores for the quality and cleanliness of the environment (Mean: 4.8, SD: 0.4). However, upon entering the ward, the patients rated the comfort of the room with one of the lowest experience scores (Mean: 4.3, SD: 1.0). The answers to the open questions show the reason for this: the patients wished to have a TV inside the wards and to have larger wards to move more easily with the orthopaedic aids they had to manage (wheelchair, crutches, etc.). One patient suggested the following solution: ‘Small hospital room for physiotherapy: creation of a dedicated space’ (Code: ORTO 63).

In the ‘Satisfaction’ items, patients recognised a high level of professionalism and competence in the healthcare staff (Mean: 4.8, SD: 0.6). However, in the ‘Experience’ questionnaire, the items concerning information received before surgery showed some of the lowest scores. The score on the information received to organise the hospitalisation and prepare for surgery was rated at 4.5 (SD: 0.8). Understandable explanations given before surgery by the doctor of everything the patient needed to know about surgery, length of stay and the postsurgery period was rated 4.3 (SD: 0.9). The same result was recorded for understandable explanations given by the anaesthesiologist on everything the patient needed to know about surgery and pain treatment. The answers to open questions showed that 29 patients would have liked more information concerning the different aspects of hospitalisation, including the necessary aids for surgery and postsurgery path. Two patients emphasised the need for more communication with family members when the patient was in the operating theatre. One patient expressed how this issue can always be improved: ‘In my opinion, improve the information given to patients on the path they have to take inside the hospital. I have been hospitalised five times and I always see an improvement, thanks for everything’ (Code: DS36).

The patient satisfaction questionnaire reported a high score on the availability of doctors and nursing and care staff (Mean: 4.7, SD: 0.7). For the experience items, the patients rated these aspects at 4.4 and 4.6, respectively, at the time of entering the ward. The average score decreased to 4.3 and 4.5, respectively, regarding the postsurgery stay. More specific data emerged from the open questions. The patients reported the need for more of a presence of and contact with doctors (38 quotes) and nurses (21 quotes), and this need was reported in particular regarding the postsurgery stay: ‘More time spent by staff in the postoperative period’ (Code: ORTO2). Twenty-one patients reported a lack of interaction with healthcare staff as a staff shortage problem: ‘Nurses are very professional and well trained, but there should be more of them’ (Code: DS37); ‘too few nurses during the shift to answer the call bells quickly’ (Code: ORTO151). Other patients added that the presence of so many students decreased their confidence in being properly cared for. For example, one patient said, ‘Stay longer with the patient without rushing, too many students unable to solve certain problems and too few nurses and doctors’ (Code: ORTO 116).

The question ‘Were you involved in decisions about your care?’ obtained the lowest score. Specifically, the average rating was 4.0 upon arrival in the ward and increased to 4.3 upon discharge. However, only one participant suggested greater patient involvement.

The satisfaction score on waiting times and admissions procedures was among the lowest (Mean: 4.5, SD: 0.8). The reasons for these scores were expanded by the answers to the open questions captured immediately after entering the ward: 53 patients reported that the waiting times between arrival at the hospital, admission procedures and room assignment were too long. One patient pointed out that hospital discharges and new entries needed to be better coordinated; another suggested that the patient should not come too early in the morning if admission was scheduled during the day; some patients asked for a reduction in the time between entering the hospital and actually entering the operating theatre.

Some hidden but not openly stated needs for adaptation by the patient to hospital rules are evident in this quote: ‘I found everything well, no complaints, I understood that having a relative’s personal assistance is impossible but I would have liked it’ (Code: ORTO26). In the presurgery period, the patients reported the desire for family members to be nearby when they wanted (Mean: 4.4, SD: 0.9). The score increased in the postoperative period (Mean: 4.7, SD: 0.6), and only nine patients stated they wanted more time with their families, with more flexible visiting hours and with their presence before surgery.

Although unsolicited, feedback on what works, in addition to what needs to be improved, was given. For example, one patient reported, ‘I did not expect to find such a comfortable environment with such professionalism from all the staff. Nothing is perfect, therefore everything is perfectible, but here, in this hospital, we are at a good point’ (Code: ORTO16). Another said, ‘Nothing to improve, on the contrary I would like to point out the particular care, attention and professionalism of the student F.A.’ (Code: ORTO33).

Table  5 reports the improvements suggested by the patients collected at the two different points of their journey.

Numerous studies have explored how different types of data collected directly from patients can improve the quality of care, while few studies have analysed whether the data reported by patients on a cross-hospital process can be useful to improve the process itself [ 29 , 30 ]. The current study was designed to explore whether patient-reported data, specifically experience data, can identify areas of improvement within the hospital and between the different units crossed by the patient journey.

By timely and simultaneously gathering preferences, satisfaction, outcomes and experience data, it is possible to have a complete picture of the hospital patient journey. In particular, the satisfaction and preference data measure the levels of importance given to different aspects by the patient— what is important for the patient experience—and outcomes data show the patient’s circumstances and present conditions— why it is important for the patient. Satisfaction data may not reveal pathology-related needs, while patient experience data can detect important areas of improvement along the hospital journey. In the current study, for instance, the score of preference for room comfort was one of the lowest items (Mean: 3.8). However, by answering to the open question on what can be improved, when the patient entered the room, he judged it not very comfortable (Mean: 4.3) because of the lack of space for movement with orthopaedic aids. The satisfaction item cannot capture this information (average score of 4.8 on the quality and cleanliness of the environments).

These same results also show how at the beginning of the journey, patients might consider an issue as not critical, but while living the hospital experience, it becomes important. As another example, although on admission, patients declared that waiting as little time as possible for a visit or for assistance was one of the least important aspects (Mean: 3.9), the satisfaction score on waiting times and admissions procedures was among the lowest (Mean: 4.5). Timely experience data show how as soon as the patient enters the room, he or she clearly remembers having waited too long from the moment of admission and suggests a better organisation of the hospitalisation.

Numerous factors can influence the patient experience. In the presurgery stage, trust and apprehension are the prevailing emotions (37.8 and 20.5% of patients, respectively), and the patient seems to lack knowledge of what to do. The experience data show that the scores related to information received to organise the hospitalisation and prepare for surgery and explanations given before surgery are very low. Improving information concerning the different aspects of hospitalisation, including the necessary aids for surgery and the postsurgery path, emerged as a fundamental need.

Even if related to a very specific case, the results of the current study show that patients do not have the technical competence to predict their needs before and after surgery; thus, nursing competence is needed to effectively anticipate patient needs and attend to the organisation of patient journeys to improve experiences of care. These data support the claim of a recent NHS report in which nurses are shown to play an essential role in the way in which data are collected, interpreted and used to improve care [ 10 ].

Because clinical conditions and the context of care change rapidly within a single hospital stay for surgery, capturing data at key moments in the journey, rather than at the end, can better represent the patient’s experience [ 31 ]. In particular, analysing the answers to the open question ‘What can we do better?’ allows for understanding what happened to the patient that may have influenced his or her experience (e.g., apprehension and pain before surgery, pathology and age-related needs, fast-track recovery, waiting without entertainment, etc.). Moreover, one patient may reveal the important needs made impossible by circumstances (e.g., need of having a family member being close to the patient before surgery made impossible by hospital organisation). For example, when redesigning fast-track recovery from major orthopaedic surgery, significant touchpoints for the patient should be treated with respect to his or her need for interaction with professionals, his or her emotional state and social conditions and by considering the changing circumstances he or she will face along the journey [ 19 , 32 , 33 ]. In particular, the emotional state should be better explored to understand how this variable affects patient experience along the journey and to improve ways of interacting with the patient: by giving more information, by offering support or simply by accompanying him or her in critical moments of the presurgery period.

In the present study, to encourage patient participation, the authors decided to ask a few questions at two critical moments of the journey: at the arrival before surgery and at discharge. Moreover, despite the older population involved, the simplicity of the questionnaire, which even used emoticons, made it possible to capture the experience of those patients who were able to read and write. In this way, a high rate of responses was achieved. Nevertheless, further studies should investigate how to collect real-time feedback from those who are unable to describe their own experience [ 34 ]. Moreover, because data were collected in paper format, the process of returning data to the management team and front-line professionals to stimulate quality improvement slowed down because of the necessary data analysis times. The challenges in handling real-time big data collection and storage in health information systems will bring new advancements in the continuous improvement process by immediately returning the patient-reported data at all organisational levels. These data should help redesign hospital care processes at the top and middle management levels in an integrated and patient-centred way. At the front-line level, healthcare professionals can immediately make corrections with micro-interventions, fixing the way of giving attention to the patient by focusing on his or her experiences.

In present study, patient-reported data on satisfaction and experience were significantly positive in almost all the items investigated, with an average score between 4 and 5 on a scale of 1 to 5. This result is in line with the literature showing that little variation occurs in the answers to questions about the quality of care with high patient satisfaction scores [ 35 , 36 ]. Further studies should investigate if the asymmetrical relationship between the health care professional and patient [ 37 ], the vulnerable situation of a patient [ 34 ] and the primary need to solve a clinical problem and fear of surgery that are deemed more important than anything else could result in high and undistributed response rates.

When asking the patient ‘What can we do better?’ the question assumes that the patient only identifies what does not work. However, patients are also able to report what worked well. Managers and health care teams should study which factors, from the patient’s point of view, determine a good experience and must be supported. Further studies should analyse whether positive patient-reported data may explain what factors produce a good patient journey experience and how they may reinforce the quality improvement solutions adopted and, hence, influence health professionals’ behaviour [ 38 ].

The results of the current study emphasise that personalised medicine should no longer only refer to the targeted therapy. This requires management teams to be able to customise the patient journey and identify different patient profiles, which should not be reduced to the clinical pathway.

The limitations of the current study are manifold; in large part, they are connected to the nature of the original project, which aimed to produce local actionable improvements in the setting. First, the results cannot be generalised: the study was conducted in a single hospital and only on how the orthopaedic surgical path; this influences the significance and transferability of the results. However, the study aimed to provide useful insights to hospital management to promote a review of the processes in a real patient-centred way. In particular, the results offer a stimulus for the debate on the use of patient experience data for the design of service delivery. Second, the orthopaedic surgical path is very different, for example, from the oncological one. Specifically, the orthopaedic surgical journey is generally shorter, has a beginning and an end and does not extend over time with a worsening of the initial clinical conditions. This aspect could influence the results and methodology should be tested on different clinical pathways, in particular by considering chronic pathologies. Finally, the authors preferred to use a questionnaire with relatively few questions instead of using the validated ones already present in the literature. The choice was made to favour real-time data collection and the patient’s response rate to the questions. These objectives have been achieved, and future studies will have to validate the single items for patients with different pathologies. However, by considering the very different and complex contexts in which each hospital operates, the literature will increasingly have to consider single longitudinal studies by starting from the analysis of the patient journey that takes place in each hospital.

Several issues would benefit from further exploration, including the impact of the patient-healthcare staff relationship in the hospital journey experience; the opportunity of bringing patients’ and professionals’ experiences together for joint knowledge of improvement solutions; and the study of new methodology to capture the real-time experiences of vulnerable patients.

Providing customers with quality experiences is a key competitive advantage in a range of service sectors, including the healthcare service. Measuring patient experiences is a practice increasingly used, and researchers and managers are seeking to understand how to use these measures to improve service delivery [ 39 , 40 , 41 ].

The current study provides insights for healthcare practitioners caring for patients in hospitals and those responsible for planning and designing the hospital patient journey. By contributing to the literature on how patient-reported data could be collected and used in hospital quality improvement, it also opens the debate about the use of real-time focused data when capturing experiences from vulnerable patients. Furthermore, the present study asks for a more positive perspective on patients’ data that can be used not only to detect what does not work, but also what is working well.

In different clinical settings, further studies should explore how to effectively use patient-reported data to improve hospital processes, profile patients’ needs and identify appropriate methodologies to capture the experiences of vulnerable patients. These topics may offer new frontiers of research to achieve a patient-centred healthcare system.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Patient-reported outcomes

Patient-reported experience

Patient-reported satisfaction

Patient-reported preferences

Number count

Standard deviation

National Health Service (UK)

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RG, CM and DT contributed to the study’s conception and design. Material preparation was performed by RG. Data collection was performed by ME. Data analysis was performed by RG and MP. The first draft of the manuscript was written by RG and CM contributed to the analysis and drafting of the article. All authors read and approved the final manuscript.

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Questionnaire. Questionnaire used for the study.

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Gualandi, R., Masella, C., Piredda, M. et al. What does the patient have to say? Valuing the patient experience to improve the patient journey. BMC Health Serv Res 21 , 347 (2021). https://doi.org/10.1186/s12913-021-06341-3

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Article Contents

Introduction, why patient journey mapping, how is patient journey mapping conducted, use of technology in patient journey mapping, future implications for patient journey mapping, conclusions, patient journey mapping: emerging methods for understanding and improving patient experiences of health systems and services.

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Lemma N Bulto and Ellen Davies Shared first authorship.

Conflict of interest: none declared.

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Lemma N Bulto, Ellen Davies, Janet Kelly, Jeroen M Hendriks, Patient journey mapping: emerging methods for understanding and improving patient experiences of health systems and services, European Journal of Cardiovascular Nursing , 2024;, zvae012, https://doi.org/10.1093/eurjcn/zvae012

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Patient journey mapping is an emerging field of research that uses various methods to map and report evidence relating to patient experiences and interactions with healthcare providers, services, and systems. This research often involves the development of visual, narrative, and descriptive maps or tables, which describe patient journeys and transitions into, through, and out of health services. This methods corner paper presents an overview of how patient journey mapping has been conducted within the health sector, providing cardiovascular examples. It introduces six key steps for conducting patient journey mapping and describes the opportunities and benefits of using patient journey mapping and future implications of using this approach.

Acquire an understanding of patient journey mapping and the methods and steps employed.

Examine practical and clinical examples in which patient journey mapping has been adopted in cardiac care to explore the perspectives and experiences of patients, family members, and healthcare professionals.

Quality and safety guidelines in healthcare services are increasingly encouraging and mandating engagement of patients, clients, and consumers in partnerships. 1 The aim of many of these partnerships is to consider how health services can be improved, in relation to accessibility, service delivery, discharge, and referral. 2 , 3 Patient journey mapping is a research approach increasingly being adopted to explore these experiences in healthcare. 3

a patient-oriented project that has been undertaken to better understand barriers, facilitators, experiences, interactions with services and/or outcomes for individuals and/or their carers, and family members as they enter, navigate, experience and exit one or more services in a health system by documenting elements of the journey to produce a visual or descriptive map. 3

It is an emerging field with a clear patient-centred focus, as opposed to studies that track patient flow, demand, and movement. As a general principle, patient journey mapping projects will provide evidence of patient perspectives and highlight experiences through the patient and consumer lens.

Patient journey mapping can provide significant insights that enable responsive and context-specific strategies for improving patient healthcare experiences and outcomes to be designed and implemented. 3–6 These improvements can occur at the individual patient, model of care, and/or health system level. As with other emerging methodologies, questions have been raised regarding exactly how patient journey mapping projects can best be designed, conducted, and reported. 3

In this methods paper, we provide an overview of patient journey mapping as an emergent field of research, including reasons that mapping patient journeys might be considered, methods that can be adopted, the principles that can guide patient journey mapping data collection and analysis, and considerations for reporting findings and recognizing the implications of findings. We summarize and draw on five cardiovascular patient journey mapping projects, as examples.

One of the most appealing elements of the patient journey mapping field of research is its focus on illuminating the lived experiences of patients and/or their family members, and the health professionals caring for them, methodically and purposefully. Patient journey mapping has an ability to provide detailed information about patient experiences, gaps in health services, and barriers and facilitators for access to health services. This information can be used independently, or alongside information from larger data sets, to adapt and improve models of care relevant to the population that is being investigated. 3

To date, the most frequent reason for adopting this approach is to inform health service redesign and improvement. 3 , 7 , 8 Other reasons have included: (i) to develop a deeper understanding of a person’s entire journey through health systems; 3 (ii) to identify delays in diagnosis or treatment (often described as bottlenecks); 9 (iii) to identify gaps in care and unmet needs; (iv) to evaluate continuity of care across health services and regions; 10 (v) to understand and evaluate the comprehensiveness of care; 11 (vi) to understand how people are navigating health systems and services; and (vii) to compare patient experiences with practice guidelines and standards of care.

Patient journey mapping approaches frequently use six broad steps that help facilitate the preparation and execution of research projects. These are outlined in the Central illustration . We acknowledge that not all patient journey mapping approaches will follow the order outlined in the Central illustration , but all steps need to be considered at some point throughout each project to ensure that research is undertaken rigorously, appropriately, and in alignment with best practice research principles.

Steps for conducing patient journey mapping.

Steps for conducing patient journey mapping.

Five cardiovascular patient journey mapping research examples have been included in Figure 1 , 12–16 to provide specific context and illustrate these six steps. For each of these examples, the problem or gap in practice or research, consultation processes, research question or aim, type of mapping, methods, and reporting of findings have been extracted. Each of these steps is then discussed, using these cardiovascular examples.

Examples of patient journey mapping projects.

Examples of patient journey mapping projects.

Define the problem or gap in practice or research

Developing an understanding of a problem or gap in practice is essential for facilitating the design and development of quality research projects. In the examples outlined in Figure 1 , it is evident that clinical variation or system gaps have been explored using patient journey mapping. In the first two examples, populations known to have health vulnerabilities were explored—in Example 1, this related to comorbid substance use and physical illness, 13 and in Example 2, this related to geographical location. 13 Broader systems and societal gaps were explored in Examples 4 and 5, respectively, 15 , 16 and in Example 3, a new technologically driven solution for an existing model of care was tested for its ability to improve patient outcomes relating to hypertension. 14

Consultation, engagement, and partnership

Ideally, consultation with heathcare providers and/or patients would occur when the problem or gap in practice or research is being defined. This is a key principle of co-designed research. 17 Numerous existing frameworks for supporting patient involvement in research have been designed and were recently documented and explored in a systematic review by Greenhalgh et al . 18 While none of the five example studies included this step in the initial phase of the project, it is increasingly being undertaken in patient partnership projects internationally (e.g. in renal care). 17 If not in the project conceptualization phase, consultation may occur during the data collection or analysis phase, as demonstrated in Example 3, where a care pathway was co-created with participants. 14 We refer readers to Greenhalgh’s systematic review as a starting point for considering suitable frameworks for engaging participants in consultation, partnership, and co-design of patient journey mapping projects. 18

Design the research question/project aim

Conducting patient journey mapping research requires a thoughtful and systematic approach to adequately capture the complexity of the healthcare experience. First, the research objectives and questions should be clearly defined. Aspects of the patient journey that will be explored need to be identified. Then, a robust approach must be developed, taking into account whether qualitative, quantitative, or mixed methods are more appropriate for the objectives of the study.

For example, in the cardiac examples in Figure 1 , the broad aims included mapping existing pathways through health services where there were known problems 12 , 13 , 15 , 16 and documenting the co-creation of a new care pathway using quantitative, qualitative, or mixed methods. 14

In traditional studies, questions that might be addressed in the area of patient movement in health systems include data collected through the health systems databases, such as ‘What is the length of stay for x population’, or ‘What is the door to balloon time in this hospital?’ In contrast, patient mapping journey studies will approach asking questions about experiences that require data from patients and their family members, e.g. ‘What is the impact on you of your length of stay?’, ‘What was your experience in being assessed and undergoing treatment for your chest pain?’, ‘What was your experience supporting this patient during their cardiac admission and discharge?’

Select appropriate type of mapping

The methods chosen for mapping need to align with the identified purpose for mapping and the aim or question that was designed in Step 3. A range of research methods have been used in patient journey mapping projects involving various qualitative, quantitative, and mixed methods techniques and tools. 4 Some approaches use traditional forms of data collection, such as short-form and long-form patient interviews, focus groups, and direct patient observations. 18 , 19 Other approaches use patient journey mapping tools, designed and used with specific cultural groups, such as First Nations peoples using artwork, paintings, sand trays, and photovoice. 17 , 20 In the cardiovascular examples presented in Figure 1 , both qualitative and quantitative methods have been used, with interviews, patient record reviews, and observational techniques adopted to map patient journeys.

In a recent scoping review investigating patient journey mapping across all health care settings and specialities, six types of patient journey mapping were identified. 3 These included (i) mapping key experiences throughout a period of illness; (ii) mapping by location of health service; (iii) mapping by events that occurred throughout a period of illness; (iv) mapping roles, input, and experiences of key stakeholders throughout patient journeys; (v) mapping a journey from multiple perspectives; and (vi) mapping a timeline of events. 3 Combinations or variations of these may be used in cardiovascular settings in the future, depending on the research question, and the reasons mapping is being undertaken.

Recruit, collect data, and analyse data

The majority of health-focused patient journey mapping projects published to date have recruited <50 participants. 3 Projects with fewer participants tend to be qualitative in nature. In the cardiovascular examples provided in Figure 1 , participant numbers range from 7 14 to 260. 15 The 3 studies with <20 participants were qualitative, 12 , 14 , 16 and the 2 with 95 and 260 participants, respectively, were quantitative. 13 , 15 As seen in these and wider patient journey mapping examples, 3 participants may include patients, relatives, carers, healthcare professionals, or other stakeholders, as required, to meet the study objectives. These different participant perspectives may be analysed within each participant group and/or across the wider cohort to provide insights into experiences, and the contextual factors that shape these experiences.

The approach chosen for data collection and analysis will vary and depends on the research question. What differentiates data analysis in patient journey mapping studies from other qualitative or quantitative studies is the focus on describing, defining, or exploring the journey from a patient’s, rather than a health service, perspective. Dimensions that may, therefore, be highlighted in the analysis include timing of service access, duration of delays to service access, physical location of services relative to a patient’s home, comparison of care received vs. benchmarked care, placing focus on the patient perspective.

The mapping of individual patient journeys may take place during data collection with the use of mapping templates (tables, diagrams, and figures) and/or later in the analysis phase with the use of inductive or deductive analysis, mapping tables, or frameworks. These have been characterized and visually represented in a recent scoping review. 3 Representations of patient journeys can also be constructed through a secondary analysis of previously collected data. In these instances, qualitative data (i.e. interviews and focus group transcripts) have been re-analysed to understand whether a patient journey narrative can be extracted and reported. Undertaking these projects triggers a new research cycle involving the six steps outlined in the Central illustration . The difference in these instances is that the data are already collected for Step 5.

Report findings, disseminate findings, and take action on findings

A standardized, formal reporting guideline for patient journey mapping research does not currently exist. As argued in Davies et al ., 3 a dedicated reporting guide for patient journey mapping would be ill-advised, given the diversity of approaches and methods that have been adopted in this field. Our recommendation is for projects to be reported in accordance with formal guidelines that best align with the research methods that have been adopted. For example, COREQ may be used for patient journey mapping where qualitative methods have been used. 20 STROBE may be used for patient journey mapping where quantitative methods have been used. 21 Whichever methods have been adopted, reporting of projects should be transparent, rigorous, and contain enough detail to the extent that the principles of transparency, trustworthiness, and reproducibility are upheld. 3

Dissemination of research findings needs to include the research, healthcare, and broader communities. Dissemination methods may include academic publications, conference presentations, and communication with relevant stakeholders including healthcare professionals, policymakers, and patient advocacy groups. Based on the findings and identified insights, stakeholders can collaboratively design and implement interventions, programmes, or improvements in healthcare delivery that overcome the identified challenges directly and address and improve the overall patient experience. This cyclical process can hopefully produce research that not only informs but also leads to tangible improvements in healthcare practice and policy.

Patient journey mapping is typically a hands-on process, relying on surveys, interviews, and observational research. The technology that supports this research has, to date, included word processing software, and data analysis packages, such as NVivo, SPSS, and Stata. With the advent of more sophisticated technological tools, such as electronic health records, data analytics programmes, and patient tracking systems, healthcare providers and researchers can potentially use this technology to complement and enhance patient journey mapping research. 19 , 20 , 22 There are existing examples where technology has been harnessed in patient journey. Lee et al . used patient journey mapping to verify disease treatment data from the perspective of the patient, and then the authors developed a mobile prototype that organizes and visualizes personal health information according to the patient-centred journey map. They used a visualization approach for analysing medical information in personal health management and examined the medical information representation of seven mobile health apps that were used by patients and individuals. The apps provide easy access to patient health information; they primarily import data from the hospital database, without the need for patients to create their own medical records and information. 23

In another example, Wauben et al. 19 used radio frequency identification technology (a wireless system that is able to track a patient journey), as a component of their patient journey mapping project, to track surgical day care patients to increase patient flow, reduce wait times, and improve patient and staff satisfaction.

Patient journey mapping has emerged as a valuable research methodology in healthcare, providing a comprehensive and patient-centric approach to understanding the entire spectrum of a patient’s experience within the healthcare system. Future implications of this methodology are promising, particularly for transforming and redesigning healthcare delivery and improving patient outcomes. The impact may be most profound in the following key areas:

Personalized, patient-centred care : The methodology allows healthcare providers to gain deep insights into individual patient experiences. This information can be leveraged to deliver personalized, patient-centric care, based on the needs, values, and preferences of each patient, and aligned with guideline recommendations, healthcare professionals can tailor interventions and treatment plans to optimize patient and clinical outcomes.

Enhanced communication, collaboration, and co-design : Mapping patient interactions with health professionals and journeys within and across health services enables specific gaps in communication and collaboration to be highlighted and potentially informs responsive strategies for improvement. Ideally, these strategies would be co-designed with patients and health professionals, leading to improved care co-ordination and healthcare experience and outcomes.

Patient engagement and empowerment : When patients are invited to share their health journey experiences, and see visual or written representations of their journeys, they may come to understand their own health situation more deeply. Potentially, this may lead to increased health literacy, renewed adherence to treatment plans, and/or self-management of chronic conditions such as cardiovascular disease. Given these benefits, we recommend that patients be provided with the findings of research and quality improvement projects with which they are involved, to close the loop, and to ensure that the findings are appropriately disseminated.

Patient journey mapping is an emerging field of research. Methods used in patient journey mapping projects have varied quite significantly; however, there are common research processes that can be followed to produce high-quality, insightful, and valuable research outputs. Insights gained from patient journey mapping can facilitate the identification of areas for enhancement within healthcare systems and inform the design of patient-centric solutions that prioritize the quality of care and patient outcomes, and patient satisfaction. Using patient journey mapping research can enable healthcare providers to forge stronger patient–provider relationships and co-design improved health service quality, patient experiences, and outcomes.

None declared.

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  • Published: 04 December 2019

“Patient Journeys”: improving care by patient involvement

  • Matt Bolz-Johnson 1 ,
  • Jelena Meek 2 &
  • Nicoline Hoogerbrugge 2  

European Journal of Human Genetics volume  28 ,  pages 141–143 ( 2020 ) Cite this article

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  • Cancer genetics
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“I will not be ashamed to say ‘ I don’t know’ , nor will I fail to call in my colleagues…”. For centuries this quotation from the Hippocratic oath, has been taken by medical doctors. But what if there are no other healthcare professionals to call in, and the person with the most experience of the disease is sitting right in front of you: ‘ your patient ’.

This scenario is uncomfortably common for patients living with a rare disease when seeking out health care. They are fraught by many hurdles along their health care pathway. From diagnosis to treatment and follow-up, their healthcare pathway is defined by a fog of uncertainties, lack of effective treatments and a multitude of dead-ends. This is the prevailing situation for many because for rare diseases expertise is limited and knowledge is scarce. Currently different initiatives to involve patients in developing clinical guidelines have been taken [ 1 ], however there is no common method that successfully integrates their experience and needs of living with a rare disease into development of healthcare services.

Even though listening to the expertise of a single patient is valuable and important, this will not resolve the uncertainties most rare disease patients are currently facing. To improve care for rare diseases we must draw on all the available knowledge, both from professional experts and patients, in order to improve care for every single patient in the world.

Patient experience and satisfaction have been demonstrated to be the single most important aspect in assessing the quality of healthcare [ 2 ], and has even been shown to be a predictor of survival rates [ 3 ]. Studies have evidenced that patient involvement in the design, evaluation and designation of healthcare services, improves the relevance and quality of the services, as well as improves their ability to meet patient needs [ 4 , 5 , 6 ]. Essentially, to be able to involve patients, the hurdles in communication and initial preconceptions between medical doctors and their patients need to be resolved [ 7 ].

To tackle the current hurdles in complex or rare diseases, European Reference Networks (ERN) have been implemented since March 2017. The goal of these networks is to connect experts across Europe, harnessing their collective experience and expertise, facilitating the knowledge to travel instead of the patient. ERN GENTURIS is the Network leading on genetic tumour risk syndromes (genturis), which are inherited disorders which strongly predispose to the development of tumours [ 8 ]. They share similar challenges: delay in diagnosis, lack of cancer prevention for patients and healthy relatives, and therapeutic. To overcome the hurdles every patient faces, ERN GENTURIS ( www.genturis.eu ) has developed an innovative visual approach for patient input into the Network, to share their expertise and experience: “Patient Journeys” (Fig.  1 ).

figure 1

Example of a Patient Journey: PTEN Hamartoma Tumour Syndrome (also called Cowden Syndrome), including legend page ( www.genturis.eu )

The “Patient Journey” seeks to identify the needs that are common for all ‘ genturis syndromes ’, and those that are specific to individual syndromes. To achieve this, patient representatives completed a mapping exercise of the needs of each rare inherited syndrome they represent, across the different stages of the Patient Journey. The “Patient Journey” connects professional expert guidelines—with foreseen medical interventions, screening, treatment—with patient needs –both medical and psychological. Each “Patient Journey” is divided in several stages that are considered inherent to the specific disease. Each stage in the journey is referenced under three levels: clinical presentation, challenges and needs identified by patients, and their goal to improve care. The final Patient Journey is reviewed by both patients and professional experts. By visualizing this in a comprehensive manner, patients and their caregivers are able to discuss the individual needs of the patient, while keeping in mind the expertise of both professional and patient leads. Together they seek to achieve the same goal: improving care for every patient with a genetic tumour risk syndrome.

The Patient Journeys encourage experts to look into national guidelines. In addition, they identify a great need for evidence-based European guidelines, facilitating equal care to all rare patients. ERN GENTURIS has already developed Patient Journeys for the following rare diseases ( www.genturis.eu ):

PTEN hamartoma tumour syndrome (PHTS) (Fig.  1 )

Hereditary breast and ovarian cancer (HBOC)

Lynch syndrome

Neurofibromatosis Type 1

Neurofibromatosis Type 2

Schwannomatosis

A “Patient Journey” is a personal testimony that reflects the needs of patients in two key reference documents—an accessible visual overview, supported by a detailed information matrix. The journey shows in a comprehensive way the goals that are recognized by both patients and clinical experts. Therefore, it can be used by both these parties to explain the clinical pathway: professional experts can explain to newly identified patients how the clinical pathway generally looks like, whereas their patients can identify their specific needs within these pathways. Moreover, the Patient Journeys could serve as a guide for patients who may want to write, in collaboration with local clinicians, diaries of their journeys. Subsequently, these clinical diaries can be discussed with the clinician and patient representatives. Professionals coming across medical obstacles during the patient journey can contact professional experts in the ERN GENTURIS, while patients can contact the expert patient representatives from this ERN ( www.genturis.eu ). Finally, the “Patient Journeys” will be valuable in sharing knowledge with the clinical community as a whole.

Our aim is that medical doctors confronted with rare diseases, by using Patient Journeys, can also rely on the knowledge of the much broader community of expert professionals and expert patients.

Armstrong MJ, Mullins CD, Gronseth GS, Gagliardi AR. Recommendations for patient engagement in guideline development panels: a qualitative focus group study of guideline-naive patients. PloS ONE 2017;12:e0174329.

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Acknowledgements

This work is generated within the European Reference Network on Genetic Tumour Risk Syndromes – FPA No. 739547. The authors thank all ERN GENTURIS Members and patient representatives for their work on the Patient Journeys (see www.genturis.eu ).

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Bolz-Johnson, M., Meek, J. & Hoogerbrugge, N. “Patient Journeys”: improving care by patient involvement. Eur J Hum Genet 28 , 141–143 (2020). https://doi.org/10.1038/s41431-019-0555-6

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DOI : https://doi.org/10.1038/s41431-019-0555-6

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Process mapping the patient journey: an introduction

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  • Peer review
  • Timothy M Trebble , consultant gastroenterologist 1 ,
  • Navjyot Hansi , CMT 2 1 ,
  • Theresa Hydes , CMT 1 1 ,
  • Melissa A Smith , specialist registrar 2 ,
  • Marc Baker , senior faculty member 3
  • 1 Department of Gastroenterology, Portsmouth Hospitals Trust, Portsmouth PO6 3LY
  • 2 Department of Gastroenterology, Guy’s and St Thomas’ NHS Foundation Trust, London
  • 3 Lean Enterprise Academy, Ross-on-Wye, Hertfordshire
  • Correspondence to: T M Trebble tim.trebble{at}porthosp.nhs.uk
  • Accepted 15 July 2010

Process mapping enables the reconfiguring of the patient journey from the patient’s perspective in order to improve quality of care and release resources. This paper provides a practical framework for using this versatile and simple technique in hospital.

Healthcare process mapping is a new and important form of clinical audit that examines how we manage the patient journey, using the patient’s perspective to identify problems and suggest improvements. 1 2 We outline the steps involved in mapping the patient’s journey, as we believe that a basic understanding of this versatile and simple technique, and when and how to use it, is valuable to clinicians who are developing clinical services.

What information does process mapping provide and what is it used for?

Process mapping allows us to “see” and understand the patient’s experience 3 by separating the management of a specific condition or treatment into a series of consecutive events or steps (activities, interventions, or staff interactions, for example). The sequence of these steps between two points (from admission to the accident and emergency department to discharge from the ward) can be viewed as a patient pathway or process of care. 4

Improving the patient pathway involves the coordination of multidisciplinary practice, aiming to maximise clinical efficacy and efficiency by eliminating ineffective and unnecessary care. 5 The data provided by process mapping can be used to redesign the patient pathway 4 6 to improve the quality or efficiency of clinical management and to alter the focus of care towards activities most valued by the patient.

Process mapping has shown clinical benefit across a variety of specialties, multidisciplinary teams, and healthcare systems. 7 8 9 The NHS Institute for Innovation and Improvement proposes a range of practical benefits using this approach (box 1). 6

Box 1 Benefits of process mapping 6

A starting point for an improvement project specific for your own place of work

Creating a culture of ownership, responsibility and accountability for your team

Illustrates a patient pathway or process, understanding it from a patient’s perspective

An aid to plan changes more effectively

Collecting ideas, often from staff who understand the system but who rarely contribute to change

An interactive event that engages staff

An end product (a process map) that is easy to understand and highly visual

Several management systems are available to support process mapping and pathway redesign. 10 11 A common technique, derived originally from the Japanese car maker Toyota, is known as lean thinking transformation. 3 12 This considers each step in a patient pathway in terms of the relative contribution towards the patient’s outcome, taken from the patient’s perspective: it improves the patient’s health, wellbeing, and experience (value adding) or it does not (non-value or “waste”) (box 2). 14 15 16

Box 2 The eight types of waste in health care 13

Defects —Drug prescription errors; incomplete surgical equipment

Overproduction —Inappropriate scheduling

Transportation —Distance between related departments

Waiting —By patients or staff

Inventory —Excess stores, that expire

Motion —Poor ergonomics

Overprocessing —A sledgehammer to crack a nut

Human potential —Not making the most of staff skills

Process mapping can be used to identify and characterise value and non-value steps in the patient pathway (also known as value stream mapping). Using lean thinking transformation to redesign the pathway aims to enhance the contribution of value steps and remove non-value steps. 17 In most processes, non-value steps account for nine times more effort than steps that add value. 18

Reviewing the patient journey is always beneficial, and therefore a process mapping exercise can be undertaken at any time. However, common indications include a need to improve patients’ satisfaction or quality or financial aspects of a particular clinical service.

How to organise a process mapping exercise

Process mapping requires a planned approach, as even apparently straightforward patient journeys can be complex, with many interdependent steps. 4 A process mapping exercise should be an enjoyable and creative experience for staff. In common with other audit techniques, it must avoid being confrontational or judgmental or used to “name, shame, and blame.” 8 19

Preparation and planning

A good first step is to form a team of four or five key staff, ideally including a member with previous experience of lean thinking transformation. The group should decide on a plan for the project and its scope; this can be visualised by using a flow diagram (fig 1 ⇓ ). Producing a rough initial draft of the patient journey can be useful for providing an overview of the exercise.

Fig 1 Steps involved in a process mapping exercise

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The medical literature or questionnaire studies of patients’ expectations and outcomes should be reviewed to identify value adding steps involved in the management of the clinical condition or intervention from the patient’s perspective. 1 3

Data collection

Data collection should include information on each step under routine clinical circumstances in the usual clinical environment. Information is needed on waiting episodes and bottlenecks (any step within the patient pathway that slows the overall rate of a patient’s progress, normally through reduced capacity or availability 20 ). Using estimates of minimum and maximum time for each step reduces the influence of day to day variations that may skew the data. Limiting the number of steps (to below 60) aids subsequent analysis.

The techniques used for data collection (table 1 ⇓ ) each have advantages and disadvantages; a combination of approaches can be applied, contributing different qualitative or quantitative information. The commonly used technique of walking the patient journey includes interviews with patients and staff and direct observation of the patient journey and clinical environment. It allows the investigator to “see” the patient journey at first hand. Involving junior (or student) doctors or nurses as interviewers may increase the openness of opinions from staff, and time needed for data collection can be reduced by allotting members of the team to investigate different stages in the patient’s journey.

 Data collection in process mapping

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Mapping the information

The process map should comprehensively represent the patient journey. It is common practice to draw the map by hand onto paper (often several metres long), either directly or on repositionable notes (fig 2 ⇓ ).

Fig 2 Section of a current state map of the endoscopy patient journey

Information relating to the steps or representing movement of information (request forms, results, etc) can be added. It is useful to obtain any missing information at this stage, either from staff within the meeting or by revisiting the clinical environment.

Analysing the data and problem solving

The map can be analysed by using a series of simple questions (box 3). The additional information can be added to the process map for visual representation. This can be helped by producing a workflow diagram—a map of the clinical environment, including information on patient, staff, and information movement (fig 3 ⇓ ). 18

Box 3 How to analyse a process map 6

How many steps are involved?

How many staff-staff interactions (handoffs)?

What is the time for each step and between each step?

What is the total time between start and finish (lead time)?

When does a patient join a queue, and is it a regular occurrence?

How many non-value steps are there?

What do patients complain about?

What are the problems for staff?

Fig 3 Workflow diagram of current state endoscopy pathway

Redesigning the patient journey

Lean thinking transformation involves redesigning the patient journey. 21 22 This will eliminate, combine and simplify non-value steps, 23 limit the impact of rate limiting steps (such as bottlenecks), and emphasise the value adding steps, making the process more patient-centred. 6 It is often useful to trial the new pathway and review its effect on patient management and satisfaction before attempting more sustained implementation.

Worked example: How to undertake a process mapping exercise

South Coast NHS Trust, a large district general hospital, plans to improve patient access to local services by offering unsedated endoscopy in two peripheral units. A consultant gastroenterologist has been asked to lead a process mapping exercise of the current patient journey to develop a fast track, high quality patient pathway.

In the absence of local data, he reviews the published literature and identifies key factors to the patient experience that include levels of discomfort during the procedure, time to discuss the findings with the endoscopist, and time spent waiting. 24 25 26 27 He recruits a team: an experienced performance manager, a sister from the endoscopy department, and two junior doctors.

The team drafts a map of the current endoscopy journey, using repositionable notes on the wall. This allows team members to identify the start (admission to the unit) and completion (discharge) points and the locations thought to be involved in the patient journey.

They decide to use a “walk the journey” format, interviewing staff in their clinical environments and allowing direct observation of the patient’s management.

The junior doctors visit the endoscopy unit over two days, building up rapport with the staff to ensure that they feel comfortable with being observed and interviewed (on a semistructured but informal basis). On each day they start at the point of admission at the reception office and follow the patient journey to completion.

They observe the process from staff and patient’s perspectives, sitting in on the booking process and the endoscopy procedure. They identify the sequence of steps and assess each for its duration (minimum and maximum times) and the factors that influence this. For some of the steps, they use a digital watch and notepad to check and record times. They also note staff-patient and staff-staff interactions and their function, and the recording and movement of relevant information.

Details for each step are entered into a simple table (table 2 ⇓ ), with relevant notes and symbols for bottlenecks and patients’ waits.

 Patient journey for non-sedated upper gastrointestinal endoscopy

When data collection is complete, the doctor organises a meeting with the team. The individual steps of the patient journey are mapped on a single long section of paper with coloured temporary markers (fig 2 ⇑ ); additional information is added in different colours. A workflow diagram is drawn to show the physical route of the patient journey (fig 3 ⇑ ).

The performance manager calculates that the total patient journey takes a minimum of 50 minutes to a maximum of 345 minutes. This variation mainly reflects waiting times before a number of bottleneck steps.

Only five steps (14 to 17 and 22, table 2 ⇑ ) are considered both to add value and needed on the day of the procedure (providing patient information and consent can be obtained before the patient attends the department). These represent from 13 to 47 minutes. At its least efficient, therefore, only 4% of the patient journey (13 of 345 minutes) is spent in activities that contribute directly towards the patient’s outcome.

The team redesigns the patient journey (fig 4 ⇓ ) to increase time spent on value adding aspects but reduce waiting times, bottlenecks, and travelling distances. For example, time for discussing the results of the procedure is increased but the location is moved from the end of the journey (a bottleneck) to shortly after the procedure in the anteroom, reducing the patient’s waiting time and staff’s travelling distances.

Fig 4 Workflow diagram of future state endoscopy pathway

Implementing changes and sustaining improvements

The endoscopy staff are consulted on the new patient pathway, which is then piloted. After successful review two months later, including a patient satisfaction questionnaire, the new patient pathway is formally adopted in the peripheral units.

Further reading

Practical applications.

NHS Institute for Innovation and Improvement ( https://www.institute.nhs.uk )—comprehensive online resource providing practical guidance on process mapping and service improvement

Lean Enterprise Academy ( http://www.leanuk.org )—independent body dedicated to lean thinking in industry and healthcare, through training and academic discussion; its publication, Making Hospitals Work 23 is a practical guide to lean transformation in the hospital environment

Manufacturing Institute ( http://www.manufacturinginstitute.co.uk )—undertakes courses on process mapping and lean thinking transformation within health care and industrial practice

Theoretical basis

Bircheno J. The new lean toolbox . 4th ed. Buckingham: PICSIE Books, 2008

Mould G, Bowers J, Ghattas M. The evolution of the pathway and its role in improving patient care. Qual Saf Health Care 2010 [online publication 29 April]

Layton A, Moss F, Morgan G. Mapping out the patient’s journey: experiences of developing pathways of care. Qual Health Care 1998; 7 (suppl):S30-6

Graban M. Lean hospitals, improving quality, patient safety and employee satisfaction . New York: Taylor & Francis, 2009

Womack JP, Jones DT. Lean thinking . 2nd ed. London: Simon & Schuster, 2003

Cite this as: BMJ 2010;341:c4078

Contributors: TMT designed the protocol and drafted the manuscript; TMT, MB, JH, and TH collected and analysed the data; all authors critically reviewed and contributed towards revision and production of the manuscript. TMT is guarantor.

Competing interests: MB is a senior faculty member carrying out research for the Lean Enterprise Academy and undertakes paid consultancies both individually and from Lean Enterprise Academy, and training fees for providing lean thinking in healthcare.

Provenance and peer review: Not commissioned; externally peer reviewed.

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  • ↵ Yanai H, Schushan-Eisen I, Neuman S, Novis B. Patient satisfaction with endoscopy measurement and assessment. Dig Dis 2008 ; 26 : 75 -9. OpenUrl CrossRef PubMed Web of Science

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The patient journey: what it is and how it’s vital for success.

10 min read In the digital age, the patient experience has become more complex but also more critical as it relates to patient retention, reimbursement, and patient satisfaction. In order to thrive in today’s healthcare landscape, it’s important to look at the patient journey when aiming to improve the patient experience.

Does your healthcare organization ask patients for feedback following clinical encounters? This is a common approach used to improve the patient experience . You may gather key insights about a specific encounter, but you’ll miss out on an untapped system of important patient interactions throughout the care journey .

Stay up-to-date on patient experience management trends with our guide

Improving patient experiences requires looking at the entire healthcare ecosystem. Patients communicate with their healthcare providers through a variety of channels, while interacting with a wide range of departments and individuals along the way.

To stand out in the market and provide an optimal experience for your patients , hospitals and health systems should look beyond clinical service delivery and begin patient journey mapping.

The patient journey is the entire sequence of events that begins when the patient first develops a need for clinical care and engages with your organization. It follows the patient’s steps as they navigate your healthcare system, from initial scheduling to treatment to continuous care.

The patient journey vs. the patient experience

Why is the patient journey important? Each touchpoint of the patient engagement journey, from a simple visit to your website to checking in for an appointment, has downstream effects that can help or hinder meeting patient needs.

As the patient experience evolves , it’s important to expand how you are listening to your patients in order to close gaps and make continuous improvements.

In recent years, emphasis on the patient experience has become the focus of regulatory programs and payment incentives. Many quality measures today center around collecting patient feedback on the healthcare experience.

To satisfy these measures and drive quality improvement efforts, many organizations turn to post-transactional patient satisfaction surveys . The feedback from these surveys often measures only a limited set of touchpoints while overlooking other critical data from the full patient journey.

A holistic view

Patient experience programs often hone in on clinical service delivery, and many regulatory programs focus solely on numerical scores to measure improvement. These approaches may fail to identify pain points occurring in dozens of patient interactions within a healthcare system.

A holistic view of the patient journey is the key to modernizing and strengthening your efforts to meet your patients’ needs . By breaking down silos into separate patient events, you can begin to identify blind spots where hidden challenges exist in your patient experiences.

By the time your patients engage with their care providers, they’ve likely interacted with your organization a number of times. These interactions can occur digitally, over the phone, or in person. Navigating your website, verifying insurance coverage, and scheduling an appointment are all examples of pain points that may be creating barriers to care.

It’s easy to assume any given touchpoint is more or less important than another. The fact is that each one provides unique value to the patient’s experience. Each of them plays a role in helping the patient achieve their goals.

Patient engagement with your organization doesn’t begin when the patient is examined by the healthcare provider, or even when they enter your medical facility. From initial awareness to ongoing care, the patient journey encompasses every separate interaction throughout the process of seeking, receiving, and continuing care within a health system.

There are several stages of the patient journey you should consider.

What triggers the patient’s need for care, and how does the patient learn about your organization?

  • Quality ratings and online reputation
  • Campaign management
  • Community involvement

Consideration

What drives a patient to choose your organization over another?

  • Coverage and benefits
  • Healthcare provider search

What impacts your patient’s ability to receive care or support from your organization?

  • Patient portal
  • Call center
  • Price transparency

Service delivery

What is your patient’s experience with their clinical care?

  • Interaction with healthcare professionals
  • Check-in and check-out
  • Discharge process

Ongoing care

What type of patient engagement occurs after a visit?

  • Wellness and care management
  • Social determinants of health
  • Population health

All of these examples influence the way in which your patients make decisions. It’s essential to understand which touchpoints along the patient’s journey are the most impactful or leave the largest gaps in care. There are patient expectations surrounding each type of interaction.

Patient journey mapping

How do you move beyond patient feedback on service delivery and focus instead on the end-to-end patient journey? Patient journey mapping can provide context around what your patients experience as they move through the various channels of your organization.

A patient journey map is a visual representation of the steps the patient takes as they engage with your organization in order to receive care. Patient journey maps should capture pre-visit and post-visit touchpoints in addition to those that occur when the patient is onsite at your medical facility.

Your patient journey may be broad and applicable to your entire patient base, or it may be specific to certain specialties, patient personas , demographics , or health events.

Start with an inventory of all the touchpoints for which you currently collect patient feedback. Next, determine what’s missing. Envision moving through your organization from your patient’s point of view. Your patient journey map should include Interactions that take place pre-visit and post-visit, which are not always captured by traditional or regulatory surveys.

Benefits of patient journey mapping

There are many benefits to capturing key moments along the whole patient journey.

  • A patient journey map allows you to walk in your patient’s shoes and think the way they think as they engage with your organization. Patient journey mapping looks at patient experiences from the outside in.
  • Mapping your patient journeys helps you to hone in on the areas where you may not be listening to your patients, but should be.
  • You can uncover inconsistencies, gaps in care, and common pain points with patient journey mapping. These are difficult to identify when you collect feedback only on service delivery. Collecting data around these areas can aid in process optimization and improve patient satisfaction.
  • A patient journey map can give you a cross-functional view of your patient experience so you can engage all teams and stakeholders in gathering and understanding patient insights .
  • Patient journey mapping provides context around behavior and attitudes as patients move throughout the channels of your organization. Are patients having to repeat paperwork? Do patients understand their follow-up care instructions? Are your patients able to easily navigate your patient portal? Patient journey mapping can help to answer these types of questions.
  • Mapping the patient journey can transform your patient care approach from a reactive one to a proactive one.

Using patient journey data

Once you can visualize the end-to-end patient journey within your organization, it’s time to listen to your patients and start gathering data.

Gather the right data

Collect data on all the touchpoints of the patient journey.  Understand how your patients are interacting with every aspect of your organization, including non-clinical interactions such as your website, scheduling, and billing. Involve multiple stakeholders during this process, including managers, doctors, nurses, other healthcare professionals, and administrative staff.

It’s important to capture all steps involved in each of these stages. For example, when looking for potential pain points surrounding the patient portal, consider how the patient sets up an account, logs in, navigates the interface, gets technical assistance, and so forth.

Also, consider patient expectations and usage–what are they using the portal to accomplish? Look for potential gaps in these experiences , such as paying a bill, contacting the provider with a question, reviewing test results, or scheduling an appointment.

Understanding the patient’s goals and actions along all the different paths of your patient journeys is essential to gathering the data you need to take action.

Understand the data

Gain insights using analytics , benchmarking, and visualizations to identify gaps and discover opportunities at each step of the patient’s journey. Trends along the various touchpoints can help you to discover pain points and identify opportunities.

It’s also important to engage all the right stakeholders when reviewing the data you collect. Involving the right teams and people is essential to understanding gaps and improving experiences.

Take action

Use the insights from all touchpoints along your patient journey to develop solutions to improve your patient experience.

A closed-loop system is ideal for taking action to close gaps along the patient journey. For example, if a patient gives a low score on a survey for your online scheduling tool, you could follow up with the patient to ensure they were able to schedule an appointment.

Using the data you collect to drive specific actions and feed into processes is vital to creating a seamless patient journey.

Why Qualtrics?

At Qualtrics, we want to enable you to listen to and understand your patients across all aspects of their journey, all within a single platform. Omnichannel distribution lets you gather feedback from patients from where they are at during each touchpoint, with powerful built-in analytics for uncovering meaningful insights.

The Qualtrics XM Platform™ provides a single source for all of your patient journey data. Real-time feedback displayed in role- and location-based dashboards helps deliver pertinent information to the right people, allowing you to take prompt action where needed.

Ready to collect data and drive action along your patient journey?

Related resources

Patient experience 12 min read, symptoms survey 10 min read, nurse satisfaction survey 11 min read, cahps surveys 6 min read, patient journey mapping 15 min read, patient feedback 15 min read, healthcare branding 13 min read, request demo.

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Patient journey 101: Definition, benefits, and strategies

Last updated

22 August 2023

Reviewed by

Melissa Udekwu, BSN., RN., LNC

Today’s patients are highly informed and empowered. They know they have choices in their healthcare, which can put healthcare providers under a lot of pressure to provide solutions and meet their patients’ expectations.

Just like any customer, patients embark on a journey that begins before they ever contact the provider. This makes understanding the journey and where improvements can be made extremely important. Mapping the patient journey can help practitioners provide better care, retain a solid customer base, and ultimately identify ways to improve patient health.

  • What is the patient journey?

The patient journey is best described as the sequence of experiences a patient has from admission to discharge. This includes all the touchpoints between the patient and provider from beginning to end.

The patient journey continues through consultation, where they meet the potential caregiver. That portion of the journey includes interactions with a doctor and support staff, how long they wait to be seen, and the steps taken for diagnosis and treatment.

The patient’s post-care journey includes follow-ups from the healthcare provider, post-treatment care, and billing. For example, if the patient has questions about post-surgery care or how to read their invoice, how quickly their questions are answered and their problems resolved will impact their satisfaction.

Mapping the patient journey helps healthcare providers improve patient satisfaction at every step of the way. By collecting data at each stage and conducting an in-depth analysis, providers can identify patient concerns and make the necessary improvements to meet their patient satisfaction goals.

What is another name for the patient journey?

The term “patient funnel” describes the journey patients take from first learning about a healthcare provider or healthcare product to actually making an appointment or purchase. This “funnel” can be applied to any type of business, describing the stages a customer goes through to obtain a service.

  • Understanding the stages of the patient journey

Each stage of the patient journey is essential to a positive patient experience . Gathering and analyzing data can alert healthcare providers to potential issues throughout the journey.

Data collection at each of the following stages will give healthcare providers the information they need to make the necessary improvements:

1. Awareness

Awareness is where the patient journey begins. This is when they first research symptoms and identify the need to see a medical professional.

They may consider at-home remedies and get advice from friends, social media, or websites. Once they identify the need for a healthcare provider, they continue their research via review sites, advertising campaigns, and seeking referrals from friends and family.

Determining the way patients become aware they need healthcare and the sources they use for research is important. The data collected at this stage could suggest your organization has an insufficient social media presence, inadequate advertising, or a website in need of an update.

To remedy these shortcomings, you might consider adding informational blogs to your website, performing a social media analysis, or closely monitoring customer reviews.

This stage in the patient journey is where the patient schedules services with the healthcare provider.

This engagement is essential for acquiring new patients and retaining current patients. Patients will contact you in several ways to schedule an appointment or get information. Most will call on the first attempt to schedule an appointment.

This is a crucial touchpoint in the journey. A new patient may become frustrated and move on if they find it difficult to access your services or are placed on hold for a long period or transferred numerous times.

Patient engagement occurs in other ways, such as your online patient portal, text messages, and emails. Your patients may interact differently, so it’s important to gather data that represents their preferred means of communication. Work to make the improvements required to correct access issues and ensure efficient communication.

The care stage can include everything from your patient’s interaction with the front desk to how long they have to wait in the examination room to see a doctor.

Check-in, check-out, admissions, discharge, billing, and of course, the actual visit with the healthcare provider are other touchpoints in the care stage.

There are a couple of ways to gather and analyze this data. Most organizations choose to analyze it holistically, even if it’s collected separately. For example, you might gather data about the patient’s interaction with the front desk, the clinical visit, and the discharge process, but you may want to analyze the care segment as a whole.

4. Treatment

Treatment may be administered in the office. For example, a patient diagnosed with hypertension may have medication prescribed. That medication is the treatment. Gathering information at this stage is critical to see how your patient views the healthcare provider’s follow-up or responses to inquiries.

In most cases, treatment extends beyond the initial clinical visit. For example, a patient might require additional tests to get a diagnosis. Providing the next steps to a patient in a timely manner and letting them know the test results is crucial to patient satisfaction .

5. Long term

A satisfied patient results in a long-term relationship and referrals to friends and family. Most of the data collected at this stage will be positive since the patient is continuing to use your services.

Gathering data after the treatment stage allows you to expand on the qualities that keep patients returning for your services in the long term.

  • Benefits of patient journey mapping

The patient benefits from their healthcare provider understanding their journey and taking steps to improve it. Healthcare providers also reap several benefits, including the following:

1. Efficient patient care

When they understand the patient journey, healthcare providers can provide care more efficiently and spend less time and money on unnecessary, unwanted communications.

2. Proactive patient care

Proactive patient care is aimed at preventing rather than treating disease. For example, women who are over a certain age should have an annual mammogram, smokers may be tested for lung disease, and elderly women may need a bone density study. These preventative measures can help keep disease at bay, improve health outcomes, and build trust with patients.

3. Value-based patient care

Patients don’t want to feel they are being charged unfairly for their healthcare. Focusing on the individual patient promotes satisfaction and yields positive outcomes.

The Center for Medicare and Medicaid Services (CMS) has issued recent guidelines for participants that help offset the costs of high-quality care through a reward system.

4. Retention and referrals

Patients who are happy with their journey will keep returning for healthcare, and happy patients equal voluntary referrals. Many providers offer rewards to incentify referrals.

  • How to get started with patient journey mapping

Follow the steps below to start the patient journey mapping process:

Establish your patient personas

Journey mapping is a great way to identify your patient’s characteristics so that their experience can be further enhanced.

Some of the following determinations can help you pinpoint your patient’s persona and establish protocols to provide a better service:

How do your patients prefer to communicate? Are they more comfortable with phone calls, texts, or other methods?

How are most patients finding your services? Are they being referred by friends or family members, or are they seeing advertisements?

Would the patient prefer in-person communication or telecommunication?

What are the patient’s expectations of care?

This data can be complex and widespread, but it can give you the information you need to more effectively and efficiently communicate with your patients.

Understand the entire patient lifecycle

Each patient is unique. Understanding the patient lifecycle can avoid confusion and miscommunication.

To positively engage the patient, you’ll need to gather data not only about communication methods but where they are in the patient journey, their health issue, and their familiarity with the healthcare provider’s procedures and treatment options.

Understand the moments of truth

With a few exceptions, most people seek healthcare services when they are ill or have a healthcare issue. These situations can cause patients to feel stressed and anxious. It’s these moments of interaction where compassion, knowledge, and understanding can provide relief and reassurance.

When patients see their healthcare provider, they are looking for solutions to problems. It’s the provider’s opportunity to identify these moments of truth and capitalize on them.

Get the data you need

Healthcare providers can collect vast amounts of data from patients, but the data collected rarely goes far enough in analyzing and determining solutions.

Your patients have high expectations regarding personalized treatment based on data. They want personalized, easy access to medical information and records, responsive treatments and follow-up, and communication in their preferred format.

You need more than clinical data to give patients what they want. You also need personal data that sets each patient apart and ensures a tailored experience.

For example, it might be challenging for parents of small children to contact the clinic and schedule appointments at certain times of the day. As a healthcare provider, you’ll need to be aware of the best times to contact this individual and offer simple methods for scheduling appointments.

Another example is patients with physical disabilities. You can take steps to improve their access to and experience at the healthcare facility.

Encourage referrals and loyalty

Although engagement on social media and online forums is becoming more and more common, the best way for new patients to find you is through referrals. Referrals stem from satisfactory experiences and trust.

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  • Pragmatic trials are needed to assess the effectiveness of enhanced recovery after surgery protocols on patient safety
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  • http://orcid.org/0000-0002-8915-4203 Antoine Duclos 1 , 2
  • 1 Research on Healthcare Performance (RESHAPE), INSERM U1290 , Université Claude Bernard Lyon 1 , Lyon , France
  • 2 Center for Surgery and Public Health, Brigham and Women's Hospital , Harvard Medical School , Boston , MA , USA
  • Correspondence to Dr Antoine Duclos, Université Claude Bernard Lyon 1, 8 Av. Rockefeller, Lyon 69008, France; antoineduclos{at}yahoo.fr

https://doi.org/10.1136/bmjqs-2023-016966

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  • Audit and feedback
  • Cluster trials
  • Healthcare quality improvement

Contemporary healthcare systems comprise a myriad of organisations and professionals committed to patient care. These systems often develop innovations that are not easily transferable from one context to another. Three decades ago, Enhanced Recovery After Surgery (ERAS) protocols originated in Northern Europe, introducing a systematic approach to perioperative care, initially focusing on major colorectal surgeries. 1 2 Using a patient-centred and evidence-based approach, their goal was to improve patient's early recovery through enhancing the quality of surgical processes. ERAS protocols were specifically designed to facilitate the dissemination of multimodal perioperative care pathways covering all aspects of the patient’s surgical journey. Addressing key factors traditionally extending post-surgery hospital stays, including the need for analgesia, intravenous fluids due to gut dysfunction and bed rest due to limited mobility, the ERAS protocols offered guidance for well-coordinated perioperative care teams. 3 These protocols have since transcended borders, catalysing transformative changes in healthcare organisations globally across multiple surgical fields. 4 5 Aiming for consistent quality across diverse healthcare systems, ERAS protocols allow the replication of standardised strategies within the unique contexts of each surgical department, thereby fostering their adaptation and improvement. The expected effectiveness of ERAS protocols in facilitating upscaling across diverse healthcare environments stems from their evidence-based foundation, comprehensive coverage, flexibility for different surgical procedures and patient populations, clear guidelines and emphasis on multidisciplinary collaboration. Focused on scientifically proven best practices, ERAS protocols also undergo periodic updates. Successful ERAS implementation typically involves participating in an education programme to form a multidisciplinary team with members from diverse perioperative care units and conducting regular meetings to discuss care processes and clinical outcomes. Additionally, audit and feedback plays a dynamic and integral role in ERAS implementation, aiding in monitoring adherence to guidelines, identifying improvement areas, and fostering team collaboration. 3

Previous research has centred on the efficacy of ERAS protocols. Explanatory clinical trials, using individual patient randomisation, have demonstrated a notable reduction in both postoperative complications and length of stay, although the impact on overall mortality and readmission rates remains uncertain. 6 7 Hospitals’ adherence to enhanced recovery criteria has also been associated with lower postoperative complications among patients undergoing elective colorectal surgery. 8 However, evidence regarding the effectiveness of ERAS protocols in real-world settings from pragmatic trials has been lacking to date. 9 The paper by Pagano 10 in this issue of BMJ Quality and Safety therefore offers valuable insights to address this knowledge gap, informing decisions on the widespread adoption of complex interventions such as ERAS protocols. The authors conducted a stepped-wedge cluster-randomised trial to evaluate the impact of implementing enhanced recovery after colorectal cancer surgery within a regional hospital network in Italy, supported by audit and feedback.

This well-designed study imparts valuable lessons grounded in a high level of scientific evidence. 10 As might be anticipated, the routine introduction of the protocol into surgical care resulted in higher compliance with many ERAS process quality criteria (eg, preadmission counselling, preoperative nutritional risk assessment and carbohydrate loading, postoperative early re-feeding and mobilisation), accompanied by a reduction in the length of inpatient stays. Such adoption of the protocol by surgical departments had the potential to enhance consistency in the quality of surgical care provided to the population within the hospital network. To achieve this, successful ERAS implementation within each surgical department required important additional features to overcome resistance and reshape existing surgical processes in diverse healthcare environments. As with any complex intervention, bundling features with distinctive mechanisms of action was intended to have synergistic effects. 11 The first feature involved the identification and commitment of a dedicated interprofessional team trained to install ERAS multimodal and multidisciplinary approaches in each centre. The second feature entailed an audit and feedback strategy to assess both process compliance and patient outcomes. Thus, the approach taken by Pagano exemplifies a comprehensive approach to teamwork and continuous data-driven support, fostering cultural and organisational change. As is common with bundled interventions, this does, however, raise the question of the real effect attributable to compliance with the ERAS process quality criteria alone, independently from other intervention components. Because engagement with audit and feedback was not reported, we cannot disentangle which elements of the bundle are most important to reproduce the intervention’s impact.

Previous explanatory clinical trials demonstrated the theoretical benefits of ERAS protocols in reducing postoperative complications among patients undergoing colorectal procedures. 6 However, these benefits do not necessarily replicate in a real-world scenario. In the present pragmatic trial, no beneficial impact on clinical outcomes was observed. Instead, there was a non-significant trend toward a higher risk of complications, reoperations, deaths or hospital readmissions in patients exposed to ERAS protocols. 10 This raises questions about the safety of ERAS protocols in practice and prompts hesitations regarding their broader adoption across all surgical departments. A plausible explanation for these results, as presented by Pagano, may be linked to the limited adherence to, on average, only two-thirds of the ERAS process items in participating centres. It may be that full compliance with all of these items is necessary to anticipate benefits similar to those achieved in the highly controlled experimental framework of previous clinical trials. Another explanation may arise from insufficient case-mix control in this pragmatic trial. An increased average complexity among patients needing surgery, coupled with limited access to critical care during periods of elevated COVID-19 hospital exposure, could have resulted in a higher risk of complications and poorer postoperative outcomes. 12 This methodological issue is particularly notable because participating hospitals experienced implementation of the ERAS protocol concurrently with the pandemic in 2020/2021. Moreover, the impact of ERAS was predominantly assessed in comparison to patients from pre-pandemic control periods.

Pagano’s paper therefore provides evidence on the impact of ERAS in reducing hospital length of stay rather than improving patient clinical outcomes. The initial objective of the ERAS approach was not only to expedite recovery and reduce hospital length of stays but, more importantly, to uphold and enhance patient safety. Over time, there has been a shift in the research agenda, moving away from the original focus when evaluating ERAS. This shift replaced crucial indicators of recovery, such as complications or readmissions, with length of stay as the most commonly chosen primary criterion. The emphasis on length of stay does not align with patient-centred outcomes and could be potentially misleading, as a decreased length of stay may coincide with an increased risk of post-discharge readmission and overall healthcare costs. 13 A substantial decrease in the length of the index stay does not automatically translate to a reduction in overall hospital bed days and costs in the mid-term, especially if subsequent readmissions occur due to post-discharge complications stemming from inadequate patient follow-up. 14 Furthermore, implementation of an ERAS protocol involves a significant input cost for hospitals that must be weighed against the expected benefits associated with reduced lengths of stays. Hence, the potential gains in the efficiency of care bundles designed to enhance post-surgery recovery deserve to be demonstrated from a societal perspective, relying on a comprehensive economic assessment.

In summary, the paper by Pagano represents an important contribution, offering a pioneering pragmatic trial testing an ERAS protocol in the real world. Additional cluster-randomised trials and large quasi-experimental studies are necessary to elucidate the true impact on patient safety of implementing the enhanced recovery after surgery across healthcare systems. Emphasising the assessment of patient clinical outcomes is crucial to promote the widespread use of ERAS. Simultaneously, accurately considering time-based confounders and dissecting intervention components are essential steps to better understand their mechanisms and effects. The challenge also lies in designing impactful implementation strategies that ensure full compliance with updated ERAS criteria and encourage patient participation in their care. 15 Pursuing top surgical quality targets, supported by tangible evidence, is a prerequisite that can ultimately lead to improved patient outcomes at a lower cost for the population. 16

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Patient consent for publication.

Not applicable.

Ethics approval

  • Ljungqvist O ,
  • ↵ The ERAS society . Available : https://erassociety.org/ [Accessed 29 Jan 2024 ].
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  • de Boer HD ,
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Contributors AD contributed to conceptualisation and writing of the paper and approved the final version.

Funding The author has not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

Linked Articles

  • Original research Implementation of an enhanced recovery after surgery protocol for colorectal cancer in a regional hospital network supported by audit and feedback: a stepped wedge, cluster randomised trial Eva Pagano Luca Pellegrino Manuela Robella Anna Castiglione Francesco Brunetti Lisa Giacometti Monica Rolfo Alessio Rizzo Sarah Palmisano Maurizio Meineri Ilaria Bachini Mario Morino Marco Ettore Allaix Alfredo Mellano Paolo Massucco Paola Bellomo Roberto Polastri Giovannino Ciccone Felice Borghi ERAS-colorectal Piemonte group Tatiana Maan Fabio Priora Sergio Gentilli Luca Portigliotti Marco Palisi Paolo Massucco Luca Pellegrino Felice Borghi Mario Solej Maurizio De Giuli Paola Bellomo Alfredo Mellano Dario Ribero Roberto Polastri Nicoletta Sveva Pipitone Federico Andrea Muratore Mauro Garino Elisabetta Castagna Andrea Caneparo Adriana Ginardi Reggina Lagana Monica Carrera Luca Panier Suffat Alberto Kiss Marcello Cucinelli Donatella Scaglione Roberto Saracco Andrea Gattolin Roberto Rimonda Francesco Battafarano Carlo Palenzona Luca Lorenzin Carmine Gianfranco Di Somma Eliana Giaminardi Marco Naddeo Marco Calgaro Emma Marchigiano Piero Cumbo Francesca Cravero Francesco Lemut Luciano Bonaccorsi Tiziana Viora Clemente De Rosa Silvio Testa Marco Brunetti Matteo Gatti Presidio Cottolengo Enrico Gibin Carlo Bima Francesco Quaglino Marco Ettore Allaix Paolo De Paolis Mario Morino Ida Marina Raciti Gitana Scozzari Felice Borghi Danilo Donati Maurizio Meineri Sarah Palmisano Luca Pellegrino Giovannino Ciccone Rosalba Galletti Eva Pagano Sergio Sandrucci Ilaria Bachini Paola Coata Barbara Mitola Paolo Massucco Alessio Rizzo Pietro Caironi Monica Rolfo Anna Orlando Oscar Bertetto Francesco Brunetti Corinna Defilè Vitor Hugo Dias Martins Lisa Giacometti Matteo Papurello Fabio Saccona Danila Turco BMJ Quality & Safety 2024; - Published Online First: 29 Feb 2024. doi: 10.1136/bmjqs-2023-016594

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Practice Innovation

A patient journey audit tool (PJAT) to assess quality indicators in a nuclear medicine service

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  • Kunthi Pathmaraj   ORCID: orcid.org/0000-0003-0788-3785 1 , 2 , 3 , 4 ,
  • Jessica Welch 1 ,
  • Wesley Ng 1 ,
  • Danny Lee 1 ,
  • Sze Ting Lee   ORCID: orcid.org/0000-0001-8641-456X 1 , 2 , 3 , 4 , 5 ,
  • Anita Brink 6 ,
  • Maurizio Dondi 6 ,
  • Diana Paez 6 &
  • Andrew M. Scott   ORCID: orcid.org/0000-0002-6656-295X 1 , 2 , 3 , 5  

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To develop a nuclear medicine specific patient journey audit tool (PJAT) to survey and audit patient journeys in a nuclear medicine department such as staff interaction with patients, equipment, quality of imaging and laboratory procedures, patient protection, infection control and radiation safety, with a view to optimising patient care and providing a high-quality nuclear medicine service.

The PJAT was developed specifically for use in nuclear medicine practices. Thirty-two questions were formulated in the PJAT to test the department’s compliance to the Australian National Safety and Quality Health Service Standards, namely clinical governance, partnering with consumers, preventing and controlling health care infection, medication safety, comprehensive care, communicating for safety, blood management and recognising and responding to acute deterioration. The PJAT was also designed to test our department’s adherence to diagnostic reference levels (DRL). A total of 60 patient journey audits were completed for patients presenting for nuclear medicine, positron emission tomography and bone mineral density procedures during a consecutive 4-week period to audit the range of procedures performed. A further 120 audits were captured for common procedures in nuclear medicine and positron emission tomography during the same period. Thus, a total of 180 audits were completed. A subset of 12 patients who presented for blood labelling procedures were audited to solely assess the blood management standard.

The audits demonstrated over 85% compliance for the Australian national health standards. One hundred percent compliance was noted for critical aspects such as correct patient identification for the correct procedure prior to radiopharmaceutical administration, adherence to prescribed dose limits and distribution of the report within 24 h of completion of the imaging procedure.

This PJAT can be applied in nuclear medicine departments to enhance quality programmes and patient care. Austin Health has collaborated with the IAEA to formulate the IAEA PJAT, which is now available globally for nuclear medicine departments to survey patient journeys.

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Introduction

Nuclear medicine services are being increasingly sought for both diagnostic imaging procedures and radionuclide therapy in the evaluation of oncological, cardiac, neurological and endocrinological conditions and infections. The patient journey in a nuclear medicine service comprises of booking the procedure, preparation for the procedure prior to the appointment, arrival and registration on the day of the appointment, patient interview for clinical history and undergoing the procedure itself which includes radiopharmaceutical administration, scanning, report generation and dispatching of results.

At each of these points in the patient journey, quality indicators should be exerted to ensure patient safety is a critical focus, procedures are conducted according to appropriate protocols and the clinical question is addressed in the final report. Quality standards in health care are increasing; therefore, knowledge and experience in the use of tools for quality management must grow accordingly. A nuclear medicine service should have an established quality and safety programme that aligns to the quality principles of its organisation, as well as the national and international health standards/guidelines.

The International Atomic Energy Agency (IAEA) has the most publications in this area, with their flagship quality programme, QUANUM (Quality Management Audits in Nuclear Medicine Practices), developed to audit a nuclear medicine department as a whole and is based on international guidelines from Society of Nuclear Medicine and Molecular Imaging (SNMMI), European Association of Nuclear Medicine (EANM) as well as prior publications from IAEA [ 1 ]. QUANUM contains relevant checklists to audit all aspects of nuclear medicine practices including clinical practice, management, operations and services.

The QUANUM programme provides guidelines and framework for maintaining a strong quality programme in a nuclear medicine service. QUANUM has sought to instil the culture of quality by encouraging the conduction of annual systematic audits, the adoption of a culture of regular analyses and reviews of internal processes and the introduction of a quality audit process that is patient-oriented, systematic and outcome-based. Adopting a culture of auditing through peer review is essential and enhances the contribution of nuclear medicine to safe practice and optimal patient care. The IAEA publications analysing the QUANUM audit missions show that QUANUM audits have overall contributed to significant improvements of clinical practice and services to patients around the world [ 2 , 3 ].

A review of Austin Health processes showed that the hospital uses a clinical audit portal to conduct patient journey audits in clinical areas, namely in the wards, intensive care unit and emergency department. These audit tools were not fit for the purpose of auditing patient journeys and clinical service delivery in a nuclear medicine department.

Recognising the need for ongoing quality management systems, internal audits and the implementation of the PDCA (plan, do, check and act) cycle in a nuclear medicine service to optimise patient care and minimise risks, the Department of Molecular Imaging and Therapy at Austin Health developed an in-house, nuclear medicine specific, patient journey audit tool (PJAT) that could be used to audit patient journeys in a nuclear medicine service. The PJAT was developed by drawing on the IAEA QUANUM principles and the clinical audit tools utilised by Austin Health and customised specifically for use in nuclear medicine practices. In developing the PJAT, the focus was kept on developing quality audit processes in nuclear medicine that can assist nuclear medicine departments/laboratories in maintaining or improving the quality of service for their patients, and thus lending itself to use by nuclear medicine services globally.

The main scope for the PJAT is to be able to review and evaluate the quality of all elements involved in a patient journey, including staff interaction, equipment and procedures, patient protection and safety, as well as its interaction with external service providers.

Materials and methods

The PJAT questions were formulated to test the department’s compliance to the Australian National Safety and Quality Health Service Standards (NSQHS) (Table  1 ), professional guidelines (e.g. diagnostic reference levels, DRL) mandated by regulatory bodies such as the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) and guidelines set by the Patient Safety and Clinical Excellence Unit of Austin Health. The PJAT was also designed to audit clinical and laboratory procedures, COVID-19–related governance and radiation safety practices. An essential feature of the PJAT audit tool is its ability to be used as part of the PDCA cycle. The audit tool was designed to have the ability to clearly identify gaps and areas that do not quite comply against audited parameters and therefore target these areas for improvements.

The PJAT was a hospital department quality evaluation project, and there was no requirement for formal Hospital Ethics Committee approval of the project.

Table 2 illustrates the 32 questions that were formulated in the PJAT database, including the method of extracting the responses to each of the questions. Two sets of random patient journey audits were conducted during a consecutive 4-week period. Initially, 60 patient journey audits were conducted, 20 patients in nuclear medicine (NM), 20 patients in positron emission tomography (PET) and 20 patients in bone mineral density (BMD), to sample the wide range of procedures performed in the department. A further subset of 120 patient journey audits was completed for common NM and PET procedures to critically assess key aspects of the patient journey in the department. This subset constituted of 20 bone scans, 20 myocardial perfusion imaging studies, 20 lung VQ scans, 40 whole-body PET scans and 20 FDG brain PET scans. In addition, 12 laboratory procedures that required withdrawal and re-administration of patient’s blood (2 gastrointestinal bleed studies and 10 cardiac gated blood pool scans) were reviewed to test compliance of the Blood Management Standard (NSQHS Standard 7). The questions in the PJAT were addressed at different time points of a patient journey in the department and were asked of the patient by the receptionist, nuclear medicine technologist and nursing staff. One technologist was responsible for each individual patient journey audit and ensured that all the data that was collected by other staff was entered accurately in the PJAT database.

All the 32 questions in the PJAT were applied to the first 60 patient journey audits and the subsequent 120 audits that were conducted for the subset of common procedures in NM and PET.

The Australian Clinical Governance Standard recognises the importance of governance, leadership, culture, patient safety systems, clinical performance and the patient care environment in delivering high-quality care. For purposes of the patient journey audit, this standard was measured by reviewing the compliance to the department’s clinical governance framework including following standard operating procedures (SOP) for patient identification, radiopharmaceutical administration, scanning, scan reporting, hand hygiene measures to reduce risk of health care-associated infections, prescribed DRL, radiation safety measures for staff and patients, correct procedures for personal protective equipment and reducing falls risk.

The standard protocol for a procedure in our department (including NM procedure, PET scan or BMD scan) is for the technologist to complete the clinical and technical datasheet (CTDS) by interviewing the patient and recording the clinical history including various other aspects of the patient journey. These aspects included patient identification, informed consent, risk assessment, review date, allergies, clean environment checks, imaging times and the administered dose of the radiopharmaceutical. During the patient interview, the assigned technologist asked the patient if (i) the patient had received and followed the preparation for the scan (questions 5 and 6) and (ii) the three patient identifiers were checked by the reception staff on arrival (question 4).

Radiation precautions following a diagnostic procedure are usually communicated verbally; however, if an outpatient is attending another medical appointment on the same day following the scan, a radiation precaution document is provided to the patient to take to their next appointment. For inpatients, in addition to providing verbal instructions, the radiation precautions are also recorded in the patient’s electronic medical record (EMR) to ensure adequate communication with ward staff. The provision of the radiation precaution document is also recorded on the clinical and technical datasheet which is scanned and electronically stored on the radiology information system (RIS) for retrospective analysis (questions 7, 8, 10, 11, 15, 18, 19, 20, 24, 27 and 30).

Auditing the infection control measures during the PJAT required the technologist to identify any missed infection control measures such as the correct application of personal protective equipment (PPE) and observation of the five moments of hand hygiene [ 5 ] (questions 9 and 26). Patient recliner chairs in the administration areas, scanner beds and imaging equipment were cleaned between each patient use and recorded on the clinical and technical datasheets which were used to audit compliance of the technologist for questions 19 and 20.

PJAT questions 7, 8, 18, 22 and 23 apply to medication safety and hence were used to audit governance processes around the administration of radiopharmaceuticals. The administered dose of the radiopharmaceutical was calculated by decay correcting the pre- and post-injection syringe to the time of injection with the use of an in-house, web-based dose calculator. The exact administered dose was then compared against the DRL specifications (question 24). To assess the compliance of the medication safety SOPs and guidelines when administering sedatives, the auditing technologist observed the administration of the sedative by nursing staff to ensure the medication safety standards have been met (questions 15, 16 and 17). More information was gathered from the patient electronic medical record or scanned medical record for auditing purposes (questions 13, 14 and 31).

The time taken from the receipt of the patient’s request to be triaged and booked for a procedurewas monitored to ensure requests were booked in a timely fashion (questions 1, 2 and 3). The patient journey time in the department was retrospectively calculated based on the patient arrival time and the time at which their procedure was completed. Additionally, the duration between scan completion and report generation was also obtained from the department information system (question 32). Reports are sent out electronically by a secure message delivery network (SMDN), allowing the secure communication of health information from one health care provider organisation to another, as soon as the report is finalised. For those referrers without access to SMDN, reports are either sent by encrypted email or posted. The patient journey audit reviewed the results dissemination process including the communication of significant and unexpected findings.

Another important aspect of the patient journey survey was to assess patient satisfaction of the nuclear medicine service. Our department utilises a booking software application which has the ability to send the appointment date and time, procedure information, patient preparation, COVID-19 questionnaire and the ability for the patient to provide feedback electronically. The booking software application automatically sends a short message service (SMS) to the patient once their scan is completed providing the patient with the option to participate in a patient satisfaction survey. During the 4-week audit period, 108 patients provided feedback. Note that feedback was not necessarily provided by the 180 patients who were randomly audited by the PJAT. Key questions in the survey include the following:

Did the staff introduce themselves?

Did the staff explain the procedure to you in a clear fashion?

Were you treated with courtesy and respect?

Did the staff give you an opportunity to ask questions before the procedure?

Were you satisfied with the service?

What was your experience with the digital app?

The data collated from the patient journey audits was categorised according to the NSQHS standards, graphed and reviewed.

Standard 1—clinical governance

Very high compliance was noted for the 180 patient journey audits for adherence to departmental SOP through all aspects of the patient journeys. In two instances, the sterile intravenous (IV) cannulation procedure was not followed according to the SOP, as eye protection was not worn (Fig.  1 ). High level of compliance of greater than 95% was noted for recording three patient identifiers by reception staff, and 100% compliance was noted for identifying the patient correctly prior to the procedure and radiation exposure.

figure 1

Standard 1 clinical governance. Ensuring safety and quality systems are followed to maintain reliability, safety and quality of health care. The n value is smaller for the first set of data because inpatients do not report to the department reception, they report directly to the nursing station

Standard 2—partnering with consumers

For the audit period, more than an 85% satisfaction rate was noted for staff introduction, explanation of procedure, courtesy and respect, asking questions before procedure and overall satisfaction with the service (Fig.  2 ). The digital application experience was recorded at just under 80% satisfaction.

figure 2

Standard 2 partnering with consumers. Survey responses from patients who attended the department during the audit period

Standard 3—preventing and controlling health care-associated infections

During the audit period, attention was paid closely to audit the compliance of staff with measures that are expected to be taken to prevent and control health care-associated infections. Results demonstrated high compliance for all the parameters that were tested, namely (i) was the iv cannula was removed when outpatients left the department, (ii) was relevant PPE used at the time of the procedure, (iii) was the scanner room cleaned before patient use, (iv) was the out-patient injection area and uptake rooms clean when patients entered and (v) were five moments of hand hygiene practiced when dealing with the patient. It was found that the department was complying with hospital infection control policies and exerting due diligence to minimise the risk of infection and COVID-19 transmission.

Standard 4—medication safety

High compliance was noted for all assessed aspects of medication safety (Fig.  3 ).

figure 3

Standard 4 medication safety. High compliance was observed for all aspects of medication safety in NM and PET. BMD was not included since no medication/radiopharmaceutical is administered for DXA scans

Our department conducts annual assessments of DRLs for all diagnostic procedures. The facility reference levels (FRL) are reviewed against DRL recommended by ARPANSA, with a view to ensure administered radioactivity is within the prescribed DRL whilst maintaining optimal image quality. Administered radioactivity was reviewed for all patients in the PJAT and for an in-depth analysis, a subset of 120 common procedures in NM and PET procedures were extracted and compared against the DRL guidelines set by ARPANSA.

Our department demonstrated excellent DRL compliance to the ARPANSA guidelines for all the procedures during the audit period, and the subset of 120 common NM and PET procedures demonstrated that we are below the maximum allowed dose (Fig.  4 ). The average dose length product (DLP) for studies requiring a low dose CT for attenuation correction and anatomical correlation was also well within the maximum dose allowed by the ARPANSA guidelines.

figure 4

Patient safety. Diagnostic reference levels for administered radiopharmaceutical dose. The chart demonstrates the average administered dose for each of the common procedures in the subset of 120 patients, where 100% depicts the maximum allowed dose as stipulated by ARPANSA

Standard 5—comprehensive care standard

In NM, PET and BMD, for patients who were assessed as falls risk, appropriate care was taken to prevent/minimise the risk of fall.

Standard 6—communicating for safety

Patient identification by three-point identifier checks, as well as procedure matching, revealed high compliance rates, assuring that the right patient for the right procedure was receiving the radiopharmaceutical injection. In one instance in BMD, there was no record in RIS that the patient was identified by administration staff at the time of registration. There was 100% compliance for identifying the patient by three-point identification prior to radiopharmaceutical administration for NM and PET procedures and scanning the patients for BMD. High compliance was noted for all the other parameters that were assessed for the communication for safety standard. For myocardial stress test patients, compliance was high for all aspects of work practice including three identifiers being used to check patient identity prior to radiopharmaceutical administration. To assist with communication of results, especially significant and unexpected findings, patients are asked if they are aware of their next review date with their specialist, general practitioner or outpatient clinics. It was demonstrated that 72% of PET patients and 37% of NM patients were aware of their review appointment. The remaining patients were unsure about their follow-up appointment.

Standard 7—blood management standard

During the audit period, 12 procedures (2 gastrointestinal studies and 10 cardiac gated blood pool studies) required venous blood being collected from the patient via an intravenous cannula, radiolabelled and readministered to the patient. In all 12 instances, blood collection vials were labelled correctly with the patient name, unit record number and date of the procedure. Additionally, patient identification was performed twice for each of these procedures, once prior to withdrawal of blood and once prior to re-injection of radiolabelled blood.

Standard 8—recognising and responding to acute deterioration

There were no patients in the audit period that fell into this category.

Patient journey time in the department and time taken to generate a report

In addition to evaluating the department’s performance against the Australian NSQHS, the patient journey audit also enabled a review of the duration of the time spent by patients in the department for a range of procedures (arrival time to completion of the procedure) and the time taken for a report to be issued after completion of a scan. This analysis was conducted for a total of 180 patient journey audits, as well as the subset of 12 patient journey audits that were conducted to audit the blood management standard. The average time a patient spends for a particular procedure is shown in Fig.  5 . Report turnaround times were very efficient, and the mean time from scan completion to report generation was 1.4 h for NM reports, 1.6 h for PET WB reports and 4.1 h for PET brain reports. These timeframes refer to reports being accessible on PACS for internal distribution within Austin Health and being distributed by the SMDN for externally referred patients.

figure 5

The time spent by patients in the department from arrival to completion of the study and time taken from scan completion to report generation

The Austin Health PJAT was used successfully to audit 180 patient journey audits and demonstrated a high compliance to the national health standards and adherence to DRL stipulations. The patient journey audits confirmed that the department is delivering a high-quality nuclear medicine service. The PJAT provides an efficient approach to establishing compliance and patient-focused quality parameters in a busy nuclear medicine department.

Self-assessment and data-driven decision-making is a critical aspect of a quality programme in any nuclear medicine service which can enhance the quality of the service and ensure alignment with local, national and international standards for best nuclear medicine practice and patient-centred care. The European Commission has published the results of the QuADRANT study (Quality Improvement Through Clinical Audit in Diagnostic Radiology, Radiotherapy and Nuclear Medicine) illustrating a snapshot of the current situation of clinical auditing in EU countries [ 6 ]. The publication suggests that regular clinical audits help close the gap between everyday clinical practice as well as describe what is recommended in the current literature. Delgado et al. [ 7 ] shine light on the necessity of a clinical audit to improve the nuclear medicine service in many nations and summarise recent findings of the QuADRANT study. National co-ordination, regulatory control, involvement of a national professional body, consideration of the barriers and enablers, accreditation and certification and education of health professionals have been found to be the key aspects of successfully implementing a clinical audit.

Berman et al. [ 8 ] have published the outcomes of a survey to assess the patient experience before and after introducing an in-house radioiodine therapy clinic allowing for a direct consultation between the nuclear medicine specialist and the patient. The patient journey through the clinic and subsequent radioiodine treatment was surveyed pre and post-treatment with favourable outcomes post-treatment relating to patient satisfaction ratings. Similar results were achieved by Moncayo et al. [ 9 ]. However, these studies are limited to the radioiodine therapy procedure and are unable to provide a wider perspective or overview of the patient journeys for other services provided by a nuclear medicine department.

These publications establish the importance of a clinical audit and that it is well recognised internationally and emphasised by many professional bodies. There is a lack of published guidelines to comprehensively audit the patient’s experience through nuclear medicine services where diagnostic and radionuclide therapy procedures are performed on a regular basis.

The PJAT we have developed was demonstrated to be an efficient audit tool to conduct patient journey audits and was developed based on the IAEA QUANUM principles to specifically assess if a nuclear medicine service in Australia meets the requirements of the Australian NSQHS standards. The PJAT is able to assess quality indicators in administrative processes such as booking of the appointment, patient preparation, correct patient identification and procedure matching, patient interview prior to a procedure, appropriate management of human resources in regard to training and clinical competence, governance around infection control measure, nuclear medicine specific aspects such as radiation safety, radiopharmaceutical administration and adherence to prescribed DRL as well as assess the time taken from the receipt of the imaging request to performing the scan and generating the scan report. The results of the patient journey audits can also be used as part of the PDCA cycle to identify gaps and hence encourage continuous improvement of all aspects of a nuclear medicine service.

Whilst the Austin Health PJAT was developed initially to test compliance of nuclear medicine practices against Australian health standards and DRL guidelines, it can easily be adapted by any nuclear medicine service for their own needs, to review their practices and ensure compliance with either local regulations or international guidelines set by organisations such as the IAEA. Towards this end, Austin Health and IAEA have collaborated to formulate a patient journey audit tool (IAEA PJAT) that draws on the fundamental principles of the Austin PJAT but is modified to accommodate nuclear medicine departments globally. The IAEA convened a group of experts in quality management to develop the IAEA PJAT. Questions in the IAEA PJAT were formulated to audit processes around all aspects within a nuclear medicine service including the assessment of the request/referral and justification of the procedure, triaging and booking the appointment, providing patient instructions and appointment details, assessing the clinical history, obtaining patient consent and preparing for procedures. Further, questions were developed to monitor radiopharmaceutical administration, the imaging, laboratory or treatment procedure; the management of adverse or unexpected events and deviations from standard practice. Finally, questions were built in the audit tool to monitor post-procedure instructions for both diagnostic and therapeutic procedures, including patient release and or discharge, report generation and result dissemination.

The IAEA PJAT has now been added to the QUANUM portfolio of products on the Human Health Campus website of the IAEA [ 10 ]. The IAEA-PJAT provides an opportunity for nuclear medicine departments globally to audit all the phases of the patient journey through a nuclear medicine procedure and nuclear medicine departments have the flexibility to tailor the questions in the audit tool and adapt it to align with their specific service requirements.

Conclusions

Our department has successfully developed a patient journey audit tool (PJAT) that can be easily implemented as part of a nuclear medicine quality programme. The results of the 180 patient journey audits conducted show that our department is complying with the expectations set by government quality standards and guidelines set by professional nuclear medicine societies and national regulatory bodies. The IAEA PJAT has now been developed and made accessible on the IAEA website which allows global nuclear medicine services to use this patient journey audit tool for use in their nuclear medicine practices, identify gaps in processes and strive for excellence in quality to ensure optimal patient care.

Data Availability

All data supporting the findings of this study are available within the paper.

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Acknowledgements

The authors wish to acknowledge the collective efforts of the nuclear medicine technologist team, nursing staff and reception staff in the Department of Molecular Imaging and Therapy, Austin Health, who contributed to the collection of the patient journey audits. The authors also wish to acknowledge the support and contribution of the International Atomic Energy Agency (IAEA) and the QUANUM advisory committee for their assistance in formulating the IAEA patient journey audit tool.

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Department of Medicine, University of Melbourne, Melbourne, Australia

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by KP, JW, WN and DL. The first draft of the manuscript was written by KP, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Pathmaraj, K., Welch, J., Ng, W. et al. A patient journey audit tool (PJAT) to assess quality indicators in a nuclear medicine service. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06627-8

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DOI : https://doi.org/10.1007/s00259-024-06627-8

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March 15, 2017 — Comments are off for this post.

Seeking Minard

After examining the history of data visualization greats I have decided to collect my learnings in the style of history’s data visualization greats. The fifth of these visual summaries is presented and discussed below. You can explore the entire series here .

patient journey quality

Minard. Carte des quantités de viande . 1858.

The large thematic map above shows the home country of 300 years worth of European data visualization pioneers, in the style of Charles Joseph Minard's 1858 quantités de viande  examination of the  supply of meat to Paris  - the first work to size pie chart bubbles and place them on a map. I have so much to get to about Minard beyond this little map

 that I will direct you to the embedded commentary located within the title and explanatory note boxes if you would like to learn more. Click either map to read the details!

Charles Joseph Minard was a French engineer famous for his depiction of Napoleon's retreat from Moscow . He produced over 50 beautiful maps, most notably dozens that draped the flow of goods and people over geography. This article intentionally dives directly past the Moscow map and deep into a most surprising story of data visualization pioneering.

Minard composed almost all of his maps in retirement after completing a decorated career as an engineer and civil servant. A full biography is warranted, but the highlights of Minard's professional career include studying as a teenager under Lagrange and Fourrier at the Ecole Polytechnique , taking part in many major public works projects, teaching at France's premier civil engineering school ( Ecole nationale des ponts et chaussée),  being named to the National Order of the Legion of Honour and becoming the Inspector General of Bridges and Roads.

An age limit decree retired Minard on his 70th birthday in 1851, and for us that's where the real fun begins. Minard went on to develop a new visual engineering practice that combined his expert drafting skills and a fascination with how commerce was shaped by transportation technology (especially waterways and railroads). I have assembled a thematically-grouped visual catalog of Minard's work, below. You can interact with the PDF and click-through each map thumbnail to visit a high quality image in the library that houses it.

patient journey quality

This visual catalog is inspired by the work of Michael Friendly, who maintains an excellent record of Minard's work. I have done my best to blend his list with images available from a number of libraries. You can inspect details of each map (including ones I couldn't find images of) and link through to other sources on Minard by exploring my working spreadsheet .

What stood out to me after assembling the visual catalog, beyond the surprising volume of work, is how often Minard returned to the same themes and chart designs year after year, updating his maps as new data became available. Some of these topics held his attention for over 20 years and provide a window into how his craft evolved as he used each data refresh to tweak design.

Beyond this personal evolution, these series also paint vivid data stories of the different commercial topics Minard was concerned with. These longitudinal visual studies are something Minard was very intentional about. He sometimes included historic predecessors with new charts for easy comparison and annotations that explained why things had shifted. My favorite series from him is on the global cotton trade, which I have highlighted in the following video short:

Imitating historic charts through this series has provided me a wonderful opportunity to slow down and really appreciate the details and design decisions that went into making them. This appreciation for Minard's work goes beyond acknowledging his innovative contributions which include perfecting both the flow map previously introduced by Henry Harness and innovating the use of Playfair's pie chart.

Many best practices can be learned from Minard's work. He employed "approximate" geographic maps that are good enough for reference, but also get out of the way of illustrating data accurately. He packed empty space with lengthy notes that explained how the chart was to be read, where the data came from, his own biases, and key insights. He augmented maps by including corresponding data tables and simple bar charts - building what we might recognize as a dashboard. His flows are often annotated with numbers so that you can get the big picture at a glance and pause to appreciate the details. The innovations are important, but these details are what makes it all sing.

Minard's work was so revered in his day that it was presented to Napoleon III and included in a state minister's official portrait, which you can enjoy in this article's companion piece: Finding Minard .

By 1869, when Minard published the Moscow map, he was 88 years old and hobbled on crutches with arthritis and rheumatism. Prussia marched on Paris the following summer. Remembering the carnage he witnessed at the 1814 Siege of Antwerp, Minard fled for Bordeaux, abandoning his workshop and carrying what he could so that map-making could continue. Almost 90 years old, he caught and succumbed to a fever six weeks after leaving his home.

The library of   l' Ecole nationale des ponts et chaussée (Minard's home institution) houses the most complete collection of Minard's work, some of which was given by Minard himself. You can explore their entire collection here . Special thanks to Anne Lacourt, responsible for archives and collections of objects and furniture at l'Ecole nationale des ponts et chaussées , who has been incredibly helpful as I've journeyed on my Minard adventure (and very patient with my french). The  Bibliothèque nationale de France and US Library of Congress also present several works of Minard.

Approaching the legacy of Minard was intimidating not only because of his incredible work, but also because of the excellent analysis by others that have helped elevate Minard's innovative contributions. Michael Friendly's study of data visualization history, and particularly his enthusiasm for Minard, has been the primary fuel for my own journey. If you want to know more about Minard's life and work I recommend starting with Friendly's paper,  Visions and Re-visions of Charles Joseph Minard . If you are still thirsty for more read Minard's 1871 obituary (original french or  translation ).

If you like this piece you will love the first installment in the series, a detailed journey through the history of data visualization based on John Ogilby’s 1675 road atlas, which features both Minard's meat map and the Napoleon-Moscow sacred cow. You can navigate the whole series using:

patient journey quality

Update: it was very exciting to see Betsy Mason include this investigation of Minard in her excellent  The Underappreciated Man Behind the “Best Graphic Ever Produced” at National Geographic's All Over The Map .

Info We Trust is an award-winning ‘data adventure’ exploring how to better humanize information. Data storyteller  RJ Andrews  is based in San Francisco. Please let me know what you think via Twitter  @infowetrust  or the  contact page .

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  • v.27(7); 2020 Jul

The bird’s-eye view: A data-driven approach to understanding patient journeys from claims data

Katherine bobroske.

o1 Cambridge Centre for Health and Leadership Enterprise, University of Cambridge, Cambridge, United Kingdom

Christine Larish

o2 Research and Development, Evolent Health, Arlington, Virginia, USA

Anita Cattrell

Margrét v bjarnadóttir.

o3 Robert H. Smith School of Business, University of Maryland, College Park, Maryland, USA

Lawrence Huan

Associated data.

In preference-sensitive conditions such as back pain, there can be high levels of variability in the trajectory of patient care. We sought to develop a methodology that extracts a realistic and comprehensive understanding of the patient journey using medical and pharmaceutical insurance claims data.

Materials and Methods

We processed a sample of 10 000 patient episodes (comprised of 113 215 back pain–related claims) into strings of characters, where each letter corresponds to a distinct encounter with the healthcare system. We customized the Levenshtein edit distance algorithm to evaluate the level of similarity between each pair of episodes based on both their content (types of events) and ordering (sequence of events). We then used clustering to extract the main variations of the patient journey.

The algorithm resulted in 12 comprehensive and clinically distinct patterns (clusters) of patient journeys that represent the main ways patients are diagnosed and treated for back pain. We further characterized demographic and utilization metrics for each cluster and observed clear differentiation between the clusters in terms of both clinical content and patient characteristics.

Despite being a complex and often noisy data source, administrative claims provide a unique longitudinal overview of patient care across multiple service providers and locations. This methodology leverages claims to capture a data-driven understanding of how patients traverse the healthcare system.

Conclusions

When tailored to various conditions and patient settings, this methodology can provide accurate overviews of patient journeys and facilitate a shift toward high-quality practice patterns.

INTRODUCTION

Medical researchers have long pointed to the importance of understanding the realistic picture of the patient journey: the chronological sequence of how a patient seeks and receives care from the healthcare system. 1 , 2 Capturing an accurate overview of the patient journey can help identify sources of variability, evaluate why patients respond differently to the same overarching treatment plan, and compare how actual realizations of the treatment plan differ from standard clinical guidelines. However, in a fragmented healthcare system, it can be difficult to derive a comprehensive understanding patient journeys based on real utilization patterns.

Understanding the patient journey is especially important for highly variable, preference-sensitive conditions such as back pain. 3–5 Because back pain has numerous clinically acceptable therapeutic options, the trajectory of patient care can be highly variable and influenced by the severity of the condition, access to healthcare services, provider preferences, and the patient’s medical history. 5–7 Adding to this complexity, treatment for back pain often occurs across service locations (eg, primary care, emergency services, physical therapy). While significant effort has been placed on extracting and analyzing patient journeys from electronic medical records and clinical workflows, these data sources tend to be centered around a single healthcare provider. 8–10

To obtain a more comprehensive overview of the patient journey across various providers and locations, we propose a data-driven methodology based on medical and pharmaceutical claims data. In the U.S. healthcare system, administrative claims data from insurance providers offer a uniquely detailed retrospective account of how individual patients receive medical treatment. 11–13 Claims data contain date, diagnostic, procedural, and provider information, which, when strung together, create an overview of services provided by a collection of clinicians.

Compared with electronic health records, insurance claims are a useful platform to study longitudinal utilization and conditions that are treated across multiple locations. However, claims data are often inherently noisy, have duplicated information, and may not accurately identify a complete list of services provided to the patient. 14 , 15 With these challenges, to our knowledge, automatic detection of representative patient journey patterns from claims data has not been successfully completed at scale.

The proposed data-driven methodology uniquely combines and builds on tools leveraged elsewhere in healthcare informatics to develop an algorithmic approach to extract and understand patient journeys from claims data. 16–25 We represent the back pain–related events of the patient’s journey as a string of letters, in which each letter corresponds to a distinct encounter with the healthcare system. We then evaluate the similarities between the strings based on both their content and their ordering (with a dynamic sequence alignment algorithm), and finally cluster the patient journeys together (using ensemble clustering) to identify representative patterns. Applied together, using careful data modeling, these analytic elements create a data-driven understanding of the patient journey.

The proposed methodology to extract patient journey patterns from claims data combines and customizes techniques from sequence alignment and clustering. Applications of sequence alignment (such as the Levenshtein edit distance) have been successfully implemented within informatics to map laboratory text into a standardized medical vocabulary, identify duplications in electronic medical records, and normalize terms in clinical text. 16–19 Clustering has been shown to be effective at compressing large clinical datasets; techniques including k-means clustering are commonly applied to image processing in the context of radiology scans and skin tissue samples. 20–23

There is a large prior literature that focuses on understanding or extracting patient journey patterns from event logs, such as electronic medical record systems. 8 , 10 , 26 , 27 When patient journey data are organized into event logs or time stamps, process mining can discover a single process map (or set of maps) that shows how entities transfer from the beginning to the end of the system. 24 , 28 Even though noise reduction techniques have been developed to address challenges such as missing data and repeated events, the frequency of such occurrences in claims data makes it difficult to apply process discovery within the claims setting. 9 , 29 , 30 Furthermore, in conditions like back pain, in which it is appropriate to revisit or repeat events such as physical therapy, it may not be appropriate to conceptualize the patient journey as an end-to-end process.

When studying the patient journey using administrative claims, analyses typically limit the analysis to specific elements of the patient journey, for instance, categorizing the first-line treatment after condition onset, or looking at the first 3 events of the treatment pathway. 31 , 32 Claims have also been used to measure outcomes of pathway effectiveness, without being leveraged to create an understanding of the patient journey itself. 33 , 34 Other work identified common pathways by frequency, but this inherently biases the outputs to display the shortest and simplest patient pathways. 35 In contrast, our proposed methodology uses a data-driven approach to identify similarities between patient journeys and understand the main patterns across the patient’s full set of interactions with the healthcare system.

MATERIALS AND METHODS

There are 3 analytical steps to the proposed data-driven approach to extract the patient journeys: claims processing, sequence alignment, and journey clustering. Details of all clinical assumptions, including codes used to process the data, are provided in the Supplementary Appendix .

Claims processing

This research utilized a nationwide U.S. dataset that included medical and pharmaceutical claims from 29 different provider networks across 23 states and the District of Columbia. While not a nationally representative dataset, the patients were insured through commercial, Medicare Advantage, and Medicaid plans and represent a variety of patient demographics and comorbidities. The research was approved by the Ethics Committee at the University of Cambridge Judge Business School as a nonhuman subject study.

We analyzed all back pain–related claims between September 2012 and March 2019, in which back pain was broadly defined to encompass patients expected to follow conservative back pain guidelines, such as those released by the American College of Practitioners. 36 , 37 Following the related back pain literature, patients were excluded if they had a history of cancer, congenital abnormalities, or certain autoimmune conditions, or if they were being treated in end-of-life care, as the care for these patients is often medically justified to deviate from the general guidelines. 36–39

We identified a random sample of 10 000 back pain episodes (corresponding to 9981 unique patients) in which the patient had an initial back pain–related claim after a minimum 6-month clean period without back pain–related claims. 38 , 40 Patients were required to be fully eligible in the dataset for at least 12 months before the start of the episode and for 6 months after the index back pain claim. We then extracted the first 6 months of back pain claims for each episode, totaling 113 215 claims.

The claims processing stage uses clinical assumptions to group the medical and pharmaceutical claims into 14 event types (see Table 1 ). For each event type, we identified the set of diagnosis, procedure, revenue, service location, and clinician specialty codes that could be used to classify the claims. For some event types such as back pain surgery (coded as event letter “S”), the event is clearly defined and the codes used to identify claims are directly drawn from the medical literature. 4 , 38

Back pain episode event categories and event type descriptions

Within the back pain setting, the order of the table from top to bottom reflects a decreasing clinical importance of the events.

Other event types require a more knowledge-driven approach to identify the combination of characteristics that classify claims into events. For instance, the unplanned care event (coded as event letter “E”) looks for claims related to an emergency department visit (revenue code starting with 045, service location code 23, or Current Procedural Terminology code between 99 281 and 99 285) or urgent care visit (revenue code starting with 0516 or 0526, service location code 20, or Current Procedural Terminology code of S90088 or S9083). Further description of how we arrived at the code classification can be found in the Supplementary Appendix .

Once the claims are assigned to an event type, the claims within each event category are aggregated based on overlapping dates into distinct interactions with the health system. For example, if a physical therapy appointment generated more than 1 medical claim, these claims would be grouped together into a single “physical therapy” (T) event. Likewise, all medical claims associated with a multiday inpatient hospital stay would be grouped together into a single “inpatient admission” (A) event.

If an event contained claims that could be classified into different event types, the event is labeled according to the claim with the highest relative importance. For example, if a patient saw a surgeon (G) while admitted to the hospital (I), the event would be labeled as an inpatient admission (I). The order of importance of various events, also known as a clinical hierarchy, is represented from top to bottom in Table 1 , with higher importance events listed first. 41

Because combining claims into a single event only occurs within the partitions of an event category (eg, diagnostic imaging), it is possible that events from different categories occur on the same day. We also apply the clinical hierarchy from Table 1 to order these same-day events, such that the general medical interactions are ordered ahead of diagnostic imaging or prescriptions. This logic assumes that the clinician associated with the general medical interaction likely ordered the diagnostic imaging or prescription drugs.

Events are each assigned a letter and then strung together in consecutive order to form a longitudinal view of the patient journey for back pain across distinct specialty appointments, prescriptions, facility visits, and diagnostic tests. As an example, the string P-T-T-I-O-W is a potential patient journey. It represents a patient that first went to their primary care physician for back pain (P), had 2 physical therapy appointments (T-T), was given diagnostic imaging in the form of magnetic resonance imaging or a computed tomography scan (I), and then was prescribed an opioid (O). A time-spacing event (W) indicates that significant time has elapsed between events or marks the end of an episode.

Depending on the specific study context, the preprocessing stage can make a significant impact in reducing the dimensionality of the dataset. In our illustration, 113 215 back pain claims were reduced to 53 820 events (a 52.5% reduction in distinct data points), representing 2863 unique variations of the 6-month back pain patient journey. Figure 1 contains a visual representation of the first 4 back pain–related events across the patient sample.

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Variation in the first 4 events of the patient back pain journey. Because back pain is a preference-sensitive condition, high variation exists in the first 6 months of the patient journey. The letters in this Sankey chart correspond to the event types displayed in Table 1 . Of the patient back pain episodes, 74% contain 4 or fewer events; 89% are completed within 6 months.

Sequence alignment

For preference-sensitive conditions such as back pain, the treatment decisions (eg, whether the patient was prescribed opioids) as well as the order of treatment decisions (eg, whether the patient was sent for advanced imaging before or after attempting physical therapy) can substantially impact patient outcomes. 4 , 37 , 40 , 42 , 43 The next stage of our proposed algorithm assesses the similarity between pairs of patient journey sequences based on both content and order, without requiring researchers to explicitly define clinical rules.

Levenshtein’s edit distance algorithm aligns 2 sequences using a combination of edits: matches, insertions (or, equivalently, deletions), and substitutions. 44 For example, the sequences G-T-T-T-P and P-T-P could be aligned by substituting the G for P at the front of the string, and inserting 2 Ts into the middle of the second sequence (see Figure 2 ). In the standard Levenshtein algorithm, each match between the 2 sequences is awarded a value of 1 and each insertion or substitution is penalized with a value of –1. 45 As such, aligning G-T-T-T-P and P-T-P as described previously (substitute + match + insert + insert + match) with the Levenshtein edit costs would result in an alignment score of −1.

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Object name is ocaa052f2.jpg

Example of sequence alignment. Our adaption of Levenshtein’s edit distance maximizes the total score of aligning 2 sequences using matches, substitutions, insertions, and transpositions. Because multiple possible alignments exist for any 2 strings, dynamic optimization is applied to maximize the sequence alignment score based on the given edit values.

Our algorithm relies on 2 expansions of the Levenshtein algorithm. First, we allow for transpositions, such that O-P and P-O could be aligned by swapping the last 2 characters instead of applying the insert-match-insert sequence. 46 This is important in the back pain context because small changes in the order of patient actions (eg, filling a prescription and getting an x-ray) are often due to scheduling constraints and are of little consequence to the patient’s overall pattern of care. Second, unlike in the Levenshtein algorithm, in which all edits are penalized with a value of 1, our algorithm customizes the edit values based on both the type of editing action and the event being edited. 19 For instance, transposing 2 letters may be awarded a smaller edit value compared with matching on the same 2 letters.

Assigning edit values can be data-driven, involve the input of medical experts, or a combination of both. 47 For back pain patients, some rarer treatment options, such as surgery, can be a defining aspect of the patient journey. Therefore, instead of weighting a match on surgery equal to a match on a primary care visit, we assign the value of matching events in proportion to the rareness of the event (which we refer to as rareness weighting). With match values scaled between 1 and 10 in our dataset, A (inpatient admission), which makes up 0.1% of events, has a match edit value of 10.0, while P (primary care visits), which makes up 11.7% of events, has a match edit value of 2.2. See the Supplementary Appendix for a complete list of edit values and the corresponding sensitivity analyses.

As each pair of sequences may be aligned with multiple sets of edit actions, we utilize dynamic optimization to efficiently calculate the highest possible alignment score. The dynamic program is based on the principle that the maximum alignment score of strings i and j must be some combination of an action (eg, substitution) on the last letter(s) of 1 or both the sequences and the optimal score before that action. Specifically, we define s i , j * ( y , z ) to represent the maximum score of aligning the first y elements of patient journey i (where 1 ≤ y ≤ i _ len ,   the number of elements in sequence i ) with the first z elements of patient journey j (where 1 ≤ z ≤ j _ len ,   the number of elements in sequence j ). The value of the yth element of i is designated as i [ y ] and the zth element of j as j [ z ] .

The values ( v ) associated with each potential edit operation are v mtc   (match), v sub (substitution), v ins (insertion), and v tns (transposition). The dynamic optimization problem to maximize similarity score s i , j * y , z can be expressed through the following formulation:

Subject to:

Where [1] inserts letter j z into string i , [2] inserts letter i y into string j , [3] matches letter i y = j z , [4] substitutes letter i y for j z , [5] transposes letters i y - 1 : y with j z - 1 : z , and [6] indicates that a transposition between i y - 1 : y and j z - 1 : z is not valid.

After obtaining the optimal similarity score, we calculate the minimum ( scale _ min i j ) and maximum ( scale _ max i j ) scores that could have been generated for the given pair of strings i , j (see Supplementary Appendix for calculation). We then transform the optimal value of aligning the 2 complete strings s i , j * ( i _ len , j _ len ) into a normalized similarity score s i , j , where 0 represents no similarity between strings and 1 implies the strings are identical:

The algorithm thus assigns high similarity scores to similar patient journeys (eg, P-X-O-W and P-X-O-O-W have a similarity score of 0.81) and lower scores to less similar journeys (eg, P-X-O-W and E-R-P-W have a similarity score of 0.21). The similarity scores s i , j for each pair of journeys are compiled into a similarity matrix (see Figure 3 ).

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Sample of the n -by- n similarity matrix. The matrix is populated using the normalized similarity scores. The index [ i , j ] in the similarity matrix s corresponds to the similarity score between patient journey i and patient journey j . Note that diagonal entries all have a normalized similarity score of 1 (as a given patient journey is identical to itself), and the lower diagonal is a reflection of the upper diagonal scores (because s i , j = s j , i ).

Journey clustering

The goal of the clustering is to summarize the main patterns of the patient journeys. As it is important for the methodology to scale to large patient samples, we leverage k-means clustering, an effective approach when classifying objects within large datasets. 21 The basic k-means algorithm (1) chooses k objects to be cluster centers, (2) assigns all other objects to their nearest cluster center, and (3) re-evaluates the center of the cluster. Steps 2 and 3 are repeated until the algorithm converges and no reassignments are made.

To choose the cluster centers, we leverage the “k-means++” seeding technique, an approach that encourages starting seeds to be widely spread across the sample. 48 After the first center K is randomly chosen, the next center is chosen by assigning a probability based on the squared distance between K and the other objects.

Then, because k-means clustering can be sensitive to its initialization, we aggregate the results from multiple iterations of k-means using ensemble clustering. Ensemble clustering forms more stable clusters, with improved robustness and less distortion. 21 , 22 , 49 After the k-means algorithm is run with different values of k and starting seeds, we calculate the percentage of times that patient journey i has been clustered together with patient journey j . These percentages are populated into what is called a co-association matrix.

Researchers can then choose the single-link threshold t , which represents the minimum percentage that a patient journey i must have been clustered together with 1 (or more) of the patient journeys j in the final data partition C n for patient journey i to be added into   C n . In the example illustrated in Figure 4 , higher thresholds (eg, 90%) yield smaller and more homogenous clusters, whereas lower thresholds (eg, 50%) yield larger and more diverse clusters.

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Aggregating k-means results using ensemble clustering. A single-link method partitions the outputs from multiple iterations of k-means into the final patient journey clusters C n . When the minimum threshold t is set to 90%, 2 clusters form: POWPO-POWPRO and GWGIGW-GXIGW; the other 4 patient journeys drop out as “noise.” When t  =   70%, patient journeys are categorized into 1 of 3 clusters: GWGIGW-GXIGW-GXIW, POWPO-POWPRO-PPRW, or EOXW-EPOPW. When t  =   50%, 2 clusters merge, resulting in 2 more heterogeneous clusters: POWPO-POWPRO-PPRW-EOXW-EPOPW and GWGIGW-GXIGW-GXIW.

The chosen threshold t should balance the specificity of the clusters (to focus on specific sets of patients) with the cluster size (to gain enough “power” for any subsequent interpretations, regressions or analyses). To gain an overview of the main patient journey patterns in this study, clinicians selected a threshold of 50% to extract 12 main patient journey patterns from the data. As detailed in the Supplementary Appendix , this threshold is appropriate for this study context in gathering a comprehensive overview of the first 6 months of back pain treatment; setting t to higher thresholds resulted in more, smaller partitions appropriate for studying more detailed clinical questions.

Using the proposed data-driven methodology, the 10 000 patient journeys were reduced into 12 primary patient journey clusters. The resulting clusters displayed in Table 2 show the distribution of patient episodes between diagnosis and treatment pathways, along with example patient journey sequences that make up each cluster.

Back pain patient journey clusters

The highest proportion of patients (17.0% in cluster 1) visit a primary care practitioner and are directed to a low-acuity next step that may include waiting at least 4 weeks, getting an x-ray diagnosis, or a physical or occupational therapy appointment. Patients in cluster 1 appear to closely follow clinical guidelines that promote noninvasive, nonopioid care after initial onset of back pain. 36 , 43 Patients in clusters 5 and 7 also begin their back pain episode in the primary care setting; however, most patients fill prescriptions (either opioid or nonopioid) as their first-line treatment.

The second most common cluster is comprised of patients who make an unplanned visit to an emergency or urgent care center and receive an x-ray (16.0% in cluster 2). In 9.7% of episodes (cluster 3), we observe a self-referral to physical or occupational therapy, in which the patient proceeds to have approximately 3-5 additional therapy appointments. There also exist small, well-defined clusters such as cluster 8 (5.7% of episodes in which patients are primarily treated with facet or epidural injections) and cluster 12 (5.0% of episodes in which pain medicine specialists are consulted but do not administer epidural or facet injections).

As described in the Materials and Methods, patient journeys were clustered solely on the sequence of the patient’s back pain events without considering the patient’s comorbidities or demographics. However, as seen in Table 3 , there is a high level of variability between clusters in terms of patient characteristics.

Cluster summary statistics

For example, patients in cluster 6 (who receive 10 or more physical or occupational therapy sessions) or clusters 9, 11, and 12 (who obtain care from specialists) are more likely to live in areas with higher average salaries compared with patients who follow different patient journeys. Meanwhile, the lowest average salaries within the sample are associated with clusters 2 and 10 (seeking care from the emergency room) or cluster 7 (being prescribed opioids by a primary care physician). The alternative medicine cluster (cluster 4) is associated with the youngest average age of the sample, whereas cluster 8 (invasive pain management procedures) is associated with the highest average age.

Driven largely by our use of rareness-weighted edit values, there is a high level of diversity between clusters in terms of the key back pain outcomes such as early advanced imaging and surgical rates. 37 For example, high rates of back pain surgery are concentrated among the patients whose initial starting encounter is a surgeon (cluster 9 at 9.1%), compared with patients who enter the system though other clinical entry points. Episodes in cluster 8 (invasive pain management procedures) average 10.1 times higher medical costs than episodes in cluster 1. In cluster 7, in which the patient’s first point of contact is typically the primary care physician, 91.2% of patients are prescribed and fill opioids within the first 6 weeks of the start of their episode. This opioid fill rate in this primary care cluster even exceeds that of clusters 2 and 10, in which patients seek care in emergency or urgent care settings.

Although early advanced imaging within 6 weeks of onset of pain is considered a major contributor to overtreatment and inappropriate medical spending, 89.8% of the patients in cluster 10, who seek care in an emergency or urgent care setting, receive magnetic resonance imaging or a computed tomography scan within 6 weeks of their index back pain claim. 4 Despite the high cost associated with this cluster, the episodes appear short-lived, with 93.4% of patients ceasing treatment for back pain within the first 6 weeks compared with the overall rate of 88.9% across the sample. An additional 27.8% of patients who seek care from a surgeon (cluster 9) receive advanced imaging within the first 6 weeks, as do 17.3% of patients whose care is managed by nonprocedural specialists (cluster 12). Although they jointly comprise only 23.4% of episodes in the sample, clusters 8-12, which rely heavily on specialists, procedures, and imaging, make up 43.2% of overall back pain spending.

While claims data have been touted as having the potential to provide a bird’s-eye view of a patient’s healthcare records and of healthcare utilization at the population level, studies have often fallen short of that goal. Claims data are notoriously noisy (owing to, for example, variation in medical coding) and are not generated for research purposes. 11–15 The proposed methodology effectively used a combination of data processing, sequence alignment, and ensemble clustering to identify primary patient journey patterns in the highly variable, preference-sensitive condition of back pain.

When a group of primary care providers in a large multispecialty clinic were presented with the preliminary cluster outputs using the group’s own data, they initially voiced concerns about the validity of the treatment pathways. However, within a short time of reviewing the outputs, the clinicians moved beyond their own recollections of individual patient cases to a more objective discussion treatment options within the context of real-life complexities. In addition to engaging with an overview of care plans, clinicians leveraged the outputs to better understand variability between outcomes and inform future medical research into how patients and providers interact to create high-quality care.

This study is the result of a close collaboration between healthcare informatics researchers and clinicians. Medical expertise on back pain was instrumental in designing an objective with clinical relevance and informing the patient sample criteria. Clinicians also helped define event types, set edit value assumptions, and interpret cluster results. While not used directly in this research, standardized logics, such as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), can aid informatics researchers when translating clinical assumptions into corresponding diagnosis codes. 50

There are multiple opportunities to expand this methodology in future work. First, the methodology could be adapted to study longitudinal healthcare events such as chronic conditions or the patient experience at the end of life. Doing so may require researchers to adjust the preprocessing steps such that the patient journey is represented in blocks of time instead of as a string of distinct events.

Second, researchers could consider different methods of assigning edit values. For instance, the rareness-weighting method of assigning edit values was developed when clinicians identified that the Levenshtein edit values caused insufficient differentiation of journeys with rare, high-cost events. In other research contexts that have well-established guidelines, a weighting strategy could assign edit values based on which events are recommended as first-line or second-line therapies.

Third, the methodology could be adjusted based on the size of the dataset. The sequence alignment step can be computationally intensive as it compares each pair of patient journeys to obtain the full matrix of similarity scores. In comparison, conformance checking in process mining uses sequence alignment to compare event-log data to a single, predetermined understanding of the process map. 19 , 24 While the conformance checking approach anchors the analysis to prior, potentially biased knowledge of the system, there likely exists a balance between its limited comparisons and our methodology that lets patterns emerge fully from the data. Researchers could also compare the efficiency of the presented clustering method to techniques such as spectral clustering.

Finally, we have not yet explored techniques used in other applications of data science and sequence alignment, including the trace-back method that identifies sources of deviations along pairs of sequences. 51 In the context of preference-sensitive conditions, the trace-back method could allow researchers to isolate key discrepancies between journeys that may have led to variation in outcome measures. The resulting clusters can also be combined with prediction algorithms to identify patients who should be targeted for early intervention. For example, certain patterns in the beginning of the journey may signal that a patient is at elevated risk for aggressive opioid prescribing or for a low-quality procedure.

Despite the clinically relevant results, we acknowledge that this study has several limitations. We used a very broad definition of back pain that captured most patients presenting new general back pain symptoms. The purpose of this definition was to identify patient episodes typically expected to follow a conservative care route, as outlined by the American College of Physicians, and to understand population-level deviation from clinical recommendations. 36 , 37 It is not known how well our selected population reflect all patients who present with back pain, as claims data were not supplemented by other data sources such as hospital notes or psychologic evaluations. Additionally, while patients are geographically dispersed throughout the United States, the sample is not nationally representative; thus, the breakdown of patient episodes into clusters may not represent national trends.

Compared to clinical guidelines that represent a top-down picture of patient behavior, the outputs from this methodology reveal a data-driven understanding of how patients traverse the healthcare system. Using a limited set of assumptions, the methodology is particularly effective in analyzing conditions with high levels of variability in patient care and those treated across service locations. In the preference-sensitive condition of back pain, we observed that treatment choices are associated with patient characteristics and procedure rates, thereby highlighting the potential public health impact of related future studies based on this methodology. When tailored to various care settings, this methodology can provide the medical community with an accurate overview of the current state of patient care and facilitate a shift toward high-quality practice patterns.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sector. It was supported internally by Evolent Health.

AUTHOR CONTRIBUTIONS

All authors were involved in revising the work for intellectual content and approved the manuscript. KB, AC, and LH made substantial contributions to the original study design. KB developed the algorithm, contributed to data analysis, interpretation, and writing the manuscript. CL contributed to data analysis, interpretation, and writing the manuscript. AC contributed to data acquisition and revising the manuscript. MB refined the algorithm and contributed to data interpretation, writing, and revising the manuscript. LH served as the primary contact with provider groups during algorithm development and contributed to clinical assumptions, data interpretation, and revising the manuscript.

SUPPLEMENTARY APPENDIX

Supplementary Appendix is available at Journal of the American Medical Informatics Association online.

Supplementary Material

Ocaa052_supplementary_data, acknowledgments.

The authors gratefully acknowledge the following individuals for their contributions: Rich King, Malcolm Charles, and Michael Freeman who advised on algorithm development; Madina Bram who provided administrative support on the project; Feryal Erhun, Stefan Scholtes, Nico Lewine, Matthias Weidlich, and Jenny Wang who provided valuable feedback and suggestions to the development and framing of this research. The authors also thank the JAMIA review team for their constructive and encouraging comments, as well as the clinicians who participated in discussions on the back pain assumptions and outputs.

CONFLICT OF INTEREST STATEMENT

None declared.

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