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Total hospital outpatient visits in the United States 1965-2019

Total number of hospital outpatient visits in the u.s. from 1965 to 2019 (in 1,000).

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United States

1965 to 2019

Data are for all AHA-registered hospitals in the United States. Data are estimated for nonreporting hospitals (except data before 1972).

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• Article Information

The dotted vertical line in panel B indicates the week of March 17, 2020, (week 11), when Medicare expanded reimbursement for telemedicine visits due to the COVID-19 pandemic. 4

a Week 21 (May 20 to May 26, 2020) includes Memorial Day, a federal holiday in the US. The work week was likely 4 days for many practices resulting in a decrease in visit volume.

• Trends in US Ambulatory Care Patterns During the COVID-19 Pandemic JAMA Original Investigation January 18, 2022 This retrospective study compares ambulatory care patterns before and in the first year of the COVID-19 pandemic among patients insured by public and private insurance programs. John N. Mafi, MD, MPH; Melody Craff, PhD, MB, BChir; Sitaram Vangala, MSc; Thomas Pu, MHI; Dale Skinner, MSc; Cyrus Tabatabai-Yazdi, MSc; Anikia Nelson, MD; Rachel Reid, MD, MPH; Denis Agniel, PhD; Chi-Hong Tseng, PhD; Catherine Sarkisian, MD, MSPH; Cheryl L. Damberg, PhD; Katherine L. Kahn, MD
• Federal Plan Proposes Improving Rural Health Care Through Telehealth JAMA Health Forum In the News September 22, 2020 Joan Stephenson, PhD

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Patel SY , Mehrotra A , Huskamp HA , Uscher-Pines L , Ganguli I , Barnett ML. Trends in Outpatient Care Delivery and Telemedicine During the COVID-19 Pandemic in the US. JAMA Intern Med. 2021;181(3):388–391. doi:10.1001/jamainternmed.2020.5928

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Trends in Outpatient Care Delivery and Telemedicine During the COVID-19 Pandemic in the US

• 1 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
• 2 Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
• 3 OptumLabs Visiting Fellow, Eden Prairie, Minnesota
• 4 RAND Corporation, Arlington, Virginia
• 5 Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
• 6 Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
• Original Investigation Trends in US Ambulatory Care Patterns During the COVID-19 Pandemic John N. Mafi, MD, MPH; Melody Craff, PhD, MB, BChir; Sitaram Vangala, MSc; Thomas Pu, MHI; Dale Skinner, MSc; Cyrus Tabatabai-Yazdi, MSc; Anikia Nelson, MD; Rachel Reid, MD, MPH; Denis Agniel, PhD; Chi-Hong Tseng, PhD; Catherine Sarkisian, MD, MSPH; Cheryl L. Damberg, PhD; Katherine L. Kahn, MD JAMA
• In the News Federal Plan Proposes Improving Rural Health Care Through Telehealth Joan Stephenson, PhD JAMA Health Forum

The coronavirus disease 2019 (COVID-19) pandemic has dramatically altered patterns of health care delivery in the US. In the context of declining in-person outpatient visits, many clinicians began using telemedicine for the first time, spurred in part by regulatory changes that expanded public and private insurer reimbursement for a wider range of telemedicine services. 1 , 2 To understand how telemedicine compensated for declining outpatient volume and geographic variation in changing patterns of outpatient care, we examined telemedicine and in-person outpatient visits in 2020 among a national sample of 16.7 million individuals with commercial or Medicare Advantage insurance.

We used insurance claims from the OptumLabs Data Warehouse 3 to capture all outpatient visits over a 24-week period from January 1, 2020, to June 16, 2020. We included enrollees with 12 months of continuous enrollment (July 2019-June 2020). We assessed data completeness using weekly childbirth rates (eAppendix in the Supplement ). We defined outpatient visits as Medicare’s list of Common Procedural Terminology (CPT) codes eligible for telemedicine 4 and telemedicine visits via modifier codes GT, GQ, or 95 or CPT codes 99441-99443.

We assessed changes in outpatient visit volume by capturing weekly rates per 1000 enrollees of telemedicine, in-person, and total visits over the study period. For each state, during the final 4 weeks of the study period (May 20 to June 16), we calculated the percent of total weekly visits delivered by telemedicine and the percent change in total weekly visits compared to the 4 week period preceding expansion of telehealth coverage by Medicare (February 12 to March 10). 5 The Harvard Medical School institutional review board exempted this study from review and informed consent because all data were deidentified.

Among 16 740 365 enrollees, the weekly rate of telemedicine visits increased during the pandemic period, peaking in the week of April 15, 2020, before declining by the week of June 10, 2020 ( Figure 1 ). From the weeks of January 1 to June 10, the rates for telemedicine visits increased from 0.8 to 17.8 visits per 1000 enrollees (increase of 17.0 or 2013% change); in-person visits dropped from 102.7 to 76.3 (decrease of 26.4 or −30.0% change); total visits (telemedicine and in-person visits combined) decreased from 103.5 to 94.1 (−9.1% change).

By the last 4 weeks of the study period, May 20 through June 16, there was wide geographic variation in the percent of total visits delivered by telemedicine (ranging from 8.4% in South Dakota to 47.6% in Massachusetts) and the percent change from baseline in total visit rates (ranging from −73.2% in Hawaii to −16.0% in Alaska) ( Figure 2 ). Some states, especially in the South, had a small decline in total visits and lower rates of telemedicine use (ie, Tennessee, −23.6% change in total visits with 10.4% of all visits as telemedicine; Alabama, −21.5% and 13.4%, respectively).

In this national study of a commercially insured population, growth in telemedicine use offset roughly two-thirds of the decline in in-person visit volume during the COVID-19 pandemic. Although there was geographic variation in the magnitude of changes, every state experienced a drop in total visits, illustrating the broad scope of deferred care during the first months of COVID-19. Although some deferred care may have represented discretionary care that could be postponed without harm, these results also substantiate concerns that patients may fall behind in chronic illness management or face complications from deferred acute medical issues. This would be consistent with evidence from natural disasters resulting in decreased access to care associated with greater morbidity and mortality not directly related to the disaster itself. 6

An important limitation is that results may not generalize to other populations (eg, traditional Medicare or Medicaid). Telemedicine use during the early COVID-19 pandemic only partially offset a drop in total outpatient care.

Accepted for Publication: August 31, 2020.

Published Online: November 16, 2020. doi:10.1001/jamainternmed.2020.5928

Corresponding Author: Michael L. Barnett, MD, MS, Department of Health Care Policy and Management, Harvard T. H. Chan School of Public Health, 677 Huntington Ave, Kresge 411, Boston, MA 02115 ( [email protected] ).

Author Contributions: Dr Patel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Patel, Mehrotra, Barnett.

Acquisition, analysis, or interpretation of data: Patel, Huskamp, Uscher-Pines, Ganguli, Barnett.

Drafting of the manuscript: Patel.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Patel, Barnett.

Obtained funding: Mehrotra, Huskamp, Uscher-Pines.

Administrative, technical, or material support: Patel.

Supervision: Mehrotra, Uscher-Pines, Barnett.

Conflict of Interest Disclosures: Dr Mehrotra reported grants from the National Institutes of Health during the conduct of the study. Dr Huskamp reported grants from the National Institute of Mental Health during the conduct of the study. Dr Ganguli reported personal fees from Haven and personal fees from Blue Cross Blue Shield Massachusetts outside the submitted work. No other disclosures were reported.

Funding/Support: This project was supported by the National Institute on Aging of the National Institutes of Health (K23 AG058806-01) and the National Institute of Mental Health (R01 MH112829, T32MH019733). We thank Rebecca Shyu for contributing to data analysis, visualization, and manuscript preparation efforts.

Role of the Funder/Sponsor: The National Institute on Aging of the National Institutes of Health (K23 AG058806-01) and the National Institute of Mental Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Impact of COVID-19 on Trends in Outpatient Clinic Utilization

Courtney e. mccracken.

* Center for Research and Evaluation, Kaiser Permanente Georgia, Atlanta, GA

Jennifer C. Gander

† Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO

Bennett McDonald

Glenn k. goodrich, heather m. tavel, sundeep basra.

‡ Mid-Atlantic Permanente Research Institute, Kaiser Permanente Mid-Atlantic States, Rockville, MD

Nancy S. Weinfield

Debra p. ritzwoller, douglas w. roblin, teaniese l. davis, background:.

The COVID-19 pandemic forced many US health care organizations to shift from mostly in-person care to a hybrid of virtual visits (VV) and in-person visits (IPV). While there was an expected and immediate shift to virtual care (VC) early in the pandemic, little is known about trends in VC use after restrictions eased.

This is a retrospective study using data from 3 health care systems. All completed visits from adult primary care (APC) and behavioral health (BH) were extracted from the electronic health record of adults aged 19 years and older from January 1, 2019 to June 30, 2021. Standardized weekly visit rates were calculated by department and site and analyzed using time series analysis.

There was an immediate decrease in APC visits following the onset of the pandemic. IPV were quickly replaced by VV such that VV accounted for most APC visits early in the pandemic. By 2021, VV rates declined, and VC visits accounted for <50% of all APC visits. By Spring 2021, all 3 health care systems saw a resumption of APC visits as rates neared or returned to prepandemic levels. In contrast, BH visit rates remained constant or slightly increased. By April 2020, almost all BH visits were being delivered virtually at each of the 3 sites and continue to do so without changes to utilization.

Conclusions:

VC use peaked during the early pandemic period. While rates of VC are higher than prepandemic levels, IPV are the predominant visit type in APC. In contrast, VC use has sustained in BH, even after restrictions eased.

Telehealth, or virtual care (VC), encompasses a range of visit types including synchronous modalities such as real time video, telephone or synchronous chats along with asynchronous modalities (ie, messages or chats not in real time) and remote patient monitoring. 1 While early forms of VC, like telephone calls with a physician, have been around since the start of the 20th century, improvements in technology, including mobile device use and federal legislation have pushed VC into the 21st century. 2 – 4 Despite the availability of VC options, both patients and providers have identified barriers to VC that have contributed to a slow adoption of VC. These barriers stem from reimbursement models, equipment issues, lack of reliable internet, and state-level policies. 5 , 6 Before the COVID-19 pandemic, coverage of VC services under Medicare was only available to those living in rural areas. 7 During the pandemic, the Centers for Medicare & Medicaid Services (CMS) and most private insurance plans expanded coverage for VC appointments, improved reimbursements, decreased or removed co-pays, and/or expanded indications for VC use. 7

The onset of the COVID-19 pandemic (March 2020) and the declaration of a national emergency (March 13, 2020) rapidly shifted the delivery of health care in the United States from a mostly in-person model to a hybrid model dominated by VC. 8 – 12 This shift was in part due to the recommendations made by the Centers for Disease Control and Prevention (CDC) encouraging VC use (eg, video and telephone visits) by both patients and providers, when possible, to reduce the spread of COVID-19 and encourage social distancing practices. 13 In addition, recommendations were made by CMS to delay nonessential medical visits and procedures to reduce the risk of COVID-19 transmission and decrease the burden on the health care system. 14 Consequently, many health care systems saw a decrease in outpatient visits for non–COVID-related issues.

Before the COVID-19 pandemic, 90% of virtual visits (VV) among insured patients were from behavioral health (BH) and primary care visits. 15 BH encounters often consist of counseling and psychotherapy and may be more amendable to delivering care virtually. In contrast to BH, many services provided in adult primary care (APC), such as vital sign checks and physical examinations, mostly require in-person visits (IPV). Given the shift to VC caused by the pandemic, little is known about the long-term trends in VC use, especially once restrictions eased and IPV were easier to come by. For several years before the COVID-19 pandemic, the integrated health care delivery systems of Kaiser Permanente (KP) have offered a wide range of VC options including video visits, chats, telephone calls, house calls, and a secure patient portal platform for messaging, appointment requests, prescription refills and laboratory and medical record review. KP is comprised of 8 health care systems that integrate their health plan departments, hospitals and medical groups creating a closed system for both outpatient and inpatient care. When COVID-19 hit the United States, KP was able to leverage their established, mature VC infrastructure to offer virtual appointments to their 12+ million membership.

The overarching goal of this study is to describe the impact of the COVID-19 pandemic on health care utilization within 3 large, independent KP health systems. We focus on 2 departments where VC appointments were utilized before the pandemic, APC and BH. Our objectives were to: (1) describe the impact of the COVID-19 pandemic on visit rates in APC and BH departments; and (2) determine how the rate of VV and IPV changed in APC and BH departments once COVID-19 restrictions eased, and clinics resumed IPV. This study may provide insights into the long-term transition to VC use as the COVID-19 pandemic continues to evolve.

Setting and Population Studied

This study used data from 3 KP health systems in the Denver and Boulder, Colorado area (KPCO), Atlanta metropolitan-area of Georgia (KPGA), and the Mid-Atlantic States (KPMAS) which encompasses Maryland, Virginia, and the Washington, DC area. These sites represent geographically and racially diverse membership of over 1.6 million adult individuals. Individuals included in the study population were adults age 19 years and older as of the first of the month of each of the 30 months from January 2019 to June 2021.

Study Design

This is a retrospective study using data from health care encounters that occurred in each health care system from January 1, 2019 to June 30, 2021. To describe changes in visit rates pre-COVID-19 and post-COVID-19 onset, we further divided the period into 3 eras of care: (1) pre-COVID=January 1, 2019–March 12, 2020; (2) national COVID-19 shutdown and recovery=March 13, 2020–December 31, 2020; (3) COVID-19 vaccination phase=January 1, 2021–June 30, 2021. Each of the 3 sites maintain a similar local implementation of a common data structure and repository developed under the governance of the Health Care Systems Research Network 16 to facilitate multisite collaborations. Demographics, visit characteristics, hospitalizations, diagnoses, procedures, and medication orders are routinely curated from each site’s electronic health record and claims databases and imported into their local virtual data warehouse (VDW). Additional details about the VDW can be found in the Supplemental Digital Content ( http://links.lww.com/MLR/C568 ). Data for this study were extracted from each site’s VDW and electronic health record data using common data definitions and variable formats. Data were aggregated and deidentified and sent to KPGA for analysis. This study was reviewed and approved by the KPGA institutional review board, which served as the institutional review board of record for all 3 sites.

Classification of Visit Model

APC visits consisted of visits, subset into: (1) IPV conducted by providers in adult or family medicine clinics or providers in urgent care clinics, and (2) VV (not in-clinic) conducted through a virtual mode (KPMAS “house calls,” telephone visits, video visits, and synchronized chats). BH visits consisted of visits, subset into: (1) IPV conducted by a BH provider (psychiatrist, clinical psychologist, licensed clinical therapist, or social worker) in a BH clinic, and (2) VV conducted through a virtual mode (telephone visits, video visits, and synchronous chats). KPMAS “house calls” allow for only patient concerns related to specific physical conditions. To provide a more congruent comparison of visit rates, encounters that could only be completed in-person (ie, did not have a virtual analog) were excluded. This included visits such as immunizations, blood pressure checks, eye examinations, or preoperative testing. To reduce the influence of COVID-19 testing and vaccination on health care utilization trends, we excluded visits solely for COVID-19 vaccination or testing.

During the study period modes of VV varied by site and era. Pre-COVID-19, KPGA offered both video visits and telephone visits, KPMAS offered house calls, video visits, and telephone visits, and KPCO offered telephone visits and synchronous chats (Supplemental Digital Content Table 1, http://links.lww.com/MLR/C568 ). During COVID-19, KPCO added video visits and KPGA added synchronous chats (APC only). Given the sparsity of some VC types, all VV types were collapsed for analysis.

Statistical Analysis

Individual visits were combined to provide weekly summaries of visit counts overall and by department. Weekly rates were further summarized by visit type; VV or IPV. Data were treated as a weekly time series and for each week, we computed total (VV+IPV), VV, and IPV rates as visits per 1000 enrolled adult beneficiaries. To reduce large variations in weekly rates, especially around weeks with holidays, consecutive weekly visit rates were averaged together, reducing the number of data points to 65 observations. Visit rates were plotted over a 30-month period using scatter plots with linear interpolation. Separate time series were created for APC and BH visits for each visit mode and site.

To evaluate trends in visit rates we applied analytic approaches appropriate for time series data including the use autoregressive error models to account for correlated time observations. Lags were included in the model to improve model fit and reduce autocorrelation. Model diagnostics were examined and included partial autocorrelation plots, white noise probability plots, and tests for autocorrelation and heteroscedasticity. To evaluate changes in trends across the 3 eras, segmented regression models were constructed. The parameter estimates obtained from the segmented regression models include 6 terms of interest. These estimates are described further in Supplemental Digital Content Table 2 ( http://links.lww.com/MLR/C568 ). Analyses were conducted using SAS Enterprise Guide, v. 8.2 and statistical significance was assessed at the 0.05 level. Additional details regarding the statistical analyses can be located in the Supplemental Digital Content ( http://links.lww.com/MLR/C568 ).

Adult Primary Care

Overall utilization trends.

Figures ​ Figures1A–C 1 A–C depict the observed weekly encounter rates at each of the 3 sites during the study period. The figures show the overall trend (red line), the IPV rate (green line) and virtual visit rate (blue line). Overall rates of APC visits in the pre-COVID era were stable at each of the 3 sites and trend patterns throughout the study period were similar. The median (25th–75th percentile) weekly visit rate per 1000 enrolled adult members was 44.8 (41.5–47.7), 44.3 (43.7–47.1), and 47.9 (46.2–49.3) at KPCO (Fig. ​ (Fig.1A), 1 A), KPGA (Fig. ​ (Fig.1B), 1 B), and KPMAS (Fig. ​ (Fig.1C), 1 C), respectively. Results from the time series analysis (Table ​ (Table1) 1 ) demonstrated a relatively flat trend (b 11 overall =0) in the weekly visit rate with some seasonal variation noted (higher utilization in first and last quarters of the calendar year). As expected, there was an immediate drop, or level shift, in APC visits coinciding with the national emergency declaration. All sites detected a significant decrease in the weekly visit rate following the shutdown with an estimated level shift (b 02 overall ) ranging from −10.1 to -9.7 visits per 1000 members/week (Table ​ (Table1). 1 ). During the second era, median weekly visit rates declined to 39.4 per 1000 members (37.2–42.0), 41.6 (37.8–43.9), and 42.9 (40.4–45.3) at KPCO, KPGA, and KPMAS, respectively. In addition to an immediate level shift, the trend (ie, slope) in the weekly visit rate significantly increased at KPCO and KPGA (b 12 overall >0) as clinics began to open and IPV were more widely available. Of note, KPMAS did not have a statistically significant change in the pre-COVID slope during the second era. During the third era (January 2021–June 2021), median weekly visit rates in APC at 2 of the 3 sites returned to prepandemic levels. At KPCO (Fig. ​ (Fig.1A) 1 A) and KPGA (Fig. ​ (Fig.1B) 1 B) median weekly visit rates were 44.0 per 1000 members (43.2–44.7) and 44.5 (42.8–45.2). In contrast at KPMAS (Fig. ​ (Fig.1C), 1 C), the median weekly visit rate of 44.9 per 1000 members was still below the median pre-COVID rate. Time series analysis at KPMAS confirmed a slower rate of recovery compared with the 2 other sites (Table ​ (Table1). 1 ). Trends in visit rates varied across the 3 sites during the third era. At KPGA, the time series analysis demonstrated a negative trend in the third era corresponding to a significant decrease in the average weekly trend (b 13 overall <0). In contrast, at KPMAS and KPCO, there was no level shift or trend change during the third era.

(A–C) Weekly encounter rates in adult primary care departments per 1000 enrolled adult members from January 1, 2019, to June 30, 2021, at Kaiser Permanente Colorado (KPCO) (A); Kaiser Permanente Georgia (KPGA) (B); Kaiser Permanente Mid-Atlantic States (KPMAS) (C). The red dashed line is the overall weekly encounter rate. The green solid line is the in-person visit weekly encounter rate. The blue solid line is the virtual visit weekly encounter rate. The dashed black lines define the 3 encounter eras [pre-COVID (era 1), COVID onset and recovery (era 2), and COVID vaccination (era 3)]. The first black dashed line corresponds to the national emergency on March 13, 2020. The second black line corresponds to December 26, 2020 which shortly follows the emergency authorization and distribution of the first 2 COVID-19 vaccines.

Model Estimated Trends and Level Changes Before and During COVID-19 Pandemic for Adult Primary Care Encounters

Avg. indicates average; Enc, encounter; KPCO, Kaiser Permanente Colorado; KPGA, Kaiser Permanente Georgia; KPMAS, Kaiser Permanente Mid-Atlantic States.

In-person and Virtual Visit Trends

Before the national shutdown, weekly VV rates in APC were stagnant with nonsignificant changes in the weekly VV rate. The median weekly VV rate pre-COVID ranged from 3.7 per 1000 members (KPGA) to 4.8 (KPMAS) to 7.8 (KPCO). VV accounted for <20% of all APC visits (Supplemental Digital Content Figs. 1a–c, http://links.lww.com/MLR/C568 ). Similar stationary trends were observed for IPV at each of the 3 sites (Table ​ (Table1). 1 ). After the shutdown at each of the 3 sites, IPV rates immediately and significantly decreased (b 02 IPV <0) to a rate of <5 IPV per week per 1000 adult members. However, during the recovery period, IPV rates slowly increased (b 12 IPV >0) with new estimated slopes of 0.98 (KPCO), 1.15 (KPGA), and 0.93 (KPMAS) visits per week per 1000 members (Table ​ (Table1). 1 ). During the second era, ∼60% of all visits were virtual (Supplemental Digital Content Figs. 1a–c, http://links.lww.com/MLR/C568 ); however, as quickly as the VV rate increased, the weekly rate began to decrease into a downward trend (Figs. ​ (Figs.1A–C) 1 A–C) in mid-2020. This visual pattern was confirmed by the time series model (b 12 VV <0) resulting in an overall decreasing trend. As clinics reopened, IPV increased and became the dominant visit mode by the third era; however, trends differed by site. At KPGA and KPMAS, the rollout of COVID-19 vaccines (also coinciding with a COVID-19 surge) resulted in an immediate decrease in the weekly IPV rate (b 03 IPV <0). This effect was not observed at KPCO. In addition, during the third era the IPV weekly visit rate was still increasing but slowed compared with the recovery period (b 13 IPV <0). All 3 sites observed an immediate increase in the VV rate in January 2021 (b 03 VV >0); however, the overall trend continued to decline at the same rate in era 3 as it did in era 2 (b 13 VV =0).

Behavioral Health

In contrast to APC, trends in weekly visit rates (shown in Figs. ​ Figs.2A–C) 2 A–C) for BH visits remained stable or even slightly increased following the onset of the COVID-19 pandemic. Overall weekly visits rates in leading up the COVID-19 shutdown visually appeared similar at each of the 3 sites but were noted to have small trend differences in our models. Results from Table ​ Table2 2 show a small but significant decreasing trend at KPCO (b 11 overall =−0.02), a stable trend at KPMAS (b 11 overall =0.006) and small but slightly increasing at KPGA (b 11 overall =0.04). In the first era, the median (25th–75th percentile) weekly visit rate per 1000 enrolled adult members was 4.8 (4.6–5.8), 8.2 (7.9–9.1), and 5.8 (5.4–6.1) at KPCO, KPGA, and KPMAS, respectively. Unlike the trends observed in APC, there were no significant level shifts or immediate changes in the weekly visit rate resulting from the national emergency (b 02 overall =0). During the second era, KPCO saw a small, but significant, increase in the weekly trend rate followed by a significant, positive, level change at the start of the third era (b 03 overall >0). Figure ​ Figure2A 2 A shows an increasing trend in the weekly visit rate which was confirmed by the time series analysis (Table ​ (Table2). 2 ). The median visit rate during the second and third eras at KPCO were 5.4 per 1000 members (4.9–5.8) and 5.8 (5.4–6.0). At KPGA, during the second and third eras, no significant changes in trends or level shifts between eras were detected. The median weekly visit rate during the third era was 9.2 (8.9–9.6) and relatively unchanged from the second era [9.3 (8.8–9.6)]. At KPMAS, median weekly visit rates increased during each era from 5.8 to 6.3 to 7.1. The time series analysis detected significant changes in the trend (increased trend rate) during the second era followed by a decreasing trend or slope in the third era (Table ​ (Table2 2 ).

(A–C) Weekly encounter rates in behavioral health departments per 1000 enrolled adult members from January 1, 2019 to June 30, 2021 at Kaiser Permanente Colorado (KPCO) (A); Kaiser Permanente Georgia (KPGA) (B); Kaiser Permanente Mid-Atlantic States (KPMAS) (C). The red dashed line is the overall weekly encounter rate. The green solid line is the in-person visit weekly encounter rate. The blue solid line is the virtual visit weekly encounter rate. The dashed black lines define the 3 encounter eras [pre-COVID (era 1), COVID onset and recovery (era 2), and COVID vaccination (era 3)]. The first black dashed line corresponds to the national emergency on March 13, 2020. The second black line corresponds to December 26, 2020 which shortly follows the emergency authorization and distribution of the first 2 COVID-19 vaccines.

Model Estimated Trends and Level Changes Before and During COVID-19 Pandemic for Behavioral Health Encounters

The onset of COVID-19 radically changed how BH departments functioned at KP. Unlike APC departments, BH visits have remained almost entirely virtual (Figs. ​ (Figs.2A–C 2 A–C and Supplemental Digital Content Figs. 2a–c, http://links.lww.com/MLR/C568 ) as in-person services have been limited in this department. As a result, following the national emergency all 3 sites had a significant increase in VV utilization rates (b 02 VV >0) and a significant decrease in IPV utilization rates (b 02 IPV <0). During the first era, median weekly IPV rates were 4.1, 5.9, and 4.5 at KPCO, KPGA, and KPMAS, respectively. IPV rates in the second and third eras we nearly 0 at each of the 3 sites. Even in the third era, VV continue to dominate BH visits at each of the 3 sites with median rates of 5.3, 8.9, and 7.0 at KPCO, KPGA, and KPMAS, respectively and accounted for >92% of all BH visits. At KPCO and KPMAS the trend in VV utilization rates continued from the pre-COVID trend but started to significantly decline during the third era (b 13 VV <0), driving the observed decreasing trend in the overall BH weekly utilization rate.

Our study found that the COVID-19 pandemic differentially impacted health care utilization in APC and BH departments within and across our 3 health care systems. Our first goal was to describe visit rates in APC and BH departments before and during the COVID-19 pandemic. In APC, visit rates remained stable in the year preceding the pandemic. At the start of the pandemic, visit rates declined but eventually rebounded to prepandemic levels in 2021. In contrast, BH visit rates remained stable or slightly increased during the pandemic.

Our second goal was to evaluate trends in virtual visit and IPV rates. In both departments, the onset of the pandemic resulted in significant decrease in IPV and increase in VV. In APC, each health care system saw a resumption of IPV in 2021; however, BH visits have remained almost entirely virtual without impacting overall visit rates. The onset of the COVID-19 pandemic and national shutdown significantly decreased the rate of APC visits and led to a large shift in care delivery. Before the pandemic, VV accounted for <20% of APC visits across each of the 3 sites; however, this rate of VV use is higher than other health care organizations. Using cross-sectional data from 2018 to 2020 from the IQVIA National Disease and Therapeutic Index, Alexander and colleagues found that before COVID-19, ∼8% of visits in primary care (including pediatrics) were conducted virtually, while in the second quarter of 2020, VV accounted for 35% of primary care visits. The proportion of APC visits conducted virtually during the second quarter of 2020 was much higher in our study (>50% of APC visits). Our study excluded visits related to COVID-19 and those that could only be conducted only in-person which may have contributed towards our higher observed rate of VV. The higher rate of VV use was not sustained throughout the pandemic and started to decline by mid-2020. By 2021, VV accounted for 35%–45% of visits in APC. Consistent with our findings, a cross-sectional survey of US adults in found a declining rate of VC use such that by April 2021 <30% of adults surveyed used VC in the last month. 17

We noted significant fluctuations in VV rates across all 3 sites and both departments at the end of 2020. These changes may be a reflection of end of year trends in ambulatory care visits when clinics and providers have reduced appointment availability during the holiday season and urgent care and emergency departments are more frequently utilized. While not as pronounced, this trend was also noted toward the end of 2019. Thus, the significant changes in visit rates from the end of the second era into the third era could be due to reduced appointment availability and not a reflection of COVID-19 trends.

Our APC visit trends are consistent with findings from other health care systems and commercially insured populations. 8 – 10 , 18 – 20 Whaley et al 20 reported that among employer-insured individuals, there was a large reduction in the use of preventative services and elective procedures. In April 2020, IPV decreased by over 1000% and VV increased by 642% compared with March 2019. A study by Kaiser Family Foundation evaluated outpatient visits from March 2019 to August 2021 using data from 126 million patients across 156 Epic organizations. 21 Consistent with our findings, the study by Lo et al 21 reported that the rate of outpatient visits had returned to prepandemic levels and while VC use continues to decline, it is still being used at a much higher rate compared with 2019. The declining trend in virtual APC visits is not unexpected as many individuals return in-person care for unmet needs related to chronic conditions and preventative care. 22 During the third era, 2 of the 3 sites saw their visit rates return to pre-COVID levels. Both sites added an additional virtual mode during the recovery period which may have impacted their overall visit rates.

At 2 of the 3 sites, overall rates of BH visits slightly increased during the pandemic. During the COVID-19 pandemic, mental health diagnoses among adults, including anxiety and depression, increased. 23 , 24 , 25 A systematic review examining the global prevalence of major depressive and anxiety disorders before and during the COVID-19 pandemic found an estimated 53.2 million cases of major depressive disorder and 76.2 million cases of anxiety disorder were due to the effects of the COVID-19 pandemic. 23 Another study from Kaiser Family Foundation found that 40% of all VV from March 2020 to August 2020 were for mental health or substance use disorder visits 26 and another large, US based, private health insurer found that BH claims were up 29% in 2020 compared with 2019. 27

Unlike APC, BH visits continue to be delivered virtually at each health care system. A report from the Commonwealth Fund 28 found that while virtual use declined as the pandemic persisted, VV use in BH remains high relative to other departments. A retrospective review of outpatient visits a Duke University Health Systems reported 98% of visits in psychiatry were still virtual in September 2020, whereas other departments like dermatology and orthopedics were almost entirely in-person. 29 While it appears that BH may be the ideal outpatient setting where care can be delivered almost entirely virtually, some have raised concerns about inequities to access especially among individual with limited digital literacy, limited English proficiency, unreliable internet access, and/or lack of devices required to connect digitally. 30 , 31 Continued efforts should be made to increase equitability of access to virtual services by improving broadband access in rural communities, offering virtual appointments in conjunction with interpreter services, and ensuring access to VC compatible devices in economically disadvantaged populations.

There are several limitations present in this study. First, this study was limited to visits observed in our systems. Therefore, overall visits rates and trends are reflective of KP members seeking care within the KP health care system and do not account for visits outside our system. In addition, our analysis focused on visit counts and not unduplicated care episodes—meaning that patients may be represented with 1 or more visits in a time interval. Many primary care departments, including urgent care, were inundated by COVID-19 visits, especially during peaks of COVID-19 infection. While we attempted to remove visits that were for COVID-19, it is possible that some trends in APC were still influenced by COVID-19 encounters. We were unable to account for several system and policy factors that may have contributed to variation in trends or overall visits rates observed at each of the 3 sites including: differences in regional or state-wide polices around COVID-19 mitigation strategies such as stay at home orders and health system factors such as limited in-person appointment availability offered in some departments. Finally, the 3 health care systems used in analysis are a part of KP, which before the COVID-19 pandemic had a number of initiatives to explore, develop and promote the use of VC. Therefore, the trends and sustained rate of VC use may not be representative of other health care organizations or insured populations.

COVID-19 fundamentally changed how outpatient health care is delivered. Many health care systems saw a dip in outpatient visits early in the pandemic but have largely returned to normal. While VC use is declining in APC, it is still being used at a much higher rate compared with prepandemic levels. In contrast, BH care continues to be delivered almost entirely virtually at each of the 3 study sites. With the expansion of VC, national efforts are needed to ensure equitable access to VC appointments across the entire US population. Additional research is needed to evaluate how health outcomes may vary when patients choose to receive their care virtually, especially for patients with chronic conditions. As COVID-19 moves from a pandemic to an endemic and restrictions continue to ease, one thing remains certain, virtual care is here to stay.

Supplementary Material

Acknowledgments.

The authors acknowledge the contributions of project team members including Jennifer C. Barrow, MSPH; Doraina Walker-Williams, MS; Julia Certa MPH; Kevin Rubenstein, MPH.

D.W.R. and T.L.D. are co-senior authors.

Funding for this study was provided by Kaiser Permanente Research, Program Office, Oakland, CA (Study Number: KPR-HPHQ-2020-01).

The authors declare no conflict of interest.

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website, www.lww-medicalcare.com .

How to Calculate Admits Per 1000

by Victoria Lee Blackstone

Published on 8 Nov 2018

"Admits per 1,000" is a term that represents how many patients are admitted to a hospital, healthcare facility or treatment center for every 1,000 people who seek help there. Many patients enter through the emergency department or in-patient admissions at regional hospitals, but other types of medical providers also are included, such as psychiatric and chemical-dependency facilities. Regardless of the type of medical facility, admits per 1,000 is a ratio that's calculated using a simple math equation.

You can calculate the number of admits per 1,000 visits by taking the number of admits over a given time, multiplying it by 1,000, and then dividing it by the total number of people who visited the facility during that identical duration of time.

How to Calculate Admits Per Thousand

In order to calculate the number of admits per thousand, you must first determine the number of patients admitted to a hospital, healthcare facility or treatment center in a given time period. Next, multiply this number by 1,000. Lastly, divide the result by the total number of people who visited that medical provider, including those who were not ultimately admitted to the facility.

By using the calculation above, if a hospital admits 500 patients from a total of 800 people who visited the hospital, the number of admits per thousand is 625 (500 x 1,000 divided by 800 = 625).

Why Is This Calculation Important?

Hospitals and other in-house medical facilities face ongoing challenges as they prepare budgets and cost estimates for future years. Admits per 1,000 is one way they can project a future year's financial needs based on the past year's actual patient statistics. This simple equation can help healthcare centers find solutions to minimize their costs, manage their supplies, modify their medical practices and adjust their budgets.

Benchmarking to Compare Standards

Admits per 1,000 also provides a benchmark, or point of reference, from which one hospital may measure its performance based on one or more other hospitals. Benchmarking can compare one hospital's statistics to other hospitals, which may be in the same community/county, state or national database. Hospitals may use the benchmarking tool to help them identify areas that need improvement toward their ongoing goal of providing the best patient care at the best costs.

Identifying Proactive Benefits

Although monitoring expenses is a primary focus of calculating admits per 1,000, hospitals can also use this calculation to help identify industry trends as they begin to emerge. This proactive management tool allows hospitals to stay on the forefront of subtle shifts in patient care so they can quickly identify and eliminate wasteful spending and unnecessary supply costs and other expenses. With a total of 140-plus million hospital visits each year just to emergency departments, according to a 2014 Centers for Disease Control and Prevention report, the data collected from a hospital's admits-per-1,000 calculations have significant potential to reduce national healthcare costs.

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• HEDIS Measures and Technical Resources
• Emergency Department Utilization

Emergency Department Utilization (EDU)

Assesses emergency department (ED) utilization among commercial (18 and older) and Medicare (18 and older) health plan members. Plans report observed rates of ED use and a predicted rate of ED use based on the health of the member population. The observed and expected rates are used to calculate a calibrated observed-to-expected ratio that assesses whether plans had more, the same or less emergency department visits than expected, while accounting for incremental improvements across all plans over time. The observed-to-expected ratio is multiplied by the emergency department visit rate across all health plans to produce a risk-standardized rate which allows for national comparison.

Why It Matters

ED visits are a high-intensity service and a cost burden on the health care system, as well as on patients. Some ED events may be attributed to preventable or treatable conditions . A high rate of ED utilization may indicate poor care management, inadequate access to care or poor patient choices, resulting in ED visits that could be prevented. 1,2 Plans can ensure that members receive appropriate, coordinated primary care to address preventable ED visits.

Results – National Averages

Emergency department utilization total rate.

*Lower rates signify better performance.

§  Not available due to CMS suspension of data reporting during COVID-19 pandemic.

This State of Healthcare Quality Report classifies health plans differently than NCQA’s Quality Compass. HMO corresponds to All LOBs (excluding PPO and EPO) within Quality Compass. PPO corresponds to PPO and EPO within Quality Compass.

Figures do not account for changes in the underlying measure that could break trending. Contact Information Products via  my.ncqa.org  for analysis that accounts for trend breaks.

• Dowd, B., M. Karmarker, T. Swenson, et al. 2014. “Emergency department utilization as a measure of physician performance.” American Journal of Medical Quality 29 (2), 135–43. http://ajm.sagepub.com/content/29/2/135.long
• Agency for Healthcare Research and Quality. 2015. Measures of Care Coordination: Preventable Emergency Department Visits. Accessed at https://www.ahrq.gov/research/findings/nhqrdr/chartbooks/carecoordination/measure2.html

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Emergency Department Visits

Data are for the U.S.

• Number of visits: 139.8 million
• Number of injury-related visits (includes poisoning and adverse effects): 40.0 million
• Number of visits per 100 persons: 42.7
• Number of emergency department visits resulting in hospital admission: 18.3 million
• Number of emergency department visits resulting in admission to critical care unit: 2.8 million
• Percent of visits with patient seen in fewer than 15 minutes: 41.8%
• Percent of visits resulting in hospital admission: 13.1%
• Percent of visits resulting in transfer to a different (psychiatric or other) hospital: 2.4%

Source: National Hospital Ambulatory Medical Care Survey: 2021 National Summary Tables, table 1, 3, 15, 23 [PDF – 830 KB]

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• Emergency Department Visits by Adults With Chronic Conditions Associated With Severe COVID-19 Illness: United States, 2017–2019  [PDF – 1,013 KB]
• Emergency Department Visits Among Adults With Mental Health Disorders: United States, 2017–2019
• National Health Interview Survey
• National Hospital Ambulatory Medical Care Survey
• American Hospital Association

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Number of outpatient department visits per 10,000 population per year

Definition :

The number of outpatient visits to health facilities during one year relative to the total population of the same geographical area. Health facilities include all public, private, non-governmental and community-based health facilities in which general health services are offered.

This indicator is calculated as:

(Number of visits to health facilities for ambulant care (not including immunization) / Total population for the same geographical area)

The ratio can be adjusted to per 10,000 population by multiplying the numerator and denominator by the same factor required for the denominator to equal 10,000.

This indicator is selected from the list of core indicators in the WHO Health System Strengthening (HSS) Handbook.  For more background on the process and criteria used in developing the WHO Handbook of indicators for HSS and for details on this and related indicators, see WHO (2010) ; USAID (2009) ; and The Global Fund (2009).

Data Requirement(s):

Data can be collected from health facility records, health information systems (HIS), and population-based surveys. A comprehensive facility survey instrument called the Service Provision Assessment (SPA) has been developed by USAID and Macro International Inc. to be used with nationally representative samples of health facilities to provide information on the characteristics of health services, inclu(ding their quality, infrastructure, utilization and availability MEASURE DHS, 2011; WHO, 2010). The accuracy and completeness of reporting need to be consistent over time and between populations to allow assessment of trends and comparisons. Data can be disaggregated by type of facility, districts,age group and sex.

If targeting and/or linking to inequity, disaggregate by relevant demographic and socioeconomic factors related to poverty-related inequities such as location (poor/not poor, urban/rural) and income.

Data Source(s):

Facility records, HIS; population-based surveys; facility sample surveys, such as the SPA.

This standardized indicator shows the levels of utilization of outpatient healthcare services and can be employed to examine trends and variations in use of services by type of facility and healthcare service, geographic districts and urban/rural locations, and will allow comparisons between countries and programs. These data can assist in planning, advocacy, and data from multiple time points will allow for monitoring progress in scaling-up health services and overall HSS (WHO, 2010). The primary aim of HSS is to improve access, quality, and utilization, and growing evidence shows that health systems capable of delivering services equitably, efficiently, and in a coordinated manner are essential for achieving improved health outcomes.

There has been a shift in the global health agenda from focusing on disease-specific approaches to emphasizing HSS to improve the effectiveness of national and district-level health ministries and programs. Strengthening outpatient service delivery and increasing utilization are fundamental to the achievement of the health-related Millennium Development Goals, which include: #4 reduce child mortality; #5 improve maternal health; and # 6 combat HIV/AIDS, tuberculosis and malaria.

The number of outpatient visits does not measure actual numbers of people utilizing services since individuals may make repeated visits. The volumes of visits at outpatient facilities do not serve as a coverage indicator because the population in need is not well defined. However, low rates are indicative of poor availability and quality of services. For example, several countries have demonstrated that outpatient department rates go up when barriers to using health services are removed, such as by bringing services closer to the people or reducing user fees (WHO, 2010). On the other hand, “higher than normal” rates of outpatient visits may signify problems such as lack of available hospital beds or lack of trained staff or available commodities for providing appropriate care and treatment for clients who should actually be receiving inpatient care.

References:

The Global Fund, 2009, Monitoring and Evaluation Toolkit: HIV, Tuberculosis and Malaria and Health Systems Strengthening ,  http://www.hivpolicy.org/Library/HPP000485.pdf

MEAS URE DHS. 2011, Service Provision Assessments (SPA) Survey Overview, DHS Website http://www.measuredhs.com/What-We-Do/Survey-Types/SPA.cfm

USAID, 2009, Measuring the Impact of Health Systems Strengthening, A Review of the Literature, Washington, DC: USAID. https://www.researchgate.net/publication/274064201_Measuring_the_Impact_of_Health_Systems_Strengthening_A_Review_of_the_Literature

WHO, 2010, Monitoring the building blocks of health systems: a handbook of indicators and their measurement strategies , Geneva: WHO. http://www.who.int/healthinfo/systems/WHO_MBHSS_2010_full_web.pdf

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