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Outpatient care (ambulatory care) in the U.S. - Statistics & Facts

Will outpatient revenue outpace inpatient revenue, outpatient care during the coronavirus pandemic, key insights.

Detailed statistics

Industry revenue of “ambulatory health care services“ in the U.S. 2012-2024

Employment in U.S. ambulatory health care services 1998-2021

Ambulatory health care establishments U.S. total number 2007-2020

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Current statistics on this topic.

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Number of U.S. Community Health Centers (CHCs) in 2021, by state

Health Professionals & Hospitals

Revenue of outpatient care in U.S. hospitals 2017-2021, by type of hospital

Number of Medicare-certified ambulatory surgery centers by state 2022

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Number of outpatient visits in the United States from 2015 to 2021, by type of hospital structure (in millions)

Number of outpatient visits in U.S. 2021, by ambulatory health care service

Number of outpatient visits in the United States in 2021, by ambulatory health care service (in millions)

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Total number of health center patient visits in the U.S. from 2010 to 2020 (in millions)

Total number of U.S. ambulatory health care establishments from 2007 to 2020

Health center sites in U.S. 2010-2020

Number of health center sites in the U.S. from 2010 to 2020

Number of Community Health Centers (CHCs) in the U.S. in 2021, by state*

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• Premium Statistic Employment in U.S. ambulatory health care services 1998-2021
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Employment in U.S. ambulatory health care services from 1998 to 2021 (in 1,000)*

Federally funded health center staff distribution in the U.S. 2020

Percentage of U.S. community health center staff that provided select services as of 2020

Community health center medical staff distribution by type 2020

Percentage of U.S. health center medical staff by type as of 2020

Community health center physician staff distribution by type U.S. 2020

Percentage of U.S. health center physicians that worked in select specialties as of 2020

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Percentage of U.S. health center dental staff by type as of 2021

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Total employee compensation in U.S. ambulatory health care services 1998-2021

Total employee compensation in ambulatory health care services in the U.S. from 1998 to 2021 (in million U.S. dollars)

Industry revenue of ambulatory health care services

• Premium Statistic Industry revenue of “ambulatory health care services“ in the U.S. 2012-2024
• Premium Statistic Value added by ambulatory health care services in the U.S. 1998-2022
• Premium Statistic Gross output of ambulatory health care services 1998-2022
• Basic Statistic Gross operating surplus of ambulatory health care services 1998-2020
• Basic Statistic Total employer firm revenue in ambulatory health care services 2001-2021
• Basic Statistic Revenue in U.S. ambulatory health care service sector by tax category 2001-2021
• Basic Statistic Total tax-exempt employer firm expenses in ambulatory health care services

Industry revenue of “ambulatory health care services“ in the U.S. from 2012 to 2024 (in billion U.S. Dollars)

Value added by ambulatory health care services in the U.S. 1998-2022

Value added by ambulatory health care services in the U.S. from 1998 to 2022 (in billion U.S. dollars)

Gross output of ambulatory health care services 1998-2022

Gross output of ambulatory health care services in the U.S. from 1998 to 2022 (in million U.S. dollars)

Gross operating surplus of ambulatory health care services 1998-2020

Gross operating surplus of ambulatory health care services in the U.S. from 1998 to 2020 (in million U.S. dollars)

Total employer firm revenue in ambulatory health care services 2001-2021

Total employer firm revenue in the U.S. ambulatory health care service sector from 2001 to 2021 (in million U.S. dollars)

Revenue in U.S. ambulatory health care service sector by tax category 2001-2021

Employer firm revenue in the U.S. ambulatory health care service sector from 2001 to 2021, by tax category (in million U.S. dollars)

Total tax-exempt employer firm expenses in ambulatory health care services

Total tax-exempt employer firm expenses in the U.S. ambulatory health care service sector 2005-2009 (in million U.S. dollars)

Industry revenue of out-patient care centers

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Industry revenue of “out-patient care centres“ in the U.S. 2012-2024

Industry revenue of “out-patient care centres“ in the U.S. from 2012 to 2024 (in billion U.S. Dollars)

Total employer firm revenue of outpatient care centers 2010-2021

Total employer firm revenue of U.S. outpatient care centers from 2010 to 2021 (in million U.S. dollars)*

Employer firm revenue of outpatient care centers by tax category 2004-2010

Employer firm revenue of U.S. outpatient care centers from 2004 to 2010, by tax category (in million U.S. dollars)

Tax-exempt employer firm expenses of outpatient care centers

Tax-exempt employer firm expenses of U.S. outpatient care centers from 2005 to 2009 (in million U.S. dollars)

Revenue of emergency & other outpatient care centers in the U.S., 2009-2014

Revenue of emergency & other outpatient care centers (NAICS 62143) in the United States from 2009 to 2014 (in billion U.S. dollars)

• Premium Statistic Health center patient distribution in the U.S. 2020, by age group
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• Premium Statistic Community health center visit in the U.S. 2020, by age and gender
• Premium Statistic Number of community health center visits in the U.S. 2020, by ethnicity or race
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• Basic Statistic Uninsured patients served by health centers in the U.S. 2020, by state
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Health center patient distribution in the U.S. 2020, by age group

Distribution of health center patients in the U.S. in 2020, by age group

Health center patients in U.S. 2020, by income status

Distribution of health center patients in the U.S. in 2020, by income (federal poverty level)

Community health center visit in the U.S. 2020, by age and gender

Distribution of visits to community health centers in the United States in 2020, by age and gender

Number of community health center visits in the U.S. 2020, by ethnicity or race

Number of visits to community health centers in the United States in 2020, by ethnicity or race

Community health center visits in the U.S. 2020, by visit reason

Distribution of visits to community health centers in the United States in 2020, by principal reason to visit

Community health center visit in the U.S. 2020, by payment source

Distribution of visits to community health centers in the United States in 2020, by expected source of payment

Uninsured patients served by health centers in the U.S. 2020, by state

Percentage of the U.S. uninsured population that were served by health centers in 2020, by state

Special populations served by health centers in U.S. 2021

Number of special populations served by health centers in the U.S. in 2021

• Premium Statistic Community health center visits in the U.S. 2020, by provided service
• Premium Statistic Community health center visits in the U.S. 2020, by initial diagnosis
• Premium Statistic Community health center visit in the U.S. 2020, by chronic condition presence
• Premium Statistic Community health center visit in the U.S. 2020, by chronic condition
• Premium Statistic Most frequently mentioned drugs named in community health centers in the U.S 2020

Community health center visits in the U.S. 2020, by provided service

Share of community health center visits in the United States in 2020, by provided service

Community health center visits in the U.S. 2020, by initial diagnosis

Share of visits to community health centers in the United States in 2020, by primary diagnosis

Distribution of visits to community health centers in the United States in 2020, by provider-assessed major reason

Community health center visit in the U.S. 2020, by chronic condition presence

Share of visits to community health centers in the United States in 2020, by number of present chronic conditions

Community health center visit in the U.S. 2020, by chronic condition

Share of visits to community health centers in the United States in 2020, by chronic condition

Most frequently mentioned drugs named in community health centers in the U.S 2020

Ten most frequently mentioned drugs during community health center visits in the United States in 2020, by number of estimated mentions

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How has healthcare utilization changed since the pandemic?

By Matthew McGough ,  Krutika Amin , and  Cynthia Cox Twitter   KFF

January 24, 2023

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Early in the COVID-19 pandemic, many outpatient visits and elective hospitalizations were delayed, avoided, or cancelled, leading to a sharp decline in healthcare  utilization . However, there have been expectations that there will be pent-up demand for this missed care.

In this chart collection, using a variety of data sources, we look at the latest available data on how health services utilization has changed over the course of the pandemic. We find that, as of mid-to-late 2022, utilization of healthcare is generally rebounding, but some of that use is likely for COVID-related treatment, testing, or vaccination, making it difficult to assess how non-COVID care compares to the amount of care people received pre-pandemic. It is likely that utilization of some services, particularly for non-COVID care, remains below expectations based on pre-pandemic trends.

In 2021, about 1 in 5 adults missed or delayed medical care due to the pandemic

There was a sharp drop in utilization in 2020, particularly during the earliest months of the pandemic. Even in 2021, as vaccines became available, about one in five people ages 18 years and older (21%) reported delaying or foregoing medical care due to the COVID-19 pandemic.

This chart and other charts below that use NHIS data are based on survey questions that specify missed or delayed “medical care.” There are other NHIS questions about missed or delayed prescriptions and metal health care due to costs, but the survey does not ask whether the pandemic was a reason for these delays, so we limit our analysis here to missed or delayed medical care. Additionally, NHIS sometimes asks about missed or delayed dental care due to cost, but the survey did not include this question in 2021. 

Both the pandemic and healthcare costs were significant barriers to medical care in 2021

In 2021, one in four adults (26%) missed or delayed medical care due to either the COVID-19 pandemic or healthcare costs.

We find that 4% of adults in the U.S. missed or delayed medical care due to both costs and the pandemic in 2021. Meanwhile, 17% of adults reported missing or delaying care due to the pandemic but not costs, and 5% reported missing or delaying medical care due to costs but not the pandemic.

In addition to costs and the pandemic, there could be additional reasons for missed or delayed care, such as an inability to take time off of work, a lack of transportation, or a lack of available appointments. 

Cost has remained a barrier to medical care into mid-2022

NHIS publishes quarterly updates to the rates of cost -related access barriers, but similar quarterly updates are not available for pandemic -related barriers. Cost-related access barriers rose in the early pandemic, likely associated with rising unemployment and resulting income instability, as well as disruption in health coverage. The rates of reported cost barriers have since declined somewhat in recent quarters, even as inflation puts strain on household budgets . The uninsured rate is currently at a record low , and Medicaid and ACA Marketplace enrollment are at record highs . Medicaid generally has little to no cost-sharing, and enhanced subsidies in the ACA Marketplaces may have helped enrollees afford health plans with lower deductibles.

However, there are other factors to consider. While the share of adults who reported delaying or not getting care due to cost reasons decreased from 2019 to 2021, part of this trend might be because COVID-19 presented another reason care was delayed or foregone. It is difficult to tease apart the various reasons one might not get the care they need. There is also variation across demographic groups in rates of cost-related access barriers (discussed more below). Additionally, as pandemic-era Medicaid continuous coverage ends and dis-enrollments resume, there will likely be an uptick in the uninsured rate, which could result in increases in cost-related barriers to care. Our earlier work has shown that many households lack the liquid assets needed to afford out-of-pocket expenses typical in private health plans. KFF polling has consistently shown the difficult decisions families make in juggling costs for essentials like housing, food, and healthcare.

Hospital discharges have increased recently but remain below pre-pandemic levels

The number of hospital discharges in the third quarter of 2022 remained below the average quarterly discharges in prior years. Quarterly hospital discharges in 2018-2019 averaged 9.8 million. Since the beginning of the COVID-19 pandemic, total discharges in a quarter peaked in the third quarter of 2021 at 9.3 million, 500,000 discharges below the pre-pandemic quarterly average in 2018-2019. Despite increases in discharges through the end of 2021, there was a drop in discharges in the first quarter of 2022 compared to both the first quarter of 2021 and the previous quarter. Total discharges in the third quarter of 2022 were 9.1 million, about 700,000 discharges below the pre-pandemic quarterly average in 2018-2019.

Nevertheless, there may still be strain on hospital resources in part because the average length of stay is increasing. Additionally, until recently, hospital employment had remained below pre-pandemic levels. 

While COVID-19 hospital admissions have increased during this most recent winter wave of infections, the level of admissions is well short of what we saw a year ago. As the virus continues to mutate, the future course of the pandemic, and what it means for health utilization and spending, is quite uncertain.

The share of adults with a doctor visit in the past year dipped early in the pandemic and remains somewhat below early 2019 levels

The National Health Interview Survey (NHIS) early release estimates provide a look at how visits to doctor’s offices and hospital emergency departments have changed from 2019 through mid-2022. Because the survey asks about utilization in the past year, though, it may mask volatility in utilization from month to month.

The share of adults with a doctor visit in the last year has recovered but has not reached early 2019 levels. In the first quarter of 2019, 85.3% of adults reported going to a doctor in the previous 12 months. The share of adults who had a doctor visit decreased in 2020 and reached the lowest level in the first quarter of 2021 with 80.1% of adults having seen a doctor in the prior year. In the most recent quarter with available data, the second quarter of 2022, 83.1% of adults saw a doctor in the past year.

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Similarly, the share of adults with a visit to an emergency department fell in mid-2020 and remains below pre-pandemic levels.

Like physician care, utilization of emergency care appears to be somewhat below pre-pandemic levels, especially considering that an unknown share of current emergency care is due to COVID-19. The share of adults reporting an emergency department visit in the previous 12 months dropped from 22.2% in the first quarter of 2019 to 17.0% in the fourth quarter of 2020. The share of adults reporting a visit to an emergency department in the past year has since rebounded but remains below early 2019 levels. Emergency department visits in the past year rebounded to 19.4% in the third quarter of 2021 and were at 19.1% in the most recent quarter (second quarter of 2022).

The number of physician visits per person is rebounding

Using data private insurers report to the National Association of Insurance Commissioners, it appears the number of physician encounters per person has mostly rebounded to pre-pandemic levels. These data have some limitations, though. For example, an unknown share of current visits is for COVID-related care (treatment, vaccination, and testing), so it is likely that non-COVID care is still below pre-pandemic levels. Additionally, although the data represent people enrolled across a variety of markets (including fully insured individual and group, as well as privately administered public coverage), the chart above does not include traditional Medicare without supplemental coverage, state-administered Medicaid, or self-insured employers, which combined represent a significant share of the U.S. population.

Health service utilization increased in 2021 after a drastic decline in 2020, when many people went without care

Another way to look at utilization trends is to use quantity indices from the Bureau of Economic Analysis (BEA). In 2021, healthcare prices increased by 2.9%, in line with previous years, but health services use increased by 7.3% relative to 2020. This increase in healthcare use in 2021 followed a sharp decrease in health utilization in 2020, largely driven by the COVID-19 pandemic, as many health services, such as elective procedures and routine care, were postponed or cancelled.

Use of pharmaceutical products continued to grow during the pandemic at similar rates as before

While the price index for drugs grew steadily since 2010 (ranging in growth from about 0.5% to 3.9% annually), it decreased by 1.6% between 2020 and 2021, following a 0.7% increase between 2019 and 2020.  The utilization index, which has been more volatile year to year, increased 5.2% in 2020 over the previous year.

Unlike health services, pharmaceutical product utilization grew in 2020 over the previous year and the 2021 annual growth rate was similar to the rate seen in recent decades. This is likely in part due to many people stockpiling needed medications early in the pandemic when lockdowns were announced. Additionally, with local delivery or mail-in pharmacies, many people were likely able to continue filling retail prescription drugs with limited interactions and risk of spreading COVID-19. Though new prescriptions likely declined with fewer doctor visits.

Across all race and ethnicity groups, more adults reported delaying or foregoing care due to the pandemic than due to cost in 2021

In 2021, the cost of care and the COVID-19 pandemic contributed to people delaying or foregoing care. Across all race and ethnicity groups, the COVID-19 pandemic was a more prevalent reason for delaying or foregoing care compared to cost. Asian adults had the lowest share of individuals who reported delaying care due to cost (4%), while those who were a part of an Other racial or ethnic group reported the highest share of adults who delayed or foregone care due to cost (13%).

Black adults had the lowest share of people who delayed or foregone care due to COVID-19 (18%). Adults who were a part of an Other racial or ethnic group also had the highest share of individuals who delayed or foregone care due to the COVID-19 pandemic (27%).

Only uninsured adults reported delaying or foregoing care due to cost more than delaying care due to the COVID-19 pandemic

Uninsured people had the highest share of adults who had delayed or foregone care due to cost (27%) but reported the lowest share of adults who had delayed or foregone care due to the COVID-19 pandemic (15%). Among those with private insurance, over one in five (22%) had delayed or foregone care due to the pandemic, the highest across all insurance groups. Among adults enrolled in Medicare, only 4% reported having delayed or foregone care due to cost, the lowest across all insurance types.

In early 2022, one in three adults said they or a family member did not get care due to cost

While NHIS shows about one in ten adult individuals delaying or forgoing care due to cost, KFF polling has found a larger share of adults report at least one person in their household has delayed or gone without care due to costs. Rates of forgone care are highest for uninsured and low-income individuals and households.

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The Peterson Center on Healthcare and KFF are partnering to monitor how well the U.S. healthcare system is performing in terms of quality and cost.

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Observation, Outpatient, or Inpatient Status Explained

• How Long Is a Stay?
• Types of Admission
• Admission Status Determination
• Dealing With Insurance

If you receive medical treatment that involves an overnight hospital stay, you are being treated on an inpatient basis. If you are treated in a hospital but don't need to stay, you are being admitted on an outpatient basis.

In between, a person may receive treatment and be admitted to a hospital so they can be watched and reassessed to determine if further treatment is needed. This is admission on an observation basis.

The decision on how you are admitted is largely directed by your condition and approvals by your insurance company. Based on medical codes that classify your condition and treatment (called CPT and ICD-10 codes), your insurer will decide how long—or if—you need to stay in a hospital.

This article explains when you need to be admitted on an outpatient, inpatient, or observation basis and how the decision is made.

How Long Is Your Hospital Stay?

For the purpose of insurance billing, the length of a hospital stay is based on how many midnights you will spend in the hospital. It is not based on the number of hours you are hospitalized.

So, even if you are admitted at 11:00 p.m., you will be billed for one hospital day (along with any accrued charges) the second it turns midnight.

The hospital bill you receive is separate from the bill you receive from your surgeon or anesthesiologist . The bill not only includes the daily room charge but also charges for food, medical supplies, medical services, and any tests or procedures, such as X-rays.

Definitions of Hospital Stays

Specific definitions are assigned to your admission status, some of which are not as straightforward as they seem. The definitions matter because they have a direct impact on both your billing and out-of-pocket costs .

By definition:

• Outpatient is when a person leaves the hospital after treatment on the same day. It can also be applied a someone who spends the night in the hospital for whom a doctor has not written an order for inpatient admission. They are still admitted and billed as an outpatient.
• Inpatient is when a person treated in a hospital is admitted for at least two midnights. It can also be applied to a person who was discharged or transferred to another hospital before two midnights and didn't occupy the bed. They are still admitted and billed as an inpatient.
• Observation is when a person is admitted to the hospital but has an unclear need for longer care. The purpose is to determine within the span of one midnight whether further treatment or inpatient admission is needed.

In practice, the term "admitted" generally infers inpatient care but can be applied to anyone who is admitted for treatment in a hospital.

Insurance and How Admission Status Is Determined

Every time you are scheduled to have a hospital-based treatment or procedure—such as surgery or to deliver a baby—your healthcare provider will submit prior authorization to your health insurance company. This is to ensure that the procedure is covered along with any hospitalization that may be needed.

The decision to pay and how much to pay is largely based on two codes submitted by your healthcare provider:

• ICD-10 code : This is an international classification of all medical diagnoses used for insurance claims processing. The U.S. version is issued by the Centers for Medicare and Medicaid Services (CMS) and the National Center for Health Statistics (NCHS).
• CPT code : Otherwise known as the current procedural terminology (CPT) codes, these classify medical services and procedures. These codes were designated by the U.S. Department of Health and Human Services under the Health Insurance Portability and Accountability Act (HIPAA) .

The codes are used by the insurer to determine what services are authorized for coverage of your condition, including whether the treatments are administered on an inpatient or outpatient basis. If inpatient care is indicated, the codes will also direct how many days you are authorized to stay.

If you require emergency care, the ER department will submit a specific CPT code after treatment designating the need for hospital observation. The code can be transitioned to inpatient care if it is decided that further treatment is needed after an overnight stay.

Asking About Overnight Hospital Bills

The amount you pay for a hospital stay is based on your insurance plan, including the deductible . If you have private or employer-sponsored insurance, there may be copayment or coinsurance costs you will need to pay out of pocket.

If out-of-pocket costs are a factor, there may be an outpatient procedure that can be used in place of an inpatient procedure. As long the treatment is appropriate and effective, it is a reasonable option to discuss with your healthcare provider.

For people with Medicare , outpatient services are covered as part of Medicare Part B , while inpatient services are covered under Medicare Part A . Medications may fall under Medicare Part D .

Because there are many rules and regulations governing payment based on the type of Medicare you have, you can reach out for assistance by calling the Medicare Helpline at 1-800-MEDICARE (1-800-633-4227).

The same applies to other federal programs like Medicaid , Children's Health Insurance Program (CHIP) , Tricare , and Veteran's Health Administration (VHA) .

On the other hand, if you have been discharged from the hospital and are confused about your bill, the hospital billing department can explain the charges and may be able to direct you to financial assistance if you foresee problems paying the bill.

A hospital outpatient, inpatient, or observation status is about more than just how long you are in hospital. The definition of each can place you in a different category of billing.

The determination of outpatient, inpatient, and observations is based on your condition and treatment recommendation. Based on the CPT and ICD-10 code assigned by your healthcare provider, your insurer will determine what form of treatment they will cover and how many days of hospitalization are needed, if any.

Centers for Medicare and Medicaid Services. Billing and coding: acute care: inpatient, observation and treatment room services .

Medicare.gov. Are you a hospital inpatient or outpatient?

Centers for Medicare and Medicaid Services. Hospital outpatient quality reporting program .

Centers for Medicare and Medicaid Services. Advanced copy- revisions to state operations manual (SOM) hospital appendix A .

Centers for Medicare and Medicaid Services. Hospital coverage under Part B .

American Medical Association. ICD-10 .

American Medical Association. CPT overview and code approval .

By Jennifer Whitlock, RN, MSN, FN Jennifer Whitlock, RN, MSN, FNP-C, is a board-certified family nurse practitioner. She has experience in primary care and hospital medicine.

• Center on Health Equity and Access
• Health Care Cost
• Health Care Delivery
• Value-Based Care

Continuity of Outpatient Care and Avoidable Hospitalization: A Systematic Review

• Wan-Hsuan Chen, MPH
• Shiao-Chi Wu, PhD
• Tung-Sung Tseng, DrPH

Higher continuity of care was statistically significant and was associated with fewer ambulatory care–sensitive condition hospitalizations.

Objectives: Continuity of care (COC) is a core element of primary care, which has been associated with improved health outcomes. Hospitalizations for ambulatory care—sensitive conditions (ACSCs) are potentially preventable if these conditions are managed well in the primary care setting. The aim of this article is to conduct a systematic review of literature on the association between COC and hospitalizations for ACSCs.

Study Design: Systematic literature review.

Methods: All published literature was searched for in PubMed and MEDLINE using PRISMA guidelines for collecting empirical studies. Studies published in English between 2008 and 2017 that measured the association between COC and at least 1 measure of ACSC hospitalizations were included in this review.

Results: A total of 15 studies met the inclusion criteria and applied claims data to examine the association between COC and ACSC hospitalizations. Most studies (93.3%) demonstrated a statistically significant association of higher COC in the outpatient setting with reduced likelihood of hospitalization for either all ACSCs or a specific ACSC. A strong association was observed among studies focusing on patients with a specific ACSC. Additionally, most studies used the Bice-Boxerman COC index to measure COC and measured COC before a period of measuring ACSC hospitalizations.

Conclusions: This systematic review identified that increased COC in outpatient care is associated with fewer hospitalizations for ACSCs. Increasing COC is favorable for patients who are managing a specific ACSC.

Am J Manag Care. 2019;25(4):e126-e134 Takeaway Points

This review analyzed findings using PRISMA guideline indicators to assess the association between continuity of care (COC) and hospitalization for ambulatory care—sensitive conditions (ACSCs).

• Higher COC was statistically significantly associated with fewer ACSC hospitalizations and specific-ACSC hospitalizations.
• The Bice-Boxerman COC index is most commonly used to measure COC in studies using claims data sets.
• Most studies assessed COC before measuring ACSC hospitalizations.

An ambulatory care—sensitive condition (ACSC) is defined as a condition for which timely and effective primary care or outpatient care can potentially reduce the risk of subsequent hospitalization. 1-4 Hence, a hospitalization for an ACSC is also called a preventable hospitalization or avoidable hospitalization. 5,6 The Agency for Healthcare Research and Quality developed a set of Prevention Quality Indicators consisting of 16 ACSCs (eg, asthma, bacterial pneumonia, congestive heart failure, chronic obstructive pulmonary disease [COPD], dehydration, diabetes, hypertension, kidney/urinary tract infection, ruptured appendix) as indicators to measure the occurrence of potentially preventable hospitalizations and to track trends in hospitalizations for ACSCs to assess the quality of primary healthcare. 7

In the United States, 1426 per 100,000 Americans were hospitalized for ACSCs in 2014, although the hospitalization rate for ACSCs has been decreasing slightly since 2005. 8 Previous literature has found that patients with ACSC hospitalizations had higher expenditures than those without this type of hospital admission. 9 Hence, hospitalizations due to ACSCs have become a critical discussion topic, because they not only reflect primary care quality 1 but also relate to the cost consciousness 10 in healthcare delivery systems. Additionally, ACSC hospitalizations have been used to measure the performance of primary care in healthcare systems around the world. 7,11-13 Therefore, it is imperative to decrease the risk of ACSC hospitalizations for patients in the current healthcare system, in which costs of inpatient admissions are rapidly increasing. 9,10

Continuity of care (COC), a core element of primary care, 14,15 represents a constant curative relationship between a patient and a care provider that is characterized by trust and responsibility. 16 Maintaining a continuous therapeutic relationship between patient and physician when treating chronic diseases has been proven to be associated with higher satisfaction, better compliance, and reduced hospitalizations and emergency department (ED) visits. 17-21 Patients who have a stable connection with their healthcare providers for chronic disease treatment may improve their health outcomes because their providers are familiar with their disease conditions and understand their needs. 21,22

Although studies have recognized COC as being positively associated with healthcare outcomes, the association between COC and all ACSCs (or a specific ACSC) is not well reviewed systematically. To our knowledge, there have been no review articles in this decade discussing the relationship between COC and ACSC hospitalizations. Therefore, this systematic review evaluated the association between COC and ACSC hospitalizations across studies published approximately in the past decade to provide a comprehensive, evidence-based perspective for clinicians and researchers who are interested in conducting research related to COC and ACSCs.

A systematic search of the PubMed and MEDLINE databases was conducted from January to February 2018 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 23 ( Figure 1 ). The initial search was limited to articles published in English from January 1, 2008, to December 31, 2017, that included COC in the title or abstract. After that, article titles or abstracts were reviewed to identify studies that included hospitalizations or admissions. Subsequently, combinations of terms relating to hospitalizations or admissions (ie, avoidable, preventable, and ambulatory care—sensitive conditions) were identified in the title or abstract. Article titles and abstracts were reviewed to assess whether the remaining articles met inclusion criteria, excluding studies and reports that had nonrelevant outcomes or that did not actually measure COC. Lastly, duplicates, books, reports, editorials, and review articles were removed. The remaining articles were assessed entirely and included in this review if criteria were met. We identified further relevant studies by searching the reference lists of included studies and using the Web of Science Core Collection to explore all potentially relevant research that cited the included studies.

A data extraction form was created to collect relevant study information from each article, including lead author name, year of publication, study design, number of study samples, age of the sample, and samples with or without a specific disease. Relevant information also included data resources, COC measurement, cutoff point for COC level, COC measuring period, healthcare outcomes measuring period, primary healthcare outcome(s) of interest, and significant results. Two researchers (Y.H.K. and W.T.L.) performed the initial search, conducted the appraisal of articles, extracted data from studies, and recorded findings in data extraction forms. Researchers summarized and synthesized these findings to evaluate inferences and conclusions made on the association between COC and ACSC hospitalizations across studies.

Figure 1 presents a diagrammatic flow of the process and search terms used to conduct the review. The search of PubMed and MEDLINE resulted in the identification of 3076 articles that mentioned COC. After applying exclusion criteria (ie, language was not English, title or abstract did not include “hospitalization[s]” or “admission[s]”), 482 articles remained. The titles of these articles were reviewed for relevance to outcomes of interest including “avoidable,” “preventable,” or “ACSC(s),” and 88 articles were retained. From the eligible articles, we excluded 50 duplicates, 3 reports or editorials, 2 review articles, and 20 articles that did not actually measure COC. Thus, 13 studies were selected. 24-36 After manually hand searching the reference lists of included studies, 2 additional articles 37,38 were selected for this review. Full articles from these 15 studies were then evaluated for inclusion. Summaries of these studies are presented in the Table 24-38 [ part A and part B ] ; an expanded version of the Table is in the eAppendix ( available at ajmc.com ).

There were 13 studies in which a retrospective study design was conducted to investigate the association between COC and ACSC hospitalizations. 25-34,36-38 The other 2 studies used a cross-sectional design. 24,35 Regarding the study population, 6 studies analyzed adults 20 years or older 27,30-33,36 ; 5 studies targeted elderly adults 24,27,29,34,35 ; 3 studies focused on infants, 38 children aged 3.5 years and younger, 37 and children aged 12 years and younger, 28 respectively; and 1 study analyzed subjects of all ages. 26 Subjects who had a chronic disease such as diabetes, 27,32 asthma, 28,29 COPD, 30,31 hypertension, 33 or heart failure 36 were considered in 8 studies. The remaining 7 studies were not limited by subjects’ diseases. Regarding the primary outcome measurement, ACSC hospitalization was used as the primary outcome in 7 studies. 24-26,34,35,37,38 The remaining studies focused on diabetes, 27,32 asthma, 28,29 COPD, 30,31 hypertension, 33 and heart failure. 36 Studies were conducted in 5 countries: United States, 25,34,35,37,38 United Kingdom, 24 Korea, 27,28,33 Taiwan, 26,29-32 and Germany. 36 They adopted claims data from 7 care systems—Medicare, 25,34,35 Children’s Hospital of Philadelphia’s greater Philadelphia primary care network, 38 Hawaii’s largest single health insurer, 37 the UK National Health Service, 24 Korean National Health Insurance, 27,28,33 Taiwan’s National Health Insurance, 26,29-32 and Germany’s biggest statutory health insurance company 36 —to investigate the association between COC and ACSC hospitalizations.

Association Between COC and ACSC Hospitalizations

Most of the studies showed a significant link between COC and hospitalization for either all ACSCs or a specific ACSC ( Figure 2 [ part A , part B , and parts C and D ] ). Compared with patients in the high COC group, patients in the low COC group tended to have a significantly higher likelihood of ACSC hospitalization in 9 studies (odds ratios [ORs] ranged from 1.34 to 8.69). 27-33,37,38 Three studies showed that an increased COC might be associated with fewer hospitalizations for ACSCs (coefficient, —0.32%; 95% CI, –0.39% to –0.25% 24 ; ORs ranged from 0.75 to 0.98 34,36 ). However, the association between COC and all ACSC hospitalizations was inconsistent in the 3 studies using low COC as a referent. In the study by Bentler et al, patient-reported affective continuity showed that better COC was associated with fewer ACSC hospitalizations, but the positive association was not observed when using Medicare claims. 25 Cheng et al found that patients with high or medium COC were less likely to have ACSC hospitalizations than those with low COC in different age groups (ORs ranged from 0.39 to 0.73). 26 Romaire et al explored the associations between COC and healthcare use among beneficiaries with primary care physicians (PCPs) or specialists as their predominant provider. Positive relationships between COC and ACSC hospitalization were found if a specialist physician was the principal provider; this association was not found when beneficiaries sought PCPs as their predominant provider. 35

COC Measurement

The Bice-Boxerman Continuity of Care Index (COCI) and Usual Provider Continuity (UPC) index were the most common objective measures of continuity. Of the 15 included studies, the COCI was adopted as the primary assessment in 11 studies to measure care continuity 25-27,29-31,33-37 ; the remaining 3 studies used the UPC index as the primary assessment. 24,28,32,38 Other indicators, such as the Sequential Continuity Index (SECON), the Modified Continuity Index, and the Modified Modified Continuity Index, were also mentioned in 2 studies. 25,36 In addition, Bentler et al measured COC from claims data and patient-reported questionnaires to measure longitudinal continuity and interpersonal continuity, respectively. 25 Twelve studies included patients who had at least 3 outpatient visits to calculate COC. 26-37 Seven of these studies analyzed study subjects with at least 4 outpatient visits to assess COC. 27-29,32-34,37 In terms of COC measurement units, 3 studies determined COC at the institute level because of data limitations 27,28,33 ; the remaining studies assessed COC at the physician level. 24-26,29-32,34-38

COC Scores and Cutoff Points

Studies that focused on subjects with a specific chronic disease had mean COC scores between 0.61 and 0.86. 27-33,36,38 Studies that considered subjects without limiting to any specific diseases had fairly low mean COC scores between 0.27 and 0.43. 24-26,34,35,37 Regarding the cutoff point of COC, 8 studies divided COC scores into 3 levels of low, medium, and high by tertiles 24-26,30-32,35 or first and third quartiles. 29 Three studies split COC scores into low and high groups by means 28,33 or quartiles. 38 Two studies considered COC as a continuous variable. 34,36 The other studies 27,37 divided COC scores into several groups by a fixed score, such as 0.20 or 0.25, respectively.

Temporal Issue for COC and Outcome Measurement

A total of 13 studies applied a longitudinal design to avoid cross-sectional design limitations and present stronger evidence of an association between COC and ACSC hospitalizations. 25-34,36-38 In these studies, 11 papers measured COC before determining hospitalization for ACSCs to strengthen the evidence of association between COC and ACSC hospitalizations. 25,27,29-34,36-38 Two studies indicated COC as a time-dependent variable and applied random intercept models to adjust for the temporal problem because COC and ACSC hospitalizations were measured simultaneously. 26,28 In the remaining studies, 1 assessed COC before determining ACSC hospitalizations, although it applied a cross-sectional analysis, 35 and the other study considered COC over the whole study period, at the end of which outcomes were measured. 24

Consideration of Confounders

Several confounders were considered across the 15 studies. Demographic factors included patient’s age, gender, race, marital status, deprivation score, level of education, income-level quintile, low-income status, health insurance type, level of insurance premium, and residential area. Patients’ clinical characteristics, such as Charlson Comorbidity Index score, medication possession ratio, and healthcare utilization history (eg, number of outpatient visits, hospital admissions, and ED visits), were also considered.

This systematic review shows that higher COC is associated with lower risk of ACSC hospitalizations. All studies in this review clearly defined the measure of COC and used claims data to estimate the association between COC and ACSC hospitalizations. The results of these studies have validated the notion that increased COC is associated with a reduced risk of ACSC hospitalizations, and the relationship has been shown in any age group with a specific chronic disease or multiple diseases. In addition, the average COC score is higher in patients with a single specific chronic disease than in those without any specific diseases; hence, it is more sensitive in identifying the association between COC and hospitalizations for ACSC with a specific disease. This finding suggests that patients with a single specific chronic disease might benefit from developing an abiding relationship with the same physician. Furthermore, the robust association between COC and hospitalizations for ACSC was observed in both referral healthcare systems 24,25,34,35,37,38 and nonreferral healthcare systems. 26-28,29-33,36

COC is a hierarchical relationship that includes informational continuity, longitudinal continuity, and interpersonal continuity. 16 Informational continuity represents the precise information exchanged from one healthcare provider to another. Longitudinal continuity is based on providers having enough information and creates a stable care pattern for patients in a familiar place of care over time. Interpersonal continuity incorporates longitudinal continuity and relates to a strong ongoing physician—patient relationship that is developed over time and incorporates trust in one another. When studies used claims data, longitudinal continuity was usually used to exhibit interpersonal continuity because repeated contacts between a patient and care provider were recorded, representing a reliant and stable relationship. 22

Many indices, such as the COCI, UPC index, and SECON, were developed to evaluate COC in claims data. 39 Each index has advantages and disadvantages, and there are no conclusions as to which is necessarily better. 15 The COCI reflects the dispersion of contact between patients and physicians 40 and identifies visit concentration of a patient with each physician. The UPC index, a density measure, focuses on the number of visits with the most frequently visited physicians, which cannot recognize whether patients reduce their visits or change healthcare providers frequently. SECON determines the sequences of change in the healthcare process, but it was limited to the detection of nonsequential issues. In this review, the COCI is the most common index adopted as the main measure for COC. A possible reason is that the COCI is less sensitive to the number of physician visits and more suitable for a higher number of outpatient visits. 40 This feature was considered and adopted by studies that used claims databases to analyze COC. Thus, according to this review, we recommend that future research can consider the COCI as the preferred COC measure if claims data are available.

All but 3 studies in our review examined medical institution continuity. 27,28,33 A previous study published in 1998, not included within this review, found that physician continuity is more important than medical site continuity in decreasing patients’ likelihood of hospitalization. 19 In addition, COC is measured at the physician level, which may provide superior information about the association between COC and avoidable hospitalization than that obtained from measurements at the level of healthcare institutions. 41 Three studies mentioned that they measured COC at the medical institution level because of data limitations and recommended that further studies try to measure COC at the physician level. 27,28,33 With this in mind, this review suggests that future studies could calculate COC at the physician level if data are available.

Our review found that the temporal relationship between COC and outcome measures is an essential issue for study design. Most studies assessed COC before measuring ACSC hospitalizations. This design may reduce the time bias to interpret the association between COC and healthcare outcomes. However, the problem of temporal ambiguity between COC and hospitalization for ACSCs might not be completely avoided. Hence, this issue should be further investigated in future studies. In addition, 2 studies considered that biased conclusions would also occur if continuity is measured concurrently with outcomes. 26,28 Therefore, these studies adopted a longitudinal design with random intercept models to assess the relationship between COC and ACSC hospitalization. We recommend that the methodological limitations in temporal design between COC and hospitalization for ACSCs should be considered in future studies that measure the association between COC and outcomes.

Most studies calculated COC in subjects with more than 3 or 4 ambulatory care visits. In addition, 2 articles added a sensitivity analysis to compare avoidable hospitalizations between patients with 3 or fewer outpatient visits and those who were in the high COC group. The results showed that patients with 3 or fewer outpatient visits might have a lower risk of hospitalization for ACSCs. Therefore, future studies could consider conducting the analyses for patients with fewer than 3 or 4 visits in the model and provide comparison results.

There are many factors, such as patient age, gender, socioeconomic status, insurance type, comorbidities, and severity of illness, that could serve as critical confounders in exploring the association between COC and ACSC hospitalizations in this review. Each of these factors might be associated with not only ACSC hospitalizations, but also COC. Hence, future studies investigating the association of COC and ACSC hospitalizations will need to consider the influence of such confounders when conducting multivariate analyses.

Limitations and Strengths

Some limitations of this review should be noted. First, some pertinent studies may have been missed because several synonymous terms could represent COC and ACSCs. Second, ACSCs include chronic diseases that could be analyzed independently, which may exclude them from our search strategy. In addition, using meta-analytic methods to compare and summarize results might be limited by the heterogeneity of study designs and methods used to measure COC. Despite this limit, higher COC scores represent better continuity with care providers. Therefore, we showed the range of COC scores across studies. Lastly, this review was limited to studies that calculated objective COC rather than subjective COC. Studies using qualitative methods are not discussed here.

Nevertheless, this systematic review has several strengths. First, our study shows that higher COC is associated with a lower risk of hospitalization in the cases of all ACSCs and a specific ACSC. Second, this review observes COCI as a mainstream indicator to measure COC in the studies using claims data sets in the past 10 years. Third, this review reveals that measuring COC before healthcare outcomes is a better method to reduce time bias and demonstrate a strong association. Fourth, the affirmative association between COC and ACSC hospitalizations is found in different healthcare systems, such as the US healthcare system, the UK National Health Service, and a single-payer national health insurance system. Finally, this review suggests that future studies should consider controlling for critical confounders with multivariate analytical models when measuring the association between COC and hospitalization for ACSC.

CONCLUSIONS

Most findings from this review support the notion that higher COC is associated with fewer ACSC hospitalizations. The COCI is often used to measure COC in studies using claims data sets. Additionally, most studies measured COC before the period of outcome measurement. Continuous patient—physician relationships should be encouraged. Also, increasing COC is favorable for patients who are managing a specific ACSC. Author Affiliations: Behavioral and Community Health Sciences, School of Public Health, Louisiana State University Health Sciences Center (YHK, TST), New Orleans, LA; Department of Global Community Health and Behavioral Sciences (WTL) and Department of Epidemiology (WHC), Tulane University School of Public Health and Tropical Medicine, New Orleans, LA; Institute of Health and Welfare Policy, National Yang-Ming University (SCW), Taipei, Taiwan.

Source of Funding: This study was supported by a grant from the Ministry of Science and Technology Postdoctoral Research Abroad Program (MOST 106-2917-I-564-039) in Taiwan.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YHK, SCW, TST); acquisition of data (YHK, WTL, WHC); analysis and interpretation of data (YHK, WTL, WHC, SCW, TST); drafting of the manuscript (YHK, WTL, WHC); provision of patients or study materials (YHK, WTL); obtaining funding (YHK, SCW, TST); administrative, technical, or logistic support (SCW, TST); and supervision (SCW, TST).

Address Correspondence to: Shiao-Chi Wu, PhD, Institute of Health and Welfare Policy, National Yang-Ming University, 155 Li-Nong St Sec 2, Peitou, Taipei, Taiwan. Email: [email protected]. Tung-Sung Tseng, DrPH, Behavioral and Community Health Sciences, School of Public Health, Louisiana State University Health Sciences Center, 2020 Gravier St, Room 213, New Orleans, LA 70112. Email: [email protected]. REFERENCES

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Inpatient vs. Outpatient: Comparing Two Types of Patient Care

More than ever, patients are engaged in their medical care, which is encouraging when you consider most medical school mission statements emphasize patient communication and education. It’s also worth noting that research shows providers are able to drive positive patient outcomes using a teach-back method that involves caring and clear language. Yet even well-informed individuals lack some knowledge, such as the distinction between inpatient versus outpatient care.

So what’s the difference, and why does it matter? This overview can help you advance your health literacy.

Inpatient vs. outpatient: Distinguishing the differences in care

What is an inpatient ? In the most basic sense, this term refers to someone admitted to the hospital to stay overnight, whether briefly or for an extended period of time. Physicians keep these patients at the hospital to monitor them more closely.

With this in mind, what is outpatient care? Also called  ambulatory care , this term defines any service or treatment that doesn’t require hospitalization. An annual exam with your primary care physician is an example of outpatient care, but so are emergent cases where the patient leaves the emergency department the same day they arrive. Any appointment at a clinic or specialty facility outside the hospital is considered outpatient care as well.

While there’s a clear difference between an inpatient and an outpatient, there is a little bit of gray area as well. Occasionally, physicians will assign a patient  observation status while they determine whether hospitalization is required. This period typically lasts for no more than 24 hours.

Also note that the location itself doesn’t define whether you’re an inpatient versus outpatient. It’s the duration of stay, not the type of establishment, that determines your status.

Inpatient vs. outpatient: Comparing services

You’re probably starting to get a sense of the varying circumstances that fit under each category. To further recognize the difference between inpatient and outpatient care, review the below treatments and services that are common for these two types of care.

Inpatient care examples

• Complex surgeries, as well as some routine ones
• Serious illnesses or medical issues that require substantial monitoring
• Childbirth, even in cases that don’t require a cesarean section
• Rehabilitation services for psychiatric illnesses, substance misuse, or severe injuries

Outpatient care examples

• X-rays, MRIs, CT scans, and other types of imaging
• Lab tests, such as bloodwork
• Minor surgeries, particularly ones that use less invasive techniques
• Colonoscopies
• Consultations or follow-ups with a specialist
• Routine physical exams
• Same-day emergent care, often treated at an urgent care facility versus the ER
• Chemotherapy or radiation treatment

Inpatient vs. outpatient: The providers in each setting

Primary care physicians  have traditionally been considered outpatient providers, while specialists are thought of as inpatient physicians. But that’s really an oversimplification, particularly when you consider that  hospitalists bridge the gap  by providing general medical care to inpatients. Effective care requires that doctors work together and effectively leverage health care technology , regardless of their specialties and settings.

Many physicians also divide their time between inpatient and outpatient services. OB/GYNs , for example, provide inpatient care when delivering babies and outpatient care when consulting with pregnant women during prenatal checkups.

Generally speaking, inpatients have contact with a larger group of providers. During a hospital stay, you could interact with physicians, nurse practitioners, lab technicians, physical therapists, pharmacists, and physician assistants.

Inpatient vs. outpatient: Cost considerations

The difference between inpatient versus outpatient care matters for patients because it will ultimately affect your eventual bill.

Outpatient care involves fees related to the doctor and any tests performed. Inpatient care also includes additional facility-based fees. The most recent cost data included in the Healthcare Cost and Utilization Project from the Agency for Healthcare Research and Quality (AHRQ) shows the average national inpatient charges can vary considerably depending on the length of stay and the treatment involved. The exact amount you pay also hinges on your insurance.

Things get a little more complicated  if you have Medicare . Outpatient care and physician-related services for inpatient care are covered by Part B. Hospital services like rooms, meals, and general nursing for inpatients are covered by Part A.

But if you stay overnight in the hospital under observation status, Medicare still considers you an outpatient and will not cover care in a skilled nursing facility. It can certainly be confusing, so don’t be afraid to ask the medical team about your status. They’re used to these types of questions.

Expand your medical knowledge

Hopefully, you now have a little more clarity concerning the definition of inpatient versus outpatient. It can go a long way towards helping you understand what you should expect during and after any sort of medical treatment.

You can further deepen your understanding of the health care world by reading our article “50 Must-Know Medical Terms, Abbreviations, and Acronyms .”

*This article was originally published in June 2019. It has since been updated to reflect information relevant to 2021.

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The association between outpatient follow-up visits and all-cause non-elective 30-day readmissions: A retrospective observational cohort study

Liping tong.

1 Advocate Health Care, Downers Grove, IL, United States of America

2 Cerner Corporation, North Kansas City, MO, United States of America

3 University of Illinois at Chicago, Department of Mathematics, Statistics, and Computer Science, Chicago, IL, United States of America

Xinyong Tian

Cole erdmann, tina esposito, associated data.

All relevant data are within the paper and its Supporting Information file.

As an effort to reduce hospital readmissions, early follow-up visits were recommended by the Society of Hospital Medicine. However, published literature on the effect of follow-up visits is limited with mixed conclusions. Our goal here is to fully explore the relationship between follow-up visits and the all-cause non-elective 30-day readmission rate (RR) after adjusting for confounders.

Methods and results

To conduct this retrospective observational study, we extracted data for 55,378 adult inpatients from Advocate Health Care, a large, multi-hospital system serving a diverse population in a major metropolitan area. These patients were discharged to Home or Home with Home Health services between June 1, 2013 and April 30, 2015. Our findings from time-dependent Cox proportional hazard models showed that follow-up visits were significantly associated with a reduced RR (adjusted hazard ratio: 0.86; 95% CI: 0.82–0.91), but in a complicated way because the interaction between follow-up visits and a readmission risk score was significant with p-value < 0.001. Our analysis using logistic models on an adjusted data set confirmed the above findings with the following additional results. First, time matter. Follow-up visits within 2 days were associated with the greatest reduction in RR (adjusted odds ratio: 0.72; 95% CI: 0.63–0.83). Visits beyond 2 days were also associated with a reduction in RR, but the strength of the effect decreased as the time between discharge and follow-up visit increased. Second, the strength of such association varied for patients with different readmission risk scores. Patients with a risk score of 0.113, high but not extremely high risk, had the greatest reduction in RR from follow-up visits. Patients with an extremely high risk score (> 0.334) saw no RR reduction from follow-up visits. Third, a patient was much more likely to have a 2-day follow-up visit if that visit was scheduled before the patient was discharged from the hospital (30% versus < 5%).

Conclusions

Follow-up visits are associated with a reduction in readmission risk. The timing of follow-up visits can be important: beyond two days, the earlier, the better. The effect of follow-up visits is more significant for patients with a high but not extremely high risk of readmission.

Introduction

Reducing hospital readmissions remains a significant challenge for many healthcare systems. In a study among Medicare patients by Jencks et al in 2009, about one in five discharged patients were re-hospitalized within 30 days and only 10% of these re-hospitalizations were planned [ 1 ]. Hospitals with excessive 30-day readmission rates among patients with acute myocardial infarction (AMI), heart failure (HF), pneumonia (PN), and chronic obstructive pulmonary disease (COPD) have suffered financial penalties through the Hospital Readmission Reduction Program (HRRP) enforced by the Centers for Medicare and Medicaid Services (CMS) [ 2 ].

Early follow-up visits are recommended by the Society of Hospital Medicine for patients discharged home—a finding based on expert opinion rather than published studies [ 3 ]. However, literature on the effect of follow-up visits to reduce readmissions is limited with mixed conclusions. Several papers based on patients with specific conditions, as well as general hospitalized patients, conclude that early follow-up visits are helpful in preventing 30-day readmissions [ 3 – 9 ]. Other articles show that patients do not meaningfully benefit from follow-up visits [ 10 – 12 ]. These mixed conclusions may reflect the truth or might be due to insufficient adjustment for potential confounding factors or the incorrect choice of methods when comparing the groups with and without follow-up visits.

As pointed out in [ 7 ], when the follow-up visit is treated as a fixed indicator with a binary outcome of yes or no, there is a potential bias resulting from the fact that patients who are readmitted earlier will not have a chance for a follow-up visit and hence are included in the “no follow-up” group [ 7 ]. This makes the “no follow-up” group seem worse and thus overestimates the benefit of a follow-up visit. To correct such bias, [ 7 ] proposed using the follow-up visit as a time-dependent covariate in Cox proportional hazard models. We show in the Results section that this is helpful in addressing the above concern of overestimation. However, it cannot readily answer the question of when a follow-up visit is the most effective. In addition, the proportional hazard assumption can be hard to validate when time-dependent covariates and interaction terms are both significant in the model. Thus, we propose a simple strategy to carefully define the groups with and without follow-up visits and apply traditional logistic models in the analysis so that the above concerns can be addressed. We show that the results from the sample-adjusted logistic models are amazingly consistent with those from survival models.

Data sources

We extracted the clinical and claims data of inpatients from eight Advocate Health Care hospitals located in the Chicago metropolitan area. The cohort included adult (age > = 18) inpatient encounters with discharge dates from June 1, 2013 to April 30, 2015. In addition, for each inpatient encounter the first post-discharge visit information was extracted up to December 31, 2015. Inpatients with a hospital service of Hospice, Obstetrics, Pediatrics, IP Pediatric Rehab, Psychiatric, Inpatient Rehab, or Skilled Nursing were excluded. If patients expired during hospitalization, we excluded the last encounter only. We initially had a data set of 99,660 encounters for 62,940 unique patients. Since patients discharged to a skilled nursing facility, inpatient rehab facility, or long-term care facility are actively monitored by healthcare professionals, a conventional follow-up visit does not serve the same purpose as it does for patients who go home after discharge. Therefore, we focused only on patients discharged to Home (self-care) or Home Health (visiting nurse), which left us with a sample of 66,400 encounters for 46,866 unique patients. Finally, we excluded inactive patients who were defined as those that didn’t have contact with any of the Advocate hospitals within 6 months after discharge from the hospital. These patients most likely switched to other hospitals or expired at home. Including such patients can underestimate the readmission rate in the control group and thus underestimate the treatment effect. We finally ended up with 55,378 encounters for 38,068 unique patients.

This study has been approved by the Advocate Health Care IRB. All data were fully anonymized and the IRB has waived the requirement for informed consent.

Response variable

A readmission is defined as a non-elective re-hospitalization within 30 days after discharge from the hospital. In the original dataset, there were 11,864 readmissions among 99,660 index admissions, resulting in a readmission rate (RR) of 11.90%. For the final data set, there were 7,217 readmissions out of 55,378 index admissions, resulting in a RR of 13.03%.

Follow-up visits

Outpatient follow-up visits have been recommended to reduce patients’ readmission risk by the Society of Hospital Medicine. There are two types of follow-up visits considered in current literature: an actual follow-up visit (AFV) and a scheduled follow-up visit (SFV). An AFV is a healthcare provider-patient encounter with encounter type labeled as appointment or outpatient that occurred after the patient was discharged from the hospital while an SFV refers to an encounter that was scheduled but which may or may not have happened.

We focused on the analysis of AFVs. Most of the AFVs occurred in physician offices. However, we did include other types of outpatient encounters such as rehab services, imaging procedures, chemotherapy treatments and so on. We chose not to differentiate between different aspects of these inpatient visits (e.g. medical, surgical, primary diagnosis, etc.) because we are more interested in seeing the general pattern of association between readmission events and outpatient visits in all types of inpatients. Our data on SFVs was restricted to high-risk patients only, which limited a direct analysis on the association between a visit and readmission risk for the general inpatient population. Therefore, assuming AFVs are effective, we were more interested in seeing how AFVs were affected by SFVs.

Confounding factors

It is important to include potential confounding factors in the model to minimize bias. The factors we included in this study were raw readmission risk score, acute myocardial infarction (AMI), heart failure (HF), chronic obstructive pulmonary disease (COPD) and pneumonia (PN). The raw risk score is the predicted readmission probability, which was developed by the Advocate Cerner Collaborative team [ 13 ]. The raw risk score is considered a representative measure of the actual readmission risk of a patient. A raw risk score of 0.068 or less is considered low risk. A score between 0.068 and 0.10 indicates moderate risk and a score above 0.10 is considered high risk. We also included the indicator variables for HF, AMI, COPD and PN in the model because patients with such conditions are more likely to receive other types of interventions such as transitional visits, heart failure clinic visits, extra education, and so on. By including these factors in the study, we hope to minimize confounding effects so that we might draw a more reliable conclusion.

Inter-correlation among inpatient encounters of the same patient

An inpatient encounter is an admission event, which might be a readmission to another inpatient encounter in the dataset. To deal with the correlations among inpatient encounters of the same patient, the method of deduplication [ 13 ] or generalized estimating equations (GEE) [ 14 ] can be applied. However, it was shown that for the purpose of predicting 30-day readmission risk, when a sample size is large enough, neither de-duplication nor GEE gains more precision over the basic models that simply ignore correlations [ 15 ]. Therefore, we decided to ignore such correlations and to use the basic models for analysis in this study.

Hospital diversity

Our data was extracted from eight Advocate hospitals in the Chicago area with patients across a wide spectrum of cultural, ethnic and socioeconomic circumstances. Besides the standard discharge protocol of readmission prevention education for high risk patients, several additional hospital-specific programs were also available in these hospitals to overcome barriers in language, culture and other factors. Considering such diversity, we can apply the random effect models in both survival and logistic analysis to examine the inter-hospital variation. Since results for the regular and random effect models in both survival and logistic analysis are very similar, we included only results from regular models in the Results section for simplicity. Fitting parameters on random effect models were listed in Tables A and B in S1 File .

Definition of the groups with and without follow-up visits

As discussed in Introduction, the survival models with a time dependent covariate can sufficiently take into consideration the time-dependent feature of both events of readmission and intervention so that a reliable assessment can be made. However, to answer the question of when is the most effective time for a follow-up visit, we need to be more creative.

Our strategy is to first define a threshold S E for an early follow-up visit. Then we compare differences in readmission rates between the yes-intervention and -no-intervention groups for different values of S E and choose the one with the greatest difference. In published literature, S E can vary from 5 days to 2 weeks. We allow S E to vary from 1 to 29 days so that we can search for an optimal choice in the space of all possibilities.

The definitions of the yes-intervention and no-intervention groups are not as trivial as they seem due to the time-dependence feature for both the readmission and follow-up events. We describe our strategy in the following. For each fixed value of S E , we first exclude encounters readmitted on or before day S E , regardless of whether or not they have a follow-up visit before the readmission. What is left in the sample are encounters at risk of readmission after day S E . Encounters in the sample with an actual follow-up visit on or before day S E are included in the yes-intervention group. Encounters without an actual follow-up visit on or before day 30 are included in the no-intervention group. Encounters with an actual follow-up visit after day S E but before day 30 are not included in either the yes-intervention or no-intervention group because there can be a partial effect from a later intervention. Including them in either of the groups can result in biased conclusions. With such defined groups, we can then compare readmission rates and apply logistic models to explore the association between an intervention and readmission risk.

Statistical analysis

We focused our analysis on AFVs. We first applied the Cox proportional hazard model to evaluate the overall association between a follow-up visit and a readmission event. As in Sharma et al (2010), to avoid time-dependent bias, a follow-up visit must be treated as a time dependent covariate. That is, patients are in the “no follow-up visit” group until they have their first follow-up visit. The proportional hazard assumption for each covariate was examined by plotting the log of cumulative hazard rates against time. The readmission event was censored by 30 days and the follow-up event was censored by 29 days.

We then used the properly defined yes- and no-intervention groups to compare the difference of readmission rates for different values of S E and to find out the optimal time for an AFV. Next, with S E being fixed at the optimal value, we applied multivariate logistic models adjusting for risk score, HF, AMI, COPD and PN, to find out the featured groups that might benefit the most from follow-up visits. Analyses were performed with R 3.2.2 and SAS 9.4.

Characteristics of the study population

Out of the 55,378 active inpatient encounters discharged to Home or Home Health, 26,436 (47.74%) had at least one follow-up visit within 30 days. There were 1,929 readmissions within 30 days in these patients, which generated a readmission rate of 7.30%. There were 28,942 (52.26%) inpatient encounters without any follow-up visit within 30 days of discharge. The number of readmissions in these patients was 5,288 which resulted in a readmission rate of 18.27%. Direct comparison of these two readmission rates was not reliable due to concerns of potential bias.

We first applied the Cox proportional hazard model in the analysis. Table 1 lists the basic characteristics of the study population and the estimated hazard ratios for potential confounding factors. The cells in columns 2 (Yes) and 3 (No) are bold if the p-value to test the difference is less than 0.05, where p-values were obtained using two-sided t-tests. The cell in column 3 is bold with stars if the hazard ratio is different from 1 at the significant level of 0.05. The interaction between follow-up visit and raw risk score was significant. Therefore, the hazard ratio for raw risk score was not listed and the effect will be explained later.

a The estimated HR is not available for Raw Risk Score due to the interaction between AFV and Raw Risk Score.

b Cells with stars in this column indicate that the estimated hazard ratios are significantly different from 1 (p ≤ 0.05).

Based on the data shown in Table 1 , we concluded that patients with follow-up visits had lower raw risk scores, lower proportion of heart failure (current or historical), lower proportion of COPD (current or historical), and lower proportion of historical pneumonia. Patients with current heart failure or pneumonia had lower hazard of readmissions comparing to those without. However, patients with historical heart failure or pneumonia had higher hazard of readmissions comparing to those without.

Results from Cox proportional hazard models

The interaction between AFV and raw risk score was significant (p < 0.001). The hazard ratio for the logit of raw risk score was 2.28 (95% CI: 2.16, 2.41) with a follow-up visit and 2.01 (95% CI: 1.95, 2.08) without a follow-up visit. This is consistent with our expectation that a higher raw risk score yields a higher hazard of readmission. In addition, Fig 1 shows the hazard ratios for a follow-up visit at different values of raw risk scores. The hazard ratio approached 1 when the raw risk score increased to 0.328, which indicated that follow-up visits were associated with lower readmission risk for most patients except those with extremely high raw risk scores. In our data, about 8.6% encounters had raw risk scores above 0.3. We applied the Cox proportional hazard model to the subset of data with raw risk scores above 0.3. The estimated hazard ratio for follow-up visits was 0.91 (95% CI: 0.81, 1.04). This indicated that follow-up visits alone might not work well for these patients at extremely high risk of readmission.

It is of interest to know that in the proportional hazard model that included all confounding factors but excluded interactions, the estimated hazard ratio of yes vs no for follow-up visit on the risk of readmission is 0.89, with a 95% confidence interval (0.84, 0.94). This indicated that on average follow-up visits were significantly associated with a reduction of readmission risk. This is consistent with the results by Sharma et al in 2010 although they were using Medicare data for COPD patients, where they concluded that patients who had a follow-up visit had a significantly reduced risk of readmission with a hazard ratio of 0.91.

Most effective time for a follow-up visit

Due to varying expectations on readmissions at different time points, we cannot directly compare the readmission rates for patients with follow-up visits. However, we can generate comparable groups with (YF) and without (NF) early follow-ups and compare the difference of RR between these two groups. To that end, we calculated the readmission rate of patients with and without follow-up visits from day 1 through day 29. The results are displayed in Fig 2 . We concluded that a follow-up visit within 2 days was associated with the greatest reduction in RR. Such association beyond two days was still significant, but the strength of it diminished over time.

Next, we focused on patients with 2-day actual follow-up visits (YF group, n = 2,412) versus those without (NF group, n = 28,290) for further analysis. It turned out that the RR in the YF group was 11.32% as compared to 16.39% in the NF group, which generated an (unadjusted) odds ratio of 0.65 (95% CI: 0.57–0.74). The adjusted odds ratio (with confounding factors, but without interactions) was 0.72 (95% CI: 0.63–0.83). Table 2 presents the basic characteristics for the groups with (YF) and without (NF) early follow-ups. The YF group had lower risk scores on average and a lower proportion of patients with historical COPD.

a The estimated OR is not available for Raw Risk Score due to the interaction between AFV and Raw Risk Score.

b Cells with stars in this column indicate that the estimated odds ratios are significantly different from 1 (p ≤ 0.05).

Results from logistic models

The last column in Table 2 shows the estimated odds ratio for each risk factor. We drew very similar conclusions when comparing to results from the proportional hazard models. That is, a higher raw risk score always predicted a higher probability of readmission. Patients with current heart failure or pneumonia had a lower readmission risk, while patients with historical heart failure or pneumonia had a higher readmission risk. Again, the interaction between follow-up visits and raw risk scores was significant. To further explain the model, we generated Fig 3 using the logistic model that included only follow-up visits, raw risk scores and the interaction between them. Fig 3 shows the effect of follow-up visits on the readmission risk for patients with different risk scores. It turned out that the effect was maximized when the raw risk score equaled 0.113. Patients with a risk score of 0.113 were considered high but not extremely high risk patients for readmission. This effect decreased as the readmission probability got further from 0.113 in either direction, and it approached 0 when the raw risk score equaled 0.334. Patients with a raw risk score of 0.334 or above were considered extremely high risk patients. Many of them had complicated conditions as well as frequent visits to the hospital. It was consistent with our experience that a single follow-up visit might not be effective in reducing readmissions for these patients. However, it did not mean that AFV would do harm to such patients. In addition, since this optimal value was sensitive to the coefficient of the interaction term, we calculated the 95% confidence interval for this coefficient and obtained optimal values with the coefficient being at the margin values. We then had 0.056 at the lower boundary and 0.146 at the upper boundary, which can be viewed as the approximate 95% confidence interval for the optimal value of the raw risk score of patients to maximize the reduction in readmission risk. In our data, there were about 45% of the encounters with raw risk scores between 0.056 and 0.146.

Scheduled follow-up visit

We used the 2-day actual follow-up visit as a threshold and checked the relationship between the actual and scheduled follow-up visits. In the 66,400 encounters discharged to Home or Home Health, we further excluded 175 encounters with an appointment date prior to discharge or greater than one year after the discharge date, as well as 686 encounters readmitted within 2 days. We ended up with a data set containing 65,539 encounters. The p-value to test independence between AFV and SFV was less than 0.001, which indicated a strong association between these two variables.

Table 3 shows the joint distribution of actual and scheduled follow-up visits. We saw that of the patients scheduled to visit a doctor within 2 days, about 30% did visit a doctor within 2 days. Patients without such a scheduled visit saw a doctor within 2 days less than 5% of the time. This indicated that scheduling 2-day follow-up visits before discharging patients to home or home health was associated with a lower readmission risk.

In this paper, we have explored the association between follow-up visits and the risk of readmission using both proportional hazard models and logistic models. In the proportional hazard models, it is critical to treat follow-up visits as a time-dependent variable. Otherwise, the effect can be overestimated, which leads to misleading conclusions. In the logistic models, it is critical to first specify a threshold to define an early follow-up visit event and then exclude patients no longer at risk as well as patients with later follow-up visits.

Both analyses concluded that timely follow-up visits for patients discharged to Home or Home Health was strongly associated with a lower risk of readmission. Follow-up visits can work differently for patients at different levels of readmission risk. Such association was the strongest for patients with high but not extremely high raw risk scores. Patients with a current condition of heart failure or pneumonia had a lower risk of readmission compared to those without, which might be due to other successful intervention programs. However, patients with a historical condition of heart failure or pneumonia had a higher risk of readmission compared to those without. This indicates that intervention programs on these patients might also be considered to further reduce readmissions.

In addition, we found out that a follow-up visit within 2 days worked best for patients with a raw risk score of 0.113. A patient was much more likely to have a 2-day follow-up visit if that visit was scheduled before the patient was discharged from the hospital. However, we understand that in practice, it might be a challenge to have patients come back within 2 days. Our results showed that the best strategy is to aim for follow-up visits within 2 days. If not, patients can still benefit from follow-up visits—especially those which occur as soon after 2 days as possible.

The major confounding factor, the raw risk score, is not necessarily available in other medical systems. However, similar scores, either automatically generated from an EMR [ 16 – 20 ] or manually calculated using algorithms such as LACE [ 21 ], can be easily obtained. Therefore, our conclusions on the complicated association between follow-up visits and readmissions can be readily double checked and possibly generalized to other health care providers to improve medical practice.

Limitations

This study has several limitations. First, it is an observational study. Although we have tried our best to adjust for potential confounding factors, there are always other possible explanations on the association between follow-up visits and reduced readmissions because of unmeasured variables such as socioeconomic status (SES), medical literacy level, medication adherence and so on. For example, patients with timely follow-ups might have higher SES, might care more about their health and might be more willing to comply with medications and to follow professional medical suggestions. The observational study only reveals an association between follow-up visits and readmissions. The causal effect conclusion can be drawn only through a controlled experiment that minimizes bias, such as the study in [ 22 ].

It is a challenge to properly exclude inactive patients from the data. The ideal way would be keeping contact with each of the patients discharged home and obtaining timely information when a patient dies or switches to another hospital so that the readmission rates for both groups with and without early follow-up visits can be estimated more accurately. However, it is very hard to keep track of every patient in practice. Our method to exclude patients without any hospital contact for six months after discharge to home is a compromise, which might be problematic for smaller hospitals with high turnover rates of patients.

The readmission rate of 13% was much lower than the RR of 19.6% as in [ 1 ] for three possible reasons: (1) patients in our sample were younger ((≥ 18) than Medicare patients (≥ 65); (2) the overall RR has become lower over time since the enforcement of HRRP began in 2012 [ 23 ]; (3) information on readmissions could be missing if patients received care at other hospitals, which could result in an underestimation of the actual RR. Since we are generally missing more information for patients with whom we have no further contact, this might result in an underestimation of the association between follow-up visits and readmission events. Our effort to define active patients is not the ideal way to solve this problem, but it can correct such an underestimation to a certain degree.

The strategy we proposed for logistic models is easy to follow and generates consistent results with survival analysis. Nevertheless, it has obvious limitations. We lose power by discarding a large proportion of data. Also, by excluding patients with later follow-up visits from the data, the estimation on readmission rate is no longer accurate. We intend to show in a future paper that such a method is appropriate under certain model assumptions but can be problematic under others. It is methodologically feasible to use all the data and make a direct fair comparison for patients with follow-up visits to those without by developing new models and testing statistics, but that is beyond the scope of this paper and is worth exploring further.

Supporting information

Table A. Raw results of the time dependent Cox proportional hazard model, both regular and random effect, including all factors, using data with a sample size being 55,378. Table B. Raw results of the logistic model, both regular and random effect, including all factors, using data with a sample size being 30,702.

S1 Data File

Acknowledgments.

The authors would like to thank Cerner and Advocate Health Care for the support of this work. The authors would also like to thank their colleagues in the Advocate Cerner Collaborative team for the constructive and professional suggestions and comments.

Funding Statement

TA and CE are employees of Cerner. The funder provided support in the form of salaries for authors TA and CE, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Data Availability

RSV Surveillance & Research

CDC has several systems that focus on monitoring and describing seasonal trends, clinical risk factors, rates of illness and hospitalization, and demographics of patients seeking care for RSV-associated illness.

• The National Respiratory and Enteric Virus Surveillance System (NREVSS) is a laboratory-based system that monitors temporal and geographic circulation patterns (patterns occurring in time and place) of respiratory syncytial virus (RSV) and other viral infections.
• The National Syndromic Surveillance Program (NSSP) is a collaboration among CDC, federal partners, state and local health departments, and academic and private sector partners to collect, analyze, and share electronic data received from multiple health care settings.
• The RSV Hospitalization Surveillance Network (RSV-NET) is a population-based surveillance system that monitors respiratory syncytial virus hospitalizations in the US among children and adults.
• The New Vaccine Surveillance Network (NVSN) is a multisite, active, population-based surveillance network for hospitalizations and outpatient visits among children that are associated with RSV and other acute respiratory illnesses.
• The Investigating Respiratory Viruses in the Acutely Ill (IVY) Network is a multisite, active surveillance network designed to assess how well vaccines work to prevent severe COVID-19, flu, and eventually RSV-associated hospitalizations among adults.
• The RSV Surveillance in Native American Persons (RSV SuNA) collaboration monitors for RSV-associated hospitalizations and outpatient visits among Native American persons and is conducted on the Navajo Nation, White Mountain Apache Tribal Lands, and in Alaska. Additional information about other research and surveillance activities among Alaska Native persons may be provided by the Arctic Investigations Program .

Each year in the United States, RSV leads to approximately:

• 2.1 million outpatient (non-hospitalization) visits among children younger than 5 years old. ( 1 )
• 58,000-80,000 hospitalizations among children younger than 5 years old. ( 1 , 2,3 )
• 60,000-160,000 hospitalizations among adults 65 years and older. ( 4-8 )
• 6,000-10,000 deaths among adults 65 years and older. ( 9-11 )
• 100–300 deaths in children younger than 5 years old. ( 11 )

How are data collected? CDC collects RSV laboratory test results performed in the United States using a surveillance system called the National Respiratory and Enteric Virus Surveillance System (NREVSS) . CDC analyzes data on RSV activity at the national, regional, and state levels and RSV Surveillance Reports  are periodically published. This is a voluntary, laboratory-based surveillance system established in the 1980s to monitor trends in several viruses, including RSV. Through NREVSS, participating laboratories report the total number of weekly RSV tests performed to detect the virus, and the number of those tests that were positive. They also report the method used for detection, and the location and date of specimen collection. Serotyping, demographic data, and clinical data are not reported. Data from NREVSS provides information to public health officials and healthcare providers about the presence of RSV in their communities.

What are the typical seasonal patterns? In most regions of the United States and other areas with similar climates, RSV season typically starts during the fall and peaks in the winter. Based on data from before the COVID-19 pandemic (2014 to 2017), in all 10 U.S. Department of Health and Human Services (HHS) regions, except Florida and Hawaii these patterns were observed ( 12 ) :

• RSV season onset (indicating a sustained rise in the number of RSV-positive tests) ranged from mid-September to mid-November.
• RSV season peak (indicating the maximum number of RSV-positive tests) ranged from late December to mid-February.
• RSV season offset (indicating a sustained drop in the number of RSV-positive tests) ranged from mid-April to mid-May.

Florida has an earlier RSV season onset and longer duration than most regions of the country.

Prior to 2020, seasonal patterns for RSV in the United States were very consistent. ( 12 ) However, the patterns of circulation for RSV and other common respiratory viruses have been disrupted since the start of the COVID-19 pandemic early in 2020. Beginning in the southern region of the United States, RSV circulation began to rise in the spring months of 2021 and peaked in July. ( 13 )  It is too soon to predict when the previous seasonal patterns will return.

• Hall CB, Weinberg GA, Iwane MK, et al.  The burden of respiratory syncytial virus infection in young children. New Engl J Med . 2009;360(6):588–98.
• Rha B, Curns AT, Lively JY, et al.  Respiratory Syncytial Virus–Associated Hospitalizations Among Young Children: 2015–2016. Pediatrics . 2020;146(1):e20193611.
• McLaughlin JM, Khan F, Schmitt H-J, et al.  Respiratory Syncytial Virus–Associated Hospitalization Rates among US Infants: A Systematic Review and Meta-Analysis. JID . 2022;225(6):1100-1111.
• Widmer K, Zhu Y, Williams JV, et al. Rates of Hospitalizations for Respiratory Syncytial Virus, Human Metapneumovirus, and Influenza Virus in Older Adults . J Infect Dis. 2012; 206(1):56-62.
• Branche AR, Saiman L, Walsh EE, et al. Incidence of Respiratory Syncytial Virus Infection Among Hospitalized Adults, 2017–2020 . CID. 2022;74(6):1004-1011.
• McLaughlin JM, Khan F, Begier E, et al. Rates of Medically Attended RSV among US Adults: A Systematic Review and Meta-analysis . Open Forum Infect Dis. 2022; 9(7): ofac300.
• Zheng Z, Warren JL, Shapiro ED, et al. Estimated incidence of respiratory hospitalizations attributable to RSV infections across age and socioeconomic groups . Pneumonia. 2022;14(1):6.
• CDC unpublished data from RSV-NET. Available at: https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2023-02/slides-02-23/RSV-Adults-04-Melgar-508.pdf .
• Thompson WW, Shay DK, Weintraub E, et al.  Mortality Associated with Influenza and Respiratory Syncytial Virus in the United States. JAMA. 2003; 289(2): 179.186
• Matias G, Taylor R, Haguinet F, et al. Estimates of mortality attributable to influenza and RSV in the United States during 1997–2009 by influenza type or subtype, age, cause of death, and risk status. Influenza Other Respir Viruses . 2014; 8(5):507-15.
• Hansen CL, Chaves SS, Demont C, Viboud C.  Mortality Associated With Influenza and Respiratory Syncytial Virus in the US, 1999-2018. JAMA Network Open . 2022 Feb 1;5(2):e220527.
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• Introduction
• Conclusions
• Article Information

Significant increases were seen for overall visits ( P  < .001), mood-related visits (5.7% to 14.0%; P  < .001), behavioral visits (3.4% to 4.6%; P  = .004), and substance use–related visits (0.6% to 1.2%; P  = .04). No significant temporal changes were seen for psychosis or other visits.

There were significant increases in the proportion of visits associated with psychotropic medications overall ( P  < .001), as well as specifically for the medication classes of antidepressants (5.3% to 13.2%; P  < .001), antipsychotics (1.9% to 2.7%; P  = .04), and stimulants (3.8% to 5.9%; P  = .001).

eTable 1.  ICD Codes for Mental Health–Related Outpatient Visits

eTable 2. Classes of Psychotropic Medications

eFigure 1. Age-Stratified Trends in Visits for Mood, Behavioral, and Substance-Use Disorder

eFigure 2. Prevalence of Mental Health–Related Outpatient Visits Among Adolescents and Young Adults by Sex

eFigure 3. Sex-Stratified Trends in Mood, Behavioral, and Substance Use Disorders

Data Sharing Statement

• Errors in Abstract, Methods, Figure 2, and Supplement 1 JAMA Network Open Correction April 4, 2024

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Ahn-Horst RY , Bourgeois FT. Mental Health–Related Outpatient Visits Among Adolescents and Young Adults, 2006-2019. JAMA Netw Open. 2024;7(3):e241468. doi:10.1001/jamanetworkopen.2024.1468

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Mental Health–Related Outpatient Visits Among Adolescents and Young Adults, 2006-2019

• 1 Department of Psychiatry, Massachusetts General Hospital, Boston
• 2 Department of Psychiatry, McLean Hospital, Belmont, Massachusetts
• 3 Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
• 4 Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
• 5 Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
• Correction Errors in Abstract, Methods, Figure 2, and Supplement 1 JAMA Network Open

Question   What are the trends in mental health–related outpatient visits and psychotropic medication use among adolescents and young adults in the US from 2006 to 2019?

Findings   In this cross-sectional analysis of nationally representative data, the proportion of mental health–related outpatient visits and visits associated with psychotropic medications increased almost 2-fold. There were significant increases specifically for visits related to mood, behavioral conditions, and substance use.

Meaning   The findings of this study suggest that youth experienced a significant and sustained increase in mental health burden for over a decade preceding the COVID-19 pandemic, and treatment and prevention strategies will need to address preexisting psychiatric needs in addition to the direct effects of the COVID-19 pandemic.

Importance   Concerns over the mental health of young people have been increasing over the past decade, especially with the rise in mental health burden seen during the COVID-19 pandemic. Examining trends in mental health–related outpatient visits provides critical information to elucidate contributing factors, identify vulnerable populations, and inform strategies to address the mental health crisis.

Objective   To examine characteristics and trends in mental health–related outpatient visits and psychotropic medication use among US adolescents and young adults.

Design, Setting, and Participants   A retrospective cross-sectional analysis of nationally representative data from the National Ambulatory Medical Care Survey, an annual probability sample survey, was conducted from January 2006 to December 2019. Participants included adolescents (age 13-17 years) and young adults (age 18-24 years) with office-based outpatient visits in the US. Data were analyzed from March 1, 2023, to September 15, 2023.

Main Outcomes and Measures   Mental health–related outpatient visits were identified based on established sets of diagnostic codes for psychiatric disorders. Temporal trends in the annual proportion of mental health–related outpatient visits were assessed, including visits associated with use of psychotropic medications. Analyses were stratified by age and sex.

Results   From 2006 to 2019, there were an estimated 1.1 billion outpatient visits by adolescents and young adults, of which 145.0 million (13.1%) were associated with a mental health condition (mean [SD] age, 18.4 [3.5] years; 74.0 million females [51.0%]). Mental health–related diagnoses were more prevalent among visits by male (16.8%) compared with female (10.9%) patients ( P  < .001). This difference was most pronounced among young adults, with 20.1% of visits associated with a psychiatric diagnosis among males vs 10.1% among females ( P  < .001). The proportion of mental health–related visits nearly doubled, from 8.9% in 2006 to 16.9% in 2019 ( P  < .001). Among all outpatient visits, 17.2% were associated with the prescription of at least 1 psychotropic medication, with significant increases from 12.8% to 22.4% by 2019 ( P  < .001).

Conclusions and Relevance   In this cross-sectional study, there were substantial increases in mental health–related outpatient visits and use of psychotropic medications, with greater overall burden among male patients. These findings provide a baseline for understanding post-pandemic shifts and suggest that current treatment and prevention strategies will need to address preexisting psychiatric needs in addition to the effects of the COVID-19 pandemic.

Concerns over the mental health of young people have been increasing over the past decade. From 2008 to 2015, hospitalization for suicidal behaviors doubled, and an estimated 1 in every 5 children in the US experienced a mental illness. 1 - 3 The COVID-19 pandemic further increased the burden of mental health illness in this population, with alarming increases in mental health–related emergency department visits and suicidal behaviors, particularly among female adolescents. 4 In response, in 2019, the American Academy of Pediatrics declared a national state of emergency in children’s mental health and called for improved strategies to effectively address mental health needs. 5 Examining trends in the prevalence of mental health conditions is necessary to address this crisis and understand contributing factors, identify vulnerable populations, and inform strategies to provide effective services.

Studies have documented an increase in emergency department visits by adolescents and young adults over the past decade, 6 - 8 but it is unknown whether there has been a similar sustained increase in ambulatory visits in this population. 9 , 10 Understanding trends in the diagnosis and treatment of mental health conditions in outpatient settings is critical as these health care encounters represent the most common avenue through which adolescents and young adults access mental health care. Most studies have traditionally focused solely on the pediatric or adult population, with few considering young adults (age 18-24 years) as a separate group, despite ample evidence that these individuals are unique in terms of clinical risk profiles and the emergence of psychiatric illnesses. 11 - 14 In addition, sex-based analyses are imperative in assessing mental health conditions because of differences in prevalence, presentation, risk factors, and course. 15 Understanding these differences can contribute to improved diagnosis, treatment, and prevention approaches for both sexes.

The objectives of this study were to examine characteristics and trends over time from 2006 to 2019 for mental health–related outpatient visits among adolescents and young adults, including the use of psychotropic medications.

This study was a retrospective cross-sectional analysis of the National Ambulatory Medical Care Survey (NAMCS), from January 2006 to December 2019, exclusive of 2017, as data for this year have not been made available. The NAMCS, administered annually by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention, is a national probability survey of visits to office-based physicians engaged in direct patient care. 16 It uses a 3-stage probability sample design based on geography, physician practices within a geographic location, and visits within physician practices. Trained health care professionals complete patient record forms for patient visits, which is the unit of analysis. Each visit is weighted to allow for the calculation of national estimates. For this study, we identified visits for adolescents (age 13-17 years) and young adults (age 18-24 years). The survey response rate varied from 31.2% to 62.4% (median, 45% [IQR: 39%-59%]) over the 14-year period and was accounted for by sampling weights. 17 The NAMCS was approved by the National Center for Health Statistics Research Ethics Review Board and did not require institutional review board approval at Massachusetts General Hospital and McLean Hospital as all data are deidentified. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

Mental health–related outpatient visits included visits with diagnoses for psychiatric or substance use disorders, which were identified based on International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD-9-CM ) (2006-2015) and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification ( ICD-10-CM ) (2016-2019) codes. 18 , 19 Up to 3 diagnoses were recorded for each visit from 2006 to 2013, and up to 5 for each visit from 2014 to 2019. We limited the number of diagnoses to the first 3 for all years for consistency. Visits were included when any 1 of the 3 diagnosis codes was for a mental health condition.

Based on prior US-based studies examining the burden of mental health conditions, we classified psychiatric diagnoses into 6 categories: (1) mood-related (eg, depression, anxiety, bipolar disorder, trauma, and stress-related conditions), (2) behavioral (eg, disruptive, impulse-control, and attention-deficit/hyperactive disorders), (3) psychosis (eg, schizophrenia, schizoaffective, and delusional disorders), (4) suicide-related (eg, suicidal ideation, suicidal attempts, and nonsuicidal self-injury), (5) substance use, and (6) other (eg, tic disorders, eating disorders, and personality disorders) (eTable 1 in Supplement 1 ). 6 , 7 Diagnosis codes for neurodevelopmental disorders, such as autism spectrum disorder, were not included, consistent with the classification of mental health conditions in the Centers for Disease Control and Prevention National Syndromic Surveillance Program. 20

Medications associated with visits were grouped using the Multum therapeutics classification system. The NAMCS collected data on up to 8 medications from 2006 to 2011, up to 10 medications from 2012 to 2013, and up to 30 from 2014 to 2019. We limited the number of medications to the first 8 for consistency across all years. We identified all psychotropic medications associated with visits and categorized these into 1 of 7 drug classes: (1) antidepressants; (2) antipsychotics; (3) central nervous system stimulants; (4) anxiolytics, sedatives, and hypnotics; (5) mood stabilizers; (6) medications for substance use; and (7) antiadrenergic agents (eTable 2 in Supplement 1 ). 21

Sociodemographic characteristics analyzed included age, sex, race and ethnicity (abstracted from electronic health records as non-Hispanic Black, Hispanic, non-Hispanic White, and non-Hispanic Other (American Indian or Alaska Native, Asian, Native Hawaiian/Other Pacific Islander, and multiple races), insurance type (private, public, self-pay, and other), geographic region (Northeast, South, West, and Southwest), and metropolitan statistical area status. Race and ethnicity were examined as these characteristics have been shown to be associated with variable use of health care services for mental health conditions. 22

Descriptive statistics were used to describe visit characteristics and examine differences in the prevalence of mental health–related outpatient visits by sex and age (adolescents and young adults). We also determined the prevalence of mental health–related visits associated with at least 1 psychotropic medication by sex and age, with χ 2 tests used for comparisons.

We assessed temporal trends in the proportion of mental health–related visits and outpatient visits associated with psychotropic medications using χ 2 tests for linear trend. Trend analyses were conducted using annual proportions, but for presentation purposes, annual data were combined into 2-year periods.

Estimates based on less than 30 unweighted observations are considered unreliable by the NCHS and were flagged in the results. 23 Analysis of suicide-related diagnoses was not possible as the total sample size consisted of less than 30 visits and did not support further stratification.

Analyses were conducted using Stata, version 18 (StataCorp LLC) from March 1, 2023, to September 15, 2023. 24 The svy commands were used to produce national estimates to account for the multistage survey sample design, as recommended by the NCHS. 25 The χ 2 tests by default were 2-sided, and statistical significance was set at P  < .05.

From 2006 to 2019, there were an estimated 1.1 billion outpatient visits by adolescents and young adults, of which 145.0 million (13.1%) were associated with a mental health condition. Demographic characteristics of patients with mental health–related visits are reported in Table 1 . Patients had a mean (SD) age of 18.4 (3.5) years, with similar representation of females (51.0%) and males (49.0%), although males made up a greater proportion of visits associated with mental health conditions compared with visits for non–mental health conditions (49.0% vs 36.7%; P  < .001). Most patients were non-Hispanic White (77.0%), had private insurance (56.0%), and were based in metropolitan areas (89.2%). In addition to patient sex, race and ethnicity, insurance type, and geographic location differed between visits with and without mental health conditions.

Overall, mental health–related diagnoses were more prevalent among visits by male patients compared with female patients, with 16.8% of visits by males associated with a psychiatric diagnosis and 10.9% of visits by females ( P  < .001) ( Table 2 ). This difference was most pronounced among young adults, where 20.1% of visits by male patients were associated with a psychiatric diagnosis compared with 10.1% of visits by female patients ( P  < .001).

The most common categories of psychiatric conditions were mood-related (8.9%) and behavioral (5.2%). Among visits by adolescents, those by female patients were more likely to be associated with a mood disorder (9.2% vs 7.0%; P  = .003). However, this trend reversed for visits by young adults, with 13.5% of visits by male patients associated with a mood disorder compared with 7.9% of visits by female patients ( P  < .001). Behavioral conditions were more common among visits by male patients in both age groups, with nearly twice the prevalence among visits by adolescents (5.0% vs 9.8%; P  < .001), and nearly triple the prevalence among visits by young adults (2.3% vs 6.6%; P  < .001).

The proportion of visits associated with any mental health diagnosis nearly doubled over the study period, from 8.9% in 2006 to 16.9% in 2019 ( P  < .001) ( Figure 1 ). There were significant increases specifically for visits related to mood disorders (from 5.7% to 14.0%; P  < .001), behavioral conditions (from 3.4% to 4.6%; P  = .004), and substance use (from 0.6% to 1.2%; P  = .04). Visits for mood disorders peaked in 2018-2019 for both adolescents (13.5%) and young adults (14.2%) (eFigure 1 in Supplement 1 ). For behavioral conditions, visits peaked in 2014-2015 at 10.2% for adolescents and 6.0% for young adults. Visits for substance use also peaked in 2014-2015 for adolescents at 0.8%, while young adults experienced a peak in 2016-2017 at 3.0%.

Increases in overall mental health–related visits were similar for female and male patients, although males had a greater burden of psychiatric illness overall (eFigure 2 in Supplement 1 ). Significant increases in mood-related visits were seen in both sexes ( P  < .001), with peaks of 12.9% for females and 15.8% for males in 2018-2019 (eFigure 3 in Supplement 1 ). Visits for behavioral disorders remained relatively constant for males, averaging 8.3% over the study period, and increased significantly for females ( P  = .002), peaking in 2014-2015 at 6.3%. There were no significant sex-based temporal changes in substance use–related visits, with averages of 0.9% for female patients and 2.0% for male patients.

Among all outpatient visits for adolescents and young adults, 17.2% were associated with the prescription of at least 1 psychotropic medication, and 6.6% with 2 or more. Antidepressants were the most commonly prescribed medication class (7.8% of all visits), followed by stimulants (6.2%), anxiolytics (4.4%), antipsychotics (2.5%), and mood stabilizers (2.1%). The percentage of visits associated with the prescription of a psychotropic medication increased significantly over the study period, from 12.8% in 2006 to 22.4% in 2019 ( P  < .001) ( Figure 2 ).

Among visits associated with a mental health diagnosis, medication use was highest for visits with behavioral conditions (84.5%), mood disorders (76.2%), and substance use (74.0%) ( Table 3 ). Among visits by adolescent patients, males were prescribed psychiatric medications more frequently than females (79.8% vs 72.7%; P  = .047). There were no sex-based differences in overall medication prescribing among young adult patients. When examining specific psychiatric diagnosis categories, visits by young adult females with behavioral disorders were associated with higher rates of psychotropic medication use compared with those by young adult males (90.8% vs 82.4%; P  = .007). In addition, visits by adolescent males with other diagnoses were associated with higher rates of psychotropic medication prescribing compared with those by females (82.8% vs 50.6%; P  < .001).

The findings of our cross-sectional study suggest that the proportion of outpatient visits for mental health–related conditions increased significantly among adolescents and young adults from 2006 to 2019. This rise was associated with increases in visits for mood, behavioral, and substance use–related conditions. Mental health–related diagnoses were more prevalent among visits by male patients, particularly among young adults. Trends in prescribing of psychotropic drugs mirrored increases in mental health–related outpatient visits, with the greatest increases seen in visits associated with antidepressants.

The annual proportion of mental health–related outpatient visits increased almost 2-fold over the study period. Our findings suggest a continuation of trends seen in earlier studies documenting increases in pediatric outpatient visits associated with psychiatric illness from 1996 to 2012. 9 , 10 In addition, our findings are consistent with increases in the burden of mental health conditions seen in other settings, including visits to emergency departments and hospitalizations for psychiatric conditions. 6 , 7 , 26 These trends are likely predominately related to changing prevalence of underlying psychiatric illness in the US population, 27 , 28 although a combination of other factors, including increased recognition and detection of mental illness, expanded access to outpatient care, and increase in help-seeking behavior in the setting of reduced stigmatization of mental illness may be contributing to these patterns.

There were significant differences in the rates of visits for mental health conditions based on patient sex. Males carried a greater burden of psychiatric illness, with a higher prevalence of mood, behavioral, psychosis, and substance use disorders compared with females. This aligns with condition-specific studies reporting increased prevalence of behavioral, psychosis, and substance-related conditions among males. 29 - 31 These sex-specific findings have been attributed to differences in underlying biological factors, timing of emergence of disease (females have later onset of psychotic disorders than males), manifestation of illness (eg, conduct disorder), and socialization. One unexpected finding, however, was the increased prevalence of mood disorders among young adult males. Consistent with prior studies, adolescent visits with mood-related disorders were more common among females. 32 , 33 However, this trend reversed among young adult males who had approximately twice the prevalence of mood disorders. The reason for this is unclear and in contrast to prior studies. 27 , 34 , 35 Non–US-based studies, including one in Norway and one examining global disease trends, reported a greater burden of mood disorders among females than males in their twenties. 34 , 35 However, a study of young adults in the US found no sex-based differences in rates of depression in the young adult population. 27 It is generally accepted that across the life span, females have a greater prevalence of mood disorders than males. 36 , 37 However, the transitional period from youth to adulthood presents unique challenges for males because of gender norms around masculinity, avoidance in seeking mental health services, increased exposure to violence, higher levels of substance use, and homelessness. 38 , 39 Other contributing factors may be related to treatment effects of stimulants, which may lead to psychotic, depressive, and/or anxiety symptoms, and an increase in subthreshold psychiatric diagnoses, although it is unclear whether this is occurring disproportionately among males. 40 - 42 Additional studies examining the potential association of these factors with the prevalence of specific mental health conditions in young adult males are needed.

Consistent with the rising proportion of visits for mental health conditions, we observed an increase in the proportion of outpatient visits associated with psychotropic prescribing over the study period. Nearly one-quarter of all outpatient visits were associated with a psychotropic medication in 2019. Our findings extend the results of previous work that showed increasing trends in psychotropic prescriptions among adolescents from 1994 through 2001. 43 Several possible factors may have contributed to these trends, including increased prevalence of mental health conditions in recent years, increased severity of illness requiring pharmacologic treatment, limited accessibility to psychotherapy, 44 and new psychotropic medication options. We were not able to assess whether changing trends were the result of increased access to psychiatric care with appropriate treatment of rising mental health conditions or whether the increases were reflective of an overreliance on medications with underuse of nonpharmacologic treatments, such as psychotherapy, exercise, and dietary changes. Irrespective of the underlying factors, use of psychotropic drugs in adolescents in particular requires careful assessment of the risk-benefit balance given the limited data on efficacy of these drugs in the pediatric population and known adverse effects, including concerns of suicidality among adolescents treated with antidepressants. 45 - 48

Concerns about youth mental health remain elevated 3 years after the onset of the COVID-19 pandemic, with multiple studies documenting the negative influence of the pandemic on the mental health of adolescents and young adults. 49 , 50 Our study provides additional context to the current mental health crisis, indicating that substantial increases in mental health conditions were occurring already for a prolonged period before the pandemic. This suggests that the high burden of mental health conditions documented since the onset of the pandemic cannot be attributed to the effects of this event alone and solutions will need to account for underlying factors predating the pandemic. In addition, while there has been a focus on the decline of female adolescent mental health related to the pandemic, our study points to young adult males as another potentially vulnerable population. 32 , 51

This study has several limitations. First, NAMCS samples visits rather than patients, and therefore there may be repeated outpatient visits by the same patient, potentially inflating the estimated prevalence of mental health conditions and psychotropic prescriptions. However, this is unlikely to have substantially impacted our results given the large number of visits sampled over geographically dispersed sites. Second, mental health–related visits were identified based on assigned diagnoses, which may not always be comprehensive or represent the principal reason for a health care encounter. Third, estimates from before and after 2016 may be prone to bias due to differences between ICD-9-CM and ICD-10-CM codes. Fourth, medication information consisted of prescriptions provided and may not correspond to prescriptions filled or administered. Fifth, surveys are limited to office-based practice, so our results may not be generalizable to other treatment settings where adolescents and young adults receive mental health care, including emergency departments, inpatient settings, residential programs, and hospital-affiliated outpatient clinics.

The findings of our cross-sectional study suggest substantial increases in mental health–related outpatient visits and use of psychotropic medications among adolescents and young adults from 2006 to 2019. Psychiatric illnesses were significantly more prevalent among visits by males, particularly among young adults. In the context of the current mental health crisis, these findings suggest that increases in mental health conditions seen among youth during the pandemic occurred in the setting of already increasing rates of psychiatric illness, and treatment and prevention strategies will need to account for factors beyond the direct and indirect effects of the pandemic.

Accepted for Publication: January 18, 2024.

Published: March 7, 2024. doi:10.1001/jamanetworkopen.2024.1468

Correction: This article was corrected on April 4, 2024, to fix errors in the Abstract, Methods, Figure 2, and Supplement 1.

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Ahn-Horst RY et al. JAMA Network Open .

Corresponding Author: Florence T. Bourgeois, MD, MPH, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115 ( [email protected] ).

Author Contributions: Drs Ahn-Horst and Bourgeois had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Both authors.

Acquisition, analysis, or interpretation of data: Both authors.

Drafting of the manuscript: Ahn-Horst.

Critical review of the manuscript for important intellectual content: Both authors.

Statistical analysis: Ahn-Horst.

Administrative, technical, or material support: Ahn-Horst.

Supervision: Bourgeois.

Conflict of Interest Disclosures: None reported.

Data Sharing Statement: See Supplement 2 .

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Affiliations.

• 1 Duke-NUS Medical School, Lien Centre for Palliative Care, Singapore, Singapore.
• 2 Duke-NUS Medical School, Lien Centre for Palliative Care, Singapore, Singapore. [email protected].
• 3 Duke-NUS Medical School, Program in Health Services and Systems Research, Singapore, Singapore. [email protected].
• PMID: 38600403
• DOI: 10.1007/s11606-024-08755-1

Background: Outpatient follow-up after a hospital discharge may reduce the risk of readmissions, but existing evidence has methodological limitations.

Objectives: To assess effect of outpatient follow-up within 7, 14, 21 and 30 days of a hospital discharge on 30-day unplanned readmissions or mortality among heart failure (HF) patients; and whether this varies for patients with different clinical complexities.

Design: We analyzed medical records between January 2016 and December 2021 from a prospective cohort study. Using time varying mixed effects parametric survival models, we examined the association between not having an outpatient follow-up and risk of adverse events. We used interaction models to assess if the effect of outpatient follow-up visit on outcomes varies with patients' clinical complexity (comorbidities, grip strength, cognitive impairment and length of inpatient stay).

Participants: Two hundred and forty-one patients with advanced HF.

Main measures: 30-day all-cause (or cardiac) adverse event defined as all cause (or cardiac) unplanned readmissions or death within 30 days of an unplanned all-cause (or cardiac) admission or emergency department visit.

Key results: We analyzed 1595 all-cause admissions, inclusive of 1266 cardiac admissions. Not having an outpatient follow-up (vs having an outpatient follow-up) significantly increased the risk of 30-day all-cause adverse event. (risk [95% CI] - 14 days: 35.1 [84.5,-1.1]; 21 days: 43.9 [48.2,6.7]; 30 days: 31.1 [48.5, 7.9]) The risk (at 21 days) was higher for those with one co-morbidity (0.25 [0.11,0.58]), mild (0.67 [0.45, 1.00]) and moderate cognitive impairment (0.38 [0.17, 0.84]), normal grip strength (0.57 [0.34, 0.96]) and length of inpatient stay 7-13 days (0.45 [0.23, 0.89]).

Conclusion: Outpatient follow-up within 30 days after a hospital discharge reduced risk of 30-day adverse events among HF patients, the benefit varying according to clinical complexity. Results suggest the need to prioritize patients who benefit from outpatient follow-up for these visits.

Keywords: co-morbidities; heart failure; outpatient follow-up; readmissions; singapore; time-dependent bias.

© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.

Grants and funding

• NMRC/HSRG/0053/2016/National Medical Research Council
• A-6300022-01-00/Lien centre for palliative care

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‘A transformative force’: Lurie Children’s Hospital breaks ground on outpatient center in Schaumburg

Six months after receiving approval, Lurie Children’s Hospital broke ground Tuesday on a new 75,000-square-foot outpatient center expected to open in Schaumburg in August 2025.

The project is an expansion for Lurie in the Northwest suburbs as well as a replacement for its smaller facilities in Arlington Heights, Hoffman Estates and Huntley, which will close upon its completion.

In a letter outlining the project last year, Lurie officials cited a 150% increase over the past decade in patient visits to their satellite locations, which have limited access and extended wait lists.

Lurie President and CEO Thomas Shanley said Tuesday the mission of the new facility will be to provide its specialized pediatric care to every child in the region who needs it.

“This project is consistent with our vision of being a transformative force,” Shanley said.

The forthcoming Schaumburg facility represents a more than $60 million investment on an undeveloped 5.67-acre site at the northwest corner of Roselle Road and Hillcrest Boulevard, south of the Extended Stay America and Holiday Inn hotels on Roselle Road.

When completed, the outpatient center will have an address of 1895 Arbor Glen Blvd. During its first year, it’s anticipated to employ 85 people and receive 60,000 patient visits.

The center will offer comprehensive pediatric specialty care services, including cardiology, neurology and urology, as well as orthopedic and pediatric surgeries. Other services will include therapeutic, rehabilitation and diagnostic and imaging services such as audiology, cardiac rehab and ultrasound.

The Lurie facility also will offer primary care and an Ambulatory Infusion Center (AIC). It will be the first pediatric-only infusion center outside of a hospital in the Chicago area.

The project is designed with 270 parking spaces and a canopy over the drop-off area for patients.

Though the initial hours of operation are expected to be 7 a.m. to 6 p.m. Monday through Friday, there’s potential to expand those hours and days, officials said during Schaumburg’s review process.

The outpatient center’s project team includes HKS as the architect, Skender as the general contractor, IMEG Corp. as the structural and mechanical engineers, and V3 Companies as the civil engineers.

Officials attending Tuesday morning’s groundbreaking ceremony included Shanley; Robert Liem, head of the Hematology, Oncology, Stem Cell Transplantation and Neuro-Oncology Division; Skender Vice President Brian Kane; and Schaumburg’s senior Trustee George Dunham.

“That Schaumburg was chosen for this center is a major milestone in our tradition of progress through thoughtful planning,” Dunham said.

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Heat-Related E.R. Visits Rose in 2023, C.D.C. Study Finds

By Noah Weiland

Reporting from Washington

The rate of emergency room visits caused by heat illness increased significantly last year in large swaths of the country compared with the previous five years, according to a study published on Thursday by the Centers for Disease Control and Prevention.

The research, which analyzed visits during the warmer months of the year, offers new insight into the medical consequences of the record-breaking heat recorded across the country in 2023 as sweltering temperatures stretched late into the year.

What the Numbers Say: People in the South were especially affected by serious heat illness.

The researchers used data on emergency room visits from an electronic surveillance program used by states and the federal government to detect the spread of diseases. They compiled the number of heat-related emergency room visits in different regions of the country and compared them to data from the previous five years.

Nearly 120,000 heat-related emergency room visits were recorded in the surveillance program last year, with more than 90 percent of them occurring between May and September, the researchers found.

The highest rate of visits occurred in a region encompassing Arkansas, Louisiana, New Mexico, Oklahoma and Texas. Overall, the study also found that men and people between the ages of 18 and 64 had higher rates of visits.

How It Happens: Heat can be a silent killer, experts and health providers say.

Last year was the warmest on Earth in a century and a half, with the hottest summer on record . Climate scientists have attributed the trend in part to greenhouse gas emissions and their effects on global warming, and they have warned that the timing of a shift in tropical weather patterns last year could foreshadow an even hotter 2024.

Heat illness often occurs gradually over the course of hours, and it can cause major damage to the body’s organs . Early symptoms of heat illness can include fatigue, dehydration, nausea, headache, increased heart rate and muscle spasms.

People do not typically think of themselves as at high risk of succumbing to heat or at greater risk than they once were, causing them to underestimate how a heat wave could lead them to the emergency room, said Kristie L. Ebi, a professor at the University of Washington who is an expert on the health risks of extreme heat.

“The heat you were asked to manage 10 years ago is not the heat you’re being asked to manage today,” she said. One of the first symptoms of heat illness can be confusion, she added, making it harder for someone to respond without help from others.

What Happens Next: States and hospitals are gearing up for another summer of extreme heat.

Dr. Srikanth Paladugu, an epidemiologist at the New Mexico Department of Health, said the state had nearly 450 heat-related emergency room visits in July last year alone and over 900 between April and September, more than double the number recorded during that stretch in 2019.

In preparation for this year’s warmer months, state officials are working to coordinate cooling shelters and areas where people can be splashed by water, Dr. Paladugu said.

Dr. Aneesh Narang, an emergency medicine physician at Banner-University Medical Center in Phoenix, said he often saw roughly half a dozen heat stroke cases a day last summer, including patients with body temperatures of 106 or 107 degrees. Heat illness patients require enormous resources, he added, including ice packs, fans, misters and cooling blankets.

“There’s so much that has to happen in the first few minutes to give that patient a chance for survival,” he said.

Dr. Narang said hospital employees had already begun evaluating protocols and working to ensure that there are enough supplies to contend with the expected number of heat illness patients this year.

“Every year now we’re doing this earlier and earlier,” he said. “We know that the chances are it’s going to be the same or worse.”

Noah Weiland writes about health care for The Times. More about Noah Weiland

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