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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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Health Maintenance Organization (HMO): What It Is, Pros and Cons

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

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What Is a Health Maintenance Organization (HMO)?

An individual shopping for health insurance may find a variety of insurance providers with unique features. One popular type of insurance provider is the health maintenance organization (HMO), which provides coverage through a network of physicians.

There are several key differences between HMO plans and the more widely-used preferred provider organization (PPO) plans. With an HMO plan, your primary care physician will refer you to specialists and you must stay within a network of providers to receive coverage. HMO plans typically have lower premiums than PPO plans.

Key Takeaways

• A health maintenance organization (HMO) is a network or organization that provides health insurance coverage for a monthly or annual fee.
• An HMO limits coverage to certain providers.
• HMO contracts allow for premiums to be lower, but they also add additional restrictions to their members.
• An HMO plan requires you first receive medical care services from your designated primary care physician (PCP).
• Preferred provider organizations (PPOs) and point-of-service (POS) plans are two types of healthcare plans that are alternatives to HMOs.

How a Health Maintenance Organization (HMO) Works

HMOs provide health insurance coverage for a monthly or annual fee. An HMO limits member coverage to medical care provided through a network of doctors and other healthcare providers who are under contract with the HMO.

These contracts allow for premiums to be lower than for traditional health insurance—since the healthcare providers have the advantage of having patients directed to them. However, they also restrict HMO members' choices for care.

When deciding whether to choose an HMO plan, you should consider:

• the cost of premiums
• out-of-pocket costs
• any requirements you have for specialized medical care
• whether it’s important to you to have your own primary care physician (PCP)

An HMO is a health insurance provider network that provides basic and supplemental health services to its subscribers. The organization secures its network of health providers contracting with PCPs, clinical facilities, and specialists.

The medical entities that enter into contracts with the HMO are paid an agreed-upon fee to offer a range of services to HMO members or subscribers. The agreed payment allows an HMO to offer lower premiums than other types of health insurance plans while retaining a high quality of care from its network.

The HMO as it exists today was established under the Health Maintenance Organization Act of 1973. The law clarified the definition of HMOs as “a public or private entity organized to provide basic and supplemental health services to its members.” The law further requires that plans provide insured individuals with basic healthcare in exchange for regular, fixed premiums that are established “under a community rating system.”

Rules for HMO Subscribers

HMO subscribers pay a monthly or annual premium to access medical services in the organization’s network of providers, but they are largely limited to receiving their care and services only from doctors within the HMO network. However, some out-of-network services, including emergency care and dialysis, can be covered under the HMO.

Those who are insured under an HMO may have to live or work in the plan’s network area to be eligible for coverage. In cases where a subscriber receives urgent care while out of the HMO network region, the HMO may cover the expenses. However, HMO subscribers who receive nonemergency, out-of-network care have to pay for it out of pocket .

In addition to low premiums, there are typically low or no deductibles with an HMO. Instead, the organization charges a co-pay for each clinical visit, test, or prescription.

Role of the Primary Care Physician (PCP)

The insured party must choose a PCP from the network of local healthcare providers under an HMO plan. A PCP is typically an individual’s first point of contact for all health-related issues. This means that an insured person cannot see a specialist without first receiving a referral from their PCP.

However certain specialized services may not require a referral. For example, screening mammograms in most cases will not require a doctor’s referral.

Specialists to whom PCPs typically refer insured members are within the HMO coverage network, so their services are covered under the HMO plan after co-pays are paid. If a PCP leaves the network, subscribers are notified so they can choose another PCP from within the HMO plan.

HMO Regulation

HMOs are regulated by both states and the federal government. The McCarran-Ferguson Act of 1945 established that states regulate the insurance industry, and that no federal law can override state regulation unless it explicitly does so.

As such, regulation of health insurance is left largely to the states. Still, some legislation—such as the HMO Act of 1973 and the Employee Retirement Income Security Act of 1974 , among others—can bring some aspects of the health insurance business under the purview of the federal government.

The federal government does maintain some oversight of HMOs. For example, the 2010 Dodd-Frank Act created the Federal Insurance Office (FIO) , which can monitor all aspects of the insurance industry.

The Affordable Care Act of 2010 created the ACA Health Insurance Marketplace , which provides access to various HMO and other plans for individuals, families, and small businesses who do not have access to employee-sponsored coverage.

HMO vs. Preferred Provider Organization (PPO)

Preferred provider organization (PPO) plans are the most common type of plan among U.S. workers who are covered through their employers, according to research firm KFF. Some 47% of covered workers are enrolled in a PPO in the most recent survey, versus 13% enrolled in an HMO. Another 29% were covered by a high-deductible health plan and 10% by point-of-service plans. Just one percent of workers are still covered by traditional indemnity plans.

A PPO is a medical care plan in which health professionals and facilities provide services to subscribed clients at reduced rates. PPO medical and healthcare personnel who are part of the PPO network are called preferred providers.

PPO participants are free to use the services of any provider within their network. Out-of-network care is available, but it costs more to the insured. In contrast to PPO plans, HMO plans require that participants receive healthcare services from an assigned provider. Medical and dental PPO plans usually have deductibles , while HMO plans typically do not.

Both programs allow for specialist services. However, in an HMO, the designated PCP must provide a referral to a specialist. PPO plans are the oldest type of managed healthcare plan and—due to their flexibility and relatively low out-of-pocket costs—have long been the most popular. That advantage has narrowed, however, as plans reduce the size of their provider networks and take other steps to control costs.

HMO vs. Point-of-Service (POS)

A point-of-service (POS) plan is like an HMO plan in that it requires a policyholder to choose an in-network PCP and get referrals from that doctor if they want the plan to cover a specialist’s services. A POS plan is also like a PPO plan: it still provides coverage for out-of-network services, but the policyholder has to pay more for those services than if they used in-network providers.

However, a POS plan will pay more toward an out-of-network service if the policyholder gets a referral from their PCP than if they don’t secure a referral. The premiums for a POS plan fall between the lower premiums offered by an HMO and the higher premiums of a PPO.

POS plans require the policyholder to make co-pays, but in-network co-pays are often just $10 to $25 per appointment. POS plans also do not have deductibles for in-network services, which is a significant advantage over PPOs.

Also, POS plans offer nationwide coverage, which benefits patients who travel frequently. A disadvantage is that out-of-network deductibles tend to be high for POS plans. Patients who use out-of-network services will pay the full cost of care out of pocket until they reach the plan’s deductible. However, a patient who never uses a POS plan’s out-of-network services probably would be better off with an HMO because of its lower premiums.

If you don’t travel frequently, you’ll be better off with an HMO plan than a POS plan because of the lower costs.

Advantages and Disadvantages of HMOs

It’s important to weigh the advantages and disadvantages of HMO plans before you choose a plan, just as you would with any other option. We’ve listed some of the most common pros and cons of the program below.

Lower out-of-pocket costs

Primary care physician directing your treatment

Higher quality of care

Must use medical professionals in the plan’s network

No specialist visits without a referral

Emergencies must meet certain conditions

Pros Explained

Lower out-of-pocket costs : You’ll pay fixed monthly or annual premiums that are lower than traditional forms of health insurance. These plans tend to come with low or no deductibles, and your co-pays are generally lower than those found in other plans. Your out-of-pocket costs will also be lower for your prescriptions. Billing tends to be less complicated.

Primary care physician directing your treatment : You will choose a PCP who is responsible for managing your treatment and care. This professional will also advocate for services on your behalf, such as making referrals for specialty services for you.

Higher quality of care : The quality of care is generally higher with an HMO plan. That is because patients are encouraged to get annual physicals and seek out treatment early.

Cons Explained

Must use medical professionals in the plan’s network : You’re restricted on how you can use the plan. You must designate a doctor within the network who will be responsible for your healthcare needs, including primary care and referrals. You are responsible for any costs incurred if you see someone out of the network, even if there’s no contracted doctor in your area.

No specialist visits without a referral : You’ll need referrals for any specialists if you want your HMO to pay for those visits. If you need to visit a rheumatologist or a dermatologist, for example, your PCP must make a referral before you can see one for the plan to pay for your visit. If not, you’re responsible for the entire cost.

Emergencies must meet certain conditions : There are usually very strict definitions of what constitutes an emergency. If your condition doesn’t fit the criteria, the HMO plan won’t pay.

What Are the Benefits of an HMO?

The main benefits are cost and quality of care. People who purchase HMO plans enjoy lower premiums than traditional forms of health insurance. The plan's focus on preventative medicine allows insured parties to get a higher quality of care from providers who are contracted with the organization. HMOs typically come with low or no deductibles and relatively low co-pays.

What Are Examples of HMOs?

Almost every major insurance company provides an HMO plan. For instance, Cigna and Humana provide their own versions of the HMO. Aetna offers individuals two options: the Aetna HMO and the Aetna Health Network Only plan.

What Is the Difference Between an HMO and a PPO?

Both an HMO and a PPO use a network of physicians and specialists to help control costs. HMOs tend to have lower premiums and require you to obtain a referral from your primary care physician to see an in-network specialist. PPOs allow you to see any specialist without requiring referrals, but the fees and deduictibles for out-of-network services are higher.

How Does an HMO Differ From Traditional Health Insurance?

Coverage under an HMO is generally fairly restrictive and comes at a lower cost to insured parties. Traditional medical insurance charges higher premiums, higher deductibles, and higher co-pays. However, non-HMO plans are much more flexible. People with health insurance don’t need to have a primary care physician to outline treatment. Health insurance also pays some of the costs for out-of-network providers.

Health insurance is an important consideration for every individual and family. Choosing the right plan depends on your personal situation, including your health, finances , and quality of life. You can choose from traditional health insurance (such as a PPO) or an HMO. The HMO provides lower out-of-pocket costs but carries more restrictive conditions, including which doctors who you see. Make sure you weigh the benefits and disadvantages of each plan before you choose your coverage .

HealthCare.gov. " Health Maintenance Organization (HMO) ."

U.S. Congress. “ H.R.7974 - Health Maintenance Organization Act .”

U.S. Department of Health and Human Services. " Special Advisory Bulletin ." Page 13.

HealthCare.gov. " How to Pick a Health Insurance Plan ."

National Breast Cancer Foundation. “ How to Schedule a Mammogram .”

Cornell University, Legal Information Institute. " 15 U.S. Code Operation of State Law ."

U.S. Department of Treasury. “ About FIO .”

KFF. " 2023 Employer Health Benefits Survey ."

HealthCare.gov. " Preferred Provider Organization (PPO) ."

University of Florida. " Choosing Your health Insurance Plan ."

Aetna. “ The Right HMO Coverage, for the Right Care .”

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A new marker of primary care utilization - annual accumulated duration of time of visits

Talya a. nathan.

1 The Department of Family Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

Arnon D. Cohen

2 Clalit Health Services, Tel Aviv; 3) Medical Division, Leumit Health Services, Tel Aviv, Israel

Shlomo Vinker

Associated data.

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

Most of the research on primary care workload has focused on the number of visits or the average duration of visits to a primary care physician (PCP) and their effect on the quality of medical care. However, the accumulated annual visit duration has yet to be examined. This measure could also have implications for the allocation of resources among health plans and across regions. In this study we aimed to define and characterize the concept of "Accumulated Annual Duration of Time" (AADT) spent with a PCP. 

A cross-sectional study based on a national random sample of 77,247 adults aged 20 and over. The study’s variables included annual number of visits and AADT with a PCP, demographic characteristics and chronic diseases. The time period was the entire year of 2012.

For patients older than 20 years, the average annual number of visits to a PCP was 8.8 ± 9.1, and the median 6 ± 10 IQR (Interquartile Range). The mean AADT was 65.8 ± 75.7 min, and the median AADT was 43 ± 75 IQR minutes. The main characteristics of patients with a higher annual number of visits and a higher AADT with a PCP were: female, older in age, a higher Charlson index and a low socio-economic status. Chronic diseases were also found to increase the number of annual visits to a PCP as well as the AADT, patients with chronic heart failure had highest AADT in comparison to others (23.1 ± 15.5 vs. 8.6 ± 8.9 visits; and 165.3 ± 128.8 vs. 64.5 ± 74 min). It was also found that the relationship between AADT and age was very similar to the relationship between visits and age.

While facing the ongoing increase in a PCP’s work load and shortening of visit length, the concept of AADT provides a new measure to compare between different healthcare systems that allocate different time frames for a single primary care visit. For Israel, the analysis of the AADT data provides support for continued use of the number of visits in the capitation formula, as a reliable and readily-accessible indicator of primary care usage.

Electronic supplementary material

The online version of this article (doi:10.1186/s13584-017-0159-y) contains supplementary material, which is available to authorized users.

How this fits in

Novel concept of "Accumulated Annual Duration of Time" spent with a primary care physician as a new measure to assess health services.

• We present a new measure "The accumulated annual visit duration" with primary care physicians that had not been evaluated in the literature.
• Our findings support cumulative duration as a parallel mean to the number of visits for health services assessment. This novel concept may serve as a new standardized comparative measure to evaluate and unify the characteristics of high quality primary care.
• New primary care guidelines should also refer to the optimal amount of time needed to be spent on health topics within the visit, rather than focusing on the number of visits.

Primary care visits

The primary care visit remains the principal opportunity for health care providers to address patient’s needs. The results of the Israel Central Bureau of Statistics (ICBS) for 2009 indicate that the annual average number of visits to the primary care physician (PCP) is 6.2 in the general population of Israel and 16.1 for ages 65 and over. Age and the number of visits of patients with chronic diseases were found to be factors that significantly increase the annual average number of visits [ 1 ]. The most recent data found by us suggests that the mean duration of a visit with an Israeli PCP is 10.4 min [ 2 ].

The annual average number of visits can vary substantially across countries. One study in the United States calculated a mean of 1.6 PCP (defined as visits to a general practitioner, family physician, pediatrician, geriatrician, or general internist) yearly visits per person as of 2008 [ 3 ]. In the WHO European Region, the average outpatient contacts per person per year in 2006 was 7.85, and country specific averages for 2006 or the latest available year were 7.0 in Germany, 9.5 in Spain, 5.4 in the United Kingdom, 5.7 in the Netherlands, 6.6 in Belgium and 11.0 in Switzerland [ 4 ].

There is also significant cross-country variation in visit duration. In the United States, 2006 data from the Centers for Disease Control and Prevention (CDC) found that the mean duration of face-to-face visits with PCPs (general or family practice) was 19.5 min [ 5 ]. In Europe, it was found that the mean length of a visit with a PCP (general practitioner) was 7.6 min in Germany, 7.8 min in Spain, 9.4 min in the United Kingdom, 10.2 min in the Netherlands, 15.0 min in Belgium and 15.6 min in Switzerland [ 6 ]. A study by Bindman et al. found in a 2001–2 cross-sectional analysis that the average duration of a face-to-face visit with a PCP in the US (general internists, general pediatricians, and family practitioners) was 16.5 min, about 10% longer than with general practitioners in Australia (14.9 min) and New Zealand (15 min). Visit lengths were longer in the US for all age and gender groups. Because the average number of primary care visits per capita was greater in New Zealand and Australia, however, the mean per capita annual exposure to primary care physicians in the US (29.7 min) was about half of that in New Zealand (55.5 min) and about a third of that in Australia (83.4 min) [ 7 ].

Studies from various countries have found that the length of an ambulatory visit with PCPs is influenced by increasing age, presence of psychosocial problems [ 8 ], gender (women) and greater number of new problems discussed in the visit [ 6 ].

Visit duration and patient outcomes

Research in the matter has shown that longer PCP visits were associated with a range of better patient outcomes [ 9 , 10 ], including more statements about health education and prevention [ 11 ], as well as higher rates of preventive medical measures such as vaccinations [ 12 , 13 ], and mammography referrals [ 14 ]. The duration of PCP care was also associated with lower costs of inpatient and outpatient care and with a lower risk of hospitalizations [ 15 ]. Wilson et al. first concluded that a PCP with a higher average visit length is more likely to provide visits that include important aspects of care, and that longer visit length can therefore be used as a quality indicator [ 16 ]. They later conducted a systemic review, which found that in interventional studies that had been performed by altering same physicians’ visit length the above mentioned effect had not been demonstrated. However, their findings were not sufficient to support or resist a policy of altering PCP visit length, and due to many limitations of the study, it was difficult for them to define length as a marker of quality of care [ 17 ].

When analyzing the primary care setting, one aspect of the visit is its content. A study by Tai-Seale et al. found that visit length was insensitive to the content of a visit - longer time spent on major topics seemed to have been compensated by limiting the time allocated to minor topics, therefore leaving the visit length more or less the same. Instead, organizational structure, physicians’ practice settings and payment incentives appeared to have more influence on visit length [ 18 ]. However, other research suggested that there was a positive association between the number of problems discussed and the mean length of visits. It was found that on average, PCPs spend 11.9 min dealing with 2.5 problems, and a linear relationship was seen at least up to six problems, with the length of visits increasing by an average of 2 min for each additional problem above a baseline of 9 min for the first problem [ 19 ]. Abbo et al. found that the number of clinical items addressed during a PCP visit increased from 5.4 in 1997 to 7.1 in 2005, resulting in a decrease in minutes spent per clinical item from 4.4 to 3.8 [ 20 ]. Approximately 8% of PCP visit duration was found to be attributable to eight-related conditions included diabetes, hypertension, hyperlipidemia, obesity, cardiovascular disease, osteoarthritis, and low back pain [ 21 ]. Chen et al.’s findings suggested that the relationship between quality of care and physician visit duration depends on the type of quality indicator being measured, namely, medication quality indicators vs counseling or screening quality indicators. In their research, they found a clear and consistent relationship between visit duration and provision of counseling and screening-based care [ 22 ].

Moreover, nearly one half of a primary care physician’s workday was found to be spent on activities outside the examination room, predominately focused on follow-up and documentation of care for patients not physically present. In the United States, Gottschalk et al. found that national estimates of visit duration overestimate the combination of face-to-face time and time spent on visit-specific work outside the examination room by 41% [ 23 ].

However, despite evidence that increasing visit length is more likely to improve primary care, and that longer visit length can therefore be used as a quality indicator, to our knowledge and according to the literature review, we did not find a study that defined the optimal annual accumulated time (complementary to the number of visits) that should be spent with a patient to achieve better quality of care.

The potential implications for resource allocation

In many countries, the allocation of financial resources among regions and/or among care providers is based on capitation formulae which try to reflect how the composition of populations served affect the need for health care services. For example, as older people tend to use more health care services, regions and providers serving populations with higher concentrations of the elderly are often given more financial resources per capita. This is done so that they will have enough resources to provide quality care and to eliminate any incentive to avoid caring for elderly persons.

In Israel, for example, when Israel distributes the National Health Insurance monies among health plans, it uses a capitation formula which includes mainly age, gender and other minor affecting parameters. In developing that formula, the government examines how age and gender are related to resource use for the key types of care consumed – hospital care, community services, and medications. As its measure of community service use, the government currently uses the number of physician visits. However, if visit duration varies significantly by age or gender, then the number of physician visits would not be a good indicator of resource use, and AADT would be a more appropriate measure to use. If visit duration does not vary significantly by age or gender then it would make sense to continue to base the capitation formula on the number of visits, as it is easier for the government to collect survey data on the number of visits than on the AADT. When the health plans distribute funds among their regions they also take into account various demographic characteristics (including location) and their relationship to service use. They too face a decision of whether to use the number of visits or AADT in resource allocation decisions, and hence they too have interest in knowing whether visit duration varies by demographic characteristics, as well as by location.

We conducted a cross-sectional study based on the electronic medical records of the largest Health Maintenance Organization (HMO) in Israel to investigate the characteristics of the concept of Accumulated Annual Duration of Time (AADT) that the PCP spends with a patient. This is an important first step towards using AADT in resource planning and allocation, and perhaps even determining the optimum level of AADT.

Population and data source

Data was retrieved from the Clalit Health Services (CHS) central computerized database. CHS is the largest HMO in Israel, covering 54% of the entire Israeli population (about 4,200,000 people in 7 districts). Every person insured by CHS is assigned to a PCP. All the visits to a PCP are fully computerized and the information from the electronic medical records is retrieved to a central repository. The central database includes demographics, information about physician visits, and a register of a selected number of chronic diseases (from the HMO’s registry, diagnosed previously to the visits in question).

The study period was the entire 2012 calendar year. The population of this study consisted of all adult members of the HMO aged 20 and over, from which we draw a national random sample of 83,707. The sampling method was a randomized computer based binary extraction of 2% of all patient data, based on the two last digits of the patients’ social security number.

Of the patients who were randomly selected from the HMO’s database, 1088 died during the study period and 2615 left the HMO. Patients older than age 100 years ( n  = 25), bed-ridden ( n  = 2059) or in a nursing home ( n  = 673) were excluded from the study; therefore, the current analysis included 77,247 patients.

Data accessed

The number and duration of visits of CHS members with a PCP were retrieved for the study period. Additional patient data included: demographic characteristics: age, gender, country of birth, year of immigration to Israel (Individuals who were born in Ethiopia and immigrated to Israel after 1984 were defined as “new immigrants”. Immigrants from other countries were defined as “new immigrants” if they immigrated after 1990. These represent the two major waves of immigration to Israel that took place in the past 30 years), residency (Large city ≥100,000 citizens, other city, collective settlement - also known as a Kibbutz, cooperative Israeli settlement, small town and non-Jewish settlement), socioeconomic status (SES; low SES was defined as exemption from social security payments); chronic diseases (malignancy, diabetes, hypertension, hyperlipidemia, ischemic heart disease (IHD), chronic heart failure (CHF), status post cerebrovascular accident (s/p CVA), asthma, chronic obstructive pulmonary disease (COPD), dementia, epilepsy, anxiety disorder and drug abuse); and a Charlson comorbidity index [ 24 , 25 ], which was calculated as well.

The study was approved by the CHS ethics committee at the Meir Medical Center, Kfar Saba, Israel.

Statistical analysis

Descriptive statistics was the primary method of analyzing the data. The annual number of visits and annual duration of visits (in minutes) were analyzed as continuous parameters. The Central Limit Theorem justifies the results despite the non-normal distribution of these variables.

Demographic characteristics were compared as well as medical characteristics for sub-groups according to number of visits and visit duration, using correlations (for differences between continuous parameters), T-tests (for differences between dichotomized parameters and averages of continuous parameters), chi-squared analysis and Fisher IS (for categorical parameters) and ANOVA (for differences between more than two categories in a parameter). If the ANOVA was found to be significant, a POST HOC analysis using Tukey’s test was performed to distinguish the different categories.

We used multivariate analysis to construct predictive models for comparison between annual number of visits and annual duration of visits.

A Multivariate Linear Regression model was applied to the data to study simultaneously the independent relationship between the demographic (age, gender, SES, residence area, and immigration status) and clinical background (chronic diseases, Charlson comorbidity index) and visit characteristics. The model predicts the probability of higher number of visits and longer annual duration of visits as a function of the explanatory variables. We addressed the non-normal distribution of these variables by using a square root transformation.

A p -value of 0.05 or less was considered statistically significant. All results were rounded to tenths (+1 decimal place). All analyses were carried out with the assistance of The Statistical Consulting Lab at The School of Mathematical Sciences at Tel Aviv University, using SPSS ver. 21 statistical software.

Table ​ Table1 1 shows the characteristics of the study population. 52.3% were female and 13.1% were new immigrants. The majority of the study population (81.3%) was between the ages 20–64 (children, up to 20 years old, were excluded from the study), with an average age of 46.5 ± 18.1 years; 41% resided in large cities and only 15.8% were considered to be of low SES. The average Charlson comorbidity index was 3.0 ± 1.1. The average annual number of visits with a PCP during 2012 was 8.8 ± 9.1 visits while the median was 6 ± 10 IQR visits. The average duration of a single visit was 7.6 ± 4.3 min while the median duration was 7 ± 4.5 IQR minutes. The mean annual duration of visits was 65.8 ± 75.8 min while the median annual duration was 43 ± 75 IQR minutes.

Characteristics of study population and visits with primary care physicians

PCP Primary Care Physician

AADT Accumulated Annual Duration of Time

SES Socioeconomic Status

IQR Interquartile Range

Table ​ Table2 2 presents the characteristics of the annual number of visits and the annual duration of visits with a PCP during 2012. A positive correlation between the annual number of visits as well as the annual duration of visits was found with both age (0.4) and the Charlson index (0.5). More visits, with a higher AADT were made by women (9.8 ± 9.2 vs. 7.7 ± 8.9 visits and 73.3 ± 76.7 vs. 57.5 ± 73.8 min); by the subgroup of low SES (14.7 ± 11.9 vs. 7.7 ± 8.0 visits and 104.5 ± 98.4 vs. 58.5 ± 68.3 min); and in kibbutzim (11.9 ± 11.9 vs. <8.9 visits and 100.3 ± 116.9 vs. <67.2 min) in comparison to large cities. Those who were new immigrants visited less frequently (7.7 ± 8.1 vs. 9.0 ± 9.2 visits) and had a lower AADT (57.1 ± 67.4 vs. 67.1 ± 76.8 min). Patients with one or more chronic diseases were also found to have made more visits and spent more time with their PCP throughout the year. The most substantial difference was seen among patients with chronic heart failure (CHF) compared to patient without the disease (23.1 ± 15.5 vs. 8.6 ± 8.9 visits, a 167.9% increase and 165.3 ± 128.8 vs. 64.5 ± 74 min, a 156.2% difference) followed by chronic obstructive pulmonary disease (COPD) (20.1 ± 15.1 vs. 8.6 ± 8.8 visits, a 135.3% difference and 143.9 ± 120.9 vs. 63.9 ± 73.4 min, a 125% difference) and hypertension (15.9 ± 11.5 vs. 6.8 ± 7.2 visits, a 133.1% difference and 115.9 ± 98.7 vs. 51.8 ± 61 min, a 123.8% difference).

Characteristics of the annual number of visits and the Annual Accumulate Duration of Time spent with a primary care physician during 2012

IHD Ischemic Heart Disease

CHF Chronic Heart Failure

s/p CVA status post Cerebrovascular Accident

COPD Chronic Obstructive Pulmonary Disease

Table ​ Table3 3 presents data on the average visit duration varied by age and gender, calculated as AADT during 2012 No . of visits during 2012 for each age and gender group. The data indicate that visit duration was found to be very similar for both men and women and across age groups.

Average visit duration in 2012 AADT during 2012 No . of visits during 2012 , by age and gender

Table ​ Table4 4 presents a Multivariate Linear Regression analysis (in square root) for the number of visits (R-squared 0.39) and the AADT (R-squared 0.34) spent with a PCP during 2012. Increase in age was initially associated with a non-linear increase in the number of visits and in the amount of time spent with a PCP, however after age 80 subsequent increases in age showed a decline in the number and duration of visits (See Additional file 1 ). Women, patients of a low SES and with a higher Charlson index spent more time and paid more visits with their PCP. Being a new immigrant meant fewer and shorter visits, and compared to persons residing in large cities, kibbutz members had the highest visiting rate and spent the most time with their PCP.

Linear regression (in square root) - number of visits and Annual Accumulate Duration of Time spent with a primary care physician during 2012

During 2012, the average annual number of visits with a PCP was 8.8 ± 9.1 and the median was 6 ± 10 IQR. The mean AADT was 65.7 ± 75.8 min and the median AADT was 43 ± 75 IQR minutes. The average duration of a single visit was 7.6 ± 4.3 min and the median was 7 ± 4.5 IQR minutes, which is lower than the data known to us prior to this study [ 2 ]. This was to be expected following the rise in the PCP’s workload due to population growth and the increase in life expectancy.

The main characteristics of patients with a higher annual number of visits and a higher AADT with a PCP were: female, older in age, a higher Charlson index (all three of which coincide with previously known data [ 1 , 6 ]), of a low SES (which could be explained by Israel’s public health care system, providing highly available/no cost primary care), and residing in a kibbutz (possibly due to greater accessibility to PCP’s). New immigrants had a lower annual number of visits and a lower AADT with a PCP.

The study also found that average visit duration was very similar for both men and women and across the various age groups. This implies that the relationships of age and gender with the number of visits are similar to their relationships with AADT. Thus, while AADT does a better job of capturing resource use (i.e. the amount of time physicians invest in the care of various types of patients) than does the number of visits, it is reasonable to continue using the number of visits as a proxy for AADT in calculating capitation formulae. In the future, it will be important to examine whether visit duration is also consistent across geographic areas.

Strengths and limitations

One of the main strengths of the study is that it was based on a national sample from the largest HMO in Israel. Another is its use of thousands of electronic medical records (and not self-reports) from hundreds of general practices. This is in comparison to other studies, where the exposure to primary care was calculated from duration of visits recorded by the physician, and reports on rates of visits to primary care for each country [ 7 , 22 , 26 , 27 ]. However, international comparisons may be affected by differences in definitions and in the circumstances in which patients see primary care physicians in different countries. It is possible that some references to outpatient attendances include in part visits with specialists.

Another issue is that there are a substantial number of physician visits that are administrative in nature (repeat prescription, fill out laboratory tests forms, etc.) and do not entail a face-to-face meeting between patient and physician. Although the type of visit is specified in the electronic file, in our experience, this information is usually not accurate and therefore the type of visit is difficult to determine. Therefore, we could not separate between face-to-face and non face-to-face visits, but we believe that they are on the continuum of primary treatment and should be part of the calculated time load on the PCP. Furthermore, some other important limitations exist.

First, an underlying assumption of use of the AADT is that a higher number of annual visits with a shorter average duration are equivalent to a lower number of annual visits with a longer average duration. If the first 2 or 3 min of each visit are used by the physician to greet the patient and look at the electronic notes of past visits, this may not be the case. In addition, these actions may require a minimum time allocated for each visit even when only one problem is raised. These issues are directly related to health care policy planning. Assuming there is a more efficient utilization of physician time with fewer yet longer visits, this aspect requires future examination, which could result in an organizational paradigm shift within the health care system.

Second, we excluded patients that died during the study year. We know that at the end of life the utilization of health care resources can be abundant [ 28 – 30 ], influencing the utilization of primary care visits as well. Therefore, to evaluate this special group, we will need another focused study.

Third, the analysis was not limited to one designated physician per patient, as it was designed to find the importance of the AADT required from primary care as a whole for the treatment of patients. This is an important aspect to be examined in future research, to investigate whether time spent with a patient’s personal primary care physician is more effective.

Another limitation of the current study is a possible information bias - some of those classified as “new immigrants” (as well as others) may live outside Israel. The fact that in recent years new immigrants to Israel usually keep their original residency increases the probability of such events.

Comparison with existing literature

As expected, chronic diseases were found to increase the number of annual visits with a PCP as well as the AADT. This coincides with previous research, which found patients with multiple chronic diseases having more outpatient visits per year, more adverse events, higher health care costs including the prescription of multiple medications, and having a lower health-related quality of life [ 31 – 33 ], This can be partially attributed to the fact that the average age and Charlson index score in our study were higher amongst patients with chronic diseases. In accordance with this finding, Østbye et al. found that chronic illnesses require more time then physicians have available for patient care [ 34 ].

In an overworked primary care system, facing growing numbers of elderly and chronically ill patients as well as mounting guidelines and tests, providing the required preventive, chronic and acute medicine and maintaining high quality of care is becoming an extremely difficult task [ 35 ].

To deal with these rising challenges on current models of primary health care, other forms of care such as shared medical appointments have been suggested [ 36 ]. This model of non-physician clinicians was also suggested by Yarnall et al., who proposed another solution in the form of many more shorter visits per year [ 37 ]. Additional recommendations include comprehensive primary care guidelines that integrate highly correlated diseases together, as well as patient education [34].

Conclusion - implications for research and/or practice

In our review, we noticed the existence of a global variety of health organizations and operative units, accompanied by an increasing workload and a growing complexity of guideline-based primary care. The various international comparisons do not take into account the variability in PCP visit duration from one country to the next as well as the differences between health care systems. This in turn results in diverse guidelines as to how to organize the schedule of PCP visits duration. We suggest that this concept of AADT may serve as a new standardized comparative measure, by facilitating the standardization of PCP’s working hours to 1000 patients and accordingly the number of allocated PCP positions required. This makes it easier to evaluate and unify the characteristics of high quality primary care. However, further research is necessary to evaluate the potential of this novel concept. ‬

Another issue to address is that of chronically-ill patients’ follow-up. Due to current time constraints and limitations, it is clear that sufficient follow up and management cannot be conducted in a single visit. Our findings support cumulative duration as a parallel indicator (to the number of visits) for quality of care, and therefore there is room to evaluate whether new PCP guidelines should also refer to the optimal amount of time needed to be spent on health topics addressed within the PCP setting, rather than focusing on the number of visits.

In our study, we found that the AADT spent with a PCP is affected by the same variables as the number of visits. This finding should be evaluated by further research, which is required to assess the benefits of new practice models dealing with the allocation of time and how well they provide quality of care in the primary setting, by relating among others AADT to clinical outcomes and other relevant quality measures.

While facing the ongoing increase in a PCP’s work-load and continuous shortening of visit length, the novel concept of AADT gives a new measure to facilitate in health care policy design, compare between different healthcare systems that allocate different time frames for a single primary care visit, and plan time-consuming tasks (such as chronic disease follow up) as well as asses their contribution in terms of ‘physician time’ vs. number of visits.

Acknowledgements

All analyses were carried out with the assistance of Ms. Ilana Gelernter of The Statistical Consulting Lab at The School of Mathematical Sciences at Tel Aviv University.

Not applicable.

Availability of data and materials

Authors’ contributions.

Author’s contribution - TN carried out the literature review, participated in the statistical analysis, conducted the interpretation of the data and the drawing of conclusions, and drafted the manuscript. SV conceived of the study, participated in its design and coordination, supervised over the interpretation of the data and the drawing of conclusions, and helped to draft the manuscript. AC participated in the conception, design and coordination of the study. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Ethics approval and consent to participate.

The study was approved by the CHS ethics committee at the Meir Medical Center. Ref. No. 118/2010.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abbreviations

Additional file.

Linear regression model of AADT (in minutes) spent with a PCP during 2012 according to age - B coeficient (Age group 20-29=1). (PDF 55 kb)

This work was performed in partial fulfillment of the M.D. thesis requirements of the Sackler School of Medicine, Tel Aviv University

Contributor Information

Talya A. Nathan, Phone: +972-524445206, Email: [email protected] .

Arnon D. Cohen, Email: li.gro.tilalc@nehocra .

Shlomo Vinker, Email: li.ten.vahaz@10rekniv .

• Introduction
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Dotted lines denote the mean visit length for the 25th percentile, 50th percentile (median), and 75th percentile primary care physician.

Coefficients and 95% CIs were from a multivariable model including physician fixed effects and all patient or visit characteristics. Markers indicate the change in mean visit length associated with each patient or visit characteristic compared with the reference category, and whiskers indicate 95% CIs, which are too small to see due to the large sample size. eTable 3 in Supplement 1 shows a bivariate model regressing visit length by each characteristic individually. Other race and ethnicity includes American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander. Chronic condition count was based on the number of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision ( ICD-10 ) codes and a 1-year look-back period. Visit diagnosis count was calculated as the number of ICD-10 diagnosis codes billed during the visit. FFS indicates fee for service.

Adjusted binned scatterplots and linear fit lines used ordinary least squares (OLS) regression. Dots indicate the mean y-value for equal-sized bins of x-values, controlling for patient and visit characteristics and including physician fixed effects. The following regression coefficients and 95% CIs were derived from the identical multivariable OLS model treating visit length as a continuous variable and including physician fixed effects and all patient or visit characteristics: A, −0.11 percentage points (95% CI, −0.14 to −0.09 percentage points); B, −0.01 percentage points (95% CI, −0.01 to −0.009 percentage points); and C, 0.004 percentage points (95% CI, 0.003-0.006 percentage points). C, Based on the Beers criteria. 27

eFigure 1. Sample Selection Diagram

eTable 1.  ICD-10 Diagnoses Used to Define Subsamples Relevant to Potentially Inappropriate Prescribing Outcomes

eTable 2. Patient and Appointment Characteristics, Within the athenahealth Sample and the National Ambulatory Medical Care Survey (NAMCS)

eTable 3. Bivariate and Multivariate Exam Length Regression Results

eFigure 2. Association of Opioid and Benzodiazepine Coprescribing With Visit Length, in Visits With a Painful Condition and Anxiety Diagnosis, 2017

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Neprash HT , Mulcahy JF , Cross DA , Gaugler JE , Golberstein E , Ganguli I. Association of Primary Care Visit Length With Potentially Inappropriate Prescribing. JAMA Health Forum. 2023;4(3):e230052. doi:10.1001/jamahealthforum.2023.0052

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Association of Primary Care Visit Length With Potentially Inappropriate Prescribing

• 1 Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis
• 2 Harvard Medical School, Boston, Massachusetts
• 3 Brigham and Women’s Hospital, Boston, Massachusetts

Question   Are primary care physicians more likely to prescribe potentially inappropriate medications during shorter visits?

Findings   In this cross-sectional study of 4 360 445 patients, those who were younger, publicly insured, Hispanic, or non-Hispanic Black had shorter primary care physician visits. Shorter visits were associated with a higher likelihood of inappropriate antibiotic prescribing for patients with upper respiratory tract infections and coprescribing of opioids and benzodiazepines for patients with painful conditions.

Meaning   In this study, shorter primary care visits were associated with some, but not all, measures of inappropriate prescribing.

Importance   Time is a valuable resource in primary care, and physicians and patients consistently raise concerns about inadequate time during visits. However, there is little evidence on whether shorter visits translate into lower-quality care.

Objective   To investigate variations in primary care visit length and quantify the association between visit length and potentially inappropriate prescribing decisions by primary care physicians.

Design, Setting, and Participants   This cross-sectional study used data from electronic health record systems in primary care offices across the US to analyze adult primary care visits occurring in calendar year 2017. Analysis was conducted from March 2022 through January 2023.

Main Outcomes and Measures   Regression analyses quantified the association between patient visit characteristics and visit length (measured using time stamp data) and the association between visit length and potentially inappropriate prescribing decisions, including inappropriate antibiotic prescriptions for upper respiratory tract infections, coprescribing of opioids and benzodiazepines for painful conditions, and prescriptions that were potentially inappropriate for older adults (based on the Beers criteria). All rates were estimated using physician fixed effects and were adjusted for patient and visit characteristics.

Results   This study included 8 119 161 primary care visits by 4 360 445 patients (56.6% women) with 8091 primary care physicians; 7.7% of patients were Hispanic, 10.4% were non-Hispanic Black, 68.2% were non-Hispanic White, 5.5% were other race and ethnicity, and 8.3% had missing race and ethnicity. Longer visits were more complex (ie, more diagnoses recorded and/or more chronic conditions coded). After controlling for scheduled visit duration and measures of visit complexity, younger, publicly insured, Hispanic, and non-Hispanic Black patients had shorter visits. For each additional minute of visit length, the likelihood that a visit resulted in an inappropriate antibiotic prescription changed by −0.11 percentage points (95% CI, −0.14 to −0.09 percentage points) and the likelihood of opioid and benzodiazepine coprescribing changed by −0.01 percentage points (95% CI, −0.01 to −0.009 percentage points). Visit length had a positive association with potentially inappropriate prescribing among older adults (0.004 percentage points; 95% CI, 0.003-0.006 percentage points).

Conclusions and Relevance   In this cross-sectional study, shorter visit length was associated with a higher likelihood of inappropriate antibiotic prescribing for patients with upper respiratory tract infections and coprescribing of opioids and benzodiazepines for patients with painful conditions. These findings suggest opportunities for additional research and operational improvements to visit scheduling and quality of prescribing decisions in primary care.

Time is a scarce and valuable resource in primary care, with the average visit lasting 18 minutes. 1 By a recent estimate, primary care clinicians would require 27 hours per day to provide all guideline-recommended preventive, chronic disease, and acute care to a typical patient panel. 2 While there is global variation in primary care visit length, 3 recent growth in visit content (eg, diagnoses recorded and medications prescribed) has outpaced growth in visit length, 4 , 5 suggesting that time available per health concern may be decreasing over time. 6 In surveys, patients routinely report needing more time with their primary care physician, 7 , 8 and visit length is one of the most prominent factors associated with patients’ satisfaction with their care. 9 , 10 Physicians also want more time with their patients and frequently report feeling rushed during visits. 11 - 13

It is widely believed that shorter visits are associated with lower-quality care for patients. 14 , 15 In particular, there is concern that clinicians make less-appropriate prescribing decisions in shorter visits since it takes time to make diagnoses, discuss existing treatment regimens, identify potential medication conflicts, and deprescribe as necessary. 16 Clinicians may view some prescriptions (eg, opioids, antibiotics) as quick fixes when discussion of alternatives (eg, physical therapy, watchful waiting) would take additional time and effort or as a strategy to resolve a tense patient interaction. 17 - 19

Yet, evidence on the association between visit duration and quality of care is limited and mixed. One study using national survey data found that providing recommended counseling or screening required additional time, but appropriate medication prescribing for chronic conditions was not associated with visit duration. 20 Another study using the same national survey data found that upper respiratory tract infection visits that included an antibiotic prescription were shorter than visits without an antibiotic prescription. 21 Other research using direct observation techniques found more complete discussion of new prescription medications during longer visits. 22 Finally, some studies have documented an association between time pressure and the provision of likely low-value prescribing but not for all outcomes studied. 23 , 24 To our knowledge, none of these studies accounted for known practice differences between clinicians in their baseline propensity for visit length and prescribing decisions.

Using a multistate sample of electronic health record (EHR) data, we first examined patient clinical and sociodemographic characteristics associated with visit length. Controlling for these characteristics, we then examined within-physician changes in potentially inappropriate prescribing decisions, including inappropriate antibiotic prescribing, coprescribing opioids and benzodiazepines, and potentially inappropriate prescribing for older adults as a function of primary care visit duration. Results from these analyses may inform policy makers and health system leaders as they balance visit volume pressures and the need to deliver high-quality care.

Because only deidentified administrative data were used, this cross-sectional study was deemed not to be research involving human participants and therefore was exempt from informed consent requirements and institutional review board review by the institutional review board at the University of Minnesota. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We used a subset of claims and EHR data from athenahealth Inc, a cloud-based health care information technology company that provides physician practices with medical billing, practice management, and EHR services. These data have been used in prior work related to visit length measurement and prescribing behavior. 1 , 24 , 25

The study sample included visits for adult patients seeing primary care physicians (defined as those with internal medicine, family practice, and general practice specialties) across the US who used the full suite of athenahealth services (ie, billing management and EHR) in calendar year 2017. Using previously validated methods, 1 , 25 we excluded visits without reliable measures of observed duration (eFigure 1 in Supplement 1 gives additional detail on sample construction). We used visit subsamples to assess specific prescribing outcomes: visits with a diagnosis of upper respiratory tract infection (for the inappropriate antibiotic prescribing outcome), visits with a pain-related diagnosis (for the coprescribing of opioids and benzodiazepines outcome), and visits for adults aged 65 years or older (for the potentially inappropriate prescribing among older adults outcome) (the Table gives patient and visit characteristics of each sample, and eTable 1 in Supplement 1 gives a list of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ ICD-10 ] diagnosis codes and medications used to define subsamples).

We measured visit length using time stamps, which document clinicians’ actions in the EHR across stages of a patient encounter from check-in and intake through face-to-face encounter, checkout, and signoff. Typically, once staff members have completed check-in (eg, confirming insurance coverage), a medical assistant conducts the intake assessment (eg, vital signs and medication reconciliation). Following intake, the physician clicks “Go to Exam” to start the visit (eg, taking patient history, performing a physical examination, and placing orders). At the end of the visit, the physician closes the examination stage to advance the encounter to the checkout stage. To measure visit length, we used previously published methods of processing time stamps recorded during the face-to-face examination stage of each primary care visit, which encompasses the interaction between patient and physician. 1 , 25

We examined 3 outcomes representing inappropriate or potentially inappropriate prescribing decisions: inappropriate antibiotics for upper respiratory tract infections, coprescribing of opioids and benzodiazepines, and potentially inappropriate prescribing for older adults. For inappropriate antibiotic prescribing, we implemented a widely used definition relying on the presence of an antibiotic prescription linked by exact patient identifier, physician identifier, and date to a visit with a primary diagnosis of upper respiratory tract infection. 26 Similarly, we defined opioid and benzodiazepine coprescribing as a visit with a pain-related primary diagnosis and both an opioid and a benzodiazepine prescription linked to the visit. 24 As a sensitivity analysis, we repeated this prescribing outcome among visits with both a pain-related primary diagnosis and an anxiety diagnosis. Finally, we identified all visits for adults aged 65 or older that were linked to prescriptions for medications listed by the 2019 updated Beers criteria 27 (ie, a consensus statement from the American Geriatrics Society on potentially inappropriate medications for older adults) as having a strong recommendation of avoid based on high-quality evidence. If a prescription was linked to the visit, this meant that the prescription was newly ordered, refilled, or confirmed at the visit. As with past work, 24 we linked prescriptions to visits by exact patient identifier, physician identifier, and date.

We used submitted insurance claims and structured EHR data based on patient self-report to collect visit-level data on patients’ age, sex, marital status, race and ethnicity (collected via patient self-report by medical practices; Hispanic, non-Hispanic Black, non-Hispanic White, other [American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander], and missing), primary insurer (ie, commercial, dual eligible [for Medicare and Medicaid], Medicare Advantage, Medicare fee-for-service, Medicaid, other payer, or uninsured), visit type (ie, new or established), scheduled visit duration (10, 15, 20, or 30 minutes), diagnosis count (number of ICD-10 diagnosis codes billed during the visit, a proxy for number of topics discussed), and chronic condition count. We used ICD-10 codes and a 1-year look-back period to replicate widely used algorithms for 27 possible chronic condition categories. 28

Data were analyzed from March 2022 through January 2023. For each primary care physician included in the sample, we calculated their mean visit length and plotted a histogram ( Figure 1 ) showing the proportion of physicians by the mean visit length. We used bivariate analyses to assess the association between patient and visit characteristics and visit length and then constructed a multivariable linear probability model with visit length as the outcome that included these characteristics. We then built a multivariable linear probability model to assess the association of visit length with potentially inappropriate prescribing, controlling for patient and visit characteristics. All models also included physician fixed effects to control for time-invariant differences across physicians in visit length and prescribing patterns. As such, results can be interpreted as comparisons of prescribing outcomes as a function of each individual physician’s variation in visit length. All inappropriate prescribing models were limited to visits with a length of 5 minutes or longer to exclude visits in which patient conditions may not have been discussed (eg, visits solely for prescription refills). All regression analyses used Huber-White robust SEs to assess statistical significance, which was defined as 2-sided P  < .05. Analyses were conducted using Stata, version 16 (StataCorp LLC). To display adjusted regression results graphically, we used the binscatter command in Stata, which creates a binned scatterplot (ie, a nonparametric method of quantifying the mean y-value for equal-sized bins of x-values, controlling for patient and visit characteristics and including physician fixed effects).

The study sample consisted of 8 119 161 visits for 4 360 445 patients (43.4% men and 56.6% women) seeing 8091 primary care physicians in 4597 practices. Of the total visits, 7.7% were for Hispanic patients, 10.4% for non-Hispanic Black patients, 68.2% for non-Hispanic White patients, 5.5% for patients with other race and ethnicity, and 8.3% for patients with missing race and ethnicity ( Table ). Compared with a national sample of 8906 patients with office-based primary care visits from the National Ambulatory Medical Care Survey (NAMCS), patients receiving visits in the study sample were less likely to be non-Hispanic White (75.1% vs 68.2%), more likely to have commercial insurance (44.1% vs 48.5%) and Medicare (34.9% vs 40.2%), less likely to have Medicaid (8.3% vs 7.7%) or be uninsured (4.9% vs 2.6%), and more likely to have no chronic conditions (34.1% vs 41.5%) (eTable 2 in Supplement 1 ). Comparing the NAMCS sample with the athenahealth sample, we found similar rates of 1 chronic condition (24.5% vs 24.4%) and 2 chronic conditions (16.8% vs 16.4%), a similar sex distribution of visits, and a similar age distribution of visits (eTable 2 in Supplement 1 ). Compared with the NAMCS, the athenahealth sample somewhat overrepresented visits in the South (43.9% vs 54.9%) and underrepresented visits in the West (21.8% vs 8.6%), with similar proportions of visits in the Northeast (17.6% vs 17.7%) and Midwest (16.8% vs 18.7%). Patient and visit characteristics varied across the 3 subsamples used for our 3 potentially inappropriate prescribing measures ( Table ).

Visit duration varied considerably between and within primary care physicians. The median physician in the sample spent a mean of 18.9 minutes with each patient ( Figure 1 ). Physicians in the top quartile of visit length spent a mean of 24.6 minutes or longer with their patients, while physicians in the bottom quartile of visit length spent a mean of 14.1 minutes or less with their patients.

When examining within-physician variation in visit length, we found that visit length was significantly associated with nearly every patient and visit characteristic ( Figure 2 and eTable 3 in Supplement 1 ). Compared with a 10-minute scheduled visit, visits scheduled for 30 minutes received 4.0 additional minutes (95% CI, 3.9-4.1 minutes). Compared with visits with only 1 recorded diagnosis, visits with 5 or more diagnoses were 9.1 minutes (95% CI, 9.1-9.2 minutes) longer. Compared with visits for established patients, visits for new patients were 4.1 minutes (95% CI, 4.1-4.2 minutes) longer. Visit length was also slightly longer for female patients compared with male patients (female: 17.2 minutes [95% CI, 17.2-17.2 minutes]; male: 17.0 minutes [95% CI, 16.9-17.0 minutes]), patients aged 65 years or older compared with the youngest age groups (eg, ≥65 years: 17.2 minutes [95% CI, 17.1-17.2 minutes]; 25-44 years: 16.8 minutes [95% CI, 16.8-16.8 minutes]), non-Hispanic White patients compared with Hispanic and non-Hispanic Black patients and patients from other race and ethnicity (non-Hispanic White: 17.2 minutes [95% CI, 17.2-17.2 minutes]; Hispanic: 16.8 minutes [95% CI, 16.7-16.8 minutes]; non-Hispanic Black: 16.7 minutes [95% CI, 16.6-16.7 minutes]; and other: 16.9 minutes [95% CI, 16.9-17.0 minutes]), and patients with commercial insurance compared with all other types of insurance (eg, commercial: 17.2 minutes [95% CI, 17.2-17.2 minutes]; Medicaid: 16.7 minutes [95% CI, 16.7-16.8 minutes]).

Within the study sample, 55.7% of 222 667 visits for upper respiratory tract infection involved an inappropriate antibiotic prescription, 3.4% of 1 571 935 visits for painful conditions involved coprescribing opioids and benzodiazepines, and 1.1% of 2 756 365 visits for adults aged 65 years or older involved the prescription of medications contraindicated by the Beers criteria. After adjusting for all patient covariates, the likelihood that an upper respiratory tract infection visit included an inappropriate antibiotic prescription decreased as visit length increased ( Figure 3 ). For every additional minute of visit length, the likelihood of inappropriate antibiotic prescribing changed by −0.11 percentage points (95% CI, −0.14 to −0.09 percentage points) and the likelihood of opioid and benzodiazepine coprescribing changed by −0.01 percentage points (95% CI, −0.01 to −0.009 percentage points). In a sensitivity analysis limiting the sample of painful condition visits to those that also had an anxiety diagnosis, the likelihood of opioid and benzodiazepine coprescribing changed by −0.05 percentage points (95% CI, −0.07 to −0.04 percentage points) for every additional minute (eFigure 2 in Supplement 1 ). Potentially inappropriate prescribing among older adults increased slightly as a function of visit length (0.004 percentage points; 95% CI, 0.003-0.006 percentage points).

In a large, multistate sample of primary care visits, we found an association between visit length and some potentially inappropriate prescribing measures. When controlling for differences in physician practice style and patient and visit characteristics, longer visits were less likely to include an inappropriate prescription for an antibiotic and slightly less likely to include coprescribing of opioids and benzodiazepines. However, there was a positive association between visit length and prescribing a collection of potentially inappropriate medications for older adults that was unlikely to be clinically meaningful. This pattern of findings may reflect that inappropriate antibiotic prescribing would likely occur during acute care visits focusing on upper respiratory tract infection symptoms for which any additional time in the visit would likely be devoted to that single issue. In contrast, the other potentially inappropriate prescribing outcomes that we assessed are not specific to an acute condition (eg, coprescribing may occur for both acute and chronic pain) and therefore may occur in visits covering a range of patient concerns for which any additional time during the visit would not necessarily be allocated to the problem relevant to the potentially inappropriate prescribing outcome. For coprescribing and older adult outcomes, many of the prescriptions that we observed may have been refills; thus, it may have taken the physician less time to refill the medication than to engage in a discussion about deprescribing.

Given that shorter visit length was associated with some risk of lower-quality care, we were particularly interested in patient and visit characteristics that were associated with time spent with the physician. Many of these associations suggest that patients with more medical complexity or with more to discuss received more time with their physicians, which may be expected. For example, visits that included more diagnoses—an imperfect proxy for number of topics discussed—were longer, as were visits for patients with more previously recorded chronic conditions and for new patients. Interestingly, while visits with longer scheduled durations had longer observed durations, this was not a 1-to-1 association; visits scheduled for 30 minutes were only 4 minutes longer than those scheduled for 10 minutes, which tended to run longer than their scheduled time. This finding suggests that scheduled visit times do not necessarily represent clinical workflows accurately and points to the challenges that primary care physicians may face in adhering to scheduled visit times to care for a wide range of patients with diverse needs.

We also have particular concerns about the associations we found between patient-visit characteristics and visit length that were not easily explained by differences in perceived patient clinical need. For example, patients with Medicaid insurance coverage, dual Medicare and Medicaid coverage, or no insurance coverage received significantly shorter visits than commercially insured patients despite the latter population being healthier on average. Similarly, non-Hispanic Black patients received visits that were shorter, on average, than non-Hispanic White patients seeing the same physician. These visit-level differences may accumulate over time, potentially contributing to racial disparities in how much time patients spend with their physicians each year. 29 Our analyses cannot explain why these differences exist but should motivate organizations and policy makers to detect, interrogate, and address underlying systemic causes such as structural racism. 30

Our analyses highlight the fundamental tension between the volume incentives embedded in fee-for-service reimbursement systems and quality of care. 25 , 31 - 33 While our results do not suggest an optimal visit length, they do suggest that physicians’ time is not always allocated based on patient complexity. 34 Such findings suggest opportunities for a more equitable distribution. While risk adjusting visit length to match individual patients’ needs may be prohibitively complex from a logistical standpoint, practice leads could consider building in more flexibility than typically exists now. For example, practices could allow for 2 different visit lengths for problem-based visits, enabling physicians to indicate in advance which patients would benefit from the longer visit.

In particular, policy makers and health system leaders wishing to advance antibiotic stewardship best practices should take note of the association between visit length and inappropriate antibiotic prescribing. Our findings suggest that lengthening upper respiratory tract infection visits may be a promising strategy to lower inappropriate antibiotic prescribing, which has been a persistent population health concern for decades. However, meaningful gains in improved patient care quality and safety require that increases in visit time be accompanied by other thoughtful implementation strategies (eg, decision supports and shared decision-making tools) that promote consistency in value-based decision-making.

This study has several limitations. First, the results of this study should not be interpreted causally, although we were able to improve on existing studies of associations by comparing within-physician (rather than across-physician) variation in visit length and associated prescribing outcomes. There could still be unobserved reasons (eg, different communication styles, language barriers) for why patients were less likely to receive inappropriate antibiotic prescriptions during longer visits. Second, the cross-sectional nature of our data mean that we were unable to examine changes over time in prescribing patterns. Third, we relied on data from a convenience sample of primary care physicians who chose to purchase the services of athenahealth, and therefore our results may not generalize to all primary care physicians in the US. However, in many respects, primary care physicians within the athenahealth network appear to resemble primary care physicians in the US. Fourth, the samples that relied on diagnosis codes (ie, inappropriate antibiotic prescribing) likely changed with visit length since physicians may code themselves out of inappropriate antibiotic prescribing by recording different diagnoses during longer visits. Relatedly, visit length and diagnosis codes are an imperfect proxy for what was discussed by patients and physicians during the encounter. Fifth, our measure of opioid-benzodiazepine coprescribing was likely an underestimate since we defined it as coprescribing within the same visit. Patients may have an active opioid prescription when they receive a benzodiazepine prescription and vice versa. Sixth and relatedly, the potentially inappropriate prescribing decisions that we captured represent a small subset of the overall quality of care provided by a physician. Notably, we were unable to examine physician decisions indicative of high-quality (rather than low-quality) care, nor did we examine other facets of primary care quality such as referral decisions or diagnostic accuracy, which may also be associated with visit length.

In this cross-sectional study of primary care physician visit length, shorter visit length was associated with higher rates of inappropriate antibiotic prescribing for upper respiratory tract infections and inappropriate coprescribing of opioids and benzodiazepines for patients with painful conditions, but similar patterns were not found for other potentially inappropriate prescribing decisions. We found considerable within-physician variation in visit length, with younger, publicly insured, Hispanic, and non-Hispanic Black patients receiving shorter visits. These findings suggest opportunities for additional research and operational improvements to visit scheduling and quality of prescribing decisions in primary care.

Accepted for Publication: January 12, 2023.

Published: March 10, 2023. doi:10.1001/jamahealthforum.2023.0052

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Neprash HT et al. JAMA Health Forum .

Corresponding Author: Hannah T. Neprash, PhD, Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 729, Minneapolis, MN 55455 ( [email protected] ).

Author Contributions: Dr Neprash and Mr Mulcahy 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: Neprash, Gaugler, Ganguli.

Acquisition, analysis, or interpretation of data: Neprash, Mulcahy, Cross, Golberstein, Ganguli.

Drafting of the manuscript: Neprash.

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

Statistical analysis: Neprash, Mulcahy, Golberstein.

Obtained funding: Neprash.

Administrative, technical, or material support: Neprash, Cross, Gaugler.

Supervision: Neprash, Gaugler, Ganguli.

Conflict of Interest Disclosures: Dr Ganguli reported receiving grants from the National Institute for Health Care Management during the conduct of the study and receiving personal fees from F-Prime and grants from Arnold Ventures and the Agency for Healthcare Research and Quality outside the submitted work. No other disclosures were reported.

Funding/Support: This research was supported by pilot grant P30AG066613 from the University of Minnesota Life Course Center, which receives funding from the National Institute on Aging (NIA) (Dr Neprash). Dr Ganguli was supported in part by grant K23AG068240 from the National Institute on Aging.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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What are HMO, PPO, EPO, POS and HDHP health insurance plans?

If you have health insurance or are even just shopping for coverage, you have likely come across the term “network” or “provider network.” You may have seen acronyms like HMO, PPO, EPO, POS or HDHP — but it may not be completely clear how choosing one over the other changes access to medical care and may affect out-of-pocket costs.

Which insurance is most affordable? Which health insurance plan is right for you? For a lot of people who get their health insurance through their employer, it comes down to what options are available if there's more than one choice. 

Frequently asked questions about health plans

Explore these common questions to learn more about the different types of health plans and how they work.

What are HMO, PPO, EPO, POS and HDHP plans?

These are common acronyms for different types of plans. Let’s go over what they mean.

• HMO stands for health maintenance organization. This is named for the overall goal of this kind of plan — which is to help maintain your health.
• PPO  stands for preferred provider organization. The name refers to its network of contracted PPO providers. With this type of plan, there are preferred providers who can offer care at the lowest out-of-pocket cost (compared to out-of-network providers).
• EPO  stands for exclusive provider organization. This refers to the rule of this type of plan that requires members to get care within the plan’s network of select providers. If you get care outside the EPO network, you’ll likely have to pay the full cost of that visit.
• POS  stands for a point-of-service plan. With this type of plan, each time you need health care (the time or “point” of service), you can decide to choose network care and allow your primary care physician to manage your care, or you can decide to go outside of the network and seek care from a doctor of your choosing.
• HDHP  stands for high deductible health plan. It’s a type of health insurance plan that offers lower premiums in exchange for higher out-of-pocket costs. With HDHPs, you’ll pay less each month, but more when you get care compared to other health plans.

What are provider networks?

A network can be made up of doctors, hospitals and other health care providers and facilities that have agreed to offer negotiated rates for services to insureds of certain medical insurance plans.

Why do health insurance companies provide access to networks?

Networks are generally developed to help keep costs down for both you, the customer using the medical insurance plan, and the insurance company itself. By negotiating rates for services, the insurance company can keep its costs down and may offer you lower out-of-pocket costs.

What are the different kinds of networks?

There are four basic kinds of networks you need to know: HMO, PPO, EPO and POS. It’s helpful to compare them in a few key categories.

Note: While we’re using common terms and definitions here, be aware that terms and definitions may vary by insurance company.

Which network should I pick?

Everyone is looking for something slightly different out of their health insurance, so this is really a question you have to answer for yourself. But there are a few pointers you can keep in mind:

• Before you start looking, make note of your “need to haves” and “want to haves” in terms of your provider network and benefits. Also, list any doctors or hospitals you want access to. Keep that information at hand while you shop.
• Check the networks you’re considering for doctors, hospitals and pharmacies near to you before making any decisions, especially if easy access to care is important.
• If your doctor’s already in-network, or you’re flexible about where you get care and can easily stay in-network, then choosing an HMO or EPO may mean a lower cost for you each month.
• If you need the freedom to go outside a narrow network and still get some benefits from your coverage, then look at PPOs or a more flexible POS plan.

Compare HMO, PPO, EPO and POS plans

Read more about hmo, ppo, epo and hdhp plans.

If you’re considering an HMO health insurance plan, it’s good to know that typically you’d need to get care from providers in the HMO network in order to use your plan benefits — and get referrals from your doctor before seeing specialists. 

PPO health insurance is a type of plan that creates a network of preferred providers. This means you’ll get the highest level of coverage when you choose to get care from providers in the plan’s network. 

With EPO plans, it’s likely that you’d pay higher deductibles and lower monthly payments compared to other plan types — and you may not need referrals before you get care, as long as you choose providers within the EPO network.

Considering an HDHP health insurance plan? With this type of plan, it’s common to pay lower premiums in exchange for higher out-of-pocket costs. So you’d pay less each month, but more when you get care compared to other plans. 

A point of service (POS) plan is a health insurance plan that partners with a group of clinics, hospitals and doctors to provide care. With this type of plan, you’ll pay less out of pocket when you get care within the plan's network. POS plans often require coordination with a primary care provider (PCP) for treatment and referrals.

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There are limits to the number of patients you can effectively care for. Here's how to determine that number, improve patient access and better manage your workload.

MARK MURRAY, MD, MPA, MIKE DAVIES, MD, AND BARBARA BOUSHON, RN

Fam Pract Manag. 2007;14(4):44-51

Dr. Murray, a family physician, is principal of Mark Murray & Associates, a health care consulting group in Sacramento, Calif. He led the creation of advanced access and has led its implementation in countless organizations. A faculty member of the Institute for Healthcare Improvement (IHI), he has served as chair for the IHI's Breakthrough Series Collaboratives on Reducing Delays and Waiting Times and has worked with diverse medical groups both in the United States and abroad. Dr. Davies, a general internist and chief of staff at the VA Black Hills Health Care System, Fort Meade, S.C., has been involved in improving access in that organization as well as numerous groups in the United States. Barbara Boushon, a faculty member and collaborative director for the IHI in Boston, has worked with a wide array of groups and organizations within the United States. Author disclosure: nothing to disclose.

Our health care system is increasingly recognizing the importance of improving patient access to care and is embracing the principles of advanced access, or “same-day scheduling.” Access improvement depends on correctly matching patient demand with appointment supply without a delay 1 – 16 and without harming continuity of care. 17 – 25 In other words, it means seeing patients when their needs arise, not bumping them to another day or to another provider.

In its interim report on primary care, the Institute of Medicine stressed the importance of the relationship between patients and their primary care providers, which it defined as a “sustained partnership.” 26 For this sustained partnership to become actualized, practices need to recognize that there are limits to the number of services each provider can deliver and the number of patients each provider can be accountable for (commonly referred to as “panel size”), and these limits must be defined. 27 This article describes the importance of panel size in balancing appointment supply and patient demand, methods to determine both the current and ideal panel size, and ways to make adjustments.

Why is it important to define a panel?

Establishing which patients are assigned to which physicians in the practice is important for a number of reasons:

It makes patients happy . Patient surveys clearly demonstrate that patients want the opportunity to choose a primary care provider; they want access to that provider when they choose; and they want a quality health care experience. Establishing a panel links each patient with a provider with whom they have a health care relationship.

It defines the workload . Establishing a panel helps divide and define workload within a practice and helps ensure that each provider is carrying his or her fair share.

It predicts patient demand . Panels are the source of demand not only for visits but also for non-visit work (paperwork, e-mail, etc.), tests, procedures and hospitalizations. Understanding the panel helps a practice anticipate that demand both.

It reveals provider performance issues . Understanding the panel allows groups to see the effects of provider variability. For example, if two providers have the same panel size but one provider has more demand than the other, then the practice can explore why this difference exists (e.g., one physician uses shorter return-visit intervals) and whether it is justified.

It helps improve outcomes . Identifying individual panels enables providers to make a commitment to continuity (that is, to taking care of their own patients for all their visits), which results in improved clinical outcomes, 17 , 18 , 28 – 30 reduced costs and enhanced revenue per visit. 13 , 16 , 19 , 31

What is the current panel size?

Panel size is simply the number of individual patients under the care of a specific provider. Panel size is easiest to determine in practices that can use enrollment data to link patients to individual providers and capture that linkage in their information system. This is most feasible in “closed” systems, such as some HMOs. In other environments, where panel size can shift rapidly or where it is not determined by enrollment or not permanently codified in the information system, other methods are required to link patients with providers and establish the panel size.

Determining the practice panel. The panel for an entire practice can be defined as the unique patients who have seen any provider (physician, NP or PA) in the last 18 months. Some practices may prefer to use data for the last 12 months; however, this method tends to underestimate the panel size, as many patients do not visit the practice within a year.

Determining the individual provider panel. Each patient on the practice's panel should then be placed on the panel of only one provider. Because patients may have seen multiple providers in a practice, this requires deciding which patients “belong” to which provider. The following “four-cut” method can be useful:

Patients who have seen only one provider for all visits are assigned to that provider.

Patients who have seen more than one provider are assigned to the provider they have seen most often.

The remaining patients who have seen multiple providers the same number of times are assigned to the provider who performed their most recent physical or health check.

The remaining patients who have seen multiple providers the same number of times but have not had a sentinel exam are assigned to the provider they saw last.

This four-cut method may not be 100-percent accurate (some patients will be assigned to the incorrect provider, and some patients will ultimately choose a different provider than the one they were initially assigned to); however, it's a good start. Panel assignments can be refined by asking and confirming at every opportunity the patient's choice of provider.

ADJUSTING FOR PRACTICE STYLE

Some providers claim that their practice style warrants a smaller panel size. For example, a provider with a highly personable style of practice may feel more effective conducting longer office visits.

In a practice where physicians' salaries are fixed, decreasing the panel size for one provider can be controversial because it increases the size of others' panels. One possible solution is to provide a salary adjustment that corresponds to the panel adjustment. For example, a physician whose practice style involves lengthy office visits, resulting in a panel size that is 80 percent the size of the typical panel in the practice, might need to be paid 80 percent of what a fully paneled provider receives. In productivity models, some degree of practice style adjustment can be accommodated; however, if the smaller panel size pulls revenue down below daily expenses, then accommodation makes no business sense.

Determining the “target” panel. The target panel is the practice panel (defined earlier) divided by the number of full-time-equivalent (FTE) clinical providers. To determine the number of FTE clinical providers, take the total FTE providers and subtract the portion of each provider's time spent on nonappointment or nonclinical duties such as hospital rounds, operating room duties, procedures, management duties and meeting time.

For example, a practice with 6,000 patients and three FTE clinical providers would have a target panel of 2,000, or 6,000/3. (See the worksheet .) The target panel size can be compared with individual provider panel sizes to get a glimpse at whether a group's workload distribution is equitable.

These calculations relate to the current panel size. But the current panel size is not always the right size.

What should the panel size be?

Practices and individual providers should not take on more work than they can manage. If a panel is too large, the excess demand results in a never-ending and ever-expanding delay in services in addition to constant deflections to other providers, resulting in discontinuity. On the other hand, if a panel is too small, demand may not be adequate to support the practice. The demand for appointments must equal the supply of appointments if timely service is desired.

A simple equation can be used to express this: Panel size × visits per patient per year (demand) = provider visits per day × provider days per year (supply).

This equation reveals each provider's ideal panel size based on his or her historical level of productivity. (See the worksheet .) However, this number is not immutable; the ideal panel size is derived from the three other variables in the equation, all of which are changeable. Often a provider will want to increase the ideal panel size (e.g., to increase capitated reimbursement, to retain current patients or to expand access to the community), which requires making adjustments to the following variables:

Visits per patient per year. The average number of visits per patient per year is 3.19, according to data we collected in one primary care practice. However, practices should calculate this figure for themselves by dividing the number of unique patients seen in the last 12 or 18 months into the number of visits to the practice that these patients generated within the same period. To increase the size of the panel that a provider can successfully care for, the number of visits per patient per year can be decreased by improving continuity (when patients see their own provider they require fewer visits), 31 lowering the visit return rate (i.e., the percentage of visits for which the provider requests a follow-up visit), 32 providing more service at each visit, increasing teamwork, 33 and using alternatives to traditional visits such as e-mail, telephone care and group visits. 34

PATIENT PANEL SIZE WORKSHEET

The following worksheet can help you capture the data you need to calculate your current and ideal panel size. Click below to download an Excel version of this spreadsheet, which performs many of the calculations for you .

Copyright © 2007 American Academy of Family Physicians. Murray M, Davies M, Boushon B. Panel size: how many patients can one doctor manage? Fam Pract Manag . April 2007:44–51. Available at: https://www.aafp.org/fpm/20070400/44pane.html.

Provider visits per day. This variable is determined by looking at historical data regarding the number of visits provided per day; it is not simply the number of appointment slots available per day. This variable can be increased by optimizing care delivery models, decreasing the no-show rate, offering more appropriate help so that providers can reduce individual visit length, 33 improving the workflow by reducing bottlenecks and providing more “just in time” support, increasing the number of exam rooms, 25 and removing unnecessary work from the providers to allow them to maximize appointment supply. 33

Provider days per year. This variable is determined by looking at the number of days a provider's schedule was booked for patient visits per year. It can be influenced by changing expectations about the number of days that should be booked with appointments and making critical decisions about how provider time is distributed (e.g., shifting providers away from nonclinical duties in favor of clinical duties). When doing this exercise, practices are sometimes surprised by the relatively small amount of provider time they have devoted to appointment work.

Isolating each of these variables helps providers understand how their practice patterns influence their panel size. For example, if a provider supplies 20 patient visits a day and works 210 days per year at an average visit rate of three visits per patient per year, then the maximum panel size is 1,400. But if the provider can increase visits per day to 25 through the strategies outlined earlier in the article, then the maximum panel size would increase to 1,750. (See “Variables that affect panel size.” )

What this demonstrates is that panel size is an outcome of the system in which providers operate. The ideal panel can be determined, but its size will necessarily differ in different environments depending on all the provider and system factors noted above.

VARIABLES THAT AFFECT PANEL SIZE

Panel size can be influenced by the number of patients seen per day, the number of days the provider is available per year and the average number of visits per patient per year. For example, a provider who sees 20 patients per day, 210 days per year, with an average of three visits per patient per year, could manage a panel of 1,400 patients. By increasing capacity to 25 patients per day, the provider could manage a panel of 1,750 patients.

What are the limits to panel size?

There is a limit to practice and individual panel sizes. If a practice or individual provider keeps saying “yes” to new patients and exceeds the limit, the overage can initially be absorbed into a waiting time. However, patients' willingness to wait has a limit. At some point, patients quit. Thus, despite saying “yes” to an endless stream of new patients with our words, we say “no” with our actions because these patients won't have access to care. Those providers who insist, “I have to say ‘yes’ to new work. I have no choice,” are simply deceiving themselves. This is an irrefutable act of denial.

In addition, the increasing wait time for an appointment leads to escalating chaos within the practice as evidenced by an increased number of phone calls to the practice; longer handling time for those calls; more patient complaints; increasing no-show, cancel and reschedule rates; greater numbers of “walk-ins” to the practice due to patients getting impatient; greater use of triage resources to determine who has to wait and who cannot wait; and an increased level of discontinuity, which worsens patient outcomes and satisfaction and increases the return visit rate and visit length, which in turn lowers productivity. 13 , 16

The main point is that if the panel is too big, the provider creates “overwork” (can't get the work done), “overtime” (needs consistent overtime support) and “over there” (sends the work away). If the panel is too small, the provider will not generate enough revenue to cover expenses.

ADJUSTING FOR AGE AND GENDER

Providers sometimes claim that their patients are older and sicker than those on the panels of other providers, which justifies a smaller panel. Sometimes these arguments can become self-fulfilling prophecies, as providers can “prove” that their patients have higher acuity by creating more return visits (which increases demand) or longer visits (which limits supply).

Still, it's true that panels equal in number are not necessarily equal in acuity at any single point in time. In some practices, panel acuity tends to balance out over time. In others, due to many factors such as patient mix and provider interests, permanent acuity differences exist.

Patients' age and gender can predict visit utilization and reflect acuity. Over a number of years, we have collected visit utilization data within a primary care practice. Patients were divided into predetermined subsets based on gender and age. The average visit rate for all patients was approximately 3.19 visits per patient per year. The number of visits in each age and gender subset was divided by the average visit rate to determine the likelihood of a visit within the subset. For example, a 0- to 11-month-old male is 5.02 times more likely to visit than a 55- to 59-year-old male, whereas a 35- to 39-year-old female is half as likely to visit as a 75- to 79-year-old female.

Practices with sophisticated information systems could use this data to adjust provider panels. However, the process is complicated and requires caution. If one panel is adjusted down due to higher acuity, there needs to be a parallel adjustment up in panels with lower acuity. In addition, practices should consider whether many of the acuity factors could be managed more effectively by providing focused team support than by adjusting panels.

What do you do with an over-paneled provider?

Once a provider's individual panel has been identified and all strategies for adjusting the panel have been dutifully applied, it might be found that the provider is indeed “over-paneled.”

If a provider is over-paneled, these strategies will reduce his or her panel:

Let attrition take its course. Every year in a typical practice, patients move away, die or change insurance.

Close the over-paneled doctor to new patients, at least temporarily, and excuse him or her from seeing the patients of absent providers.

Shift more resources to support that provider. This may take the form of additional nursing or clerical staff, or possibly additional exam rooms.

Move patients away from that panel. In this situation, providers will need to inform their patients directly, for example, by sending a letter to patients informing them that they are being moved to another provider's panel.

The bottom line

There is a limit to the number of patients each provider can effectively care for. That limit depends on the system in which the provider practices, but it can be defined using the methodology described in this article. Having an appropriate panel size is key to managing clinical workloads and optimizing patient access to care.

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Murray M. Waiting for healthcare: physician offices can dramatically reduce how long patients wait for appointments. Postgrad Med . 2003;113(2):13-17.

O'Hare CD, Corlett J. The outcomes of open access scheduling. Fam Pract Manag . February 2004:35-38.

Randolph GD, Murray M, Swanson JA, Margolis PA. Behind schedule: improving access to care for children one practice at a time. Pediatrics . 2004;113(3):e230-e237.

Berry LL, Seiders K, Wilder SS. Innovations in access to care: a patient-centered approach. Ann Intern Med . 2003;139:568-574.

Lewandowski S, O'Connor PJ, Solberg LI, et al. Increasing primary care physician productivity: a case study. Am J Manag Care . 2006;12(10):573-576.

Ettner SL. The relationship between continuity of care and the health behaviors of patients: does having a usual physician make a difference?. Med Care . 1999;37:547-555.

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Christakis DA, Mell L, Koepsell TD, Zimmerman FJ, Connell FA. Association of lower continuity of care with greater risk of emergency department use and hospitalization in children. Pediatrics . 2001;107:524-529.

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Characteristics of Office-based Physician Visits, 2016

Key findings, do office-based physician visit rates vary by patient age and sex, what was the primary expected source of payment at office-based physician visits, and did it vary by age, what were the major reasons for office-based physician visits, what were the services ordered or provided at office-based physician visits, and did they vary by age, definitions, data source and methods, about the authors, suggested citation.

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Jill J. Ashman, Ph.D., Pinyao Rui, M.P.H., Titilayo Okeyode

Data from the National Ambulatory Medical Care Survey

• In 2016, there were an estimated 278 office-based physician visits per 100 persons.
• The visit rate among females exceeded the rate for males, and the rates for both infants and older adults exceeded the rates for those aged 1–64 years.
• Private insurance was the primary expected source of payment for the majority of visits by children under age 18 and adults aged 18–64, whereas Medicare was the primary expected source of payment for the majority of visits by adults aged 65 and over.
• Compared with adults, a larger percentage of visits by children were for either preventive care or a new problem.
• Compared with children, a larger percentage of visits by adults included an imaging service that was ordered or provided.

In 2016, most Americans had a usual place to receive health care (86% of adults and 96% of children) ( 1 , 2 ). The majority of children and adults listed a doctor’s office as the usual place they received care ( 1 , 2 ). In 2016, there were an estimated 883.7 million office-based physician visits in the United States ( 3 , 4 ). This report examines visit rates by age and sex. It also examines visit characteristics—including insurance status, reason for visit, and services—by age. Estimates use data from the 2016 National Ambulatory Medical Care Survey (NAMCS).

Keywords : ambulatory health care, insurance, NAMCS

• In 2016, there were 278 office-based physician visits per 100 persons ( Figure 1 ).
• The visit rate for both infants under 1 year of age (736 per 100 infants) and adults aged 65 and over (498 per 100 adults aged 65 and over) was higher than the rate for children aged 1–17 years (213 per 100 children aged 1–17 years), adults aged 18–44 (190 per 100 adults aged 18–44), and adults aged 45–64 (302 per 100 adults aged 45–64).
• The visit rate among females (315 visits per 100 females) was higher than the rate for males (239 visits per 100 males).

Figure 1. Visit rates, by selected demographics: United States, 2016

1 Significant difference in estimates among all age groups. 2 Significant difference in estimates between females and males. NOTES: Visit rates are based on the July 1, 2016, set of estimates of the civilian noninstitutionalized population of the United States, as developed by the Population Division, U.S. Census Bureau. Total visits includes all visits by patients of all ages. For more information, see the 2016 National Ambulatory Medical Care Survey Documentation , Access data table for Figure 1 . SOURCE: NCHS, National Ambulatory Medical Care Survey, 2016.

• Private insurance was the primary expected source of payment at over one-half (54%) of all office-based physician visits, followed by Medicare (26%), Medicaid (15%), and no insurance (3%) ( Figure 2 ).
• Private insurance was the primary expected source of payment for the majority of visits by children under age 18 years (63%) and adults aged 18–64 (71%), whereas Medicare was the primary expected source of payment for the majority of visits by adults aged 65 and over (82%).
• Medicaid as the primary expected source of payment decreased with increasing age, 32% among children, 15% among adults aged 18–64, and 2% among adults aged 65 and over.
• No insurance or self-pay as the primary expected source of payment varied by age (5% among adults aged 18–64, 3% among children under 18, and 1% among adults 65 and over).

Figure 2. Primary expected source of payment, by age: United States, 2016

1 Significant difference in estimates between those under age 65 and those aged 65 and over. 2 Significant difference in estimates among all age groups. NOTES: All sources of payment were combined into one mutually exclusive primary source of payment using the following hierarchy: Medicare, Medicaid or Children’s Health Insurance Program, or other state-based program; private insurance; and no insurance. Total visits includes all visits by patients of all ages. No insurance is defined as having only self-pay, no charge, or charity as payment sources. Other sources of payment and missing or blank data are not included in this figure and represent 6.9% (weighted) of visits. Figures exclude 5.3% (weighted) of visits for which data were missing or blank. For more information, see the 2016 National Ambulatory Medical Care Survey documentation , Access data table for Figure 2 . SOURCE: NCHS, National Ambulatory Medical Care Survey, 2016.

• A chronic condition was listed as the major reason for 37% of all office-based physician visits, followed by a new problem (27%), preventive care (23%), an injury (7%), and pre- or post-surgery care (6%) ( Figure 3 ).
• Both chronic conditions (17% among children under age 18 years, 37% among those aged 18–64, and 51% among those aged 65 and over) and pre- and post-surgery care (2% among children under age 18, 6% among those aged 18–64, and 8% among those aged 65 and over), as the major reason for visit, increased with increasing age.
• Both preventive care (32% among children under age 18 years, 24% among those aged 18–64, and 15% among those aged 65 and over) and new problem (41% among children under age 18 years, 24% among those aged 18–64, and 21% among those aged 65 and over), as the major reason for visit, decreased with increasing age.
• Injury was listed as the major reason for visit at a higher percentage of visits by children (8%) and adults aged 18–64 (8%) than adults aged 65 and over (5%).

Figure 3. Major reason for office-based physician visit, by age: United States, 2016

1 Significant difference in estimates between those aged 65 and over and both those aged under 18 and those aged 18–64. 2 Significant difference in estimates among all age groups. NOTES: Provider-assessed major reason for visit was combined with injury to create a combined mutually exclusive reason for visit, with an injury visit having precedence over all other reasons. In 2016, the definition of injury changed due to the switch from using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD–9–CM) to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD–10–CM) to code injury and poisoning diagnoses. Therefore, estimates for injury should not be considered comparable with previous years of injury estimates. Total visits includes all visits by patients of all ages. Numbers may not add to 100% due to rounding. Figures exclude 2.3% (weighted) of visits for which data were missing either injury or reason for visit. For more information, please see the 2016 NAMCS documentation , Access data table for Figure 3 . SOURCE: NCHS, National Ambulatory Medical Care Survey, 2016.

• An examination or screening was ordered or provided at almost one-half (48%) of all office-based physician visits, followed by laboratory tests (29%), health education and counseling (22%), imaging (14%), and procedures (14%) ( Figure 4 ).
• A higher percentage of examinations and screenings were ordered or provided at visits by children under age 18 years than adults aged 18–64, but a higher percentage of laboratory visits occurred among adults aged 18–64 compared with children.
• A higher percentage of health education and counseling services were ordered or provided at visits by children than adults, but there was a higher percentage of visits with imaging services among adults compared with children.
• A higher percentage of procedures were ordered or provided at visits by adults aged 65 and over than younger adults and children.

Figure 4. Selected services ordered or provided at office-based physician visits, by age: United States, 2016

*Estimate does not meet NCHS standards of reliability. 1 Significant difference in estimates between those under age 18 and those aged 18–64. 2 Significant difference in estimates between those under age 18 and those aged 18–64 and 65 and over. For imaging services, estimate for those under age 18 is significantly lower than estimates for those aged 18–64 and 65 and over. 3 Estimate for those aged 65 and over is significantly higher than estimates for those aged under 18 and aged 18–64. NOTES: More than one service may be reported per visit. Total visits includes all visits by patients of all ages. Note that due to the switch to ICD–10–CM in 2016, the method used to derive examinations or screenings is different from that used in prior years. Therefore, estimates for examinations and screenings should not be considered comparable to previous years of examinations and screenings estimates. See the definitions section for the specific services included in each category. For the complete list of services, see the 2016 National Ambulatory Medical Care Survey summary documentation , Access data table for Figure 4 . SOURCE: NCHS, National Ambulatory Medical Care Survey, 2016.

During 2016, the overall rate of office-based physician visits was 278 visits per 100 persons. The visit rate for infants and older adults was higher than the rate for other age groups. The visit rate for females was higher than the rate for males.

The majority of visits by children (63%) and adults aged 18–64 (71%) listed private insurance as the primary expected source of payment, whereas the majority of visits by older adults listed Medicare as the primary expected source of payment (82%). Approximately 3% of office-based physician visits were made by those with no insurance. A higher percentage of visits by adults 18–64 (5%) had no insurance compared with adults aged 65 and over (1%) and children (3%).

A chronic condition was the major reason for 37% of all office-based physician visits, and visits for chronic conditions were higher among adults than children. A higher percentage of visits by children than adults were for a new problem or preventive care, whereas the reverse was true for visits related to pre- or post-surgery care.

Almost one-half (48%) of all office-based physician visits included an examination or screening that was ordered or provided. Compared with adults, a higher percentage of visits by children included health education and counseling. Compared with children, a higher percentage of visits by adults included imaging services.

Major reason for this visit : A variable was created by merging the “INJURY” variable with the provider-assessed major reason for this visit ( 5 ). Injury was given preference over all other reasons. The five categories for major reason for this visit include:

• Injury: A visit defined as injury or poisoning related, based on any listed reason for visit and diagnosis ( 5 ). In 2016, the definition of injury changed due to the switch from using using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD–9–CM) to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD–10–CM) to code injury and poisoning diagnoses. Therefore, estimates for injury in this report should not be considered comparable with previous years of injury estimates.
• New problem: A visit for a condition or illness having a relatively sudden or recent onset (within 3 months of this visit).
• Chronic condition: A visit primarily to receive care or examination for a preexisting chronic condition or illness (onset of condition was 3 months or more before this visit). This includes both routine visits and flare-ups; a visit primarily due to a sudden exacerbation of a preexisting chronic condition.
• Pre- and post-surgery: A visit scheduled primarily for care required prior to or following surgery (e.g., presurgery tests or removing sutures).
• Preventive care: General medical examinations and routine periodic examinations. Includes prenatal care, annual physicals, well-child exams, screening, and insurance examinations.

Selected services : Included are services that were ordered or provided during the sampled visit for the purpose of screening (i.e., early detection of health problems in asymptomatic individuals) or diagnosis (i.e., identification of health problems causing individuals to be symptomatic) ( 5 ). Each selected service item was grouped into five categories as follows

• Examinations or screenings: Alcohol misuse, breast, depression, domestic violence, foot, neurologic, pelvic, rectal, retinal or eye, skin, and substance abuse. Note that due to the switch to ICD–10–CM in 2016, the method used to derive examinations and screenings in this report is different from that used in prior years. Therefore, estimates for examinations and screenings in this report should not be considered comparable with previous years of examinations and screenings estimates.
• Health education or counseling: Alcohol abuse counseling, asthma, asthma action plan given to patient, diabetes education, diet or nutrition, exercise, family planning or contraception, genetic counseling, growth or development, injury prevention, sexually transmitted disease prevention, stress management, substance abuse counseling, tobacco use or exposure, and weight reduction.
• Imaging services: Includes bone mineral density, CT scan, echocardiogram, ultrasound, mammography, MRI, and X-ray.
• Laboratory tests: Includes basic metabolic panel, complete blood count, chlamydia test, comprehensive metabolic panel, creatinine or renal function panel, culture (blood, throat, urine, or other), glucose, gonorrhea test, HbA1c, hepatitis testing, HIV test, human papillomavirus DNA test, lipid profile, liver enzymes or hepatic function panel, pap test, pregnancy or HCG test, prostate-specific antigen, rapid strep test, thyroid-stimulating hormone or thyroid panel, urinalysis, and vitamin D test.
• Procedures: Includes audiometry, biopsy, cardiac stress test, colonoscopy, cryosurgery or destruction of tissue, EKG or ECG, electroencephalogram, electromyogram, excision of tissue, fetal monitoring, peak flow, sigmoidoscopy, spirometry, tonometry, tuberculosis skin testing, and upper gastrointestinal endoscopy.

Data for this report are from the National Ambulatory Medical Care Survey (NAMCS), which is conducted by the National Center for Health Statistics. NAMCS is an annual, nationally representative survey of office-based physicians and visits to their practices ( 3 , 5 ). The target universe of NAMCS is physicians classified as providing direct patient care in office-based practices. Radiologists, anesthesiologists, and pathologists are excluded, as are physicians in community health centers. The 2016 sample consists of 3,699 physicians. Participating physicians provided 13,165 visit records. The participation rate—the percentage of in-scope physicians for whom at least one visit record was completed—was 39.3%. The response rate—the percentage of in-scope physicians for whom at least one-half of their expected number of visit records was completed—was 32.7%.

Data analyses were performed using the statistical packages SAS version 9.4 (SAS Institute, Cary, N.C.) and SAS-callable SUDAAN version 11.0 (RTI International, Research Triangle Park, N.C.). Differences in the distribution of selected characteristics of office-based physician visits are based on chi-square tests ( p < 0.05). If a difference was found to be statistically significant, additional pairwise tests were performed. Statements of difference in paired estimates are based on two-tailed t tests with statistical significance at the p < 0.05 level. Terms such as “higher” or “lower” indicate that the differences are statistically significant.

Jill J. Ashman, Pinyao Rui, and Titilayo Okeyode are with the National Center for Health Statistics, Division of Health Care Statistics.

• Black LI, Benson, V. Tables of summary health statistics for U.S. children: 2016 National Health Interview Survey . 2018. Available from:
• Blackwell DL, Villarroel MA. Tables of summary health statistics for U.S. adults: 2016 National Health Interview Survey . National Center for Health Statistics. 2018.
• National Center for Health Statistics. 2016 NAMCS micro-data file. Hyattsville, MD. 2019.
• Rui P, Okeyode T. National Ambulatory Medical Care Survey: 2016 national summary tables . 2019.
• National Center for Health Statistics. 2016 NAMCS micro-data file documentation. Hyattsville, MD. 2019.

Ashman JJ, Rui P, Okeyode T. Characteristics of office-based physician visits, 2016. NCHS Data Brief, no 331. Hyattsville, MD: National Center for Health Statistics. 2019.

Copyright information

All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.

National Center for Health Statistics

Charles J. Rothwell, M.S., M.B.A., Director Jennifer H. Madans, Ph.D., Associate Director for Science

Division of Health Care Statistics

Denys T. Lau, Ph.D., Director Alexander Strashny, Ph.D., Associate Director for Science

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