cms avoidable ed visits

• HEDIS Measures and Technical Resources
• Emergency Department Utilization

Emergency Department Utilization (EDU)

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

Why It Matters

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

Results – National Averages

Emergency department utilization total rate.

*Lower rates signify better performance.

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

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

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

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

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Predicting Avoidable Emergency Department Visits Using the NHAMCS Dataset

Yuyang yang.

1-6 Northwestern University, Chicago, IL, USA

Hanyin Wang

Scott dresden.

Despite the important role avoidable emergency department (ED) visits play in healthcare costs and quality of care, there has been little work in development of predictive models to identify patients likely to present with an avoidable ED visit. We use a conservative definition of ‘avoidable’ ED visits defined as visits that do not require diagnostic or screening services, procedures, or medications, and were discharged home to classify visits as avoidable. Models trained using data from emergency departments across the US yielded a training AUC of 0.723 and a testing AUC of 0.703. Models trained using the full dataset were tested on demographic groups (race, gender, insurance status), finding comparable performance between white/black patients and male/female with reductions in performance in Hispanic populations and patients with Medicaid. Predictors strongly associated with non-avoidable ED visits included increased age, increasing number of total chronic diseases, and general as well as digestive symptoms. Reasons for visit stated as injuries and psychiatric symptoms influenced the model to predict an avoidable visit.

Introduction

Avoidable emergency department (ED) visits put significant strain on the healthcare system by increasing overall cost and leading to ED overcrowding 1 , 2 . This problem has received more attention in the US in recent years due to significant increases in annual ED visits and reductions in ED admission rates among the elderly, the Medicare-reimbursed, and patients with multiple comorbidities 3 . Previous studies suggest that diverting ED visits for nonurgent conditions that are treatable at retail clinics or urgent care facilities may lead to a projected saving of $4.4 billion annually 4 . Avoidable ED visits are also believed to compromise quality of care by contributing to excessive testing and treatment as well as compromising the longitudinal relationship between the patients and the primary care physicians 2 , 5 . Yet despite the heightened interest in reducing potentially avoidable ED visits, only a handful of studies had explored the factors associated with avoidable ED visits such as reasons for visit, demographics features, and encounter characteristics. In one national study of ED visits and utilization, non-urgent ED visits were shown to be more prevalent in the older, non-Hispanic white, and Medicare-insured patients 6 . Another review showed that, based on limited evidence, younger age, convenience of the ED compared to alternatives, and referral to the ED by a physician all contributed to driving up non-urgent ED use 2 .

To our knowledge, none of the studies had developed advanced models to classify and predict avoidable ED visits, the insights from which could be used to inform more efficient and cost-effective care management. Thus, the goal of this study is to use several machine learning algorithms to predict avoidable ED visits based on patient and visit characteristics known before the point of triage using a conservative definition of avoidable ED visit 7 .

Study Population

The emergency department visits data used for this study comes from the Center for Disease Control (CDC) National Hospital Ambulatory Medical Care Survey (NHAMCS), which is an annual survey tool to collect the utilization of ambulatory services in hospital emergency and outpatient departments 8 . NHAMCS surveys a sample of non-institutional and general hospitals across the United States 8 . For this study, data from the NHAMCS survey collected between the years 2014 to 2018 were included in our sample.

Variables of Interest

From the NHAMCS dataset, we included the following variables of interest: age, gender, race and ethnicity, arrival time, whether the patient was seen at the ED with the past 72 hours, alcohol abuse, substance abuse, comorbidities, including Alzheimer’s, Asthma, Cancer, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Chronic Heart Failure, Coronary Arterial Disease, Depression, Diabetes, End Stage Renal Disease, HIV, Hyperlipidemia, Hypertension, Obstructive Sleep Apnea, Osteoporosis, and Obesity, the total number of chronic conditions, time of day, day in the week of visit, month of visit, and reasons for visit codes. The NHAMCS dataset also contains patient stated reasons for visit, broken down into broad categories of chief complaints by organ system. Reasons for visit, in the form of organ system categories were used as variables in this study. In addition, patient time of arrival, which is encoded as a 24-hour time was recoded into arrival at daytime (between 7 AM and 5PM), night (5 PM and midnight), and overnight (midnight to 7AM).

Data Preprocessing and Avoidable ED Definition

Variables with more than 30% missing values (n=7 variables) and observations with missing data values (18.55%) were dropped. Training and testing datasets were created via a 7:3 split. Subsets of the dataset were created by stratifying by race, gender, and insurance type.

The avoidable ED visit definition of this study is based on a prior avoidable ED visit study by Hsia et al that uses NHAMCS data from earlier years 7 . An avoidable ED visit is defined as discharged ED visits not requiring any diagnostic tests, procedures or medications. Patients admitted for observation, hospitalized, transferred, died in the ED or were dead on arrival were considered to be non-avoidable ED visits and excluded ( Figure 1 ).

Flowchart for defining avoidable ED visit and removing data with missingness or errors

Modeling Methods & Statistical Analysis

To determine the best performing method for the classification task, multiple machine learning methods were experimented, including logistic regression, random forest, gradient boosted tree (XGboost), and multi-layer perceptron (MLP). Random forest and XGboost models were tuned to improve accuracy. After training 4 models with each machine learning method, we compared the area under curve (AUC) of each model. Modeling was performed using python 3.8.

To understand the performance of our model in various key demographic groups, we tested our full model on the demographic-specific testing sets. In addition, we created demographic-specific models, by retraining on demographic-specific training sets, and tested those models on the corresponding demographic-specific testing sets.

In addition, we examined the importance of variables used in the XGboost model. We obtained the Shapley Additive Explanations (SHAP) values from each model using the shap python package. Individual SHAP plots for the main model is shown in Figure 2 and SHAP plots for demographic subgroups are included as supplementary figures.

Feature Importance Chart of Full XGBoost Model.

There are a total of 77,714 individuals in our sample population. There are more females (55%) than males in the data and the mean age is 37.06. 57% of the population is white, 23% is black, and 16% is Hispanic. Private insurance makes up 18% of the population, with 27% of patients using Medicare and 35% using Medicaid. In comparison to the white and black population, the Hispanic population was significantly younger, had decreased usage of Medicare and increased usage of Medicaid ( Table 1 ).

Baseline characteristics of the sample population

We tested four types of models XGBoost, Random Forest, Logistic Regression, and MLP. Out of these models XGBoost was found to have the best testing AUC ( Table 2 ). The training AUC of our full model is 0.723 and the testing AUC is 0.703. The AUC of the full model tested on demographic-specific groups is shown in Table 3 . The AUC of the full model is comparable between the female-only (AUC = 0.706) and male-only (AUC = 0.700) testing sets, as well as between white (AUC = .6984) and black patients (AUC = .7006) testing sets. There is a decrease in model performance among the Hispanic group (AUC = .6724). Within the insurance subgroups, the performance of the model is highest in the Medicare-only group (AUC = 0.7289) and lower in the private insurance-only group (AUC = 0.685) and Medicaid-only group (AUC = 0.677).

Accuracy metrics for best machine learning model experimented in this study

Race Models

We identified several variables to have strong influence on model prediction of non-avoidable or avoidable ED visits. ( Figure 2 ). Age, increasing number of total chronic diseases, and general as well as digestive symptoms all strongly influenced the model to label a visit as avoidable. Reasons for visit stated as injuries, psychiatric symptoms, and younger age strongly influenced the model to predict an avoidable visit. The time of day and day of week of the visit were not considered to be particularly important by our model. We also noted several differences in the feature importance of models trained on specific demographic subgroups. Genitourinary symptoms were strong predictors of non-avoidable ED visits in women (Supplementary Figure 1 a). The presence of Psychiatric symptoms was seen as important in the Medicaid population, but not in either Medicare or Private insurance (Supplementary Figure 3 ).

Supplementary figure 3a. Private-Insurance only model.

In this study, we developed a classification algorithm using machine learning methods to predict avoidable ED visits prior to the patients arriving at the ED. Our results suggest a moderate ability to predict avoidable ED visits using variables known prior to the point of triage, with a max testing AUC just over 0.70. There was little difference in model performance between genders, and between black and white populations, suggesting that our model is not sensitive to racial differences between the two groups. Model performance was worse in Hispanic, as well as patients on Medicaid, suggesting that our model may be biased against these populations. Compared to the Black and White populations, the Hispanic population was significantly younger, used Medicaid more often, and had less comorbidities. Differences in the Hispanic population within the data in comparison to Black and White groups as well as confounding factors not represented in the data such as language or cultural barriers, which have been noted in machine learning work involving Hispanic groups, may explain reduced model performance in this group 9 .

Some of the variables identified in figure 2 as strong predictors of avoidable/non-avoidable determination are consistent with existing knowledge surrounding avoidable ED visits. It is noted that the emergency department setting is not well equipped to treat mental health disorders and substance abuse 7 , which may explain why psychiatric symptoms were associated with avoidable ED visits. Our finding that pediatric age is associated with increased likelihood of avoidable ED visits is consistent with previous findings 10 , 11 , potentially due to a mismatch in perceived severity of a child’s symptoms between parents and healthcare providers. The presence of Medicare insurance was not considered an impactful metric, although PAYTYPER 1(Private Insurance) was associated with increased avoidability and 5(Self-Pay) and -8(Unknown Payment) were associated with decreased avoidability. The time-of-day variable was not considered to be important in the model, suggesting that there is no difference in avoidable visits between overnight and daytime admissions. The day of week variable was also not considered to be important. It is known that patient access to primary care is an important factor in ED utilization. It has been shown that patients seek ED care during windows when primary care physicians are unavailable, such as overnight arrivals and weekend visits in single site studies 12 , 13 . As our data comes from the national level, the impact of primary care availability that may be visible at a local level may be obscured.

Some features were more important in the race, gender, and insurance specific models compared to the base model. Genitourinary symptoms were important in females but not males, possibly due to an increased abundance of urinary tract infections and other gynecological symptoms that present more commonly in females. Arrival by ambulance was important in females but not males and digestive symptoms were more important in females than males. Women are known to have higher rates of digestive primary complaints compared to men (and lower admissions, but same when adjusted for age and comorbidities) 14 . Pregnant women have been noted to use emergency department services non-urgently 15 .

Our work represents the first attempt to use machine learning in the classification of avoidable emergency visits. We identified several factors associated with avoidable and non-avoidable ED visits that may be useful for clinicians and patients in managing their care options. For example, in a primary care or community clinic setting, our model could be deployed to provide patient guidance on whether to schedule a meeting with their physician or go to the emergency department.

The addition of longitudinal care data from healthcare providers would likely improve the accuracy of our model in this setting. Future work should examine the effect of additional variables known prior to the point of triage to improve predictive performance, such as information regarding prior ED utilization.

Figures & Table

Supplementary figure 1a. Female only model.

Supplementary figure 1b. Male only model.

Supplementary figure 2a. Black only model.

Supplementary figure 2b. Hispanic only model.

Supplementary figure 2c. White only model.

Supplementary figure 3b. Medicare only model.

Supplementary figure 3c. Medicaid only model.

Gender Models

Insurance Models

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3M™ Potentially Preventable Emergency Department Visits (PPVs)

Identify potentially preventable emergency department visits using powerful clinical grouping logic

3M PPVs find opportunities for better discharge planning, care coordination and follow up.

Potentially preventable emergency room visits are inefficient and expensive either because the care could have been provided in a less expensive setting that was not available, or because inadequate care of a chronic or sub-acute problem in the outpatient setting resulted in an acute deterioration, or a combination of both.

3M PPVs can identify patterns of potentially avoidable emergency department visits, and may suggest areas where primary care services should be improved.

Why choose the 3M™ Potentially Preventable Emergency Department Visits (PPV) methodology?

Every year, millions of patients visit the emergency department for conditions that either could have been prevented with better management in the community or would be more appropriately treated elsewhere. Reducing potentially preventable ED visits simultaneously saves money and improves patient outcomes.

Accurate and fair PPV identification requires careful, clinically defined risk adjustment. The widely accepted 3M™ Clinical Risk Groups (CRG) methodology measures baseline population health status in comparing actual versus expected PPV rates.

3M PPVs are one of the five 3M™ Potentially Preventable Event methodologies that generate specific results for clinicians and health care managers to use in improving care and reducing cost.

Nursing facilities, intermediate care facilities and residential treatment centers that care for patients around the clock can be held to higher standards. The 3M PPV methodology includes additional logic for potentially preventable ED visits when patients are under the care of a residential nursing care facility.

All about 3M PPVs

The 3M PPV methodology identifies emergency room visits that may result from a lack of adequate access to care or ambulatory care coordination. Similar to 3M PPAs, 3M PPVs are ambulatory sensitive conditions which adequate patient monitoring and follow-up should be able to reduce or eliminate.

3M PPVs are emergency department visits for conditions that could otherwise be treated by a care provider in a nonemergency setting. 3M PPVs may also result from a lack of adequate care or ambulatory care coordination, such as access to urgent care facilities, availability of primary care physicians, etc. 3M PPVs include visits that could have been reduced or eliminated if adequate patient monitoring and treatment (e.g., medication management) had been available. High 3M PPV rates may therefore represent a failure of the ambulatory care provided to the patient.

3M PPVs can be used by payers, employers, government agencies, researchers, integrated health delivery systems (e.g., managed care organizations and accountable care organizations) and residential nursing care facilities (e.g., nursing facilities, intermediate care facilities, and residential treatment centers). For example, employers or government insurance programs can use 3M PPVs to measure the performance of contracted integrated health delivery systems or residential nursing care facilities. In turn, integrated health delivery systems and residential nursing care facilities themselves can use the 3M PPV methodology to improve their own performance by improving outcomes from year to year.

Here are a few examples of the value the 3M PPV methodology can bring to customers.

• Quantifying opportunities for improvement . In the Medicare program, 55 percent of ED visits (excluding those patients who are admitted) were defined as potentially preventable, suggesting to the Medicare Payment Advisory Commission (MedPAC) that “ample opportunities” exist for improvement in the ambulatory care provided to Medicare beneficiaries.
• Understanding population health . The New York Department of Health reports risk-adjusted 3M PPV rates by county for both the all-payer and Medicaid populations, with reports dating back to 2011. Learn more.
• Managing managed care . The Texas Medicaid program reports risk-adjusted 3M PPV rates for Medicaid-managed care organizations along with detailed drill-down capability and downloadable data files.
• Measuring outcomes in long-term care . In analyzing data for more than 400,000 Medicare nursing facility residents, 3M researchers found considerable variation both within and across states in risk-adjusted 3M PPV rates. One implication: Medicare’s method of assigning nursing facility quality scores should say more about outcomes.

While 3M PPA classification logic is the same for every client, an organization can use different versions of the methodology and apply their own reimbursement calculation tables. 3M Health Information Systems always recommends that clients use the latest version of the software, but each organization can decide how to apply it (for research, public reporting, reimbursement, or a combination). Note: At present, 3M Health Information Systems does not offer software that replicates a specific organization’s PPA analysis.

3M PPVs are integrated with the other 3M patient classification methodologies.

• 3M PPVs, along with the 3M™ Potentially Preventable Admissions and 3M™ Potentially Preventable Services , are the 3M™ Population-focused Preventables (PFPs). 3M offers a suite of five Potentially Preventable Event methodologies, also including the 3M™ Potentially Preventable Complications (PPCs) and 3M™ Potentially Preventable Readmissions (PPRs) . All 3M PPE methodologies are used to measure and reduce costly, clinically significant adverse outcomes.
• 3M PPVs are distinct from the 3M™ Potentially Preventable Revisits to the Emergency Department (PPR EDs), a component of the 3M PPR methodology that measures potentially preventable returns to the hospital (inpatient or ED) after discharge.
• The 3M PPVs are defined using the 3M™ Enhanced Ambulatory Patient Groups (EAPGs) methodology for ambulatory visits.

3M PPVs are available in the following 3M products:

• 3M™ 360 Encompass™ System
• 3M™ Core Grouping Software (CGS)
• 3M™ Grouper Plus Content Services (GPCS)
• 3M™ Data to Action Solution

Available to Licensees on the 3M Customer Support Website

• PFP Methodology Overview
• PFP Definitions Manual
• PFP Setup Guide
• PFP Summary of Changes

3M experts are available to advise health plans, government agencies and other interested parties on how to obtain maximum value from the use of 3M PPVs. For example, 3M consultants can help you measure the incidence of potentially preventable emergency department visits, compare the results with appropriate benchmarks and design improvement programs. 3M consultants can also help payers and other organizations measure 3M PPVs across health plans and other patient populations, design pay-for-outcomes incentive methods and facilitate learning collaboratives for provider groups.

The 3M PPV methodology can identify PPVs using standard, inpatient claims data derived from institutional and professional claims (i.e., the UB-04 and CMS-1500 paper forms and their corresponding X12N 837 electronic formats). Consistent, unique patient identifiers are essential. Comparing 3M PPV rates across different populations requires the 3M™ Clinical Risk Grouping (CRG) Software to perform risk adjustment by individual health status. Thus, 3M PPA analysis typically involves creation of a static data set comprising at least one full year of data. If available, pharmacy data in NCPDP format is optional but recommended. Note: 3M CRG and 3M PPV methodologies do not need to be built into the claim processing systems.

Inappropriate use of the emergency department has long been identified as a major problem in the health care system. The twin challenges have been exactly how to define “inappropriate” and who should be held responsible. Many payers have penalized the hospital or the patient for individual visits deemed inappropriate, but this approach can clearly be unfair. In 2012, 3M met these challenges by releasing the 3M™ Potentially Preventable Emergency Department Visit (PPV) methodology as one of the three 3M™ Population-focused Preventables. Learn more. (The others are 3M™ Potentially Preventable Admissions and 3M™ Potentially Preventable Services .)

As with the other 3M Potentially Preventable Event methodologies, three core concepts are essential. First, we recognize that not all ED visits are potentially preventable. Second, what matters is not the individual visit, but rather the overall rate of potentially preventable ED visits. Instead of approaching quality with the mindset of “This should never happen,” we use a more realistic and meaningful approach of “This has happened too often.” Third, any comparisons across populations of patients must be risk-adjusted. In practice, that means that the actual 3M PPV experience of a population is compared with the experience that would be expected for a population with the same case mix.

The 3M PPV logic is divided into two phases.

1. Identify potentially preventable events

By definition, “ED visits” include only those visits where patients were treated and released. (Nationally, 14 percent of ED patients are admitted to the same hospital; their ED services are included within the inpatient stay.) Learn more (PDF, 274.35 KB).

All ED visits are grouped using the 3M™ Enhanced Ambulatory Patient Group (EAPG) methodology . Of the 560 3M EAPGs (as of February 2019), many principal diagnoses within 196 3M EAPGs are considered potentially preventable in the general population. In a Minnesota all-payer analysis, the most common 3M PPVs were upper respiratory tract infections, abdominal pain, and musculoskeletal systems and connective tissue diagnoses such as back pain. When a population is under the care of a residential nursing care facility (such as a nursing facility, intermediate care facility, or residential treatment center), additional trauma, infections, and certain other diagnoses are considered potentially preventable.

2. Determine patient risk adjustment

In any rate-based comparison of outcomes, risk adjustment is essential for a fair comparison across populations. Although 3M PPVs are generally preventable, they will never be totally eliminated, even with optimal care. As a result, there will be a residual rate of 3M PPVs in even the best-performing systems. More importantly, the rate at which PPVs occur depends on the burden of illness of the population.

The 3M PPV software measures the burden of illness of each patient (and therefore of the population) using the 3M™ Clinical Risk Groups (CRGs) . In 3M CRG v2.1, there are approximately 390 base 3M CRGs and 1,470 total 3M CRGs, taking into account severity levels. For example, 3M CRG 70602 describes a person with congestive heart failure, diabetes and chronic obstructive pulmonary disease, severity level 2. ED visits for this person are more likely to be preventable than for a person in severity level 5 (i.e., 3M CRG 70605).

The 3M CRGs can be rolled up into three levels of aggregation (i.e., approximately 676, 254 or 54 groups) and nine health status group levels. The aggregated 3M CRGs sacrifice some clinical precision, but with only a slight loss of explanatory power. 3M recommends that the ACRG3 level of 54 groups (as of 2019) be used for setting risk-adjusted 3M PPV norms.

Further information on the 3M PPV logic is available in the 3M™ Population-focused Preventables Methodology Overview. Detailed information is available to licensees in the online 3M PFP definitions manual.

The 3M PPV clinical logic is maintained by a team of 3M clinicians, data analysts, nosologists, programmers and economists. The methodology is updated annually to reflect changes in the standard diagnosis and procedure code sets as well as 3M enhancements to the clinical logic.

Learn more about 3M PPVs

Publicly available documentation, articles and reports

Please note that documents not published by 3M do not necessarily reflect 3M recommendations and have not been approved by 3M. These documents are listed here for the information of readers interested in the various ways that 3M patient classification methodologies have been applied. Also note that listing these references does not imply endorsement of 3M methodologies by individual authors, other organizations or government agencies.

Some articles and reports are available from the publishers at no charge, while others require a fee.

This manual describes the 3M Population-focused Preventables (PFP) Software, a clinically-based classification system that identifies preventable hospital admissions, ED visits and ancillary services.

Whether your organization needs to monitor patient safety, calculate expected reimbursement or design incentive models for community-based care, 3M software gives you the insights you need.

This eguide can help you understand 3M solutions for population health, patient safety and cost-effective care.

This article details the use of potentially preventable events and demonstrates how they are being applied to achieve health care value.

Find out how leveraging data enabled the Egyptian Health Department to support its at-risk Medicaid youth population with proactive education, outreach and resources.

This article offers five principles for success, emphasizing clinically credible initiatives that generate actionable information for clinicians.

This report explores one set of opportunities for health care system improvement and potential savings by analyzing Emergency Department (ED) visits, hospital admissions and hospital readmissions to uncover the volume and make-up of potentially preventable health care events.

This report from Texas Health and Human Services describes the agency’s efforts to reduce potentially preventable emergency department utilization by Medicaid recipients and current and proposed initiatives to improve Medicaid recipients’ health outcomes.

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Article Contents

Introduction, acknowledgements.

• < Previous

Avoidable emergency department visits: a starting point

• Article contents
• Figures & tables
• Supplementary Data

Renee Y Hsia, Matthew Niedzwiecki, Avoidable emergency department visits: a starting point, International Journal for Quality in Health Care , Volume 29, Issue 5, October 2017, Pages 642–645, https://doi.org/10.1093/intqhc/mzx081

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To better characterize and understand the nature of a very conservative definition of ‘avoidable’ emergency department (ED) visits in the United States to provide policymakers insight into what interventions can target non-urgent ED visits.

We performed a retrospective analysis of a very conservative definition of ‘avoidable’ ED visits using data from the National Hospital Ambulatory Medical Care Survey from 2005 to 2011.

We examined a total of 115 081 records, representing 424 million ED visits made by patients aged 18–64 years who were seen in the ED and discharged home.

We defined ‘avoidable’ as ED visits that did not require any diagnostic or screening services, procedures or medications, and were discharged home.

In total, 3.3% (95% CI: 3.0–3.7) of all ED visits were ‘avoidable.’ The top five chief complaints included toothache, back pain, headache, other symptoms/problems related to psychosis and throat soreness. Alcohol abuse, dental disorders and depressive disorders were among the top three ICD-9 discharge diagnoses. Alcohol-related disorders and mood disorders accounted for 6.8% (95% CI: 5.7–8.0) of avoidable visits, and dental disorders accounted for 3.9% (95% CI: 3.0–4.8) of CCS-grouped discharge diagnoses.

A significant number of ‘avoidable’ ED visits were for mental health and dental conditions, which the ED is not fully equipped to treat. Our findings provide a better understanding of what policy initiatives could potentially reduce these ‘avoidable’ ED visits to address the gaps in our healthcare system, such as increased access to mental health and dental care.

The rhetoric surrounding ‘avoidable’ emergency department (ED) visits in the United States has been contentious. Since the Deficit Reduction Act of 2005, states may impose mandatory cost-sharing for non-urgent ED visits for Medicaid patients. Most recently, Indiana set copayments for ‘non-emergency care’ delivered in the ED as part of its Medicaid expansion waiver in 2015. Health plans, too, have emphasized the importance of avoiding the ED to reduce costs. Estimates of ‘avoidable’ ED visits run as low as 4.8% [ 1 ] and as high as 90% [ 2 ], and come from various methodologies to determine what constitutes a necessary versus unnecessary ED visit. Defining what is ‘non-urgent’, ‘unnecessary’ or ‘inappropriate’, is perhaps the first problem, as these terms are often conflated due to the lack of a consensus for a standard definition of a non-urgent visit and the complex nature of its categorization [ 3 – 5 ].

Current literature use retrospective diagnoses [ 3 , 5 ], hospital admissions [ 2 ], triage scores [ 3 ] and patient self-reported data [ 6 ], among others, to define ‘avoidable’. However, using chief complaints derived from diagnoses, which are determined post hoc , can be dangerous because visits that are eventually determined to be non-emergent after physician examination and diagnostic testing are virtually indistinguishable from emergent visits [ 7 ]. Additionally, triage scores, while correlated with the true severity of disease, are notoriously poor predictors [ 3 , 8 ].

Because of these difficulties, creating broad policy initiatives to deter potentially avoidable visits could be risky. However, even advocates against restricting ED access for non-urgent visits admit that a sub-population of ED patients can be better treated elsewhere at lower costs. To address this issue, we sought to present a characterization of a very restrictive definition of ‘avoidable’ ED visits, creating a baseline for which many practitioners would agree potentially would not warrant an ED visit, and providing insight as to why these patients present to the ED and some direction for policymakers to better target interventions in the US.

Our retrospective analysis used the National Hospital Ambulatory Medical Care Survey (NHAMCS) for years 2005–2011. NHAMCS is a national sample survey conducted by the National Center for Health Statistics and represents 136 million visits in the US annually [ 9 ], providing information on ED visits to non-federal, general and acute care hospitals in the United States. We examined visits by patients aged 18–64 years, the population affected by the current and potential Medicaid cost-sharing increases, seen and discharged from the ED to characterize and determine the proportion of non-urgent visits, using our proposed definition of ‘avoidable’.

We conservatively defined ‘avoidable’ ED visits as discharged ED visits not requiring any diagnostic tests, procedures or medications. Diagnostic tests included imaging (x-rays, CT scans, MRI), blood tests (CBC, BUN/creatinine, electrolytes) or other tests (cardiac monitor, EKG/ECG, toxicology). Procedures included IV fluids, suturing/staples and nebulizer therapy. Medications included over-the-counter and prescription medications administered or prescribed.

We excluded patients admitted for observation, hospitalized, transferred, died in the ED or were dead on arrival. Because NHAMCS has been known to underreport diagnostic testing and procedures, we excluded visits where any stated reason was coded as diagnostic tests (3300–3399), other screening and preventive procedures (3400–3499), medications (4100–4199), preoperative and postoperative care (4200–4299), specific types of therapy (4400–4499) or specific therapeutic procedures (4500–4599). We also excluded visits where tests or procedures were blank/missing and those that did not receive care, including patients who left before triage, medical screening, or being seen, walked out, were not seen by a physician, left against medical advice, were not authorized to received treatment, were transferred to another facility, or saw another specialist. Overall, 115 081 records representing 424 million visits met our selection criteria.

We analyzed the primary International Classification of Disease, Ninth Revision (ICD-9) discharge diagnosis and the Healthcare Cost and Utilization Project's (HCUP) Clinical Classification Software (CCS) grouping of primary ICD-9 discharge diagnoses for ‘avoidable’ visits, as well as the rate of avoidable visits. The University of California, San Francisco deemed this study exempt from human subjects review.

We found that 3.3% (95% CI: 3.0–3.7) of all ED visits met our definition of ‘avoidable’ (Fig. 1 ). The average age of patients was 36, with 52% female, 70% white, 25% black, 33% privately-insured, 28% uninsured, 22% Medicaid-insured and 8% Medicare-insured. And 14% of these visits arrived by ambulance. The top five chief complaints were toothache, back pain, headache, other symptoms/problems related to psychosis and throat soreness. The top three ICD-9 diagnoses of ‘avoidable’ visits included alcohol abuse, dental disorders and depressive disorders.

Study sample of avoidable ED visits 279 × 215 mm 2 (300 × 300 DPI).

Top 10 reasons for visit and discharge diagnoses for avoidable emergency department visits

While the main analyses examined the proportion of total ED visits that would be ‘avoidable’ by diagnosis grouping, we also determined the percentages of each diagnosis grouping that met our definition of ‘avoidable’. We found that 10.4% (95% CI: 7.7–13.1) of visits diagnosed with alcohol-related disorders, 16.9% (95% CI: 13.5–20.2) of mood disorders and 4.9% (95% CI: 3.9–6.0) of disorders of the teeth and jaw met our criteria of ‘avoidable’.

Our analysis using a very restrictive definition of ‘avoidable’ showed that 3.3% of all ED visits in the US did not require any diagnostic testing, procedures or medications. The 6.8% of these visits were for alcohol-related and mood disorders and 3.9% for disorders related to the teeth and jaw. We also found that alcohol-related and mood disorders and disorders of the teeth and jaw had the highest percentages of ‘avoidable’ visits. At the same time, the vast majority of visits with alcohol-related and mood disorders and disorders of the teeth and jaw were not deemed ‘avoidable’, and therefore, it would be an incorrect assumption that all patients with these conditions should be not seen in the ED.

While previous studies have examined the appropriateness of ED use [ 2 , 3 ], accurate classification of non-urgent visits has remained a challenge. One study using triage scores estimated 10.1% of visits to be ‘non-urgent’ [ 3 ], higher than our estimate of 3.3%. Ours is a more conservative estimate, using a purposefully more restrictive definition of ‘avoidable’ by excluding visits that involved the use of diagnostic tests, procedures and medications. International studies evaluating non-urgent ED visits [ 10 – 13 ] found that a large proportion of visits (ranging from 10 to 70%) could be more suitably treated in primary care settings. These studies all defined ‘non-urgent’ differently, which highlight the importance of having a very conservative baseline definition to isolate a small cohort of ‘avoidable’ visits that is likely more acceptable than using triage scores, self-report, physician reports or discharge diagnoses. By using a very restrictive definition and finding what could be just a proportion of truly avoidable ED visits, potentially policy interventions could be initiated on a small group of patients for which less harm is possible, given that they required no imaging, diagnostics, procedures or medications.

Our most striking finding is that a significant number of avoidable visits are for conditions the ED is not equipped to treat. Emergency physicians are trained to treat life- and limb-threatening emergencies, making it inefficient for patients with mental health, substance abuse, or dental disorders to be treated in this setting. One potential mechanism to more appropriately direct limited healthcare resources could be to increase access to mental health and dental care, which have traditionally been treated as separate categories of healthcare. For example, of the 46 states in the US that offer dental coverage for non-pregnant adult Medicaid enrollees, 28 provide coverage for preventive services and 18 provide emergency services only [ 14 ]. Although providing dental coverage is a step towards increasing access to dental care, less than half of dentists treat any Medicaid-insured patients [ 15 ]. Mental health and substance abuse patients similarly face difficulty in gaining access to healthcare due to insurance-imposed restrictions, and while the Mental Health Parity and Addiction Equity Act of 2008 has begun to address these disparities, not all avoidable ED visits are related to mental health, substance abuse or dental disorders, highlighting a need for greater access to care.

Our study has several limitations. First, because NHAMCS does not code some minor procedures, our definition conservatively overestimates the number of patients receiving no tests, procedures or medications. Second, NHAMCS data is based on survey responses from EDs in the sample, which can introduce potential for error. However, NHAMCS thoroughly investigates and reconciles any discrepancies. Finally, some literature shows underreporting of medications, which could apply to procedures or diagnostic testing. As NHAMCS statisticians have stated, reported excess are perhaps more accurate than underreports [ 12 ]. However, even if our findings are an overestimate, it lends even more credence that a very small proportion of patients could be identified as ‘avoidable’.

Our findings serve as a start to addressing gaps in the US healthcare system, rather than penalizing patients for lack of access, and may be a better step to decreasing ‘avoidable’ ED visits.

The authors thank Sarah Sabbagh, MPH, and Joanna Guo, BA for editorial and administrative assistance.

Mitchell TA , Remmel RJ . Level of uncompensated care delivered by emergency physicians in Florida . Ann Emerg Med 1992 ; 21 : 1208 – 14 .

Google Scholar

Lowe RA , Bindman AB . Judging who needs emergency department care: a prerequisite for policy-making . Am J Emerg Med 1997 ; 15 : 133 – 6 .

Honigman LS , Wiler JL , Rooks S et al.  . National study of non-urgent emergency department visits and associated resource utilization . West J Emerg Med 2013 ; 14 : 609 – 16 .

Richardson LD , Hwang U . Access to care: a review of the emergency medicine literature . Acad Emerg Med 2001 ; 8 : 1030 – 6 .

Mistry R , Brousseau D , Alessandrini E . Urgency classification methods for emergency department visits: do they measure up? Pediatr Emerg Care 2008 ; 24 : 870 – 4 .

Mortensen K . Copayments did not reduce Medicaid enrollees’ nonemergency use of emergency departments . Health Aff (Millwood) 2010 ; 29 : 1643 – 50 .

Raven MC , Lowe RA , Maselli J et al.  . Comparison of presenting complaint vs discharge diagnosis for identifying ‘nonemergency’ emergency department visits . J Am Med Assoc 2013 ; 309 : 1145 – 53 .

Durand AC , Gentile S , Gerbeaux P et al.  . Be careful with triage in emergency departments: interobserver agreement on 1,578 patients in France . BMC Emerg Med 2011 ; 11 : 19 .

McCaig LF , Burt CW . Understanding and interpreting the National Hospital Ambulatory Medical Care Survey: key questions and answers . Ann Emerg Med 2012 ; 60 : 716 – 21e1 .

Canadian Institute for Health Information . Understanding emergency department wait times: who is using emergency departments and how long are they waiting? 2005 .

Law C , Yip P . Acute care service utilisation and the possible impacts of a user-fee policy in Hong Kong . Hong Kong Med J 2002 ; 8 : 348 – 53 .

Nagree Y , Camarda V , Fatovich D et al.  . Quantifying the proportion of general practice and low-acuity patients in the emergency department . Med J Aust 2013 ; 198 : 612 – 5 .

Read JG , Varughese S , Cameron PA . Determinants of non-urgent emergency department attendance among females in Qatar . Qatar Med J 2014 ; 2014 : 98 – 105 .

Medicaid and CHIP Payment and Access Commission . Chapter 2: Medicaid coverage of dental benefits for adults. Report to congress on Medicaid and CHIP . Washington, DC; 2015 .

United States Government Accountability Office . Oral health: efforts under way to improve children's access to dental services, but sustained attention needed to address ongoing concerns. GAO-11–96 . Washington, DC; 2010 .

• alcohol abuse
• alcohol-related disorders
• dental procedures
• dental clinics
• depressive disorders
• emergency service, hospital
• mental health
• mood disorder
• psychotic disorders
• tooth diseases
• international classification of diseases
• sore throat
• health care systems
• outcome measures
• chief complaint

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Emergency Department Visits

Data are for the U.S.

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

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

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Emergency Department CAHPS (ED CAHPS)

Background:  The emergency department (ED) is a unique environment within the health care system, bridging the worlds of outpatient and inpatient care. In particular, the ED is a pivotal arena for the provision of acute care services. In 2017, there were nearly 139 million emergency room visits in the United States. Further, under EMTALA – the Emergency Medical Treatment and Active Labor Act of 1986 – everyone who comes to a hospital-based ED for care is entitled to a screening exam and stabilizing treatment (including hospitalization, if needed) without regard to ability to pay, making the ED a resource for those who may have no other place to receive care.

About the ED CAHPS Survey : As the leading organization spearheading national implementation of patient experience of care surveys, CMS has made considerable investments in developing and testing the Emergency Department Consumer Assessment of Healthcare Providers and Systems (ED CAHPS ) Survey.  In 2012, CMS launched an initiative to develop a reliable, valid, standardized survey to measure patients’ experience of ED care that would provide meaningful and actionable information for EDs. The survey development process followed the principles and guidelines outlined by the Agency for Healthcare Research and Quality (AHRQ) and its CAHPS Consortium in developing a patient experience of care survey. The ED CAHPS Survey is designed for adult patients (18 and older) of hospital-based emergency rooms who are discharged to home (also known as “treat and release” visits), which account for about 90% of all ED visits. The ED CAHPS Survey includes 35 questions that focus on communication and coordination, including arrival at the ED, care during the ED visit, and discharge from the ED; the survey also includes key demographic items.  

The ED CAHPS survey, which is in the public domain and available at no cost, allows EDs to collect information about their patients’ experience of care and identify aspects of care that could be improved. On its web site, CMS provides the ED CAHPS Survey and detailed recommendations on how EDs can implement, administer and score the survey; please see below.  Use of the ED CAHPS Survey is entirely voluntary and is not required by CMS.

The ED CAHPS Survey was designed to be administered several ways: a mail survey with telephone follow-up of non-respondents; a web-based survey with telephone follow-up; or a web-based survey with mail and then telephone follow-up. These “mixed modes” of survey administration can best capture the wide range of patients that EDs serve. The ED CAHPS Survey Recommended Guidelines includes templates and detailed specifications for implementing each survey mode; please see below.    

ED CAHPS Survey Supporting Materials: Provided below are links to the ED CAHPS Survey and the recommended guidelines for implementation, administration and scoring. Please note that earlier versions of the survey were called the Emergency Department Patient Experience of Care (EDPEC) Survey. This survey received the CAHPS  trademark in March 2020, and was then renamed the ED CAHPS  Survey. To maintain continuity, below we provide links to earlier versions of the survey, findings from its mode experiment and field tests, and relevant publications by the EDPEC Project Team (now known as the ED CAHPS Project Team). For more information, please contact: [email protected]

Please note that the ED CAHPS Survey development and testing occurred before the COVID-19 pandemic.

Mathews M, Parast L, Elliott MN, Lehrman WG, Stark D, Waxman D (2023). Associations Between Emergency Severity Index and Patient Experience of Care in the Emergency Department.  Academic Emergency Medicine, 30 (1). https://onlinelibrary.wiley.com/doi/10.1111/acem.14604 .

Tolpadi A, Elliott MN, Waxman D, Becker K, Flow-Delwiche E, Lehrman WG, Stark D, Parast L (2022). National Travel Distances for Emergency Care.  BMC Health Services Research, 22 (388).  https://doi.org/10.1186/s12913-022-07743-7 . 

Ye F, Parast L, Hays RN, Elliott MN, Becker K, Lehrman WG, Stark D, Martino D (2021). Development and Validation of a Patient Experience of Care Survey for Emergency Departments. Health Services Research.   https://doi.org/10.1111/1475-6773.13853 .

Chen PG, Tolpadi A, Elliott MN, Hays R, Lehrman WG, Stark DS, Parast L (2021). Gender Differences in Patients’ Experience of Care in the Emergency Department. Journal of General Internal Medicine. https://link.springer.com/article/10.1007/s11606-021-06862-x .

Parast L, Mathews M, Martino S, Lehrman WG, Stark D, Elliott MN (2021). Racial/Ethnic Differences in Emergency Department Utilization and Experience.  Journal of General Internal Medicine, 36 (4).  https://link.springer.com/article/10.1007/s11606-021-06738-0 .

Parast L, Mathews M, Elliott MN, Tolpadi A, Flow-Delwiche E, Lehrman WG, Stark D, Becker K (2019). Effects of Push-To-Web Mixed Mode Approaches on Survey Response Rates: Evidence from a Randomized Experiment in Emergency Departments. Survey Practice , 12(1): 10.29115/SP-2019-0008.

Mathews M, Parast L, Tolpadi A, Elliott MN, Flow-Delwiche E, Becker K (2019). Methods for Improving Response Rates in an Emergency Department Setting - A Randomized Feasibility Study. Survey Practice , 12(1): 10.29115/SP-2019-0007.                                                                                                                                   

Parast L, Mathews M, Tolpadi A, Elliott MN, Flow-Delwiche E, Becker K (2019). National Testing of the Emergency Department Patient Experience of Care (EDPEC) Discharged to Community (DTC) Survey and Implications for Adjustment in Scoring. Medical Care , 57(1): 42-48.

Weinick RM, Becker K, Parast L, Stucky BD, Elliott MN, Mathews M, Chan C, Kotzias V (2014). Emergency Department Patient Experience of Care Survey: Development and Field Test. RAND Corporation, RR-761-CMS.

ED CAHPS 1.0 2-Column Survey ENGLISH July 2020 (PDF)

ED CAHPS 1.0 2-Column Survey SPANISH July 2020 (PDF)

ED CAHPS Recommended Guidelines 07-09-2020 (PDF)

ED CAHPS Fact Sheet (PDF)

ED CAHPS Frequently Asked Questions (PDF)

EDPEC 5.0 2-Column Survey ENGLISH (PDF)

EDPEC 5.0 2-Column Survey SPANISH (PDF)

EDPEC 3.0, Mode Experiment and Feasibility Test I, 2016 (ZIP)

EDPEC 4.1, Feasibility Test II, 2018 (ZIP)

EDPEC Admit Version A (DOCX)

EDPEC Admit Version B (DOCX)

Medicare Advantage patients have fewer hospitalizations but more ED visits for potentially avoidable conditions, study finds

March 3, 2023 – Patients insured by Medicare Advantage (MA) had fewer hospitalizations for potentially avoidable conditions—such as dehydration, bacterial pneumonia, or urinary tract infection—compared to patients insured by traditional Medicare, according to a new study . Overall, however, patients with Medicare Advantage were more likely than patients with traditional Medicare to require some form of acute care for these same conditions, such as an emergency department (ED) visit or an outpatient hospital stay, also known as an observation stay.

These findings raise the possibility that a reduction in hospitalizations in Medicare Advantage may be related to shifting patients with the same medical conditions to cheaper sites of care, rather than higher-quality preventive care avoiding those conditions entirely, according to the study’s lead author, Adam Beckman , a graduating MD, MBA student at Harvard Medical School and Harvard Business School. Co-authors of the study included Harvard T.H. Chan School of Public Health’s Austin Frakt , Ciara Duggan, Jie Zheng, E. John Orav , Thomas Tsai , and Jose Figueroa .

The study, published February 24 in JAMA Health Forum, looked at data from 2018 from more than 10 million Medicare beneficiaries. The researchers compared information on hospitalizations among people on traditional Medicare with that of people on MA—plans that are offered through Medicare-approved private companies and that may include additional benefits but also may limit service providers.

The study comes at a moment of national dialogue about the MA program, as the federal Centers for Medicare & Medicaid Services recently released a final rule that would affect future MA payments.

Researchers found that apparent decreases in the number of potentially avoidable hospitalizations among MA patients coincided with more direct discharges from EDs and more observation stays in EDs—in which a patient is cared for longer but is not admitted to the hospital.

This study cannot determine what’s causing these trends, but one possible explanation is that MA is more aggressive in using tools like prior authorizations to manage utilization, Beckman said in a social media post and a February 28 Fierce Healthcare article.

Beckman and Figueroa said that it is not clear how these trends ultimately affect patients. On one hand, more cost-effective care can be beneficial. On the other hand, if that efficiency comes with tradeoffs in quality, patient outcomes, or patient finances, these trends would be concerning, they said. They emphasized the need for future research into those questions.

Read the Fierce Healthcare article: Medicare Advantage patients account for fewer avoidable hospitalizations. Here’s why

Read the study: Evaluation of Potentially Avoidable Acute Care Utilization Among Patients Insured by Medicare Advantage vs Traditional Medicare

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News: medpac recommends national coding guidelines for ed visits by 2022.

Following their April 4 meeting, members of the Medicare Payment Advisory Commission (MedPAC) asked the U.S. Department of Health and Human Services (HHS) to create national coding guidelines for emergency department (ED) visits by 2022, according to JustCoding .

ED visit coding has shifted to higher evaluation and management (E/M) visit levels over the last decade, according to MedPAC. Specifically, hospitals are billing level 5 visits at a higher concentration, which likely means that Medicare payments are too high for many patients.

To address this issue, at a March meeting, commission staff suggested that HHS revisit the idea of developing national guidelines for ED visit coding. According to MedPAC, establishing guidelines would give hospitals clearer rules for coding ED visits and provide CMS with a firm foundation for assessing and auditing coding behavior.

MedPAC staff also recommended that HHS implement the following policies to encourage ED staff to report resources more appropriately:

• Expanding the quality measurement of avoidable ED use to provider types who frequently administer non-urgent care
• Improving care coordination between EDs and primary care physicians
• Initiating a beneficiary education campaign to improve the understanding of appropriate ED and urgent care center use related to non-urgent care

At a meeting on April 4 , commission staff unanimously voted to approve these recommendations to HHS.

Editor’s note: This article originally appeared in JustCoding . To read about the American Medical Association’s planned E/M documentation and coding changes, click here .

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Ruxin T , Feldmeier M , Addo N , Hsia RY. Trends by Acuity for Emergency Department Visits and Hospital Admissions in California, 2012 to 2022. JAMA Netw Open. 2023;6(12):e2348053. doi:10.1001/jamanetworkopen.2023.48053

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Trends by Acuity for Emergency Department Visits and Hospital Admissions in California, 2012 to 2022

• 1 School of Medicine, University of California, San Francisco
• 2 Department of Emergency Medicine, University of California, San Francisco
• 3 Philip R. Lee Institute for Health Policy Studies, San Francisco, California

Emergency departments (EDs) are an integral component of the US health care system, as they care for all patients and provide an entry point. 1 However, ED visits have outpaced population growth while the number of EDs has decreased. 2 Between 2011 and 2021, California ED visits increased by 7.4% while the number of EDs decreased by 3.8%. 2 Furthermore, from 2006 to 2019, high complexity treat-and-release ED visits billed as high intensity increased from 4.8% to 19.2% nationally. 3

Knowing the proportion of ED visits by acuity resulting in hospital admission may provide benchmarking trends for adequate hospital capacity and planning. Therefore, we investigated trends in California ED visits and hospital admissions from EDs by acuity from January 1, 2012, to December 31, 2022.

This retrospective cohort study used publicly available ED visit data from the California Department of Healthcare Access and Information (HCAI), which mandates reporting from all hospitals in California. 4 The University of California, San Francisco, Institutional Review Board approved the study and waived the need for informed consent owing to the use of publicly available data. The study followed the STROBE reporting guideline.

We defined ED visit acuity using Current Procedural Technology ( CPT ) codes based on classifications from the California HCAI. We stratified ED visits as nonurgent (99281), urgent (99282), moderate (99283), severe (99284), and critical (99285 and 99291) and as resulting in discharge or hospital admission. Data were analyzed using R, version 4.1 (R Project for Statistical Computing). Changes in the proportion of visits by CPT code were assessed with a χ 2 trend test, and linear patterns in visits and admission were assessed using linear regression models. Two-sided P < .05 indicated statistical significance.

The overall number of ED visits grew from 12.5 to 14.3 million from 2012 to 2022 (14.0% [95% CI, 2.3%-28.7%]) ( Table ). During the study period, nonurgent visits decreased by −55.2% (95% CI, −61.3% to −47.0%) ( Figure ), whereas severe visits increased by 34.8% (95% CI, 20.9%-52.3%), and critical visits increased by 75.8% (95% CI, 62.5%-91.4%).

Likewise, the annual number of ED visits resulting in hospital admission increased by 12.0% (95% CI, 8.6%-15.7%). This increase was not equally distributed by acuity. The proportion of hospital admissions from nonurgent ED visits decreased from 1.1% in 2012 to 0.5% in 2022 (−54.5% [95% CI, −74.7% to −34.8%]). Notably, the proportion of hospital admissions from severe ED visits decreased from 15.5% in 2012 to 8.1% in 2022 (−47.8% [95% CI, −52.7% to −41.8%]), even as the proportion of these ED visits increased in that time. The proportion of critical ED visits resulting in admission decreased from 52.7% in 2012 to 37.5% in 2022 (−28.8% [95% CI, −30.7% to −26.8%]).

We found that from 2012 to 2022, the increase in severe and critical ED visits outpaced corresponding rates of hospital admissions from EDs. Severe visits rose by 34.8% and critical visits by 75.8%, aligning with findings of previous studies 5 and suggesting that patients may be presenting with higher-acuity conditions. However, this observed rise in high-acuity ED visits was not met by a rise in hospital admissions; the proportion of severe and critical ED visits resulting in admission decreased from 15.5% to 8.1% and from 52.7% to 37.5%, respectively.

The findings of this cohort study raise questions about whether the increase in high-acuity ED visits can be fully explained by sicker patients or whether external factors (eg, more stringent hospital admissions criteria, changing demographics, and shifting practice patterns 3 , 6 ) and/or upcoding (ie, documenting lower-acuity visits as higher-acuity, yielding higher payout) are substantial contributors. Study limitations include our use of administrative data, which do not have the granularity of clinical data or medical charts. These findings may inform policy makers and health care stakeholders when determining ED funding and resource allocation.

Accepted for Publication: November 2, 2023.

Published: December 18, 2023. doi:10.1001/jamanetworkopen.2023.48053

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Ruxin T et al. JAMA Network Open .

Corresponding Author: Renee Y. Hsia, MD, MSc, Department of Emergency Medicine, University of California, San Francisco, 1001 Potrero Ave, Bldg 5, Ste 6A, Box 1377, San Francisco, CA 94110 ( [email protected] ).

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

Concept and design: Ruxin, Feldmeier, Hsia.

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

Drafting of the manuscript: Ruxin, Feldmeier.

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

Statistical analysis: All authors.

Administrative, technical, or material support: Ruxin, Feldmeier, Hsia.

Supervision: Hsia.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by a University of California, San Francisco, School of Medicine Summer Explore Grant (Ms Ruxin).

Role of the Funder/Sponsor: The funder 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 the Supplement .

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Data show overall ED visit rate 47 visits per 100 people in 2022

by Elana Gotkine

In 2022, the emergency department visit rate was 47 visits per 100 people, according to an August data brief published by the National Center for Health Statistics.

Christopher Cairns, M.P.H., from the National Center for Health Statistics in Hyattsville, Maryland, and colleagues used data from the 2022 National Hospital Ambulatory Medical Care Survey to present characteristics of emergency department visits by age group, sex, race and ethnicity, payment source, and mention of COVID-19.

The researchers found that in 2022, the overall emergency department visit rate was 47 visits per 100 people. Emergency department visit rates were highest for infants younger than 1 year and adults aged 75 years and older (99 and 76 visits per 100 infants and people, respectively).

Among the selected racial and ethnic groups , the emergency department visit rate for Black or African American non-Hispanic people was the highest (91 visits per 100 people).

The lowest emergency department visit rate was seen for patients with private insurance compared with all other primary expected sources of payment that were considered; the highest rate was seen for patients with Medicaid or the Children's Health Insurance Program. A COVID-19 diagnosis was confirmed for 4.8 percent of all emergency department patient visits in 2022.

"In 2022, there were an estimated 155 million emergency department visits in the United States, with a total emergency department visit rate of 47 visits per 100 people," the authors write.

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