Inpatient admissions from the ED for adults with injuries: the role of clinical and nonclinical factors.

INTRODUCTION: Inpatient hospital costs represent nearly a third of heath care spending. The proportion of inpatients visits that originate in the emergency department (ED) has been growing, approaching half of all inpatient admissions. Injury is the most common reason for adult ED visits, representing nearly one-quarter of all ED visits. OBJECTIVE: The objective was to explore the association of clinical and nonclinical factors with the decision to admit ED patients with injury. RESEARCH DESIGN AND PARTICIPANTS: This is a retrospective cohort study of injury-related ED encounters by adults in select states in 2009. We limited the study to ED visits of persons with moderately severe injuries. We used logistic regression to calculate the marginal effects, estimating 4 equations to account for different risk patterns for older and younger adults, and types of injuries. Regression models controlled for comorbidities, injury characteristics, demographic characteristics, and state fixed effects. RESULTS: Injury location, type, and mechanism and comorbidities had large effects on hospitalization rates as expected. We found higher inpatient admission rates by level of trauma center designation and hospital size, but findings differed by age and type of injury. For younger adults, patients with private insurance and patients who traveled more than 30 miles were more likely to be admitted. CONCLUSIONS: There is great variation in inpatient admission decisions for moderately injured patients in the ED. Decisions appear to be dominated by clinical factors such as injury characteristics and comorbidities; however, nonclinical factors, such as type of insurance, hospital size, and trauma center designation, also play an important role.

Emergency department transfers and transfer relationships in United States hospitals.

OBJECTIVES: The objective was to describe transfers out of hospital-based emergency departments (EDs) in the United States and to identify different characteristics of sending and receiving hospitals, travel distance during transfer, disposition on arrival to the second hospital, and median number of transfer partners among sending hospitals. METHODS: Emergency department records were linked at transferring hospitals to ED and inpatient records at receiving hospitals in nine U.S. states using the 2010 Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases and State Inpatient Databases, the American Hospital Association Annual Survey, and the Trauma Information Exchange Program. Using the Clinical Classification Software (CCS) to categorize conditions, the 50 disease categories with the highest transfer rates were studied, and these were then placed into nine clinical groups. Records were included where both sending and receiving records were available; these data were tabulated to describe ED transfer patterns, hospital-to-hospital distances, final patient disposition, and number of transfer partners. RESULTS: A total of 97,021 ED transfer encounters were included in the analysis from the 50 highest transfer rate disease categories. Among these, transfer rates ranged from 1% to 13%. Circulatory conditions made up about half of all transfers. Receiving hospitals were more likely to be nonprofit, teaching, trauma, and urban and have more beds with greater specialty coverage and more advanced diagnostic and therapeutic resources. The median transfer distance was 23 miles, with 25% traveling more than 40 to 50 miles. About 8% of transferred encounters were discharged from the second ED, but that varied from 0.6% to 53% across the 50 conditions. Sending hospitals had a median of seven transfer partners across all conditions and between one and four per clinical group. CONCLUSIONS: Among high-transfer conditions in U.S. EDs, patients are often transferred great distances, more commonly to large teaching hospitals with greater resources. The large number of transfer partners indicates a possible lack of stable transfer relationships between U.S. hospitals.

The transfer instability index: a novel metric of emergency department transfer relationships.

OBJECTIVES: In this study, the objective was to characterize emergency department (ED) transfer relationships and study the factors that predict the stability of those relationships. A metric is derived for ED transfer relationships that may be useful in assessing emergency care regionalization and as a resource for future emergency medicine research. METHODS: Emergency department records at transferring hospitals were linked to ED and inpatient records at receiving hospitals in nine U.S. states using the 2010 Healthcare Cost and Utilization Project State Emergency Department Databases and State Inpatient Databases, the American Hospital Association Annual Survey, and the Trauma Information Exchange Program. Using the Clinical Classification Software to categorize conditions, high transfer rate conditions were placed into nine clinical groups. The authors created a new measure, the "transfer instability index," which estimates the effective number of "transfer partners" for each sending ED: this is designed to measure the stability of outgoing transfer relationships, where higher values of the index indicate less stable relationships. The index provides a measure of how many hospitals a transferring hospital sends its patients to (weighted by how often each transfer partner is used). Regression was used to analyze factors associated with higher values of the index. RESULTS: Sending hospitals had a median of 3.5 effective transfer partners across all conditions. The calculated transfer instability indices varied from 1 to 2.4 across disease categories. In general, higher index values were associated with treating a higher proportion of publicly insured patients: 10 and 12% increases in the Medicare and Medicaid share of ED encounters, respectively, were associated with 10 and 14% increases in the effective number of transfer partners. This public insurance effect held while studying all conditions together as well as within individual disease categories, such as cardiac, neurologic, and traumatic conditions. CONCLUSIONS: United States EDs that transfer patients to other hospitals often have multiple transfer partners. The stability of the transfer relationship, assessed by the transfer instability index, differs by condition. Less stable transfer relationships (i.e., hospitals with greater numbers of transfer partners) were more common in EDs with higher proportions of publicly insured patients.

The association of hospital cost-inefficiency with certificate-of-need regulation.

Certificate-of-need (CON) regulations can promote hospital efficiency by reducing duplication of services; however, there are practical and theoretical reasons why they might be ineffective, and the empirical evidence generated has been mixed. This study compares the cost-inefficiency of urban, acute care hospitals in states with CON regulations against those in states without CON requirements. Stochastic frontier analysis was performed on pooled time-series, cross-sectional data from 1,552 hospitals in 37 states for the period 2005 to 2009 with controls for variations in hospital product mix, quality, and patient burden of illness. Average estimated cost-inefficiency was less in CON states (8.10%) than in non-CON states (12.46%). Results suggest that CON regulation may be an effective policy instrument in an era of a new medical arms race. However, broader analysis of the effects of CON regulation on efficiency, quality, access, prices, and innovation is needed before a policy recommendation can be made.

Admit or transfer? The role of insurance in high-transfer-rate medical conditions in the emergency department.

STUDY OBJECTIVE: We study the association of payer status with odds of transfer compared with admission from the emergency department (ED) for multiple diagnoses with a high percentage of transfers. METHODS: This was a retrospective study of adult ED encounters using the Healthcare Cost and Utilization Project 2010 Nationwide Emergency Department Sample. We used the Clinical Classification Software to identify disease categories with 5% or more encounters resulting in transfer (27 categories; 3.7 million encounters based on survey weights). We sorted encounters by condition into 12 groups according to expected medical or surgical specialist needs. We used logistic regression to assess the role of payer status on odds of transfer compared with admission and report adjusted odds ratios (ORs). RESULTS: Among high-transfer conditions in 2010, uninsured patients had double the odds of transfer compared with privately insured patients (OR 2.12; 95% confidence interval [CI] 1.72 to 2.62). Medicaid patients were also more likely to be transferred (OR 1.2; 95% CI 1.04 to 1.38). Uninsured patients had higher odds of transfer in all specialist categories (significant in 9 of 12). The categories with the highest odds of transfer for the uninsured included nephrology (OR 2.44; 95% CI 1.07 to 5.55), psychiatry (OR 2.26; 95% CI 1.65 to 3.25), and hematology-oncology (OR 2.21; 95% CI 1.50 to 3.25); the highest for Medicaid were general surgery (OR 1.61; 95% CI 1.09 to 1.83), hematology-oncology (OR 1.55; 95% CI 1.05 to 2.30), and vascular surgery (OR 1.55; 95% CI 1.02 to 2.28). CONCLUSION: Insurance status appears to play a role in ED disposition (transfer versus admission) for many high-transfer conditions.

Variation in emergency department admission rates across the United States.

There were more than 19 million hospitalizations in 2008 from hospital-based emergency departments (EDs), representing nearly 50% of all U.S. admissions. Factors related to variation in hospital-level ED admission rates are unknown. Generalized linear models were used to assess patient-, hospital-, and community-level factors associated with ED admission rates across a sample of U.S. hospitals using Healthcare Cost and Utilization Project data. In 1,376 EDs, the mean ED admission rate, when defined as direct admissions and also transfers from one ED to another hospital, was 17.5% and varied from 9.8% to 25.8% at the 10th and 90th percentiles. Higher proportions of Medicare and uninsured patients, more inpatient beds, lower ED volumes, for-profit ownership, trauma center status, and higher hospital occupancy rates were associated with higher ED admission rates. Also, hospitals in counties with fewer primary care physicians per capita and higher county-level ED admission rates had higher ED admission rates.

Duration of patients' visits to the hospital emergency department.

BACKGROUND: Length of stay is an important indicator of quality of care in Emergency Departments (ED). This study explores the duration of patients' visits to the ED for which they are treated and released (T&R). METHODS: Retrospective data analysis and multivariate regression analysis were conducted to investigate the duration of T&R ED visits. Duration for each visit was computed by taking the difference between admission and discharge times. The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) for 2008 were used in the analysis. RESULTS: The mean duration of T&R ED visit was 195.7 minutes. The average duration of ED visits increased from 8 a.m. until noon, then decreased until midnight at which we observed an approximately 70-minute spike in average duration. We found a substantial difference in mean duration of ED visits (over 90 minutes) between Mondays and other weekdays during the transition time from the evening of the day before to the early morning hours. Black / African American patients had a 21.4-minute longer mean duration of visits compared to white patients. The mean duration of visits at teaching hospitals was substantially longer than at non-teaching hospitals (243.8 versus 175.6 minutes). Hospitals with large bed size were associated with longer duration of visits (222.2 minutes) when compared to hospitals with small bed size (172.4 minutes) or those with medium bed size (166.5 minutes). The risk-adjusted results show that mean duration of visits on Mondays are longer by about 4 and 9 percents when compared to mean duration of visits on non-Monday workdays and weekends, respectively. CONCLUSIONS: The duration of T&R ED visits varied significantly by admission hour, day of the week, patient volume, patient characteristics, hospital characteristics and area characteristics.

Achieving efficiency in crowded emergency departments: a research agenda.

In 2011, Academic Emergency Medicine convened a consensus conference entitled "Interventions to Assure Quality in the Crowded Emergency Department." This article, a product of the breakout session on "interventions to safeguard efficiency of care," explores various elements of the research agenda on efficiency and quality in crowded emergency departments (EDs). The authors discuss four areas identified as critical to achieving progress in the research agenda for improving ED efficiency: 1) What measures can be used to understand and improve the efficiency and quality of interventions in the ED? 2) Which factors outside of the ED's control affect ED efficiency? 3) How do workforce factors affect ED efficiency? 4) How do ED design, patient flow structures, and use of technology affect efficiency? Filling these knowledge gaps is vital to identifying interventions that improve the delivery of emergency care in all EDs.

What have we learned from the application of stochastic frontier analysis to U.S. hospitals?

This article focuses on the lessons learned from stochastic frontier analysis studies of U.S. hospitals, of which at least 27 have been published. A brief discussion of frontier techniques is provided, but a technical review of the literature is not included because overviews of estimation issues have been published recently. The primary focus is on the correlates of hospital inefficiency. In addition to examining the association of market pressures and hospital inefficiency, the authors also examined the relationship between inefficiency and hospital behavior (e.g., hospital exits) and inefficiency and other measures of hospital performance (e.g., outcome measures of quality). The authors found that consensus is emerging on the relationship of some factors to hospital efficiency; however, further research is needed to better understand others. The application of stochastic frontier analysis to specific policy issues is in its infancy; however, the methodology holds promise for being useful in certain contexts.

Translating frontiers into practice: taking the next steps toward improving hospital efficiency.

Frontier techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), have been used to measure health care provider efficiency in hundreds of published studies. Although these methods have the potential to be useful to decision makers, their utility is limited by both methodological questions concerning their application, as well as some disconnect between the information they provide and the insight sought by decision makers. The articles in this special issue focus on the application of DEA and SFA to hospitals with the hope of making these techniques more accurate and accessible to end users. This introduction to the special issue highlights the importance of measuring the efficiency of health care providers, provides a background on frontier techniques, contains an overview of the articles in the special issue, and suggests a research agenda for DEA and SFA.

Defining and measuring successful emergency care networks: a research agenda.

The demands on emergency services have grown relentlessly, and the Institute of Medicine (IOM) has asserted the need for "regionalized, coordinated, and accountable emergency care systems throughout the country." There are large gaps in the evidence base needed to fix the problem of how emergency care is organized and delivered, and science is urgently needed to define and measure success in the emerging network of emergency care. In 2010, Academic Emergency Medicine convened a consensus conference entitled "Beyond Regionalization: Integrated Networks of Emergency Care." This article is a product of the conference breakout session on "Defining and Measuring Successful Networks"; it explores the concept of integrated emergency care delivery and prioritizes a research agenda for how to best define and measure successful networks of emergency care. The authors discuss five key areas: 1) the fundamental metrics that are needed to measure networks across time-sensitive and non-time-sensitive conditions; 2) how networks can be scalable and nimble and can be creative in terms of best practices; 3) the potential unintended consequences of networks of emergency care; 4) the development of large-scale, yet feasible, network data systems; and 5) the linkage of data systems across the disease course. These knowledge gaps must be filled to improve the quality and efficiency of emergency care and to fulfill the IOM's vision of regionalized, coordinated, and accountable emergency care systems.

Inefficiency differences between critical access hospitals and prospectively paid rural hospitals.

The Medicare prospective payment system (PPS) contains incentives for hospitals to improve efficiency by placing them at financial risk to earn a positive margin on services rendered to Medicare patients. Concerns about the financial viability of small rural hospitals led to the implementation of the Medicare Rural Hospital Flexibility Program (Flex Program) of 1997, which allows facilities designated as critical access hospitals (CAHs) to be paid on a reasonable cost basis for inpatient and outpatient services. This article compares the cost inefficiency of CAHs with that of nonconverting rural hospitals to contrast the performance of hospitals operating under the different payment systems. Stochastic frontier analysis (SFA) was used to estimate cost inefficiency. Analysis was performed on pooled time-series, cross-sectional data from thirty-four states for the period 1997-2004. Average estimated cost inefficiency was greater in CAHs (15.9 percent) than in nonconverting rural hospitals (10.3 percent). Further, there was a positive association between length of time in the CAH program and estimated cost inefficiency. CAHs exhibited poorer values for a number of proxy measures for efficiency, including expenses per admission and labor productivity (full-time-equivalent employees per outpatient-adjusted admission). Non-CAH rural hospitals had a stronger correlation between cost inefficiency and operating margin than CAH facilities did.

Emergency department care in the United States: a profile of national data sources.

STUDY OBJECTIVE: Emergency departments (EDs) are an integral part of the US health care system, and yet national data sources on the care received in the ED are poorly understood, thereby limiting their usefulness for analyses. We provide a comparison of data sources that can be used to examine utilization and quality of care in the ED nationally. DATA SOURCES AND COMPARISONS: This article compares 7 data sources available in 2005 for conducting analyses of ED encounters: the American Hospital Association Annual Survey Database(), Hospital Market Profiling Solution(c), National Emergency Department Inventory, Nationwide Emergency Department Sample, National Hospital Ambulatory Medical Care Survey, National Electronic Injury Surveillance System-All-Injury Program, and the National Health Interview Survey. In addition to describing the type and scope of data collection, available characteristics, and sponsor of the ED data sources, we compare (where possible) estimates of the total number of EDs, national and regional volume of ED visits, national and regional admission rates (percentage of ED visits resulting in hospital admission), patient characteristics, hospital characteristics, and reasons for visit generated by the various data sources. MAJOR FINDINGS: The different data sources yielded estimates of the number of EDs that ranged from 4,609 to 4,884 and the number of ED encounters from more than 109 million to more than 116 million. Admission rates across data sources varied from 12.0% to 15.3%. Although comparisons of the 7 data sources were somewhat limited by differences in available information and operational definitions, variation in estimates of utilization and patterns of care existed by region, expected payer, and patient and hospital characteristics. The rankings and estimates of the top 5 first-listed conditions seen in the ED are relatively consistent between the 2 data sources with diagnoses, although the Nationwide Emergency Department Sample estimates 1.3 to 5.8 times more ED visits for each chronic and acute all-listed condition examined relative to the National Hospital Ambulatory Medical Care Survey. CONCLUSION: Each of the data sources described in this article has unique advantages and disadvantages when used to examine patterns of ED care, making the different data sources appropriate for different applications. Analysts should select a data source according to its construction and should bear in mind its strengths and weaknesses in drawing conclusions based on the estimates it yields.

The effects of hospital competition on inpatient quality of care.

Existing empirical studies have produced inconclusive, and sometimes contradictory, findings on the effects of hospital competition on inpatient quality of care. These inconsistencies may be due to the use of different methodologies, hospital competition measures, and hospital quality measures. This paper applies the Quality Indicator software from the Agency for Healthcare Research and Quality to the 1997 Healthcare Cost and Utilization Project State Inpatient Databases to create three versions (i.e., observed, risk-adjusted, and "smoothed") of 38 distinct measures of inpatient quality. The relationship between 12 different hospital competition measures and these quality measures are assessed, using ordinary least squares, two-step efficient generalized method of moments, and negative binomial regression techniques. We find that across estimation strategies, hospital competition has an impact on a number of hospital quality measures. However, the effect is not unidirectional: some indicators show improvements in hospital quality with greater levels of competition, some show decreases in hospital quality, and others are unaffected. We provide hypotheses based on emerging areas of research that could explain these findings, but inconsistencies remain.

Hospital quality, efficiency, and input slack differentials.

OBJECTIVE: To use an advance in data envelopment analysis (DEA) called congestion analysis to assess the trade-offs between quality and efficiency in U.S. hospitals. STUDY SETTING: Urban U.S. hospitals in 34 states operating in 2004. STUDY DESIGN AND DATA COLLECTION: Input and output data from 1,377 urban hospitals were taken from the American Hospital Association Annual Survey and the Medicare Cost Reports. Nurse-sensitive measures of quality came from the application of the Patient Safety Indicator (PSI) module of the Agency for Healthcare Research and Quality (AHRQ) Quality Indicator software to State Inpatient Databases (SID) provided by the Healthcare Cost and Utilization Project (HCUP). DATA ANALYSIS: In the first step of the study, hospitals' relative output-based efficiency was determined in order to obtain a measure of congestion (i.e., the productivity loss due to the occurrence of patient safety events). The outputs were adjusted to account for this productivity loss, and a second DEA was performed to obtain input slack values. Differences in slack values between unadjusted and adjusted outputs were used to measure either relative inefficiency or a need for quality improvement. PRINCIPAL FINDINGS: Overall, the hospitals in our sample could increase the total amount of outputs produced by an average of 26 percent by eliminating inefficiency. About 3 percent of this inefficiency can be attributed to congestion. Analysis of subsamples showed that teaching hospitals experienced no congestion loss. We found that quality of care could be improved by increasing the number of labor inputs in low-quality hospitals, whereas high-quality hospitals tended to have slack on personnel. CONCLUSIONS: Results suggest that reallocation of resources could increase the relative quality among hospitals in our sample. Further, higher quality in some dimensions of care need not be achieved as a result of higher costs or through reduced access to health care.

Measuring hospital inefficiency: the effects of controlling for quality and patient burden of illness.

OBJECTIVE: To assess the impact of employing a variety of controls for hospital quality and patient burden of illness on the mean estimated inefficiency and relative ranking of hospitals generated by stochastic frontier analysis (SFA). STUDY SETTING: This study included urban U.S. hospitals in 20 states operating in 2001. DATA DESIGN/DATA COLLECTION: We took hospital data for 1,290 hospitals from the American Hospital Association Annual Survey and the Medicare Cost Reports. We employed a variety of controls for hospital quality and patient burden of illness. Among the variables we used were a subset of the quality indicators generated from the application of the Patient Safety Indicator and Inpatient Quality Indicator modules of the Agency for Healthcare Research and Quality, Quality Indicator software to the Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases. Measures of a component of patient burden of illness came from the application of the Comorbidity Software to HCUP data. DATA ANALYSIS: We used SFA to estimate hospital cost-inefficiency. We tested key assumptions of the SFA model with likelihood ratio tests. PRINCIPAL FINDINGS: The measures produced by the Comorbidity Software appear to account for variations in patient burden of illness that had previously been masquerading as inefficiency. Outcome measures of quality can provide useful insight into a hospital's operations but may have little impact on estimated inefficiency once controls for structural quality and patient burden of illness have been employed. CONCLUSIONS: Choices about controlling for quality and patient burden of illness can have a nontrivial impact on mean estimated hospital inefficiency and the relative ranking of hospitals generated by SFA.

Stochastic frontier analysis of hospital inefficiency: a review of empirical issues and an assessment of robustness.

Twenty stochastic frontier analysis (SFA) studies of hospital inefficiency in the United States were analyzed. Results from best-practice methods were compared against previously used methods in hospital studies to ascertain the robustness of SFA in estimating cost inefficiency. To compare past studies and analyze new data, SFA methods were varied by (a) the assumptions of the structure of costs and distribution of the error term, (b) inclusion of quality and product descriptor measures, and (c) use of simultaneous and two-stage estimation techniques. SFA results were relatively insensitive to several model variations.