Herbert S. Wong

Selection bias in HMOs: a review of the evidence.

Early reviews found that health maintenance organizations (HMOs) attracted healthier beneficiaries in the Medicare program and healthier employees in the market for employer-based insurance. This review finds that HMOs still attract healthier Medicare beneficiaries, that HMOs no longer attract healthier employees, and that HMOs attract healthier Medicaid recipients. This review also found conflicting evidence about whether Medicare HMOs are overpaid, no evidence that HMOs are overpaid in the market for employer-based insurance, and evidence that concerns about overpaying Medicaid HMOs have diminished because many states are adopting mandatory programs.

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.

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 hospitals 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.

Estimating Inpatient Hospital Prices from State Administrative Data and Hospital Financial Reports

OBJECTIVE To develop a tool for estimating hospital-specific inpatient prices for major payers. DATA SOURCES AHRQ Healthcare Cost and Utilization Project State Inpatient Databases and complete hospital financial reporting of revenues mandated in 10 states for 2006. STUDY DESIGN Hospital discharge records and hospital financial information were merged to estimate revenue per stay by payer. Estimated prices were validated against other data sources. PRINCIPAL FINDINGS Hospital prices can be reasonably estimated for 10 geographically diverse states. All-payer price-to-charge ratios, an intermediate step in estimating prices, compare favorably to cost-to-charge ratios. Estimated prices also compare well with Medicare, MarketScan private insurance, and the Medical Expenditure Panel Survey prices for major payers, given limitations of each dataset. CONCLUSIONS Public reporting of prices is a consumer resource in making decisions about health care treatment; for self-pay patients, they can provide leverage in negotiating discounts off of charges. Researchers can also use prices to increase understanding of the level and causes of price differentials among geographic areas. Prices by payer expand investigational tools available to study the interaction of inpatient hospital price setting among public and private payers--an important asset as the payer mix changes with the implementation of the Affordable Care Act.

Duration of patients’ visits to the hospital emergency department

BackgroundLength 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).MethodsRetrospective 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.ResultsThe 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.ConclusionsThe duration of T&R ED visits varied significantly by admission hour, day of the week, patient volume, patient characteristics, hospital characteristics and area characteristics.

The Effects of US Hospital Consolidations on Hospital Quality

Abstract The spate of hospital consolidations that occurred in the late 1990s and early 2000s had a profound impact on the US hospital industry. However, only two published studies using data from five states examined the effects of these consolidations on inpatient quality of care. This paper examines the impacts of hospital consolidations that occurred in 1999 and 2000 in 16 states on 25 measures of quality using pre–post, difference‐in‐differences models. We categorize hospitals participating in consolidations as acquiring institutions, target institutions, or participants in a “merger of equals”. We find that the quality effects of consolidations differ by the hospital’s role and the quality measure used. Acquiring hospitals experienced significantly improved quality with respect to iatrogenic pneumothorax and postoperative hemorrhage or hematoma, but the quality impacts for target hospitals and “mergers of equals” were mixed. Hospital consolidations appear to have complex, inconsistent effects on quality, as measured using all‐payer administrative data, suggesting the need for antitrust agencies to conduct prospective and retrospective reviews of individual mergers.

Provider Competition and Health Care Quality: Challenges and Opportunities for Research

On May 28, 2003, the Agency for Healthcare Research and Quality and the Federal Trade Commission co-sponsored an invitational conference entitled, “Provider Competition and Quality: Latest Findings and Implications for the Next Generation of Research.” The main objectives of this conference were to share and discuss the latest findings on provider competition and quality, to identify implications for antitrust policy, and to develop an agenda for further research in this area. While it is impossible to completely capture the rich exchange of ideas and perspectives that transpired at the conference, we highlight several key themes that emerged and present a research agenda to guide future investigations.

Geographic variation in cesarean delivery in the United States by payer

BackgroundThe rate of cesarean delivery in the United States is variable across geographic areas. The aims of this study are two-fold: (1) to determine whether the geographic variation in cesarean delivery rate is consistent for private insurance and Medicaid (2) to identify the patient, population, and market factors associated with cesarean rate and determine if these factors vary by payer.MethodsWe used the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) to measure the cesarean rate at the Core-Based Statistical Area (CBSA) level. We linked the hospitalization data to data from other national sources to measure population and market characteristics. We calculated unadjusted and risk-adjusted CBSA cesarean rates by payer. For the second aim, we estimated a hierarchical logistical model with the hospitalization as the unit of analysis to determine the factors associated with cesarean delivery.ResultsThe average CBSA cesarean rate for women with private insurance was higher (18.9 percent) than for women with Medicaid (16.4 percent). The factors predicting cesarean rate were largely consistent across payers, with the following exceptions: women under age 18 had a greater likelihood of cesarean section if they had Medicaid but had a greater likelihood of vaginal birth if they had private insurance; Asian and Native American women with private insurance had a greater likelihood of cesarean section but Asian and Native American women with Medicaid had a greater likelihood of vaginal birth. The percent African American in the population predicted increased cesarean rates for private insurance only; the number of acute care beds per capita predicted increased cesarean rate for women with Medicaid but not women with private insurance. Further we found the number of obstetricians/gynecologists per capita predicted increased cesarean rate for women with private insurance only, and the number of midwives per capita predicted increased vaginal birth rate for women with private insurance only.ConclusionsFactors associated with geographic variation in cesarean delivery, a frequent and high-resource inpatient procedure, vary somewhat by payer. Using this information to identify areas for intervention is key to improving quality of care and reducing healthcare costs.

Predicting inpatient hospital payments in the United States: a retrospective analysis

BackgroundThe Affordable Care Act (ACA) has increased rates of public and private health insurance in the United States. Increasing coverage could raise hospital revenue and reduce the need to shift costs to insured patients. The consequences of ACA on hospital revenues could be examined if payments were known for most hospitals in the United States. Actual payment data are considered confidential, however, and only charges are widely available. Payment-to-charge ratios (PCRs), which convert hospital charges to an estimated payment, have been estimated for hospitals in 10 states. Here we evaluated whether PCRs can be predicted for hospitals in states that do not provide detailed financial data.MethodsWe predicted PCRs for 5 payer categories for over 1,000 community hospitals in 10 states as a function of state, market, hospital, and patient characteristics. Data sources included the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases, HCUP Hospital Market Structure file, Medicare Provider of Service file, and state information from several sources. We performed out-of-sample prediction to determine the magnitude of prediction errors by payer category.ResultsMany individual, hospital, and state factors were significant predictors of PCRs. Root mean squared error of prediction ranged from 32 to over 100 % of the mean and varied considerably by which states were included or predicted. The cost-to-charge ratio (CCR) was highly correlated with PCRs for Medicare, Medicaid, and private insurance but not for self-pay or other insurance categories.ConclusionsInpatient payments can be estimated with modest accuracy for community hospital stays funded by Medicare, Medicaid, and private insurance. They improve upon CCRs by allowing separate estimation by payer type. PCRs are currently the only approach to estimating fee-for-service payments for privately insured stays, which represent a sizable proportion of stays for individuals under age 65. Additional research is needed to improve the predictive accuracy of the models for all payers.

Patient Factors Contributing to Variation in Same-Hospital Readmission Rate

The Centers for Medicare & Medicaid Services Hospital Readmission Reduction Program and the Centers for Medicare & Medicaid Innovations Bundled Payments for Care Improvement Initiative hold hospitals accountable for readmissions that occur at other hospitals. A few studies have described the extent to which hospital readmissions occur at the original place of treatment (i.e., same-hospital readmissions). This study uses data from 16 states to describe variation in same-hospital readmissions by patient characteristics across multiple conditions. We found that the majority of 30-day readmissions occur at the same hospital, although rates varied considerably by condition. A significant number of hospitals had very low rates of same-hospital readmissions, meaning that the majority of their readmissions went to other hospitals. Future research should examine why some hospitals are able to retain patients for a same-hospital readmission and others are not.