We examined the quality of adult epilepsy care using the Quality Indicators in Epilepsy Treatment (QUIET) measure, and variations in quality based on the source of epilepsy care.

Racial and ethnic disparities pervade US health care.^1-9 Many studies show blacks and Hispanics receiving fewer medical services and spending less than whites. For example, minorities receive fewer cardiac procedures, prescriptions for life-saving medications, and narcotic medications for pain relief. Despite efforts by policy makers to address these disparities, they persist.^5,10 At the end of life, however, this pattern may be reversed.¹¹ Several studies have found higher Medicare costs and service use for nonwhites at life's end.^2,12-16 These studies examined differences in sociodemographic and geographic factors as contributors to these disparities. Shugarman et al¹⁴ reported that in the 2 years before the last year of life, spending by blacks was significantly lower. However, in the last year, this deficit “flipped”; estimated final-year spending was 19% higher for blacks than for whites (P = .10). They did not study Hispanics.

Use of emergency medical services (EMS) aids the rapid evaluation and stabilization of patients and serves the critical function of transportation to a suitable emergency department (ED). Proximity is generally believed to be the primary criteria for identifying the ED destination, but capabilities of the ED are also important for specific conditions (eg, stroke, cardiac events, and trauma). The American College of Emergency Physicians recommends that patients “should be transported to the nearest appropriate ED in accordance with applicable laws, regulations and guidelines.”^1(p6) However, little population-level evidence is available to date on the extent to which proximity determines the ED destination.²

Background: The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) use administrative data to detect potentially preventable in-hospital adverse events. However, few studies have determined how accurately the PSIs identify true safety events. Objectives: We examined the criterion validity, specifically the positive predictive value (PPV), of 12 selected PSIs using clinical data abstracted from the Veterans Health Administration (VA) electronic medical record as the gold standard. Methods: We identified PSI-flagged cases from 28 representative hospitals by applying the AHRQ PSI software (v.3.1a) to VA fiscal year 2003 to 2007 administrative data. Trained nurse-abstractors used standardized abstraction tools to review a random sample of flagged medical records (112 records per PSI) for the presence of true adverse events. Interrater reliability was assessed. We evaluated PPVs and associated 95% confidence intervals of each PSI and examined false positive (FP) cases to determine why they were incorrectly flagged and gain insight into how each PSI might be improved. Results: PPVs ranged from 28% (95% CI, 15%−43%) for Postoperative Hip Fracture to 87% (95% CI, 79%−92%) for Postoperative Wound Dehiscence. Common reasons for FPs included conditions that were present on admission (POA), coding errors, and lack of coding specificity. PSIs with the lowest PPVs had the highest proportion of FPs owing to POA. Conclusions: Overall, PPVs were moderate for most of the PSIs. Implementing POA codes and using more specific ICD-9-CM codes would improve their validity. Our results suggest that additional coding improvements are needed before the PSIs evaluated herein are used for hospital reporting or pay for performance.