Identification of Emergency Department Visits in Medicare Administrative Claims: Approaches and Implications.

OBJECTIVES: Administrative claims data sets are often used for emergency care research and policy investigations of healthcare resource utilization, acute care practices, and evaluation of quality improvement interventions. Despite the high profile of emergency department (ED) visits in analyses using administrative claims, little work has evaluated the degree to which existing definitions based on claims data accurately captures conventionally defined hospital-based ED services. We sought to construct an operational definition for ED visitation using a comprehensive Medicare data set and to compare this definition to existing operational definitions used by researchers and policymakers. METHODS: We examined four operational definitions of an ED visit commonly used by researchers and policymakers using a 20% sample of the 2012 Medicare Chronic Condition Warehouse (CCW) data set. The CCW data set included all Part A (hospital) and Part B (hospital outpatient, physician) claims for a nationally representative sample of continuously enrolled Medicare fee-for-services beneficiaries. Three definitions were based on published research or existing quality metrics including: 1) provider claims-based definition, 2) facility claims-based definition, and 3) CMS Research Data Assistance Center (ResDAC) definition. In addition, we developed a fourth operational definition (Yale definition) that sought to incorporate additional coding rules for identifying ED visits. We report levels of agreement and disagreement among the four definitions. RESULTS: Of 10,717,786 beneficiaries included in the sample data set, 22% had evidence of ED use during the study year under any of the ED visit definitions. The definition using provider claims identified a total of 4,199,148 ED visits, the facility definition 4,795,057 visits, the ResDAC definition 5,278,980 ED visits, and the Yale definition 5,192,235 ED visits. The Yale definition identified a statistically different (p < 0.05) collection of ED visits than all other definitions including 17% more ED visits than the provider definition and 2% fewer visits than the ResDAC definition. Differences in ED visitation counts between each definition occurred for several reasons including the inclusion of critical care or observation services in the ED, discrepancies between facility and provider billing regulations, and operational decisions of each definition. CONCLUSION: Current operational definitions of ED visitation using administrative claims produce different estimates of ED visitation based on the underlying assumptions applied to billing data and data set availability. Future analyses using administrative claims data should seek to validate specific definitions and inform the development of a consistent, consensus ED visitation definitions to standardize research reporting and the interpretation of policy interventions.

A national study of attrition in general surgery training: which residents leave and where do they go?

OBJECTIVE(S): Implementation of the 80-hour mandate was expected to reduce attrition from general surgery (GS) residency. This is the first quantitative report from a national prospective study of resident/program characteristics associated with attrition. METHODS: Analysis included all categorical GS residents entered on American Board of Surgery residency rosters in 2007 to 2008. Cases of attrition were identified by program report, individually confirmed, and linked to demographic data from the National Study of Expectations and Attitudes of Residents in Surgery administered January 2008. RESULTS: All surgical categorical GS residents active on the 2007-2008 resident rosters (N = 6,303) were analyzed for attrition. Complete National Study of Expectations and Attitudes of Residents in Surgery demographic information was available for 3959; the total and survey groups were similar with regard to important characteristics. About 3% of US categorical residents resigned in 2007 to 2008, and 0.4% had contracts terminated. Across all years (including research), there was a 19.5% cumulative risk of resignation. Attrition was highest in PGY-1 (5.9%), PGY-2 (4.3%), and research year(s) (3.9%). Women were no more likely to leave programs than men (2.1% vs. 1.9%). Of several program/resident variables examined, postgraduate year-level was the only independent predictor of attrition in multivariate analysis. Residents who left GS whose plans were known most often pursued nonsurgical residencies (62%), particularly anesthesiology (21%) and radiology (11%). Only 13% left for surgical specialties. CONCLUSIONS: Attrition rates are high despite mandated work hour reductions; 1 in 5 GS categorical residents resigns, and most pursue nonsurgical careers. Demographic factors, aside from postgraduate year do not appear predictive. Residents are at risk for attrition early in training and during research, and this could afford educators a target for intervention.

Use of statistical analysis in the biomedical informatics literature.

Statistics is an essential aspect of biomedical informatics. To examine the use of statistics in informatics research, a literature review of recent articles in two high-impact factor biomedical informatics journals, the Journal of American Medical Informatics Association (JAMIA) and the International Journal of Medical Informatics was conducted. The use of statistical methods in each paper was examined. Articles of original investigations from 2000 to 2007 were reviewed. For each journal, the results by statistical methods were analyzed as: descriptive, elementary, multivariable, other regression, machine learning, and other statistics. For both journals, descriptive statistics were most often used. Elementary statistics such as t tests, chi(2), and Wilcoxon tests were much more frequent in JAMIA, while machine learning approaches such as decision trees and support vector machines were similar in occurrence across the journals. Also, the use of diagnostic statistics such as sensitivity, specificity, precision, and recall, was more frequent in JAMIA. These results highlight the use of statistics in informatics and the need for biomedical informatics scientists to have, as a minimum, proficiency in descriptive and elementary statistics.