Publications (15) View all
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Article: Comparing 2 Methods of Assessing 30-Day Readmissions: What is the Impact on Hospital Profiling in the Veterans Health Administration?
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ABSTRACT: BACKGROUND:: The Centers for Medicare and Medicaid Services' (CMS) all-cause readmission measure and the 3M Health Information System Division Potentially Preventable Readmissions (PPR) measure are both used for public reporting. These 2 methods have not been directly compared in terms of how they identify high-performing and low-performing hospitals. OBJECTIVES:: To examine how consistently the CMS and PPR methods identify performance outliers, and explore how the PPR preventability component impacts hospital readmission rates, public reporting on CMS' Hospital Compare website, and pay-for-performance under CMS' Hospital Readmission Reduction Program for 3 conditions (acute myocardial infarction, heart failure, and pneumonia). METHODS:: We applied the CMS all-cause model and the PPR software to VA administrative data to calculate 30-day observed FY08-10 VA hospital readmission rates and hospital profiles. We then tested the effect of preventability on hospital readmission rates and outlier identification for reporting and pay-for-performance by replacing the dependent variable in the CMS all-cause model (Yes/No readmission) with the dichotomous PPR outcome (Yes/No preventable readmission). RESULTS:: The CMS and PPR methods had moderate correlations in readmission rates for each condition. After controlling for all methodological differences but preventability, correlations increased to >90%. The assessment of preventability yielded different outlier results for public reporting in 7% of hospitals; for 30% of hospitals there would be an impact on Hospital Readmission Reduction Program reimbursement rates. CONCLUSIONS:: Despite uncertainty over which readmission measure is superior in evaluating hospital performance, we confirmed that there are differences in CMS-generated and PPR-generated hospital profiles for reporting and pay-for-performance, because of methodological differences and the PPR's preventability component.Medical care 04/2013; · 3.24 Impact Factor -
Article: Surgery volume, quality of care and operative mortality in coronary artery bypass graft surgery: a re-examination using fixed-effects regression
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ABSTRACT: For many surgical procedures, apparent volume–outcome relationships may reflect differences in patient risk-profiles as well as quality of care. As some important patient profile differences may be unobserved, we use fixed effects (FE) regression to estimate the relationship between operative mortality and surgeon and hospital volumes, and compare this method with the more commonly used random effects (RE) regression approach. The 1998 and 1999 Medicare Inpatient and Denominator files for Medicare Fee for Service enrollees aged 65–99. Operative mortality rates are estimated for different surgeon and hospital volume tertiles (high, medium, low) using FE and RE regression methods, adjusted for patient demographics and morbidities. The data were collected by the Centers for Medicare and Medicaid Services (CMS). FE regression estimates that lowest volume tertile hospitals have 1.4 and lowest volume tertile surgeons have 1.6 additional operative deaths (for every 100 CABG surgeries) compared to their highest volume tertile counterparts. The corresponding RE estimates are 0.5 and 1.4 respectively. The substantially higher FE hospital volume effect compared to RE indicates the presence of unobserved “protective” characteristics in lower volume providers, including a less complicated patient profile. Lower hospital and surgeon volumes are associated with substantially higher excess operative mortality from CABG surgeries than previously estimated. KeywordsHospital volume-Surgeon volume-Fixed effects-Random effectsHealth Services and Outcomes Research Methodology 04/2012; 10(1):16-32. -
Article: Longitudinal patterns in survival, comorbidity, healthcare utilization and quality of care among older women following breast cancer diagnosis.
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ABSTRACT: To compare longitudinal patterns of health care utilization and quality of care for other health conditions between breast cancer-surviving older women and a matched cohort without breast cancer. Prospective five-year longitudinal comparison of cases and matched controls. Newly identified breast cancer patients recruited during 1997-1999 from four geographic regions (Los Angeles, CA; Minnesota; North Carolina; and Rhode Island; N = 422) were matched by age, race, baseline comorbidity and zip code location with up to four non-breast-cancer controls (N = 1,656). Survival; numbers of hospitalized days and physician visits; total inpatient and outpatient Medicare payments; guideline monitoring for patients with cardiovascular disease and diabetes, and bone density testing and colorectal cancer screening. Five-year survival was similar for cases and controls (80% and 82%, respectively; p = 0.18). In the first follow-up year, comorbidity burden and health care utilization were higher for cases (p < 0.01), with most differences diminishing over time. However, the number of physician visits was higher for cases (p < 0.01) in every year, driven partly by more cancer and surgical specialist visits. Cases and controls adhered similarly to recommended bone density testing, and monitoring of cardiovascular disease and diabetes; adherence to recommended colorectal cancer screening was better among cases. Breast cancer survivors' health care utilization and disease burden return to pre-diagnosis levels after one year, yet their greater use of outpatient care persists at least five years. Quality of care for other chronic health problems is similar for cases and controls.Journal of General Internal Medicine 10/2010; 25(10):1045-50. · 2.83 Impact Factor -
Article: Erratum to: Longitudinal Patterns in Survival, Comorbidity, Healthcare Utilization and Quality of Care among Older Women Following Breast Cancer Diagnosis.
Journal of General Internal Medicine 09/2010; · 2.83 Impact Factor -
Article: Examining the Impact of the AHRQ Patient Safety Indicators (PSIs) on the Veterans Health Administration: The Case of Readmissions.
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ABSTRACT: BACKGROUND:: By focusing primarily on outcomes in the inpatient setting one may overlook serious adverse events that may occur after discharge (eg, readmissions, mortality) as well as opportunities for improving outpatient care. OBJECTIVE:: Our overall objective was to examine whether experiencing an Agency for Healthcare Research and Quality Patient Safety Indicator (PSI) event in an index medical or surgical hospitalization increased the likelihood of readmission. METHODS:: We applied the Agency for Healthcare Research and Quality PSI software (version 4.1.a) to 2003-2007 Veterans Health Administration inpatient discharge data to generate risk-adjusted PSI rates for 9 individual PSIs and 4 aggregate PSI measures: any PSI event and composite PSIs reflecting "Technical Care," "Continuity of Care," and both surgical and medical care (Mixed). We estimated separate logistic regression models to predict the likelihood of 30-day readmission for individual PSIs, any PSI event, and the 3 composites, adjusting for age, sex, comorbidities, and the occurrence of other PSI(s). RESULTS:: The odds of readmission were 23% higher for index hospitalizations with any PSI event compared with those with no event [confidence interval (CI), 1.19-1.26], and ranged from 22% higher for Iatrogenic Pneumothorax (CI, 1.03-1.45) to 61% higher for Postoperative Wound Dehiscence (CI, 1.27-2.05). For the composites, the odds of readmission ranged from 15% higher for the Technical Care composite (CI, 1.08-1.22) to 37% higher for the Continuity of Care composite (CI, 1.26-1.50). CONCLUSIONS:: Our results suggest that interventions that focus on minimizing preventable inpatient safety events as well as improving coordination of care between and across settings may decrease the likelihood of readmission.Medical care 09/2012; · 3.24 Impact Factor
