Objective. To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public. Data Sources/Setting. Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011. Study Design. Cohort analysis using a Bayesian approach, comparing the present assumptions of HC (using a constant mean and constant variance for all hospital random effects), versus an expanded model that allows for the inclusion of hospital characteristics to permit the data to determine whether they vary with attributes of hospitals, such as volume, capabilities, and staffing. Hospital predictions are then created using directly standardized estimates to facilitate comparisons between hospitals. Data Collection/Extraction Methods. Medicare fee-for-service claims. Principal Findings. Our model that included hospital characteristics produces very different predictions from the current HC model, with higher predicted mortality rates at hospitals with lower volume and worse characteristics. Using Chicago as an example, the expanded model would advise patients against seeking treatment at the smallest hospitals with worse technology and staffing. Conclusion. To aid patients when selecting between hospitals, the Centers for Medicare and Medicaid Services (CMS) should improve the HC model by permitting its predictions to vary systematically with hospital attributes such as volume, capabilities, and staffing.

Objective. To develop a method to allow a hospital to compare its performance using its entire patient population to the outcomes of very similar patients treated elsewhere. Data Sources/Setting. Medicare claims in orthopedics and common general, gynecologic, and urologic surgery from Illinois, New York, and Texas from 2004 to 2006. Study Design. Using two example "focal" hospitals, each hospital's patients were matched to 10 very similar patients selected from 619 other hospitals. Data Collection/Extraction Methods. All patients were used at each focal hospital, and we found the 10 closest matched patients from control hospitals with exactly the same principal procedure as each focal patient. Principal Findings. We achieved exact matches on all procedures and very close matches for other patient characteristics for both hospitals. There were few to no differences between each hospital's patients and their matched control patients on most patient characteristics, yet large and significant differences were observed for mortality, failure-to-rescue, and cost. Conclusion. Indirect standardization matching can produce fair audits of quality and cost, allowing for a comprehensive, transparent, and relevant assessment of all patients at a focal hospital. With this approach, hospitals will be better able to benchmark their performance and determine where quality improvement is most needed. Key Words. Quality of care, outcomes research, health care research, costThe aim of this paper was to develop a new tool for examining how well hospitals treat their patients and to aid hospitals in identifying subgroups of patients displaying especially good or bad outcomes compared to matched patients treated at other hospitals. We will base this evaluation on a new approach we call indirect standardization matching (ISM).

Laboratory quality control (QC) and quality assurance (QA) programs have been extended and applied to point-of-care testing (POCT) both in hospitals and in the community. Some POCT analyzers have an integrated QC requirement and even operator lockout on QC omission or failure. Other POCT QC and QA schemes are paper based, the single biggest disadvantage being the time lag between the performance of the test and finding out if the achieved result was ''correct.'' This article describes an ''on-line'' quality program for POCT through the hospital intranet. The intranet site was developed to provide improved management of the QC and QA programs by the POCT team of all POCT systems in use, ''real time'' report to the POCT operator on input of QC or QA results, and POCT analyzer ''operating instructions,'' ''troubleshooting,'' and educational material. This intranet site provides the basis of a total quality program for hospital/clinic POCT. It currently covers QC and QA for capillary blood glucose, dipstick urinalysis, blood gases, hemoglobin, pregnancy test, and fecal occult blood (FOBs) but can be extended to include prothrombin time, activated clotting time (ACT), oximetry, and other POCT systems. The system is available in demonstration mode at www.poctquality.co.uk.