No abstract

Payment models that are based on stratified comparisons might result in a more equitable payment system while at the same time providing transparency regarding disparities based on these factors. No model, yet available, discriminates potentially modifiable readmissions from those not subject to intervention highlighting the fact that the optimum readmission rate for any given condition is yet to be identified.

This research investigated the effect of computer-assisted test interpretation (CATI) on physicians' readings of electrocardiograms (ECGs). The authors used an experimental method based on direct observations of 22 cardiologists, each reading 80 ECGs, for a total of 1,760 (of which 1,745 were used in the study). There were 40 sets of clinically-matched pairs of ECGs, one with CATI and one without. Reading time was observed and interpretation accuracy was measured by criterion-referenced aggregate scoring. To control for potential biases, the findings were subjected to multivariate analyses using ordinary least-squares regressions. The impact of CATI on cardiologists' readings of ECGs is demonstrably beneficial: the main empirical conclusion of this study is that, compared with conventional interpretation, the use of computer-assisted interpretation of ECGs cuts physician time by an average of 28% and significantly improves the concordance of the physician's interpretation with the expert benchmark, without increasing the false-positive rate. Moreover, CATI is the most accurate and saves the most time when the ECGs have many unambiguous diagnoses. Given that computers alone cannot perform the task of cardiovascular diagnosis, and that cardiologists' ECG interpretations are greatly enhanced by ubiquitous CATI technology, it appears that the best approach is one that combines person and machine.

Objective. To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally. Data Source. Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database. Study Design. Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-standardized in-hospital mortality rate (RSMR), and assessed hospital characteristics associated with the distinct patterns using multinomial logistic regression. Principal Findings. During 2005-2011, mean hospital RSC decreased from $12,003 to $10,782, while mean hospital RSMR declined from 3.9 to 3.2 percent. We identified five distinct hospital patterns: highest cost and low mortality (3.2 percent of the hospitals), high cost and low mortality (20.4 percent), medium cost and low mortality (34.6 percent), medium cost and high mortality (6.2 percent), and low cost and low mortality (35.6 percent). Longer hospital stay and greater use of intensive care unit and surgical procedures were associated with phenotypes with higher costs or greater mortality. Conclusions. Hospitals vary substantially in the joint longitudinal patterns of cost and mortality, suggesting marked difference in value of care. Understanding determinants of the variation will inform strategies for improving the value of hospital care.