“Phenotyping” Hospital Value of Care for Patients with Heart Failure

Abstract

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.

Big Data And New Knowledge In Medicine: The Thinking, Training, And Tools Needed For A Learning Health System

“…[28] We determined value phenotypes among groups of hospitals, exposing similarities among institutions that, on the surface, did not appear to have much in common. [27,28] The next step is to understand what hidden factors influenced the similar performance.…”

“…[28] We determined value phenotypes among groups of hospitals, exposing similarities among institutions that, on the surface, did not appear to have much in common. [27,28] The next step is to understand what hidden factors influenced the similar performance. This type of analysis opens the way for investigations of the determinants of these differences, perhaps leveraging methods that have illuminated factors that produce positive deviance and ultimately developing interventions to improve performance.…”

Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research

“…Overall, we are interested in clustering POs in our sample frame according to the trajectories of TCC with the ultimate goal of stratifying the sample according to TCC trajectory shape and level. The literature contains dozens of clustering methods, including some, such as developmental trajectory mixture modeling that have been applied to examining hospital‐level cost and care outcomes . While few, if any, of these methods were developed for sampling purposes any of them could be used to segment the sampling frame for purposes of stratification for sample selection.…”

Hospital Phenotypes in the Management of Patients Admitted for Acute Myocardial Infarction

“…Methods for estimating in-hospital cost are detailed elsewhere (3, 13). Briefly, we used accounting cost data available in the Premier database and removed geographic variation in input prices by applying average unit cost across all hospitals for each service item by year.…”

“…Using advanced analytics of pattern recognition, recent big data research revealed important similarities and differences among hospitals in provision of care (1-3). In contrast with conventional studies that analyze how hospitals deviate from an average tendency in care, the approach of pattern recognition explores whether there are distinct “phenotypes” among hospitals, representing fundamentally different patterns of care (4).…”