Global Health: Predicting patient cost blooms
Prediction tools for identifying the patients who will accrue the bulk of spending are crucial for many smaller healthcare organizations to succeed under new payment reforms. However, scant progress has been made in improvement of cost-prediction tools for over a decade. Using population health data for over two million residents of Western Denmark between 2004 and 2011, we aimed to improve prediction of high-cost patients—i.e., the top 10% of patients in a sample based on annual health spending.
We evaluated our prediction models on the most recent year of Danish data. We conducted both a whole-population analysis (N=1,557,950), and, considering only those participants who were not in the top 10% of population health spending in the prior year, a separate “cost bloom” analysis (N=1,402,155). Compared to a widely used diagnosis-based prediction tools developed in the US, our best model achieved a 21% improvement in prediction of high-cost spending at the population-level, and a 30% imporvement in the prediction of cost bloom spending.
We expect our study to inform payers and providers, who need better tools to identify future high-cost patients. More details on our study, which involved researchers at the Center for Biomedical Inforamtics Research and the Clinical Excellence Research Center, will appear in a forthcoming manuscript.
