@Article{info:doi/10.2196/jmir.4976,
author="Hu, Zhongkai
and Hao, Shiying
and Jin, Bo
and Shin, Andrew Young
and Zhu, Chunqing
and Huang, Min
and Wang, Yue
and Zheng, Le
and Dai, Dorothy
and Culver, Devore S
and Alfreds, Shaun T
and Rogow, Todd
and Stearns, Frank
and Sylvester, Karl G
and Widen, Eric
and Ling, Xuefeng",
title="Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study",
journal="J Med Internet Res",
year="2015",
month="Sep",
day="22",
volume="17",
number="9",
pages="e219",
keywords="health care costs; electronic medical record; prospective studies; statistical data analysis; risk assessment",
abstract="Background: The increasing rate of health care expenditures in the United States has placed a significant burden on the nation's economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. Objective: This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. Methods: In the HealthInfoNet, Maine's health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient's next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree--based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Results: Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. Conclusions: The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes. ",
issn="1438-8871",
doi="10.2196/jmir.4976",
url="http://www.jmir.org/2015/9/e219/",
url="https://doi.org/10.2196/jmir.4976",
url="http://www.ncbi.nlm.nih.gov/pubmed/26395541"
}