Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study

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.

[1]  N. Powe,et al.  Predicting expenditures for Medicare beneficiaries with diabetes. A prospective cohort study from 1994 to 1996. , 1999, Diabetes care.

[2]  Zhen Li,et al.  Real-Time Web-Based Assessment of Total Population Risk of Future Emergency Department Utilization: Statewide Prospective Active Case Finding Study , 2015, Interactive journal of medical research.

[3]  Chris Salisbury,et al.  Implications of comorbidity for primary care costs in the UK: a retrospective observational study. , 2013, The British journal of general practice : the journal of the Royal College of General Practitioners.

[4]  Huan Liu,et al.  Predicting Future High-Cost Patients: A Real-World Risk Modeling Application , 2007, 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007).

[5]  H. Lazenby,et al.  Age Estimates in the National Health Accounts , 2004, Health care financing review.

[6]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[7]  U. John,et al.  Predictive modeling of health care costs: do cardiovascular risk markers improve prediction? , 2010, European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology.

[8]  Lisa I. Iezzoni,et al.  Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model , 2004, Health care financing review.

[9]  Santosh S. Vempala,et al.  Algorithmic Prediction of Health-Care Costs , 2008, Oper. Res..

[10]  D. Bates,et al.  Using Diagnoses to Describe Populations and Predict Costs , 2000, Health care financing review.

[11]  Arlene S Ash,et al.  Risk-adjusted Payment and Performance Assessment for Primary Care , 2012, Medical care.

[12]  H A Pincus,et al.  Comparing the national economic burden of five chronic conditions. , 2001, Health affairs.

[13]  D Y Lin,et al.  Methods for analyzing health care utilization and costs. , 1999, Annual review of public health.

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  Ning Liu,et al.  Utilization and costs of home care for patients with colorectal cancer: a population-based study. , 2014, CMAJ open.

[16]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[17]  J. Fleishman,et al.  Using information on clinical conditions to predict high-cost patients. , 2010, Health services research.

[18]  T. Bodenheimer,et al.  Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job? , 2009, Health affairs.

[19]  Gerard F Anderson,et al.  Cross-national comparisons of health systems using OECD data, 1999. , 2002, Health affairs.

[20]  Jean X. Gao,et al.  Multiple Interacting Subcellular Structure Tracking by Sequential Monte Carlo Method , 2007, 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007).

[21]  J. Brox,et al.  Costs of shoulder pain and resource use in primary health care: a cost-of-illness study in Sweden , 2012, BMC Musculoskeletal Disorders.