title: Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study.
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.
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
authors.forename: Zhongkai
authors.identifier: http://orcid.org/0000-0003-2541-6187
authors.initials: Z
authors.lastname: Hu
authors.forename: Shiying
authors.identifier: http://orcid.org/0000-0002-7527-2460
authors.initials: S
authors.lastname: Hao
authors.forename: Bo
authors.identifier: http://orcid.org/0000-0001-9268-7134
authors.initials: B
authors.lastname: Jin
authors.forename: Andrew Young
authors.identifier: http://orcid.org/0000-0001-6713-0609
authors.initials: AY
authors.lastname: Shin
authors.forename: Chunqing
authors.identifier: http://orcid.org/0000-0003-0649-4403
authors.initials: C
authors.lastname: Zhu
authors.forename: Min
authors.identifier: http://orcid.org/0000-0001-9019-0044
authors.initials: M
authors.lastname: Huang
authors.forename: Yue
authors.identifier: http://orcid.org/0000-0002-0981-935X
authors.initials: Y
authors.lastname: Wang
authors.forename: Le
authors.identifier: http://orcid.org/0000-0003-4744-367X
authors.initials: L
authors.lastname: Zheng
authors.forename: Dorothy
authors.identifier: http://orcid.org/0000-0001-9778-198X
authors.initials: D
authors.lastname: Dai
authors.forename: Devore S
authors.identifier: http://orcid.org/0000-0001-5826-2079
authors.initials: DS
authors.lastname: Culver
authors.forename: Shaun T
authors.identifier: http://orcid.org/0000-0003-1752-4823
authors.initials: ST
authors.lastname: Alfreds
authors.forename: Todd
authors.identifier: http://orcid.org/0000-0003-0061-9535
authors.initials: T
authors.lastname: Rogow
authors.forename: Frank
authors.identifier: http://orcid.org/0000-0003-2049-9163
authors.initials: F
authors.lastname: Stearns
authors.forename: Karl G
authors.identifier: http://orcid.org/0000-0002-8559-0155
authors.initials: KG
authors.lastname: Sylvester
authors.forename: Eric
authors.identifier: http://orcid.org/0000-0002-8999-4659
authors.initials: E
authors.lastname: Widen
authors.forename: Xuefeng
authors.identifier: http://orcid.org/0000-0002-5386-3884
authors.initials: X
authors.lastname: Ling
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003695:Delivery of Health Care / Q000639:trends*; D057286:Electronic Health Records / Q000458:organization & administration*; D005260:Female; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States; D054928:Validation Studies as Topic; D055815:Young Adult
keywords: electronic medical record; health care costs; prospective studies; risk assessment; statistical data analysis