title: Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study.
abstract: BACKGROUND
  An easily accessible real-time Web-based utility to assess patient risks of future emergency department (ED) visits can help the health care provider guide the allocation of resources to better manage higher-risk patient populations and thereby reduce unnecessary use of EDs.
  
  
  OBJECTIVE
  Our main objective was to develop a Health Information Exchange-based, next 6-month ED risk surveillance system in the state of Maine.
  
  
  METHODS
  Data on electronic medical record (EMR) encounters integrated by HealthInfoNet (HIN), Maine's Health Information Exchange, were used to develop the Web-based surveillance system for a population ED future 6-month risk prediction. To model, a retrospective cohort of 829,641 patients with comprehensive clinical histories from January 1 to December 31, 2012 was used for training and then tested with a prospective cohort of 875,979 patients from July 1, 2012, to June 30, 2013.
  
  
  RESULTS
  The multivariate statistical analysis identified 101 variables predictive of future defined 6-month risk of ED visit: 4 age groups, history of 8 different encounter types, history of 17 primary and 8 secondary diagnoses, 8 specific chronic diseases, 28 laboratory test results, history of 3 radiographic tests, and history of 25 outpatient prescription medications. The c-statistics for the retrospective and prospective cohorts were 0.739 and 0.732 respectively. Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. Cluster analysis in both the retrospective and prospective analyses revealed discrete subpopulations of high-risk patients, grouped around multiple "anchoring" demographics and chronic conditions. With the Web-based population risk-monitoring enterprise dashboards, the effectiveness of the active case finding algorithm has been validated by clinicians and caregivers in Maine.
  
  
  CONCLUSIONS
  The active case finding model and associated real-time Web-based app were designed to track the evolving nature of total population risk, in a longitudinal manner, for ED visits across all payers, all diseases, and all age groups. Therefore, providers can implement targeted care management strategies to the patient subgroups with similar patterns of clinical histories, driving the delivery of more efficient and effective health care interventions. To the best of our knowledge, this prospectively validated EMR-based, Web-based tool is the first one to allow real-time total population risk assessment for statewide ED visits.
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: Bo
authors.identifier: http://orcid.org/0000-0001-9268-7134
authors.initials: B
authors.lastname: Jin
authors.forename: Andrew Y
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: Yifan
authors.identifier: http://orcid.org/0000-0001-5506-4341
authors.initials: Y
authors.lastname: Zhao
authors.forename: Shiying
authors.identifier: http://orcid.org/0000-0002-7527-2460
authors.initials: S
authors.lastname: Hao
authors.forename: Le
authors.identifier: http://orcid.org/0000-0003-4744-367X
authors.initials: L
authors.lastname: Zheng
authors.forename: Changlin
authors.identifier: http://orcid.org/0000-0002-8237-027X
authors.initials: C
authors.lastname: Fu
authors.forename: Qiaojun
authors.identifier: http://orcid.org/0000-0003-0927-7578
authors.initials: Q
authors.lastname: Wen
authors.forename: Jun
authors.identifier: http://orcid.org/0000-0003-3194-2183
authors.initials: J
authors.lastname: Ji
authors.forename: Zhen
authors.identifier: http://orcid.org/0000-0003-4133-4835
authors.initials: Z
authors.lastname: Li
authors.forename: Yong
authors.identifier: http://orcid.org/0000-0002-2311-871X
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaolin
authors.identifier: http://orcid.org/0000-0002-3152-0901
authors.initials: X
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 B
authors.identifier: http://orcid.org/0000-0002-5386-3884
authors.initials: XB
authors.lastname: Ling
keywords: ED; EMR; HIE; machine learning; modeling