ArticleJournal of medical Internet research2015
Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Trial
- Article
- Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.Frontiers in immunology · 2022Article
- Article
- Estimating Population Benefits of Prevention Approaches Using a Risk Tool: High Resource Users in Ontario, Canada.Healthcare policy = Politiques de sante · 2021Article
- Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization.Chiropractic & manual therapies · 2020Article
- CaliForest: Calibrated Random Forest for Health Data.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- Prediction of health care expenditure increase: how does pharmacotherapy contribute?BMC health services research · 2019Article
- Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation.JMIR medical informatics · 2019Article
- Digital Health Transformation of Integrated Care in Europe: Overarching Analysis of 17 Integrated Care Programs.Journal of medical Internet research · 2019Article
- Review
- Predicting High Health Care Resource Utilization in a Single-payer Public Health Care System: Development and Validation of the High Resource User Population Risk Tool.Medical care · 2018Article
- Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine.JMIR medical informatics · 2017Article
- Defining and characterizing the critical transition state prior to the type 2 diabetes disease.PloS one · 2017Article
- Neuroinflammation - using big data to inform clinical practice.Nature reviews. Neurology · 2016Review
- Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.Journal of medical Internet research · 2016Article
- Big data analytics to improve cardiovascular care: promise and challenges.Nature reviews. Cardiology · 2016Review
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis 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.
methodsIn 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.
resultsProspectively 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.
conclusionsThe 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.
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