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Using Primary Health Care Electronic Medical Records to Predict Hospitalizations, Emergency Department Visits, and
Rebecca Johnson1, Thomas Chang1, Rahim Moineddin1
1From the Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada (RJ, ADP); Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (RJ, RM, ADP); Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (TC); Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada (TU); Department of Family and Community Medicine, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada (NC); Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (NC, ADP); Department of General Practice, University College Cork, Cork, Ireland (EW); Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP).
Predictive analytics using primary care electronic medical record (EMR) data shows promise for forecasting emergency department visits and hospitalizations. Further research is needed to improve model quality and clinical utility.
Area of Science:
- Health Informatics
- Predictive Analytics
- Primary Care Research
Background:
- High-quality primary care reduces avoidable emergency department visits and hospitalizations.
- Electronic medical record (EMR) data availability enables predictive analytics for healthcare.
- This systematic review explores EMR data for predicting adverse health events.
Approach:
- Systematic review of 31 studies published up to February 5, 2020.
- Searched six major databases for relevant peer-reviewed literature.
- Analyzed studies predicting emergency department visits, hospitalizations, and mortality using primary care EMR data.
Key Points:
- Predictive model performance (C-statistics) ranged from 0.57 to 0.95.
- Common predictors included age, diagnoses, sex, medication, and prior service use.
- External validation and reporting on clinical utility were limited; AI methods used in less than half of studies.
Conclusions:
- Primary care EMR data holds significant potential for predictive analytics.
- Addressing bias and enhancing model reporting are crucial for future development.
- Improved predictive models can support better healthcare delivery and resource allocation.
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