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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Using EHR Information to Estimate Mortality Risk for People With Dementia in an Australian Nursing Home Setting
Ross Bicknell1, Alistair McLean2, Wen Kwang Lim3
1Department of Aged Care, Royal Melbourne Hospital, Parkville, Victoria, Australia.
Summary
Electronic health records in Australian nursing homes can predict dementia patient mortality risk. Key factors include age, sex, weight, and assistance needed for eating, aiding anticipatory care planning.
Area of Science:
- Gerontology
- Palliative Care
- Health Informatics
Background:
- End-of-life care for dementia patients primarily occurs in nursing homes.
- Prognostic uncertainty in dementia hinders timely palliative care and planning.
- Current electronic health record (EHR) systems in Australian nursing homes lack predictive capabilities for resident deterioration.
Purpose of the Study:
- To explore nursing home EHR data in Australia.
- To identify factors associated with mortality risk in nursing home residents with dementia.
Main Methods:
- Secondary analysis of the IMPETUS-D cluster randomized trial data.
- Utilized baseline EHR data from 922 Australian nursing home residents with dementia.
- Developed a Cox proportional hazards model using backward elimination.
Main Results:
- Five key predictors of mortality were identified: age, male sex, oral intake assistance, baseline weight, and 6-month weight change.
- The model demonstrated moderate mortality discrimination (c-statistic 0.61).
- The model showed acceptable calibration (calibration slope 0.82).
Conclusions:
- Routinely collected EHR data can identify key mortality predictors in Australian nursing home residents with dementia.
- These predictors can be integrated into EHR systems to enhance anticipatory care planning.
- Further validation is needed to fully leverage these predictors for improved dementia care.
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