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POLAR Diversion: Using General Practice Data to Calculate Risk of Emergency Department Presentation at the Time of Consultation.

Appl Clin Inform. 2019 Jan;10(1):151-157

Authors: Pearce C, McLeod A, Rinehart N, Patrick J, Fragkoudi A, Ferrigi J, Deveny E, Whyte R, Shearer M

Abstract
OBJECTIVE:  This project examined and produced a general practice (GP) based decision support tool (DST), namely POLAR Diversion, to predict a patient’s risk of emergency department (ED) presentation. The tool was built using both GP/family practice and ED data, but is designed to operate on GP data alone.
METHODS:  GP data from 50 practices during a defined time frame were linked with three local EDs. Linked data and data mapping were used to develop a machine learning DST to determine a range of variables that, in combination, led to predictive patient ED presentation risk scores. Thirteen percent of the GP data was kept as a control group and used to validate the tool.
RESULTS:  The algorithm performed best in predicting the risk of attending ED within the 30-day time category, and also in the no ED attendance tests, suggesting few false positives. At 0 to 30 days the positive predictive value (PPV) was 74%, with a sensitivity/recall of 68%. Non-ED attendance had a PPV of 82% and sensitivity/recall of 96%.
CONCLUSION:  Findings indicate that the POLAR Diversion algorithm performed better than previously developed tools, particularly in the 0 to 30 day time category. Its utility increases because of it being based on the data within the GP system alone, with the ability to create real-time “in consultation” warnings. The tool will be deployed across GPs in Australia, allowing us to assess the clinical utility, and data quality needs in further iterations.

PMID: 30812041 [PubMed – in process]

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