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Appl Clin Inform 2019; 10(01): 151-157
DOI: 10.1055/s-0039-1678608
Research Article
Georg Thieme Verlag KG Stuttgart · New York

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

Christopher Pearce
1   Outcome Health, East Burwood, Victoria, Australia
,
Adam McLeod
1   Outcome Health, East Burwood, Victoria, Australia
,
Natalie Rinehart
1   Outcome Health, East Burwood, Victoria, Australia
,
Jon Patrick
2   Health Language Analytics, Eveleigh, New South Wales, Australia
,
Anna Fragkoudi
1   Outcome Health, East Burwood, Victoria, Australia
,
Jason Ferrigi
1   Outcome Health, East Burwood, Victoria, Australia
,
Elizabeth Deveny
3   South East Melbourne Primary Health Network, Melbourne, Australia
,
Robin Whyte
4   Eastern Melbourne Primary Health Network, Box Hill, Victoria, Australia
,
Marianne Shearer
5   Gippsland Primary Health Network, Moe, Victoria, Australia
› Author Affiliations Funding This research was undertaken with the generous assistance from the HCF Research Foundation. The foundation exerted no influence over the design or the progress of the study.
Further Information

Publication History

04 August 2018

04 January 2019

Publication Date:
27 February 2019 (online)

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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.

Keywords

electronic health record data - primary care - artificial intelligence - machine learning - risk prediction

Protection of Human and Animal Subjects

No human subjects were involved in this project.