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Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study.

Christopher M Pearce¹, Adam McLeod¹, Jon Patrick²

¹Melbourne East General Practice Network, Burwood East, Australia.

JMIR Research Protocols

|December 22, 2016

Summary

This summary is machine-generated.

This study developed a predictive algorithm using linked patient records to identify individuals at high risk of emergency department visits. The tool aims to support general practitioners in primary care, potentially reducing hospital admissions.

Keywords:

avoidable presentation data linkage electronic health record machine learning primary care

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Area of Science:

Health Informatics
Predictive Analytics
Primary Care Research

Background:

Many hospital admissions stem from conditions manageable in primary care, indicating a need for early intervention strategies.
Lack of shared patient information hinders comprehensive understanding of medical history, complicating risk assessment for general practitioners.
Existing predictive algorithms often lack generalizability beyond their development populations, highlighting the need for localized solutions.

Purpose of the Study:

To develop a real-time predictive tool for general practice using linked primary and secondary healthcare data.
To create an algorithm that generates risk reports for patients likely to present to an emergency department.

Main Methods:

De-identified records of approximately 100,000 patients were pooled from general practices and emergency departments within a specific health network.
A machine learning algorithm will be developed iteratively to identify the optimal combination of variables for predictive accuracy.
The study utilizes de-identified data from general practice and emergency department attendances.

Main Results:

Data from approximately 97,000 patients with linked general practice and emergency department records have been identified.
These records are currently being utilized for the development of the predictive model.

Conclusions:

Pooled records from general practice and emergency departments are foundational for algorithm development.
Future phases include algorithm validation and live testing in a general practice setting.
The developed algorithm will form the basis of a clinical decision support tool for general practitioners, with initial efficacy testing planned.