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Using machine learning to predict subsequent events after EMS non-conveyance decisions.

Jani Paulin¹, Akseli Reunamo², Jouni Kurola³

¹Department of Clinical Medicine, University of Turku and Turku University of Applied Sciences, Turku, Finland. jani.paulin@utu.fi.

BMC Medical Informatics and Decision Making

|June 23, 2022

Summary

This summary is machine-generated.

Machine learning can predict subsequent events after Emergency Medical Services (EMS) non-conveyance. Musculoskeletal and infection-related symptoms were key predictors, but inadequate EMS documentation also contributed to adverse outcomes.

Keywords:

Documentation Emergency medical service Machine learning Non-conveyance Patient safety Subsequent event Text classification

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Area of Science:

Prehospital Emergency Care
Health Informatics
Machine Learning in Healthcare

Background:

Predicting patient outcomes after Emergency Medical Services (EMS) non-conveyance is crucial for patient safety.
Electronic patient care records (ePCR) contain valuable data for predicting these events.
The role of machine learning in analyzing narrative ePCR data for outcome prediction is under-explored.

Purpose of the Study:

To evaluate the efficacy of machine learning in predicting subsequent events following EMS non-conveyance.
To identify key predictors of subsequent events using narrative text from ePCRs.

Main Methods:

A prospective cohort study involving EMS patients in Finland.
Data collected from June to November 2018 across three regions.
Machine learning (FastText text classification) and manual evaluation used to analyze clinical notes.

Main Results:

The FastText model achieved an AUC of 0.654 in predicting subsequent events.
Many subsequent events were pre-planned, with EMS guiding patients to primary care or EDs.
Musculoskeletal, infection-related, and non-specific complaints were frequent predictors; 20% of EMS documentation was inadequate, leading to events.

Conclusions:

Machine learning effectively predicts subsequent events after EMS non-conveyance.
Subsequent events are not always indicative of compromised patient safety, as many are planned.
Further research is needed to identify harmful events where non-conveyance risks patient safety.