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

Jani Paulin; Akseli Reunamo; Jouni Kurola; Hans Moen; Sanna Salanterä; Heikki Riihimäki; Tero Vesanen; Mari Koivisto; Timo Iirola

doi:10.1186/s12911-022-01901-x

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

BMC Med Inform Decis Mak. 2022 Jun 23;22(1):166. doi: 10.1186/s12911-022-01901-x.

Authors

Jani Paulin¹, Akseli Reunamo², Jouni Kurola³, Hans Moen⁴, Sanna Salanterä⁵, Heikki Riihimäki⁶, Tero Vesanen⁶, Mari Koivisto⁷, Timo Iirola⁸

Affiliations

¹ Department of Clinical Medicine, University of Turku and Turku University of Applied Sciences, Turku, Finland. jani.paulin@utu.fi.
² Department of Biology, University of Turku, Turku, Finland.
³ Centre for Prehospital Emergency Care, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland.
⁴ Department of Computing, University of Turku, Turku, Finland.
⁵ Department of Nursing Science, University of Turku and Turku University Hospital, Turku, Finland.
⁶ Department of Nursing Science, University of Turku, Turku, Finland.
⁷ Department of Biostatistics, University of Turku, Turku, Finland.
⁸ Emergency Medical Services, Turku University Hospital and University of Turku, Turku, Finland.

Abstract

Background: Predictors of subsequent events after Emergency Medical Services (EMS) non-conveyance decisions are still unclear, though patient safety is the priority in prehospital emergency care. The aim of this study was to find out whether machine learning can be used in this context and to identify the predictors of subsequent events based on narrative texts of electronic patient care records (ePCR).

Methods: This was a prospective cohort study of EMS patients in Finland. The data was collected from three different regions between June 1 and November 30, 2018. Machine learning, in form of text classification, and manual evaluation were used to predict subsequent events from the clinical notes after a non-conveyance mission.

Results: FastText-model (AUC 0.654) performed best in prediction of subsequent events after EMS non-conveyance missions (n = 11,846). The model and manual analyses showed that many of the subsequent events were planned before, EMS guided the patients to visit primary health care facilities or ED next or following days after non-conveyance. The most frequent signs and symptoms as subsequent event predictors were musculoskeletal-, infection-related and non-specific complaints. 1 in 5 the EMS documentation was inadequate and many of these led to a subsequent event.

Conclusion: Machine learning can be used to predict subsequent events after EMS non-conveyance missions. From the patient safety perspective, it is notable that subsequent event does not necessarily mean that patient safety is compromised. There were a number of subsequent visits to primary health care or EDs, which were planned before by EMS. This demonstrates the appropriate use of limited resources to avoid unnecessary conveyance to the ED. However, further studies are needed without planned subsequent events to find out the harmful subsequent events, where EMS non-conveyance puts patient safety at risk.

Keywords: Documentation; Emergency medical service; Machine learning; Non-conveyance; Patient safety; Subsequent event; Text classification.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Documentation
Emergency Medical Services*
Humans
Machine Learning
Patient Safety
Prospective Studies