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

Jani Paulin*, Akseli Reunamo, Jouni Kurola, Hans Moen, Sanna Salantera, Heikki Riihimaki, Tero Vesanen, Mari Koivisto, Timo Iirola

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli166
Sivumäärä12
JulkaisuBMC Medical Informatics and Decision Making
Vuosikerta22
Numero1
DOI - pysyväislinkit
TilaJulkaistu - 23 kesäk. 2022
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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