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There is growing interest in including big data analytics into clinical decision support tools. This research will focus on developing, validating and verifying an artificial intelligence algorithm to identify non-ICU patients at risk for cardiac arrest, ICU transfer and death during their inpatient hospitalization. Known as an early warning score (EWS), this algorithm can be used to investigate, manage and mitigate these potential events. This Geisinger model will be benchmarked against other real-time inpatient risk algorithm performance. This project involves the use of electronic records from more than 75,000 Geisinger patients and is expected to outperform other models such as modified early warning scores (MEWS) and national early warning scores (NEWS). By having a better EWS, Geisinger clinicians can better plan for — and hopefully mitigate — the effects of adverse events, improving patient care.
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