Background: Early warning scores are clinical decision support tools that incorporate multiple physiologic variables to detect patient deterioration. Previous studies have highlighted the value of integrating measures of provider intuition into predictive models. (1-3) For example, incorporating the Patient Acuity Rating, a Likert-based measure of nurse worry, into the six-variable Modified Early Warning Score (MEWS) [...]

Background: Despite the introduction of Early Warning Scores (EWSs), clinical deterioration (CD) remains an actual problem on the general ward. A next step to counter CD would be to intensify measurement from intermittent 8 hours to continuous measurements. This leads to big data sets of patient monitoring data with great potential. Use of advanced predictive [...]

Background: The use of early warning systems (EWS) to augment clinical care is of increasing interest with recent publications showing EWS algorithm alerts coupled with clinical response actions to have significant morbidity and mortality benefit.1 At Stanford Hospital we are developing an alert and response EWS using a machine learning (ML) model predicting clinical deterioration [...]

Background: Previous studies examining continuous patient monitoring in the ICU and emergency department suggest that substantial telemetry alarm burden combined with a high proportion of false positive alerts may jeopardize patient outcomes. Currently, little is known about the burden and value of specific alarms on the general wards. Methods: As part of a quality improvement [...]