Open AccessCan Routinely Collected, Patient-Reported Wellness Predict National Early Warning Scores? A Multilevel Modeling Approach
Author(s) -
Abigail Albutt,
Jane O’Hara,
Mark Conner,
Rebecca Lawton
Publication year - 2021
Publication title -
journal of patient safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.546
H-Index - 34
eISSN - 1549-8425
pISSN - 1549-8417
DOI - 10.1097/pts.0000000000000672
Subject(s) - early warning score , medicine , warning system , multilevel model , prospective cohort study , medline , emergency medicine , family medicine , machine learning , computer science , law , political science , engineering , aerospace engineering
Measures exist to improve early recognition of and response to deteriorating patients in hospital. However, management of critical illness remains a problem globally; in the United Kingdom, 7% of the deaths reported to National Reporting and Learning System from acute hospitals in 2015 related to failure to recognize or respond to deterioration. The current study explored whether routinely recording patient-reported wellness is associated with objective measures of physiology to support early recognition of hospitalized deteriorating patients.