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Piloting electronic medical record–based early detection of inpatient deterioration in community hospitals
Author(s) -
Escobar Gabriel J.,
Turk Benjamin J.,
Ragins Arona,
Ha Jason,
Hoberman Brian,
LeVine Steven M.,
Ballesca Manuel A.,
Liu Vincent,
Kipnis Patricia
Publication year - 2016
Publication title -
journal of hospital medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.128
H-Index - 65
eISSN - 1553-5606
pISSN - 1553-5592
DOI - 10.1002/jhm.2652
Subject(s) - software deployment , medicine , electronic medical record , medical emergency , hospital medicine , early warning score , warning system , medical record , intensive care unit , medline , emergency medicine , early warning system , health care , intensive care medicine , family medicine , computer science , telecommunications , political science , law , economics , radiology , economic growth , operating system
Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2‐hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk‐adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun. Journal of Hospital Medicine 2016;11:S18–S24. © 2016 Society of Hospital Medicine