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www.PosterPresentations.com AN ELECTRONIC HEALTH RECORD BASED TOOL TO AID IN THE IDENTIFICATION OF CRITICALLY ILL NON-ICU HOSPITAL PATIENTS Barry Aaronson, Andrew White, Reena Julka, David Stone, Matthew Schaft, Dorthea McMahon, Laura Nelson University of Washington Medical Center and Harborview Medical Center, Seattle, Washington Background Conclusion Contact information Many hospitals have instituted Rapid Response Teams (RRT) to prevent potentially avoidable deaths in general medical surgical unit patients. However, studies to date have not found consistent improvement in clinical outcomes as a result of these RRTs. This may be due to RRT activation relying primarily upon recognition of critically abnormal vital signs by floor staff, a process that is not always reliable. As a result, some patients may not receive timely life saving interventions resulting in potentially avoidable deaths. Purpose The purpose of our EHR innovation is to ameliorate the problem of under-detection of potentially critically ill hospital patients. This goal is achieved by implementing an Early Warning System (EWS) that improves RRT awareness of patients with critically abnormal vital signs by displaying within the EHR a real time list of these patients. Description of Early Warning System Two lists of potentially critically ill patients are displayed within the EHR (Cerner using Mpages): RRT list- SBP<90, HR>130, RR>24, SaO2 <90% Score of 1-4 based on the number of vital signs meeting criteria. Modified Early Warning System Score (MEWS)- See Table below. Scores color coded Lists refresh every 5 minutes Can be sorted by RRT or MEWS CCU and ER patients automatically excluded Comfort care patients manually excluded. Short notes about patients can be entered directly on the list. Clicking on the patient opens the patient’s chart. Hovering over the score displays score details. Clicking on the score shows the trend for the score. Snooze Function for chronically unstable patients Subsets of hospital patients can be selected by the user (specific unit or team) User Option Tab to set defaults After correcting for number of EWS days on vs. number of EWS days off, the numbers of codes and deaths during the trial period were too few to draw meaningful conclusions from the data. There was no change in the number of patients unexpectedly transferred to the ICU. At HMC there was a significant increase in the number of times the RRT was activated. The EWS system has been enthusiastically adopted by the RRT teams at both hospitals since they believe it has significantly improved their ability to proactively identify deteriorating general ward patients. The efficacy of the system is limited in large part by the limited frequency that the RRT nurses are able to view the EWS. A pocket based device with active alerts carried by the RRT nurses that displays the EWS may improve its efficacy. A larger and/or longer trial will be required to assess for differences in clinical outcomes. Barry Aaronson MD FACP FHM Hospitalist Associate Medical Director of Clinical Informatics Virginia Mason Medical Center Clinical Associate Professor of Medicine University of Washington Seattle, Washington [email protected] (206) 859-9573 Clinical Trial Evaluation Modified Early Warning System (MEWS) Score Early Warning System within EHR Intervention Setting The list is viewed by the nurses that staff the RRT. Physician and Charge Nurses also have access to the list. When the RRT nurse sees a patient on the list that he or she is concerned about, the nurse would usually contact the patient’s primary nurse to gather more data and make subsequent management decisions. A qualitative questionnaire for users was administered at the completion of the trial. The system is piloted at our 450 bed urban university based teaching hospital (UW) and the affiliated 413 bed university operated county hospital (HMC). Sample MEWS Calculation Hi gh Results Moving from Fire Station Model To an Air Traffic Control Surveillance Model Normal High Low Normal Low High
