<p> Background: Sepsis is one of the most lethal and expensive in hospital conditions in the Unites States and around the world. International consensus guidelines for the diagnosis and management of sepsis have been established. Compliance with these guidelines has been demonstrated to substantially improve outcomes such as hospital length of stay (LOS), intensive care unit (ICU) LOS, and mortality. However, there are significant delays in timely and appropriate recognition of sepsis, as well as delays in timely and appropriate treatment after diagnosis. </p><p> Objective: To develop and implement a sepsis detection and alert system for use in the ICU setting. Several knowledge gaps must be closed to achieve this goal. </p><p> Methods: First, an optimal electronic medical record (EMR)-based algorithm for the detection of failure to recognize severe sepsis was developed. An algorithm for the detection of failure of timely and appropriate treatment of severe sepsis was also developed. Second, the best method of alert delivery for failure to recognize and treat severe sepsis was developed. This process was performed in the context of alert fatigue, interruption, human error, and information overload. Third, to demonstrate efficacy, this surveillance system for the detection of failure to recognize and treat severe sepsis was implemented in the ICU setting. </p><p> Results: A failure to recognize and treat severe sepsis detection and alert system was successfully developed and implemented in the ICU setting. </p><p> Conclusion: The work presented in this thesis proved the feasibility of iterative development, testing, and real-world implementation of electronic surveillance of sepsis resuscitation. This research paves the way for meaningful EMR use to enhance the safety of hospitalized patients.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3702545 |
| Date | 23 May 2015 |
| Creators | Harrison, Andrew Marc |
| Publisher | College of Medicine - Mayo Clinic |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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