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摘要:In order to improve the continuum of care for hospitalized Heart Failure(HF) patients,accurate and early identification during hospitalization is crucial.Manual identification of hospitalized HF patients is time consuming and challenged by inconsistent and poorly sensitive processes.We developed two new clinical decision support(CDS) applications and the HF patient Identification and Risk Stratification Daily report to facilitate the early identification and care of HF patients.The first application uses natural language processing(NLP) to improve our ability to identify HF patients based on outpatient as well as inpatient information while the other application uses that information with additional data in our electronic medical record to automatically predict the 30-day all-cause readmission and 30-day mortality scores.This information is automatically provided to cardiovascular clinicians each day and allows them to prioritize their limited time based on the identified HF patients rather than spending time to identify them manually.The addition of NLP compared to just using ICD9 codes to help identify HF patients increased the sensitivity from 82.6%to 95.3%and specificity from 82.7%to 97.5%and a current positive predictive value of 97.45%.CDS coupled with a multidisciplinary care process pathway(CPP) was found to be an effective method to improve HF patient identification and information.Use of that information reduced patient evaluation time from 40 to 10 minutes,and significantly reduced 30-day mortality(7%vs.19%,p=0.03) and significantly increased patient discharges to home health rather than a skilled nursing facility(34%vs.19%,p=0.02).Both applications along with the CPP have now been installed at the other Intermountain Healthcare hospitals that treat HF patients.
会议名称:

A New Era in Cardiology Research and Therapy——BIT’s 8th Annual International Congress of Cardiology-2016

会议时间:

2016-05-28

会议地点:

Barcelona, Spain

  • 专辑:

    医药卫生科技

  • 专题:

    临床医学

  • 分类号:

    R473.5

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