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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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A Natural Language Processing Pipeline based on the Columbia-Suicide Severity Rating Scale.

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    Summary

    A new natural language processing (NLP) algorithm accurately detects suicidal ideation (SI) and behavior (SB) in electronic health records. This NLP approach significantly outperforms traditional diagnostic codes for identifying patients at risk.

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    Area of Science:

    • Medical Informatics
    • Clinical Natural Language Processing
    • Public Health Surveillance

    Background:

    • Electronic Health Records (EHRs) have limitations in capturing patient suicidality and its severity.
    • Existing diagnostic codes (ICD-9/10) are insufficient for detailed assessment of suicide risk.

    Purpose of the Study:

    • To develop and validate a portable Natural Language Processing (NLP) algorithm for detecting suicidal ideation (SI) and suicide-related behavior/attempts (SB/SA) within EHR data.
    • To compare the performance of the NLP algorithm against traditional International Statistical Classification of Diseases (ICD) diagnostic codes.

    Main Methods:

    • An NLP algorithm was designed based on the Columbia-Suicide Severity Rating Scale (C-SSRS) criteria.
    • Clinical notes from three major academic medical centers were manually annotated to create a "Gold Standard" for algorithm evaluation.
    • The algorithm's accuracy was assessed using F1 scores, and its detection rates were compared to ICD codes across demographic groups.

    Main Results:

    • The NLP algorithm achieved high accuracy scores (F1: 0.86-0.97) across all participating sites.
    • NLP demonstrated significantly higher detection rates for SB/SA (almost 30x) and SI (almost 10x) compared to ICD codes.
    • The algorithm showed no performance bias related to race/ethnicity and performed comparably in both psychiatric and non-psychiatric EHRs.

    Conclusions:

    • The developed NLP algorithm accurately identifies and differentiates SI and SB/SA from clinical notes.
    • NLP significantly enhances the ascertainment of suicide risk compared to current ICD coding practices.
    • This algorithm offers a more effective tool for identifying at-risk individuals within EHR systems, potentially rendering ICD codes obsolete for this purpose.