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Related Concept Videos

Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

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Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
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Healthcare Associated Infections II: Preventive Measures01:22

Healthcare Associated Infections II: Preventive Measures

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Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
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Health Literacy01:21

Health Literacy

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Health literacy is an individual's or a community's capacity to comprehend, receive, read, and use relevant healthcare information and services. The World Health Organization (WHO, 2018) defines health literacy as the cognitive and social skills that determine the ability of individuals to gain access to, understand, and use information in ways that promote and maintain good health. As a result, the WHO helps individuals manage long-term health concerns, participate in preventative...
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Data Reporting and Recording01:24

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Methods of Documentation VI: Case Management Model01:15

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
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SBAR II: Application of SBAR01:14

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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
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Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit.

Jiyoun Song, Marietta Ojo, Kathryn H Bowles

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    Natural Language Processing (NLP) can identify concerning language in home healthcare notes, helping to predict patient hospitalizations. This technology aids in understanding patient needs and preventing adverse events.

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    Area of Science:

    • Healthcare Informatics
    • Natural Language Processing
    • Clinical Documentation

    Background:

    • Approximately 20% of home healthcare (HHC) patients experience hospitalization or emergency department (ED) visits.
    • Early identification of at-risk patients is crucial for preventing negative outcomes in HHC.
    • Risk indicators are often embedded within narrative clinical notes, making them difficult to detect.

    Purpose of the Study:

    • To develop an automated Natural Language Processing (NLP) algorithm.
    • To identify concerning language in HHC clinical notes indicative of hospitalization or ED visit risk.

    Main Methods:

    • Utilized the Omaha System, a standardized nursing terminology.
    • HHC experts identified concerning concepts within the Omaha System.
    • Developed and validated an NLP algorithm to detect these concepts in 2.3 million clinical notes from 66,317 patients.

    Main Results:

    • Identified 160 concerning Omaha System signs/symptoms across 31 problems.
    • The NLP algorithm demonstrated good performance in detecting concerning concepts.
    • Over 18% of notes contained concerning concepts; pain was the most frequent.

    Conclusions:

    • Concerning symptoms indicating increased hospitalization risk are prevalent in HHC clinical notes.
    • NLP offers a method for automated information extraction from narrative notes.
    • Future research will evaluate which identified concepts predict hospitalizations or ED visits.