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Capturing key function‐focused care problems, nursing diagnoses, and interventions for nursing home patients using a web‐based case management system.

  • Published In: International Journal of Nursing Knowledge, 2024, v. 35, n. 1. P. 93 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lee, Su Jung; Park, Min Sun; Chang, Sung Ok 3 of 3

Abstract

Purpose: The aim of this study is to identify the key functional care problems, NANDA‐I nursing diagnoses, and intervention plans related to function‐focused care (FFC) using a web‐based case management system for patients who present different cognitive status. Methods: This study employed a retrospective descriptive research design. Data were obtained from system records on patients after the research team trained the case management system at a nursing home in Dangjin in South Chungcheong Province, South Korea. A total of 119 inpatient records were analyzed. Results: The key physical, cognitive, and social functional problems, nursing diagnoses in six domains (health promotion, elimination and exchange, activity/rest, perception/cognition, coping/stress tolerance, and safety/protection), and intervention plans were identified. Conclusion: The identified FFC case management information of interdisciplinary caregivers will provide evidence for the implementation of effective interventions according to a patient's functional status. Additional studies related to the establishment of a large clinical database of advanced case management systems focusing on interdisciplinary caregivers' functional management are needed to support the prioritization of functional care. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Nursing Knowledge. 2024/01, Vol. 35, Issue 1, p93
  • Document Type:Article
  • Subject Area:Nursing and Allied Health
  • Publication Date:2024
  • ISSN:2047-3087
  • DOI:10.1111/2047-3095.12420
  • Accession Number:174779610
  • Copyright Statement:Copyright of International Journal of Nursing Knowledge is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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