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Updated: Jun 19, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
Published on: April 12, 2021
The sensitivity outcome index system for home care of elderly liver transplant patients was developed based on the
Bin Wang1, Xia Huang1, Guofang Liu1
1The Affiliated Hospital of Qingdao University, Qingdao City, 266071, Shandong Province, China.
Objective:
Based on the Omaha problem classification system, a sensitivity outcome index system for home nursing of elderly liver transplant patients was established.
Methods:
Through a comprehensive literature review and rigorous application of the Delphi method, a panel of 20 experts completed two rounds of effective letter consultation to obtain expert consensus opinions. The contents of indicators were determined based on this process, and the analytic hierarchy process was employed to confirm the weightage assigned to each indicator. Consequently, we established a sensitivity outcome index system for home care in elderly liver transplant patients.
Results:
The effective recovery rate of the questionnaire in two rounds of expert consultation was 100%, and the proportion of experts who gave opinions was 55% and 15%, respectively, indicating that the experts were highly active. The expert authority coefficients were calculated as 0.904 and 0.905, respectively, indicating a high degree of expert authority. In the second round, Kendall's coordination coefficients for primary, secondary, and tertiary indicators were determined to be 0.419, 0.418, and 0.394 (P < 0.001), indicating that expert opinions tended to be consistent. Finally, we established a comprehensive sensitivity outcome index system comprising 4 first-level indexes, 20 s-level indexes, and 72 third-level indexes specifically designed for elderly liver transplantation patients.
Conclusion:
The sensitivity outcome index system of home nursing for elderly liver transplant patients can provide theoretical basis for nursing staff to build accurate individualized continuous nursing model.
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