Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05953948 |
| Other study ID # |
ICMP20230704 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2024 |
| Est. completion date |
December 31, 2027 |
Study information
| Verified date |
July 2023 |
| Source |
Chang Gung University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
This study is a prospective, quasi-experimental design, with an experimental group and a
control group, will be created. The aims of this study are as follows: 1. Describe the
self-management and information needs of liver transplant recipients, 2. Create content or
modules related to the self-management of liver transplant recipients, 3. Build an
intelligent case management platform, 4. Evaluate the usability of the platform, and 5.
Conduct deep learning and examine the effects of the intelligent case management platform on
self-efficacy, self-management, health outcomes, and health-related quality of life. Data
will be collected at discharge (baseline data) and 1, 3, 6, 9, and 12 months after discharge.
An estimated 133 patients will be involved in this experiment: 44 in the experimental group
and 89 in the control group. Statistical package software (SPSS 22.0) will be used to analyze
the data. A generalized estimation equation model will examine the differences in
self-efficacy, self-management, and health-related quality of life between the experimental
and control groups. Survival analysis and the Kaplan-Meier method will be used to analyze
health outcomes, including hospital readmission, emergency visits, episodes of infection and
rejection of organs, and death.
Description:
Background: Liver transplant recipients require proper self-management to avoid the risk of
various complications, reduce hospital readmission and medical costs, and improve their
quality of life. They also face diverse challenges in self-management. Therefore, enhancing
the self-management of liver transplant recipients after liver transplantation is important.
Hospitals and medical facilities taking care of such patients should facilitate
individualized care, access to healthcare resources, and planned post-discharge support. The
use of information technology, artificial intelligence, and deep learning to identify and
confirm the characteristics and types of self-management requirements of liver transplant
recipients and provide individualized self-management may help improve their self-management
skills and health outcomes. The quality and continuity of care can also be improved. However,
no studies have been conducted in this regard.
Purpose: To establish an intelligent case management platform that combines artificial
intelligence and deep learning to enhance the self-efficacy and self-management of liver
transplant recipients, thereby improving clinical outcomes and health-related quality of
life. The aims of this study are as follows: 1. Describe the self-management and information
needs of liver transplant recipients, 2. Create content or modules related to self-management
of liver transplant recipients, 3. Build an intelligent case management platform, 4. Evaluate
the usability of the platform, and 5. Conduct deep learning and examine the effects of the
intelligent case management platform on self-efficacy, self-management, health outcomes, and
health-related quality of life.
Methods and materials: This study is a prospective, quasi-experimental design, with an
experimental group and a control group, will be created. First, the self-management care and
information needs of liver transplant patients will be integrated to create the foundation of
the intelligent case management platform. For this purpose, an estimated 50 liver transplant
recipients and 10 medical staff will be interviewed. The data will be analyzed by qualitative
content analysis. Based on these contents, the intelligent case management platform will be
developed and evaluated. For the evaluation, data from 200 liver transplant recipients will
be collected to assess platform availability, performance, and usage status. Data related to
the recipient's use of the platform and reception of self-management from the platform will
also be collected for deep learning. The importance and clinical relevance of self-management
provided by the platform will be assessed by the medical staff involved in liver transplant
care. Deep learning techniques will be utilized, and the effectiveness of the intelligent
case management platform in terms of self-efficacy, self-management, health outcomes, and
health-related quality of life will be examined. An estimated 133 patients will be involved
in this experiment: 44 in the experimental group and 89 in the control group. Data will be
collected at discharge (baseline data) and 1, 3, 6, 9, and 12 months after discharge from the
hospital. Statistical package software (SPSS 22.0) will be used to analyze the data. A
generalized estimation equation model will analyze the differences in self-efficacy,
self-management, and health-related quality of life over time between the experimental and
control groups. This study proposes innovative applications for information technology, deep
learning, and artificial intelligence. It is hoped that multidisciplinary cooperation can
improve liver transplant recipients' self-management and health outcomes.
