Developing of addressing non-communicable diseases (NCDs) model by artificial intelligence for the District Health Board (DHB) in Udon Thani Province.

Abstract

      This study aimed to examine the situation of non-communicable diseases (NCDs), the process of driving the District Health Board (DHB) in Udon Thani Province, and to study the development of a model for solving NCDs using artificial intelligence for the DHS in Udon Thani Province. This is a research and development (R&D) project spanning 24 months, divided into two phases: Phase 1 (research phase, October 2023 to September 2024, 12 months) and Phase 2 (development phase, October 2024 to September 2025, 12 months). The sample group consists of 500 members from each of the 20 District Health Board (DHB) in Udon Thani Province, with 25 members per district. The tools used in the activities include the development of a model for solving NCDs using artificial intelligence for the DQDCs in Udon Thani Province, and the existing model for solving NCDs using artificial intelligence. The District Health Board (DHB) of Udon Thani Province used the following tools for data collection: a record form for the implementation of the DQDC's processes in addressing health problems in the area; an evaluation of performance using the UCCARE criteria (UCCARE Assessment); and a self-assessment form for the DQDC's readiness in addressing health problems in the area. Data was analyzed using content analysis, frequency, percentage, mean, standard deviation, F-test with two-way MANOVA, dependent t-test, F-test with repeated measurement, and Duncan's pairwise comparisons.

      The results of the study, following the development of a model for solving non-communicable diseases (NCDs) using artificial intelligence for the District Health Board (DHB) in Udon Thani Province, showed that the UCCARE implementation results in 5 districts had a score of 5. The readiness of the District Health Board (DHB) in solving health problems in the area of ​​Udon Thani Province, both overall and in specific aspects, was at the highest level. The readiness to implement the UCCARE model was also at the highest level overall and in specific aspects. It was found that there was a statistically significant difference (p < .05) between the implementation periods of before (2023), during (2024), and after (2025), with the implementation after (2025) being better than before (2023) and during (2024).