An intelligent algorithm for identification of optimum mix of demographic features for trust in medical centers in Iran.
Journal:
Artificial intelligence in medicine
Published Date:
Apr 26, 2018
Abstract
Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for treatment outcomes. The presented study identifies optimum mixture of patient demographic features with respect to trust in three large and busy medical centers in Tehran, Iran. The presented algorithm is composed of adaptive neuro-fuzzy inference system and statistical methods. It is used to deal with data and environmental uncertainty. The required data are collected from three large hospitals using standard questionnaires. The reliability and validity of the collected data is evaluated using Cronbach's Alpha, factor analysis and statistical tests. The results of this study indicate that middle age patients with low level of education and moderate illness severity and young patients with high level of education, moderate illness severity and moderate to weak financial status have the highest trust to the considered medical centers. To the best of our knowledge this the first study that investigates patient demographic features using adaptive neuro-fuzzy inference system in healthcare sector. Second, it is a practical approach for continuous improvement of trust features in medical centers. Third, it deals with the existing uncertainty through the unique neuro-fuzzy approach.
Authors
Keywords
Adolescent
Adult
Age Factors
Delivery of Health Care
Educational Status
Female
Fuzzy Logic
Hospitals
Humans
Income
Iran
Male
Middle Aged
Models, Statistical
Patient Acceptance of Health Care
Patients
Physician-Patient Relations
Reproducibility of Results
Severity of Illness Index
Socioeconomic Factors
Surveys and Questionnaires
Trust
Uncertainty
Young Adult