ArticlePatient preference and adherence2026
Artificial Intelligence in Medication Adherence: A National Assessment of Knowledge, Attitudes, and Perceptions Among Chronic Disease Patients in Jordan.
Article in Patient preference and adherence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: To assess knowledge, attitudes, and perceptions toward AI-based medication adherence tools in a national cross-sectional survey among Jordanian adults with chronic diseases and to identify factors associated with favorable acceptance. Patients and Methods: A national cross-sectional online survey was conducted between January and July 2025 using convenience and snowball sampling and a self-developed, pilot-tested, and content-validated Arabic questionnaire. The questionnaire captured sociodemographic characteristics, digital literacy, chronic disease information, and KAP toward AI-based medication adherence tools. Knowledge was assessed using eight statements describing AI capabilities and rated on a five-point Likert scale, which were recoded dichotomously for scoring (score range 0-8; ≥5 indicating good knowledge). Attitudes and perceptions were measured using five-point Likert scales (mean scores ≥3.5 indicating positive or high levels). Descriptive statistics, chi-square tests, multivariable logistic regression, sensitivity analyses, and polypharmacy subgroup analyses were performed to identify factors associated with favorable acceptance. Results: Among 552 participants (mean age 52.7 ± 12.6 years; 56.3% female), the most prevalent chronic diseases were hypertension (213, 38.6%) and diabetes mellitus (179, 32.4%), with 154 (27.9%) reporting polypharmacy (≥5 medications). 59.2% demonstrated good knowledge (mean score 5.3 ± 1.8). Moderately positive attitudes were observed in 48.6% (mean 3.6 ± 0.9), while 52.7% reported high perceptions (mean 3.7 ± 0.9). Strong support was reported for AI-based reminders (80%) and educational functions (76%), whereas endorsement of predictive features was lower (49-58%). Major concerns included privacy (71%), technical reliability, and reduced human interaction. Chi-square tests showed significant associations with age and digital literacy (p < 0.001). Multivariable logistic regression confirmed that younger age, higher education, advanced digital literacy, and smartphone ownership independently predicted favorable KAP (p < 0.001). Polypharmacy was associated with greater receptivity in unadjusted analyses but was not an independent predictor after adjustment. Findings remained robust in sensitivity analyses. Conclusion: Jordanian patients with chronic diseases show moderate-to-good knowledge and cautiously positive attitudes toward AI-enabled medication adherence tools. Acceptance is shaped mainly by digital literacy and trust, underscoring the need for governance frameworks, clinician oversight, Arabic-language design, and targeted digital literacy initiatives to support equitable integration into national platforms such as Hakeem.
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