Impact of AI-assisted Medication Dosing on Adherence, Cognition, and Treatment Perception in Elderly Patients
Contributors
- Abiha Zainab M.Phil. Scholar, Saulat Institute of Pharmaceutical Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
- Qurat-ul-Ain Zia BS Graduate, Saulat Institute of Pharmaceutical Sciences, Quaid-I-Azam University, Islamabad, Pakistan.
- Walija Maryum BS Graduate, Saulat Institute of Pharmaceutical Sciences, Quaid-I-Azam University, Islamabad, Pakistan.
- Hunaina Nadeem BS Graduate, Saulat Institute of Pharmaceutical Sciences, Quaid-I-Azam University, Islamabad, Pakistan.
- Fiza Iman M.Phil. Scholar, National Institute of Psychology, Quaid-I-Azam University, Islamabad, Pakistan.
Abstract
The focus of this study is to evaluate the effectiveness of an AI-assisted medication dosing system in improving medication compliance, cognitive function related to medication management, and treatment perception among elderly patients. 80 elderly patients undergo a quantitative, quasi-experimental pretest–posttest design, who received AI-guided dosing recommendations with clinician oversight for 12 weeks. Morisky Medication Adherence Scale (MMAS-8) is used to assess medication adherence, cognitive performance using the Mini-Mental State Examination (MMSE), and Structured Technology Acceptance Questionnaire is used for treatment perception. Pre- and post-intervention scores were compared by using paired sample t-tests and Pearson’s correlation analysis inspects relationships among study variables. The results showed a substantial increase in compliance and therapeutic adherence (pre-intervention mean = 5.42 ± 1.21; post-intervention mean = 7.13 ± 0.96; t = 8.74, p < 0.001), cognitive function (pre-intervention mean = 24.18 ± 2.64; post-intervention mean = 26.03 ± 2.31; t = 6.11, p < 0.001), and treatment evaluation (pre-intervention mean = 3.01 ± 0.54; post-intervention mean = 4.21 ± 0.47; t = 10.38, p < 0.001). Important positive interrelations were observed between adherence and cognitive performance (r = 0.58, p < 0.001), adherence and treatment perspective (r = 0.66, p < 0.001), and cognitive function and treatment performances (r = 0.49, p < 0.001). The conclusion illustrated that AI-assisted medication dosing significantly enhances adherence, cognitive ability for medication management, and patient acceptance, supporting its amalgamation into geriatric pharmacotherapy to enhance its clinical efficacy
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Corresponding Author
Abiha Zainab
M.Phil. Scholar, Saulat Institute of Pharmaceutical Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
Corresponding Author: [email protected]
