Digital and artificial intelligence-assisted medication management for older adults with chronic diseases: a systematic review and meta-analysis of randomized controlled trials
- 1School of Nursing, Changchun University of Chinese Medicine
Abstract
Objective: This systematic review and meta-analysis aims to synthesize the results of randomized controlled trials to determine whether medication management approaches based on digital technologies and artificial intelligence/machine learning can indeed bring measurable clinical improvements in older adults with at least one chronic diseases.
Methods: To identify relevant RCTs, we performed a systematic search of PubMed, Embase, Cochrane Library, CNKI, Wanfang, and other databases from their inception through June 30, 2026, for studies of digital and AI/ML-based medication management in older adults with at least one chronic diseases. All stages of study selection, data extraction, and risk-of-bias assessment were conducted independently by two reviewers, applying the Cochrane RoB 2.0 tool. Meta-analytic pooling was conducted via RevMan 5.4.
Results: Nineteen RCTs met the inclusion criteria, of which 6 were included in the meta-analysis and 13 in the descriptive synthesis. For medication adherence, three studies reported highly heterogeneous results ( I ² = 97%), and because of this heterogeneity and the small number of studies, the data were not pooled; the individual study estimates ranged from SMD = −0.28–SMD = 1.89. For PIMs, only one study reported a comparable outcome; no pooled estimate was calculated. For hospitalization and mortality, the two included studies used different effect measures (HR and OR) and were therefore presented separately: Blum et al. reported an HR of 0.95 (95% CI 0.77–1.17), and Rieckert et al. reported an OR of 0.88 (95% CI 0.73–1.07).
Conclusion: The current body of evidence does not establish clinically meaningful benefits of digital or AI/ML-based medication management on hard endpoints such as hospitalization and mortality among older adults with at least one chronic diseases. For medication adherence, the evidence remains inconclusive and highly heterogeneous, and no reliable pooled estimate could be obtained. Given the pronounced heterogeneity and the limited pool of high-quality RCTs, current evidence is insufficient to establish clinical benefit for the routine implementation of digital or AI/ML-based medication management systems in geriatric care and further rigorously designed studies are warranted.
Systematic review registration: CRD420261460445.
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BibTeX
@article{cheng2026digital,
title = {{Digital and artificial intelligence-assisted medication management for older adults with chronic diseases: a systematic review and meta-analysis of randomized controlled trials}},
author = {Yuyan Cheng and Weidong Huang and Jianan Tian},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1956051},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1956051/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Digital and artificial intelligence-assisted medication management for older adults with chronic diseases: a systematic review and meta-analysis of randomized controlled trials
AU - Yuyan Cheng
AU - Weidong Huang
AU - Jianan Tian
PY - 2026
DA - 2026/10/22
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1956051
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1956051/full
AB - Objective: This systematic review and meta-analysis aims to synthesize the results of randomized controlled trials to determine whether medication management approaches based on digital technologies and artificial intelligence/machine learning can indeed bring measurable clinical improvements in older adults with at least one chronic diseases. Methods: To identify relevant RCTs, we performed a systematic search of PubMed, Embase, Cochrane Library, CNKI, Wanfang, and other databases from their inception through June 30, 2026, for studies of digital and AI/ML-based medication management in older adults with at least one chronic diseases. All stages of study selection, data extraction, and risk-of-bias assessment were conducted independently by two reviewers, applying the Cochrane RoB 2.0 tool. Meta-analytic pooling was conducted via RevMan 5.4. Results: Nineteen RCTs met the inclusion criteria, of which 6 were included in the meta-analysis and 13 in the descriptive synthesis. For medication adherence, three studies reported highly heterogeneous results ( I ² = 97%), and because of this heterogeneity and the small number of studies, the data were not pooled; the individual study estimates ranged from SMD = −0.28–SMD = 1.89. For PIMs, only one study reported a comparable outcome; no pooled estimate was calculated. For hospitalization and mortality, the two included studies used different effect measures (HR and OR) and were therefore presented separately: Blum et al. reported an HR of 0.95 (95% CI 0.77–1.17), and Rieckert et al. reported an OR of 0.88 (95% CI 0.73–1.07). Conclusion: The current body of evidence does not establish clinically meaningful benefits of digital or AI/ML-based medication management on hard endpoints such as hospitalization and mortality among older adults with at least one chronic diseases. For medication adherence, the evidence remains inconclusive and highly heterogeneous, and no reliable pooled estimate could be obtained. Given the pronounced heterogeneity and the limited pool of high-quality RCTs, current evidence is insufficient to establish clinical benefit for the routine implementation of digital or AI/ML-based medication management systems in geriatric care and further rigorously designed studies are warranted. Systematic review registration: CRD420261460445.
SN - 2296-2565
ER -