ArticleJMIR mHealth and uHealth2019
Impact of Social Determinants of Health and Demographics on Refill Requests by Medicare Patients Using a Conversational Artificial Intelligence Text Messaging Solution: Cross-Sectional Study.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Observational
- Nudges in A Learning Health System: Applications to Cardiovascular-Kidney-Metabolic Care.JACC. Advances · 2025Review
- Conversational agents for pharmaceutical use: insights from the eCCo database.International journal of clinical pharmacy · 2025Review
- Factors Affecting Adherence in Allergic Disorders and Strategies for Improvement.The journal of allergy and clinical immunology. In practice · 2024Review
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- The Development of a Text Messaging Platform to Enhance a Youth Diabetes Prevention Program: Observational Process Study.JMIR formative research · 2024Article
- Article
- Using Text Messages and Fotonovelas to Increase Return of Home-Mailed Colorectal Cancer Screening Tests: Mixed Methods Evaluation.JMIR cancer · 2023Article
- Remodeling the Medication Collection Process With Prescription in Locker Box (PILBOX): Prospective Cross-sectional Study.Journal of medical Internet research · 2022Article
- A VOSviewer-Based Bibliometric Analysis of Prescription Refills.Frontiers in medicine · 2022Review
- Machine Learning and Medication Adherence: Scoping Review.JMIRx med · 2021Article
- Medication Adherence: Expanding the Conceptual Framework.American journal of hypertension · 2021Article
- A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.SSM - population health · 2021Article
- Analysis of the Effects of a Patient-Centered Rideshare Program on Missed Appointments and Timeliness for MRI Appointments at an Academic Medical Center.Journal of the American College of Radiology : JACR · 2021Article
- Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases.Frontiers in digital health · 2021Review
- Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities.JMIR medical informatics · 2020Article
- Mobile Fotonovelas Within a Text Message Outreach: An Innovative Tool to Build Health Literacy and Influence Behaviors in Response to the COVID-19 Pandemic.JMIR mHealth and uHealth · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNonadherence among patients with chronic disease continues to be a significant concern, and the use of text message refill reminders has been effective in improving adherence. However, questions remain about how differences in patient characteristics and demographics might influence the likelihood of refill using this channel.
objectiveThe aim of this study was to evaluate the efficacy of an SMS-based refill reminder solution using conversational artificial intelligence (AI; an automated system that mimics human conversations) with a large Medicare patient population and to explore the association and impact of patient demographics (age, gender, race/ethnicity, language) and social determinants of health on successful engagement with the solution to improve refill adherence.
methodsThe study targeted 99,217 patients with chronic disease, median age of 71 years, for medication refill using the mPulse Mobile interactive SMS text messaging solution from December 2016 to February 2019. All patients were partially adherent or nonadherent Medicare Part D members of Kaiser Permanente, Southern California, a large integrated health plan. Patients received SMS reminders in English or Spanish and used simple numeric or text responses to validate their identity, view their medication, and complete a refill request. The refill requests were processed by Kaiser Permanente pharmacists and support staff, and refills were picked up at the pharmacy or mailed to patients. Descriptive statistics and predictive analytics were used to examine the patient population and their refill behavior. Qualitative text analysis was used to evaluate quality of conversational AI.
resultsOver the course of the study, 273,356 refill reminders requests were sent to 99,217 patients, resulting in 47,552 refill requests (17.40%). This was consistent with earlier pilot study findings. Of those who requested a refill, 54.81% (26,062/47,552) did so within 2 hours of the reminder. There was a strong inverse relationship (r10=-0.93) between social determinants of health and refill requests. Spanish speakers (5149/48,156, 10.69%) had significantly lower refill request rates compared with English speakers (42,389/225,060, 18.83%; X
conclusionsMultiple factors impacted refill request rates, including a strong association between social determinants of health and refill rates. The findings suggest that higher refill requests are linked to language, race/ethnicity, age, and social determinants of health, and that English speakers, whites, those younger than 75 years, and those with lower social determinants of health barriers are significantly more likely to request a refill via SMS. A neural network-based predictive model with an accuracy level of 78% was used to identify patients who might benefit from additional outreach to narrow identified gaps based on demographic and socioeconomic factors.
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