Evidence map›Paper›PMID 31738170›Full record

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.

Rena Brar Prayaga, Ridhika Agrawal, Benjamin Nguyen, Erwin W Jeong, Harmony K Noble, Andrew Paster, Ram S Prayaga

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Review
  4. Review
  5. Factors Affecting Adherence in Allergic Disorders and Strategies for Improvement.The journal of allergy and clinical immunology. In practice · 2024
    Review
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  13. Medication Adherence: Expanding the Conceptual Framework.American journal of hypertension · 2021
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Rena Brar PrayagamPulse Mobile, Inc, Encino, CA, United States.ORCID 0000-0001-9299-9074
Ridhika AgrawalmPulse Mobile, Inc, Encino, CA, United States.ORCID 0000-0002-5173-1319
Benjamin NguyenmPulse Mobile, Inc, Encino, CA, United States.ORCID 0000-0002-5343-1504
Erwin W JeongMedicare Medication Therapy Management & Medication Adherence programs, Kaiser Permanente Southern California, Downey, CA, United States.ORCID 0000-0001-8927-0709
Harmony K NobleRegional Pharmacy Clinical Operations, Kaiser Permanente Southern California, Downey, CA, United States.ORCID 0000-0002-2435-9670
Andrew PastermPulse Mobile, Inc, Encino, CA, United States.ORCID 0000-0001-6273-7460
Ram S PrayagamPulse Mobile, Inc, Encino, CA, United States.ORCID 0000-0001-5445-0670

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Drug MonitoringSocial Determinants of HealthAgedArtificial IntelligenceCaliforniaCross-Sectional StudiesDemographyFemaleHumansMaleMedicareMedication AdherenceMiddle AgedPilot ProjectsQualitative ResearchText Messagingconversational AIhealth disparitiesmachine learningMedicare patientsmedication adherencepredictive modelingrefill adherenceSMSsocial determinants of healthtext messaging

Identifiers

PMID31738170
PMCPMC6887813

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.