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. 2019 Nov 18;7(11):e15771.
doi: 10.2196/15771.

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  1 , Ridhika Agrawal  1   2 , Benjamin Nguyen  1   2 , Erwin W Jeong  3 , Harmony K Noble  4 , Andrew Paster  1 , Ram S Prayaga  1
Affiliations

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 et al. JMIR Mhealth Uhealth. .

Abstract

Background: Nonadherence 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.

Objective: The 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.

Methods: The 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.

Results: Over 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%; X21 [n=273,216]=1829.2; P<.001). There were also significantly different rates of refill requests by age band (X26 [n=268,793]=1460.3; P<.001), with younger patients requesting refills at a higher rate. Finally, the vast majority (284,598/307,484, 92.23%) of patient responses were handled using conversational AI.

Conclusions: Multiple 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.

Keywords: Medicare patients; SMS; conversational AI; health disparities; machine learning; medication adherence; predictive modeling; refill adherence; social determinants of health; text messaging.

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Conflict of interest statement

Conflicts of Interest: RBP, AP, and RSP are current employees of mPulse Mobile, Inc, which is a vendor for Kaiser Permanente. RA and BN contributed to this study as part of their internship at mPulse Mobile, Inc.

Figures

Figure 1
Figure 1
Overview of message flow within refill dialogue.
Figure 2
Figure 2
Conversion of refill reminder to refill request. DOB: date of birth.
Figure 3
Figure 3
Refills requests by hour from initial reminder.
Figure 4
Figure 4
Refill rates versus social determinants of health bands.
Figure 5
Figure 5
Social determinants of health impact on response rates and percentage of responders who request refill. SDOH: social determinants of health.
Figure 6
Figure 6
Impact of race/ethnicity on response rates and percentage of responders who request refill.
Figure 7
Figure 7
Minimizing the number of false positives.
See this image and copyright information in PMC

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