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

Factors Affecting Adherence in Allergic Disorders and Strategies for Improvement

Addressing patient adherence is a key element in ensuring positive health outcomes and improving health-related quality of life for patients with atopic and immunologic disorders. Understanding the complex etiologies of patient nonadherence and identifying real-world solutions is important for clinicians, patients, and systems to design and effect change. This review serves as an important resource for defining key issues related to patient nonadherence and outlines solutions, resources, knowledge gaps, and advocacy areas across five domains: health care access, financial considerations, socioenvironmental factors, health literacy, and psychosocial factors. To allow for more easily digestible and usable content, we describe solutions based on three macrolevels of focus: patient, clinician, and system. This review and interactive tool kit serve as an educational resource and call to action to improve equitable distribution of resources, institutional policies, patient-centered care, and practice guidelines for improving health outcomes for all patients with atopic and immunologic disorders.

The Development of a Text Messaging Platform to Enhance a Youth Diabetes Prevention Program: Observational Process Study

BACKGROUND

Approximately 1 in 5 adolescents in the United States has prediabetes, and racially and ethnically minoritized youths are disproportionately impacted. Unfortunately, there are few effective youth diabetes prevention programs, and in-person interventions are challenging because of barriers to access and engagement.

OBJECTIVE

We aimed to develop and assess the preliminary feasibility and acceptability of a youth-informed SMS text messaging platform to provide additional support and motivation to adolescents with prediabetes participating in a diabetes prevention workshop in East Harlem, New York City, New York, United States. We collaborated with our youth action board and a technology partner (mPulse Mobile) to develop and pilot-test the novel interactive platform.

METHODS

The technology subcommittee of our community action board (comprising youths and young adults) used the results from focus groups that we had previously conducted with youths from our community to develop 5 message types focused on healthy eating and active living: goal setting, behavior tracking, individually tailored guidance, motivational messages, and photo diary. We used an iterative process to develop and pilot the program with our internal study team, including youths from our community action board and mPulse Mobile developers. We then conducted a pilot of the 12-week SMS text messaging program with 13 youths with prediabetes.

RESULTS

Participants (aged 15-21 years; 10/13, 77% female; 3/10, 23% Black and 10/13, 77% Hispanic or Latinx) received an average of 2 automated messages per day. The system correctly sent 84% (2231/2656) of the messages at the time intended; the remaining 16% (425/2656) of the messages were either sent at the incorrect time, or the system did not recognize a participant response to provide the appropriate reply. The level of engagement with the program ranged from 1 (little to no response) to 5 (highly responsive) based on how frequently participants responded to the interactive (2-way) messages. Highly responsive participants (6/13, 46%) responded >75% (1154/1538) of the time to interactive messages sent over 12 weeks, and 69% (9/13) of the participants were still engaged with the program at week 12. During a focus group conducted after program completion, the participants remarked that the message frequency was appropriate, and those who had participated in our in-person workshops reflected that the messages were reminiscent of the workshop content. Participants rated goal setting, behavior tracking, and tailored messages most highly and informed planned adaptations to the platform. Participants described the program as: "interactive, informative, enjoyable, very convenient, reliable, motivational, productive, and reflective."

CONCLUSIONS

We partnered with youths in the initial content development and pilot testing of a novel SMS text messaging platform to support diabetes prevention. This study is unique in the triple partnership we formed among researchers, technology experts, and diverse youths to develop a mobile health platform to address diabetes-related disparities.

Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia

Chronic limb-threatening ischemia (CLTI) is the most advanced form of peripheral artery disease. CLTI has an extremely poor prognosis and is associated with considerable risk of major amputation, cardiac morbidity, mortality, and poor quality of life. Early diagnosis and targeted treatment of CLTI is critical for improving patient's prognosis. However, this objective has proven elusive, time-consuming, and challenging due to existing health care disparities among patients. In this article, we reviewed how artificial intelligence (AI) and machine learning (ML) can be helpful to accurately diagnose, improve outcome prediction, and identify disparities in the treatment of CLTI. We demonstrate the importance of AI/ML approaches for management of these patients and how available data could be used for computer-guided interventions. Although AI/ML applications to mitigate health care disparities in CLTI are in their infancy, we also highlighted specific AI/ML methods that show potential for addressing health care disparities in CLTI.

Tailoring a Text Messaging and Fotonovela Program to Increase Patient Engagement in Colorectal Cancer Screening in a Large Urban Community Clinic Population: Quality Improvement Project

BACKGROUND

Appropriate annual screenings for colorectal cancer (CRC) are an essential preventive measure for the second-leading cause of cancer-related death in the United States. Studies have shown that CRC screening rates are influenced by various social determinants of health (SDOH) factors, including race, ethnicity, and geography. According to 2018 national data, participation in screening is lowest among Hispanic or Latinx individuals (56.1%). At an urban Federally Qualified Health Center, a quality improvement project was conducted to evaluate a texting program with a motivational fotonovela-a short narrative comic. Fotonovelas have previously been used in programs to improve knowledge of cervical cancer and human papillomavirus, vaccinations, and treatments for depression.

OBJECTIVE

This study aimed to encourage compliance with fecal immunochemical test (FIT) screening. Patient engagement involved a texting program with fotonovelas informed by behavior change techniques. This study sought to understand the qualitative characteristics of patient motivation, intention, and barriers to completing their screening.

METHODS

A total of 5241 English-speaking or Spanish-speaking Federally Qualified Health Center patients aged 50 to 75 years were randomized to either intervention (a 4-week tailored 2-way texting program with a fotonovela comic) or usual care (an SMS text message reminder and patient navigator phone call). The texting vendor used a proprietary algorithm to categorize patients in the intervention group into SDOH bands based on their home addresses (high impact=high social needs and low impact=low social needs). Over 4 weeks, patients were texted questions about receiving and returning their FIT, what barriers they may be experiencing, and their thoughts about the fotonovela.

RESULTS

The SDOH index analysis showed that most of the patient population was in the SDOH band categories of high impact (555/2597, 21.37%) and very high impact (1416/2597, 54.52%). Patients sent 1969 total responses to the texting system. Thematic analysis identified 3 major themes in these responses: messages as a reminder, where patients reported that they were motivated to return the FIT and had already done so or would do so as soon as possible; increasing patients' understanding of screening importance, where patients expressed an increased knowledge about the purpose and importance of the FIT; and expressing barriers, where patients shared reasons for not completing the FIT.

CONCLUSIONS

The texting program and fotonovela engaged a subset of patients in each SDOH band, including the high and very high impact bands. Creating culturally tailored messages can encourage patient engagement for accepting the content of the messaging, confirming intentions to complete their FIT, and sharing insights about barriers to behavior change. To better support all patients across the continuum of care with CRC screening, it is important to continue to develop and assess strategies that engage patients who did not return their home-mailed FIT.

Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review

OBJECTIVE

Artificial Intelligence (AI) offers potential opportunities to optimize clinical pharmacy services in community or hospital settings. The objective of this systematic literature review was to identify and analyse quantitative studies using or integrating AI for clinical pharmacy services.

MATERIALS AND METHODS

A systematic review was conducted using PubMed/Medline and Web of Science databases, including all articles published from 2000 to December 2021. Included studies had to involve pharmacists in the development or use of AI-powered apps and tools..

RESULTS

19 studies using AI for clinical pharmacy services were included in this review. 12 out of 19 articles (63.1%) were published in 2020 or 2021. Various methodologies of AI were used, mainly machine learning techniques and subsets (natural language processing and deep learning). The datasets used to train the models were mainly extracted from electronic medical records (6 studies, 32%). Among clinical pharmacy services, medication order review was the service most targeted by AI-powered apps and tools (9 studies), followed by health product dispensing (4 studies), pharmaceutical interviews and therapeutic education (2 studies). The development of these tools mainly involved hospital pharmacists (12/19 studies).

DISCUSSION AND CONCLUSION

The development of AI-powered apps and tools for clinical pharmacy services is just beginning. Pharmacists need to keep abreast of these developments in order to position themselves optimally while maintaining their human relationships with healthcare teams and patients. Significant efforts have to be made, in collaboration with data scientists, to better assess whether AI-powered apps and tools bring value to clinical pharmacy services in real practice.

Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review

BACKGROUND

Artificial intelligence (AI)-based chatbots can offer personalized, engaging, and on-demand health promotion interventions.

OBJECTIVE

The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change.

METHODS

A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

Of the 15 included studies, several demonstrated the high efficacy of AI chatbots in promoting healthy lifestyles (n=6, 40%), smoking cessation (n=4, 27%), treatment or medication adherence (n=2, 13%), and reduction in substance misuse (n=1, 7%). However, there were mixed results regarding feasibility, acceptability, and usability. Selected behavior change theories and expert consultation were used to develop the behavior change strategies of AI chatbots, including goal setting, monitoring, real-time reinforcement or feedback, and on-demand support. Real-time user-chatbot interaction data, such as user preferences and behavioral performance, were collected on the chatbot platform to identify ways of providing personalized services. The AI chatbots demonstrated potential for scalability by deployment through accessible devices and platforms (eg, smartphones and Facebook Messenger). The participants also reported that AI chatbots offered a nonjudgmental space for communicating sensitive information. However, the reported results need to be interpreted with caution because of the moderate to high risk of internal validity, insufficient description of AI techniques, and limitation for generalizability.

CONCLUSIONS

AI chatbots have demonstrated the efficacy of health behavior change interventions among large and diverse populations; however, future studies need to adopt robust randomized control trials to establish definitive conclusions.

Remodeling the Medication Collection Process With Prescription in Locker Box (PILBOX): Prospective Cross-sectional Study

Background

Traditionally, patients wishing to obtain their prescription medications have had to physically go to pharmacy counters and collect their medications via face-to-face interactions with pharmacy staff. Prescription in Locker Box (PILBOX) is a new innovation allowing patients and their caregivers to collect medication asynchronously, 24/7 at their convenience, from medication lockers instead of from pharmacy staff.

Objective

This study aimed to determine the willingness of patients and caregivers to use this new innovation and factors that affect their willingness.

Methods

This prospective cross-sectional study was conducted over 2 months at 2 public primary health care centers in Singapore. Patients or caregivers aged 21 years and older who came to pharmacies to collect medications were administered a 3-part questionnaire face-to-face by trained study team members after they gave their consent to participate in the study.

Results

A total of 222 participants completed the study. About 40% (89/222, 40.1%) of participants were willing to use PILBOX to collect their medications. Among participants who were keen to use the PILBOX service, slightly more than half (47/89, 53%) were willing to pay for the PILBOX service. Participants felt that ease of use (3.5 [SD 1.2]) of PILBOX was the most important factor affecting their willingness to use the medication pickup service. This was followed by waiting time (3.4 [SD 1.3]), cost of using the medication pickup service (3.0 [SD 1.4]), and 24/7 accessibility (2.6 [SD 1.4]). This study also found that age (P=.01), language literacy (P<.001), education level (P<.001), working status (P=.01), and personal monthly income (P=.01) were factors affecting the willingness of patients or caregivers to use PILBOX.

Conclusions

Patients and caregivers are keen to use PILBOX to collect their medications for its convenience and the opportunity to save time if it is easy to use and not costly.

A VOSviewer-Based Bibliometric Analysis of Prescription Refills

Purpose

Prescription refills are long-term prescriptions for chronic patients in stable status, which varies from country to country. A well-established prescription refill system is beneficial for chronic patients’ medication management and facilitates the efficacy of clinical care. Therefore, we carried out a bibliometric analysis to examine the development of this field.

Summary

Publications on prescription refills from 1970 to 2021 were collected in the Web of Science Core Collection (WoSCC). Search strategy TS = “prescri* refill*” OR “medi* refill*” OR “repeat prescri*” OR “repeat dispens*” OR TI = refill* was used for search. VOSviewer was applied to visualize the bibliometric analysis. A total of 319 publications were found in WoSCC. Study attention on prescription refills has shown a steady rise but is still low in recent years. The United States was the most productive country, which had the highest total citations, average citations per publication, and the highest H-index, and participated in international collaboration most frequently. The University of California system was the most productive institution. The U.S. Department of Veterans Affairs was the institution with the most citations, most average citation, and highest H-index. Sundell was the most productive author, and Steiner J. F. was the most influential author. “Adherence,” “medication,” and “therapy” were the most prominent keywords.

Conclusion

Publications on prescription refills have increased rapidly and continue to grow. The United States had the leading position in the area. It is recommended to pay closer attention to the latest hotspots, such as “Opioids,” “Surgery,” “Differentiated care,” and “HIV.”

Machine Learning and Medication Adherence: Scoping Review

BACKGROUND

This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.

OBJECTIVE

This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.

METHODS

PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

RESULTS

Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.

CONCLUSIONS

Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.

Medication Adherence: Expanding the Conceptual Framework

Interventions targeting traditional barriers to antihypertensive medication adherence have been developed and evaluated, with evidence of modest improvements in adherence. Translation of these interventions into population-level improvements in adherence and clinical outcomes among older adults remains suboptimal. From the Cohort Study of Medication Adherence among Older adults (CoSMO), we evaluated traditional barriers to antihypertensive medication adherence among older adults with established hypertension (N = 1,544; mean age = 76.2 years, 59.5% women, 27.9% Black, 24.1% and 38.9% low adherence by proportion of days covered (i.e., PDC <0.80) and the 4-item Krousel-Wood Medication Adherence Scale (i.e., K-Wood-MAS-4 ≥1), respectively), finding that they explained 6.4% and 14.8% of variance in pharmacy refill and self-reported adherence, respectively. Persistent low adherence rates, coupled with low explanatory power of traditional barriers, suggest that other factors warrant attention. Prior research has investigated explicit attitudes toward medications as a driver of adherence; the roles of implicit attitudes and time preferences (e.g., immediate vs. delayed gratification) as mechanisms underlying adherence behavior are emerging. Similarly, while associations of individual-level social determinants of health (SDOH) and medication adherence are well reported, there is growing evidence about structural SDOH and specific pathways of effect. Building on published conceptual models and recent evidence, we propose an expanded conceptual framework that incorporates implicit attitudes, time preferences, and structural SDOH, as emerging determinants that may explain additional variation in objectively and subjectively measured adherence. This model provides guidance for design, implementation, and assessment of interventions targeting sustained improvement in implementation medication adherence and clinical outcomes among older women and men with hypertension.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Background

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

Methods

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

Results

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

Conclusions

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

Mobile Fotonovelas Within a Text Message Outreach: An Innovative Tool to Build Health Literacy and Influence Behaviors in Response to the COVID-19 Pandemic

With all 50 US states reporting cases of coronavirus disease (COVID-19), people around the country are adapting and stepping up to the challenges of the pandemic; however, they are also frightened, anxious, and confused about what they can do to avoid exposure to the disease. Usual habits have been interrupted as a result of the crisis, and consumers are open to suggestions and strategies to help them change long-standing attitudes and behaviors. In response, a novel and innovative mobile communication capability was developed to present health messages in English and Spanish with links to fotonovelas (visual stories) that are accessible, easy to understand across literacy levels, and compelling to a diverse audience. While SMS text message outreach has been used to build health literacy and provide social support, few studies have explored the benefits of SMS text messaging combined with visual stories to influence health behaviors and build knowledge and self-efficacy. In particular, this approach can be used to provide vital information, resources, empathy, and support to the most vulnerable populations. This also allows providers and health plans to quickly reach out to their patients and members without any additional resource demands at a time when the health care system is severely overburdened.