Artificial Intelligence-Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management

BACKGROUND

The use of artificial intelligence (AI) to optimize medication therapy management (MTM) in identifying drug interactions may potentially improve MTM efficiency. ChatGPT, an AI language model, may be applied to identify medication interventions by integrating patient and drug databases. ChatGPT has been shown to be effective in other areas of clinical medicine, from diagnosis to patient management. However, ChatGPT's ability to manage MTM related activities is little known.

OBJECTIVES

To evaluate the effectiveness of ChatGPT in MTM services in simple, complex, and very complex cases to understand AI contributions in MTM.

METHODS

Two clinical pharmacists rated and validated the difficulty of patient cases from simple, complex, and very complex. ChatGPT's response to the cases was assessed based on 3 criteria: the ability to identify drug interactions, precision in recommending alternatives, and appropriateness in devising management plans. Two clinical pharmacists validated the accuracy of ChatGPT's responses and compared them to actual answers for each complexity level.

RESULTS

ChatGPT 4.0 accurately solved 39 out of 39 (100 %) patient cases. ChatGPT successfully identified drug interactions, provided therapy recommendations and formulated general management plans, but it did not recommend specific dosages. Results suggest it can assist pharmacists in formulating MTM plans to improve overall efficiency.

CONCLUSION

The application of ChatGPT in MTM has the potential to enhance patient safety and involvement, lower healthcare costs, and assist healthcare providers in medication management and identifying drug interactions. Future pharmacists can utilize AI models such as ChatGPT to improve patient care. The future of the pharmacy profession will depend on how the field responds to the changing need for patient care optimized by AI and automation.

Opportunities to integrate nutrigenomics into clinical practice and patient counseling

BACKGROUND

Little progress has been made in translating nutrigenomics knowledge into clinical counseling in the past decade. Currently, clinicians are overwhelmed by nutrigenomics information without the proper scientific guidelines on patient counseling.

METHODS

In this study, we conducted a scoping review of the primary literature to assess the current evidence of nutrigenomics counseling. A literature search using PRISMA guidelines identified the current challenges and opportunities facing nutrigenomics counseling in clinical practice.

RESULTS

We identified four main themes: inadequate training, lack of awareness, underdeveloped nutrigenomics counseling skills, and unreliable evidence-based practice information. Many clinicians did not have the necessary knowledge to perform nutrigenomic counseling and were unaware of the available scientific information source. Moreover, there are no guidelines in the scientific community to counsel patients on nutrigenomics testing.

CONCLUSION

Opportunities exist for government and non-government entities to create an evidence-based information platform using clinical guidelines to integrate nutrigenomics knowledge from bench to bedside successfully.

Working with Data in Adult English Classrooms: Lessons Learned about Communicative Justice during the COVID-19 Pandemic

Throughout COVID-19, health officials have relied on data visualizations to communicate urgent messages about the spread of the virus and preventative measures. Relatively few efforts have employed participatory engagement with communities who have experienced a disproportionate burden of COVID-19 illness to shape these communications. Sociologist W.E.B. Du Bois viewed data visualization as an approach to changing the way people think about themselves. This paper describes a community-engaged approach to data literacy skill-building with bilingual Latina learners in an adult English program in Northern California, Bay Area. The curriculum combines data visualization activities with language instruction and preventive health themes. Early work on COVID-19 in 2020-21 emphasized improving health knowledge and message interpretation but later shifted to a critical data literacy perspective, focusing on myth-busting, improving risk messaging in their own social networks, and supporting learners to see the power of their own experiences in data story-telling processes. This pedagogical approach, guided by Charles Brigg's idea of communicative justice priorities, locates adult learners' data visualization work as part of a broader effort to be included in the perspectives that shape knowledge production in today's healthcare system. This approach can be used to examine disparities in information access in linguistically minoritized communities and guide future education interventions.

Artificial Intelligent Context-Aware Machine-Learning Tool to Detect Adverse Drug Events from Social Media Platforms

INTRODUCTION

Pharmacovigilance (PV) has proven to detect post-marketing adverse drug events (ADE). Previous research used the natural language processing (NLP) tool to extract unstructured texts relevant to ADEs. However, texts without context reduce the efficiency of such algorithms. Our objective was to develop and validate an innovative NLP tool, aTarantula, using a context-aware machine-learning algorithm to detect existing ADEs from social media using an aggregated lexicon.

METHOD

aTarantula utilized FastText embeddings and an aggregated lexicon to extract contextual data from three patient forums (i.e., MedHelp, MedsChat, and PatientInfo) taking warfarin. The lexicon used warfarin package inserts and synonyms of warfarin ADEs from UMLS and FAERS databases. Data was stored on SQLite and then refined and manually checked by three clinical pharmacists for validation.

RESULTS

Multiple organ systems where the most frequent ADE were reported at 1.50%, followed by CNS side effects at 1.19%. Lymphatic system ADEs were the least common side effect reported at 0.09%. The overall Spearman rank correlation coefficient between patient-reported data from the forums and FAERS was 0.19. As determined by pharmacist validation, aTarantula had a sensitivity of 84.2% and a specificity of 98%. Three clinical pharmacists manually validated our results. Finally, we created an aggregated lexicon for mining ADEs from social media.

CONCLUSION

We successfully developed aTarantula, a machine-learning algorithmn based on artificial intelligence to extract warfarin-related ADEs from online social discussion forums automatically. Our study shows that it is feasible to use aTarantula to detect ADEs. Future researchers can validate aTarantula on the diverse dataset.

A Survey of Mobile Apps for the Care Management of Patients with Dementia

Objective: Dementia is a progressive neurocognitive disorder that currently affects approximately 50 million people globally and causes a heavy burden for their families and societies. This study analyzed mobile apps for dementia care in different languages and during the COVID-19 pandemic. Methods: We searched PubMed, Cochrane Collaboration Central Register of Con-trolled Clinical Trials, Cochrane Systematic Reviews, Google Play Store, Apple App Store, and Huawei App Store for mobile applications for dementia care. The Mobile Application Rating Scale (MARS) was used to assess the quality of applications. Results: We included 99 apps for dementia care. No significant difference in MARS scores was noted between the two language apps (Overall MARS: English: 3.576 ± 0.580, Chinese: 3.569 ± 0.746, p = 0.962). In the subscale analysis, English apps had higher scores of perceived impact than Chinese apps but these were not significant (2.654 ± 1.372 vs. 2.000 ± 1.057, p = 0.061). (2) Applications during the COVID-19 pandemic had higher MARS scores than those before the COVID-19 pandemic but these were not significant (during the COVID-19 pandemic: 3.722 ± 0.416; before: 3.699 ± 0.615, p = 0.299). In the sub-scale analysis, apps during the COVID-19 pandemic had higher scores of engagement than apps before the COVID-19 pandemic but these were not significant (3.117 ± 0.594 vs. 2.698 ± 0.716, p = 0.068). Conclusions: Our results revealed that there is a minor but nonsignificant difference between different languages and during the COVID-19 pandemic. Further cooperation among dementia professionals, technology experts, and caregivers is warranted to provide evidence-based and user-friendly information to meet the needs of users.

The promise of digital health in healthcare equity and medication adherence in the disadvantaged dementia population

Tweetable abstract Pharmacogenomics cascade testing in a digital health solution can improve medication adherence in dementia care for disadvantaged populations.

PGxKnow: a pharmacogenomics educational HoloLens application of augmented reality and artificial intelligence

Aim: To develop and assess an augmented reality tool for pharmacogenomics (PGx) education based on artificial intelligence. Materials & methods: A HoloLens application was developed using feedback from three clinical PGx-trained pharmacists. 15 Participants independently reviewed the application and assessed usability using the system usability scale (SUS). Results & conclusion: Eighteen different frames were developed. Each video module was 2-3 min for the education. The application included textual information and 3D structures of PGx concepts. The mean SUS score for 15 participants (11 pharmacy students and four pharmacists) was 83, with a standard deviation of 6.6. Results suggest that PGxKnow has the potential to bridge the gap in PGx education, further widespread utilization of PGx and boost its impact on precision medicine.

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review

BACKGROUND

Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care.

OBJECTIVE

This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care.

METHODS

A systematic scoping review was conducted according to the 5-stage framework by Arksey and O'Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted.

RESULTS

Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study.

CONCLUSIONS

The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Scoping review: the empowerment of Alzheimer's Disease caregivers with mHealth applications

Alzheimer's Disease (AD) is one of the most prevalent neurodegenerative chronic diseases. As it progresses, patients become increasingly dependent, and their caregivers are burdened with the increasing demand for managing their care. Mobile health (mHealth) technology, such as smartphone applications, can support the need of these caregivers. This paper examines the published academic literature of mHealth applications that support the caregivers of AD patients. Following the PRISMA for scoping reviews, we searched published literature in five electronic databases between January 2014 and January 2021. Twelve articles were included in the final review. Six themes emerged based on the functionalities provided by the reviewed applications for caregivers. They are tracking, task management, monitoring, caregiver mental support, education, and caregiver communication platform. The review revealed that mHealth applications for AD patients' caregivers are inadequate. There is an opportunity for industry, government, and academia to fill the unmet need of these caregiver.

Persistence as a Robust Indicator of Medication Adherence-Related Quality and Performance

Medication adherence is a priority for health systems worldwide and is widely recognised as a key component of quality of care for disease management. Adherence-related indicators were rarely explicitly included in national health policy agendas. One barrier is the lack of standardised adherence terminology and of routine measures of adherence in clinical practice. This paper discusses the possibility of developing adherence-related performance indicators highlighting the value of measuring persistence as a robust indicator of quality of care. To standardise adherence and persistence-related terminology allowing for benchmarking of adherence strategies, the European Ascertaining Barriers for Compliance (ABC) project proposed a Taxonomy of Adherence in 2012 consisting of three components: initiation, implementation, discontinuation. Persistence, which immediately precedes discontinuation, is a key element of taxonomy, which could capture adherence chronology allowing the examination of patterns of medication-taking behaviour. Advances in eHealth and Information Communication Technology (ICT) could play a major role in providing necessary structures to develop persistence indicators. We propose measuring persistence as an informative and pragmatic measure of medication-taking behaviour. Our view is to develop quality and performance indicators of persistence, which requires investing in ICT solutions enabling healthcare providers to review complete information on patients' medication-taking patterns, as well as clinical and health outcomes.