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

Roosan, Don; Chok, Jay; Karim, Mazharul; Law, Anandi V.; Baskys, Andrius; Hwang, Angela; Roosan, Moom R.

doi:10.2196/21659

Cited by 27 publications

(10 citation statements)

References 51 publications

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“…For example, patients are traditionally required to commit to a physician’s consultative appointment that could be relatively inflexible because of a long list of patients, and one could be forced to skip the consultation because of a conflict in their schedule. AI confers the benefit of information collection and dissemination beyond the constraints of time and place, which have been shown to improve medication adherence through an AI-based smartphone app [ 51 ] and diet and exercise adherence through an AI-based virtual health assistant [ 52 ]. Our findings also demonstrated that AI is valued for its potential to speed up health care processes such as diagnosis, waiting time, communication with care teams, decisional support, and other routine tasks (eg, progress monitoring) that can be automated.…”

Section: Discussionmentioning

confidence: 99%

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

Chew

Achananuparp

2022

J Med Internet Res

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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.

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Section: Discussionmentioning

confidence: 99%

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

Chew

Achananuparp

2022

J Med Internet Res

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“…Future intuitive interfaces taking consideration of heuristics and artificial intelligence methods may fill in the gap. [56][57][58] Future studies may evaluate the coverage of the 27 if-then logic heuristics for their usefulness in system design. Developing a metric to quantify the outcome of these heuristics in system design may be necessary.…”

Section: Implications For Health It System Designmentioning

confidence: 99%

Power of Heuristics to Improve Health Information Technology System Design

et al. 2022

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Background Clinical decision-making can be prone to error if health system design does not match expert clinicians' higher cognitive skills. There is a gap in understanding the need for the importance of heuristics in clinical decision-making. The heuristic approach can provide cognitive support in designing intuitive health information systems for complex cases. Objective We explored complex decision-making by infectious diseases (ID) clinicians focusing on fast and frugal heuristics. We hypothesized that ID clinicians use simple heuristics to understand complex cases using their experience. Methods The study utilized cognitive task analysis and heuristics-based decision modeling. We conducted cognitive interviews and provided clinicians with a fast-and-frugal tree algorithm to convert complex information into simple decision algorithms. We conducted a critical decision method–based analysis to generate if–then logic sentences from the transcript. We conducted a thematic analysis of heuristics and calculated the average time to complete and the number of crucial information in the decision nodes. Results A total of 27 if–then logic heuristics sentences were generated from analyzing the data. The average time to construct the fast-and-frugal trees was 1.65 ± 0.37 minutes, and the average number of crucial pieces of information clinicians focused on was 5.4 ± 3.1. Conclusion Clinicians use shortcut mental models to reduce complex cases into simple mental model algorithms. The innovative use of artificial intelligence could allow clinical decision support systems to focus on creative and intuitive interface design matching the higher cognitive skills of expert clinicians.

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“…Integrating pharmacy claims data with more complete data sources, such as clinical and health outcomes, would provide information on the clinical indication for each medication and reasons for medication changes or cessation [ 39 , 40 ]. This integration could also be used to establish the association between gaps or lapses in medication refills and adverse health outcomes and healthcare resource use.…”

Section: Real-world Challengesmentioning

confidence: 99%

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

Menditto

Cahir

Malo

et al. 2021

IJERPH

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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.

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

Cited by 27 publications

References 51 publications

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

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

Power of Heuristics to Improve Health Information Technology System Design

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

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