4.6 Article

Developing an Artificial Intelligence-Driven Nudge Intervention to Improve Medication Adherence: A Human-Centred Design Approach

Journal

JOURNAL OF MEDICAL SYSTEMS
Volume 48, Issue 1, Pages -

Publisher

SPRINGER
DOI: 10.1007/s10916-023-02024-0

Keywords

Medication adherence; Qualitative research; Behaviour change; Design thinking

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To improve medication adherence, a digital AI-driven nudge intervention was co-developed with stakeholders. The intervention was found to address deficits in medication adherence tracking and was well-received by participants. Flexibility and reminder nudges were identified as critical success factors, while incentive-based nudges were viewed negatively by patients. Leveraging existing software and simplifying data entry requirements were suggested to minimize user burden. The findings demonstrate the acceptability and appropriateness of an AI-driven nudge tool, provided it is flexible to user requirements.
To improve medication adherence, we co-developed a digital, artificial intelligence (AI)-driven nudge intervention with stakeholders (patients, providers, and technologists). We used a human-centred design approach to incorporate user needs in creating an AI-driven nudge tool. We report the findings of the first stage of a multi-phase project: understanding user needs and ideating solutions. We interviewed healthcare providers (n = 10) and patients (n = 10). Providers also rated example nudge interventions in a survey. Stakeholders felt the intervention could address existing deficits in medication adherence tracking and were optimistic about the solution. Participants identified flexibility of the intervention, including mode of delivery, intervention intensity, and the ability to stratify to user ability and needs, as critical success factors. Reminder nudges and provision of healthcare worker contact were rated highly by all. Conversely, patients perceived incentive-based nudges poorly. Finally, participants suggested that user burden could be minimised by leveraging existing software (rather than creating a new App) and simplifying or automating the data entry requirements where feasible. Stakeholder interviews generated in-depth data on the perspectives and requirements for the proposed solution. The participatory approach will enable us to incorporate user needs into the design and improve the utility of the intervention. Our findings show that an AI-driven nudge tool is an acceptable and appropriate solution, assuming it is flexible to user requirements.

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