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. 2020 Nov 9;9(11):e21659.
doi: 10.2196/21659.

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

Don Roosan #  1 , Jay Chok  2 , Mazharul Karim  1 , Anandi V Law  1 , Andrius Baskys  3 , Angela Hwang  1 , Moom R Roosan #  4
Affiliations

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

Don Roosan et al. JMIR Res Protoc. .

Abstract

Background: Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients' cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients.

Objective: Our objective is to improve the design of an innovative AI- and human factor-based interface that supports patients' medication information comprehension that could potentially improve medication adherence.

Methods: This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks.

Results: Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022.

Conclusions: This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides.

International registered report identifier (irrid): PRR1-10.2196/21659.

Keywords: artificial intelligence; complex medication information; machine learning; medication adherence; mobile phone; patient cognition; smartphone app.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Study design. The participants are randomized first at R: one group uses only the app (A) and another group is given the app with medication information text (AT). At time point 1 (T1), after 4 weeks, both groups are evaluated for information recall (>80%). Then, after a second randomization (R), the participants are divided into the following groups: app with medication information text (AT), app with contextual medication information text (ACT), and app with visualization (AV). T0: baseline; T2: time point 2 (after 12 weeks).
See this image and copyright information in PMC

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