Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Designing visual hierarchies for the communication of health data

OBJECTIVES

Visual hierarchy underlies all visual design decisions related to information presentation. This manuscript describes the experience of a multidisciplinary health data visualization and software design team in using visual hierarchy to redesign a hereditary colorectal cancer lab report.

MATERIALS AND METHODS

A series of interviews with representative users were conducted to identify target user groups and determine information hierarchy for each user type. Visual elements (eg, size, color, contrast, etc.) were then assigned to mirror the information hierarchy and workflow for each user type.

RESULTS

User research identified 2 distinct user groups as consumers of the redesigned lab report. An interactive design employing a 2-level page hierarchy was developed, which stratified the content to support the needs of each user type.

CONCLUSIONS

The challenges related to displaying the complex nature of digital and personal health data can be addressed by applying foundational design methods such as visual hierarchy.

DISCUSSION

Visual hierarchy, a foundational design principle, can be used by visualization teams to clearly and efficiently present complex datasets associated with healthcare.

Digital health interventions to improve adherence to oral antipsychotics among patients with schizophrenia: a scoping review

OBJECTIVES

To assess the current evidence on the potential of digital health interventions (DHIs) to improve adherence to oral antipsychotics among patients with schizophrenia by assessing the methodologies, feasibility and effectiveness of DHIs as well as the perceptions of relevant stakeholders.

DESIGN

The scoping review was conducted based on the methodologies outlined by Levac et al and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

DATA SOURCES

PubMed, Embase, Web of Science, Scopus, CINAHL, PsycINFO and the Cochrane Library were searched in August 2023 to identify relevant publications from the previous decade.

ELIGIBILITY CRITERIA

Studies published in English focused on improving medication adherence among adult patients with schizophrenia or schizoaffective disorder via DHIs were selected. Protocols, editorials, comments, perspectives, reviews, correspondence and conference abstracts were excluded.

DATA EXTRACTION AND SYNTHESIS

The extracted data included general information about the study, framework, participants, features and strategies of DHIs, measurement tools for adherence used, and main findings.

RESULTS

In total, 64 studies were included in the qualitative synthesis. Features used in DHIs to improve medication adherence included phone calls, text messages, mobile apps, sensors, web-based platforms and electronic devices. Strategies included medication reminders and monitoring, providing medication-related information and suggestions, other illness management suggestions and individual support. Texting and mobile apps were commonly used as medication reminders and monitoring methods. Additionally, the use of sensors combined with other digital technologies has garnered significant attention. All the interventions were considered acceptable and feasible, and several were assessed in pilot trials. Preliminary findings suggest that DHIs could enhance medication adherence in patients with schizophrenia. However, further validation of their effectiveness is required.

CONCLUSION

DHIs are a promising approach to enhancing medication adherence among patients with schizophrenia. Future interventions should be interactive, focusing on user preference, experience and privacy.

Gamification to Improve Medication Adherence: A Mixed-method Usability Study for MedScrab

Medication non-adherence is a prevalent healthcare problem with poor health outcomes and added healthcare costs. MedScrab, a gamification-based mHealth app, is the first attempt to deliver crucial life-saving medication information to patients and increase their medication adherence. The paper presents the development of MedScrab and a two-phase mixed-method usability evaluation of MedScrab. Phase I qualitatively evaluated MedScrab using a think-aloud protocol for its usability. With 51 participants, qualitative data analysis of Phase I revealed two themes: positive functionality of the app and four areas of improvement. The improvement recommendations were incorporated into MedScrab's design. Phase I also validated a widely used mHealth App Usability Questionnaire (MAUQ). Quantitative data analysis of Phase I reduced the original 18-item MAUQ scale to a 15-item scale with two factors: ease of use (4 items) and usefulness and satisfaction (11 items). Phase II surveyed 83 participants from Amazon's Mechanical Turk using a modified MAUQ. The modified MAUQ scale showed strong internal consistency (Cronbach alpha = 0.959) and high factor loadings (between 0.623 and 0.987). The study design of the usability evaluation can serve as a methodological guide for designing, evaluating, and improving mHealth apps.The usability study showed that MedScrab was perceived as ease of use (6.24 out of 7) with high usefulness and satisfaction (5.72 out of 7). The quantitative data analysis results support the use of the modified MAUQ as a valid instrument to measure the usability of the MedScrab. However, the instrument should be used with adaptation based on the app's characteristics.

A usability evaluation of the perceived user friendliness, accessibility, and inclusiveness of a personalized digital care pathway tool

OBJECTIVE

This study aimed to acquire insight into the perceived user friendliness, accessibility and inclusiveness of a personalized digital care pathway.

MATERIALS & METHODS

Usability of the tool was tested in an experimental setting. Mixed methods data collection consisted of scenario-based eye tracking tests in a web- or mobile-based prototype of the tool, followed by a questionnaire assessing user friendliness (System Usability Scale; SUS) and a structured interview. Inclusiveness was assessed by subgroup comparisons based on language proficiency, age and education level. Via purposive sampling a heterogeneous population of users (N = 24) was recruited. Eye tracking was used to measure gaze behavior.

RESULTS

Overall, participants were satisfied with the tool (scale 0-10, 7.5; SD = 1.29). User friendliness of the mobile version (68.3; SD = 21.6) was higher than the web version (50.9; SD = 17.3) measured by SUS score (0-100). With regard to accessibility, eye tracking scenarios showed that the menu bar was hard to find (17% mobile, 55% web). In all scenario's, information was found faster in the mobile version than the web version. Attention was easily drawn to images. Regarding inclusiveness of the tool, we found significantly longer completing time of the scenario tasks for low language proficiency (p-value = 0.029) and higher age subgroups (p-value = 0.049). Lower language proficiency scored a significant lower SUS score (p-value = 0.012).

CONCLUSIONS

Overall, user friendliness and accessibility were positively evaluated. Assessment of inclusiveness emphasized the need for tailoring digital tools to those with low language proficiency and/or an older age. Co-creation of digital care tools with users is therefore important to match users' needs, make tools easily understandable and accessible to all users, and ultimately result in better uptake and impact.

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.

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.

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.

Detailed analysis of 'work as imagined' in the use of intravenous insulin infusions in a hospital: a hierarchical task analysis

OBJECTIVE

Variable rate intravenous insulin infusions (VRIII) is a high-risk medication that has a potential to cause significant patient harm if used in error. Complex preparation of VRIII in clinical areas and the need for frequent monitoring and adjustment increase the complexity of using VRIII. An emerging approach, called Resilient Healthcare, proposes understanding complexity of work by exploring how work is assumed to be done and compare it with everyday work. This study aimed to explore how VRIII is perceived to be used by healthcare practitioners, focusing on one aspect of Resilient Healthcare: understanding how work is assumed to be done, using a method called hierarchical task analysis (HTA).

DESIGN

A qualitative study using document analysis and focus groups.

SETTING

A vascular surgery unit in an acute National Health Service teaching hospital in the UK.

PARTICIPANTS

Stakeholders/users in different professional roles involved in the process of using VRIII.

RESULTS

The HTA showed the complexity of using VRIII and highlighted more than 115 steps required to treat elevated blood glucose. The process of producing hospital-specific guidelines was iterative. Careful consideration was taken to coordinate the development and implementation of guidelines. Documents provided detailed clinical instructions related to the use of VRIII but practitioners selectively used them, often in deference to senior colleagues. Intentional adaptations, for example, proactively asking for a VRIII prescription occurred and were acknowledged as part of providing individualised patient care.

CONCLUSION

Using VRIII to treat elevated blood glucose is a complex but necessary process mediated by a range of factors such as organisational influences. Adaptive strategies to mitigate errors were common and future research can build on insights from this study to develop a broader understanding of how VRIII is used and to understand how adaptations are made in relation to the use of VRIII.

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

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.

The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods: We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.