Development and usability of a mobile tool for identification of depression and suicide risk in Fiji

Resilience Informatics: Role of Informatics in Enabling and Promoting Public Health Resilience to Pandemics, Climate Change, and Other Stressors

Climate change, local epidemics, future pandemics, and forced displacements pose significant public health threats worldwide. To cope successfully, people and communities are faced with the challenging task of developing resilience to these stressors. Our viewpoint is that the powerful capabilities of modern informatics technologies including artificial intelligence, biomedical and environmental sensors, augmented or virtual reality, data science, and other digital hardware or software, have great potential to promote, sustain, and support resilience in people and communities. However, there is no "one size fits all" solution for resilience. Solutions must match the specific effects of the stressor, cultural dimensions, social determinants of health, technology infrastructure, and many other factors.

Nurses' Roles in mHealth App Development: Scoping Review

BACKGROUND

Although mobile health (mHealth) apps for both health consumers and health care providers are increasingly common, their implementation is frequently unsuccessful when there is a misalignment between the needs of the user and the app's functionality. Nurses are well positioned to help address this challenge. However, nurses' engagement in mHealth app development remains unclear.

OBJECTIVE

This scoping review aims to determine the extent of the evidence of the role of nurses in app development, delineate developmental phases in which nurses are involved, and to characterize the type of mHealth apps nurses are involved in developing.

METHODS

We conducted a scoping review following the 6-stage methodology. We searched 14 databases to identify publications on the role of nurses in mHealth app development and hand searched the reference lists of relevant publications. Two independent researchers performed all screening and data extraction, and a third reviewer resolved any discrepancies. Data were synthesized and grouped by the Software Development Life Cycle phase, and the app functionality was described using the IMS Institute for Healthcare Informatics functionality scoring system.

RESULTS

The screening process resulted in 157 publications being included in our analysis. Nurses were involved in mHealth app development across all stages of the Software Development Life Cycle but most frequently participated in design and prototyping, requirements gathering, and testing. Nurses most often played the role of evaluators, followed by subject matter experts. Nurses infrequently participated in software development or planning, and participation as patient advocates, research experts, or nurse informaticists was rare.

CONCLUSIONS

Although nurses were represented throughout the preimplementation development process, nurses' involvement was concentrated in specific phases and roles.

A visual analytic tool, VIADS, to assist the hypothesis generation process in clinical research—A usability study using mixed methods (Preprint)

BACKGROUND

Visualization can be a powerful tool for comprehending datasets, especially when they can be represented via hierarchical structures. Enhanced comprehension can facilitate the development of scientific hypotheses. However, the inclusion of excessive data can make a visualization overwhelming.

OBJECTIVE

We developed a Visual Interactive Analytic tool for filtering and summarizing large health Data Sets (VIADS) coded with hierarchical terminologies. In this study, we evaluated the usability of VIADS for visualizing data sets of patient diagnoses and procedures coded in the International Classification of Diseases, ninth revisions, clinical modification (ICD-9-CM).

METHODS

We used mixed methods in the study. A group of 12 clinical researchers participated in the generation of data-driven hypotheses using the same datasets and time frame (a 1-hour training session and a 2-hour study session), utilizing VIADS via the think-aloud protocol. The audio and screen activities were recorded remotely. A modified version of the System Usability Scale (SUS) survey and a brief survey with open-ended questions were administered after the study to assess the usability of VIADS and verify their intense usage experience of VIADS.

RESULTS

The range of SUS scores was 37.5 - 87.5. The mean SUS score for VIADS was 71.88 (out of a possible 100, standard deviation: 14.62 ), and the median SUS was 75. The participants unanimously agreed that VIADS offers new perspectives on data sets (100%), while 75% agreed that VIADS facilitates understanding, presentation, and interpretation of underlying datasets. The comments on the utility of VIADS were positive and aligned well with the design objectives of VIADS. The answers to the open-ended questions in the modified SUS provided specific suggestions regarding potential improvements in VIADS, and identified problems in usability were used to update the tool.

CONCLUSIONS

This usability study demonstrates that VIADS is a usable tool for analyzing secondary datasets with good average usability, SUS score, and favorable utility. Currently, VIADS accepts datasets with hierarchical codes and their corresponding frequencies. Consequently, only specific types of use cases are supported by the analytical results. Participants agreed, however, that VIADS provides new perspectives on datasets and is relatively easy to use. The functionalities mostly appreciated by participants were VIADS' ability to filter, summarize, compare, and visualize data.

CLINICALTRIAL

INTERNATIONAL REGISTERED REPORT

RR2-10.2196/39414.

Information processing by community health nurses using mobile health (mHealth) tools for early identification of suicide and depression risks in Fiji Islands

Objectives

High rates of depression and suicide and a lack of trained psychiatrists have emerged as significant concerns in the low-income and middle-income countries (LMICs) such as the Pacific Island Countries (PICs). Readily available smartphones were leveraged with community health nurses (CHNs) in task-sharing for early identification of suicide and depression risks in Fiji Islands, the largest of PICs. This investigation examines how CHNs can efficiently and effectively process patient information about depression and suicide risk for making diagnostic and management decisions without compromising safety. The research is driven by the theoretical framework of text comprehension (knowledge representation and interpretation) and decision-making.

Methods

Mobile health (mHealth) Application for Suicide Risk and Depression Assessment (ASRaDA) was designed to include culturally useful clinical guidelines for these disorders. A representative sample of 48 CHNs was recruited and presented with two clinical cases (depression and suicide) in a simulated setting under three conditions: No support, paper-based and mobile-based culturally valid guideline support. Data were collected as the nurses read through the scenarios, ‘thinking aloud’, before summarising, diagnoses and follow-up recommendations. Transcribed audiotapes were analysed using formal qualitative discourse analysis methods for diagnostic accuracy, comprehension of clinical problems and reasoning patterns.

Results

Using guidelines on ASRaDA, the CHNs took less time to process patient information with more accurate diagnostic and therapeutic decisions for depression and suicide risk than with paper-based or no guideline conditions. A change in reasoning pattern for nurses’ information processing was observed with decision support.

Discussion

Although these results are shown in a mental health setting in Fiji, there are reasons to believe they are generalisable beyond mental health and other lower-to-middle income countries.

Conclusions

Culturally appropriate clinical guidelines on mHealth supports efficient information processing for quick and accurate decisions and a positive shift in reasoning behaviour by the nurses. However, translating complex qualitative patient information into quantitative scores could generate conceptual errors. These results are valid in simulated conditions.