How Patient Work Changes Over Time for People With Multimorbid Type 2 Diabetes: Qualitative Study

The use of PrEP among men who have sex with men and transgender women as Biomedical Prevention Work: A conceptual framework

Based on ethnographic fieldwork among men who have sex with men and transgender women in Tanzania, this article explores the various types of work that may go into enrolment into PrEP programming and using pre-exposure prophylaxis (PrEP). PrEP protects against HIV acquisition and is widely touted as an essential tool in 'ending AIDS by 2030'. While taking PrEP has often been portrayed as 'just taking a pill a day' in public health campaigns, a striking observation during fieldwork was that enrolling in PrEP programming and adhering to PrEP involved a wide range of tasks. Inspired by this fieldwork experience and the literature on sociology of work, more specifically illness work and patient work, we started to think of these tasks as work. This paper identifies the range of tasks that PrEP users in Dar es Salaam had to perform as part of their enrolment and usage of PrEP. We provide a description of these tasks, organised into three categories of work that we refer to as (a) readying work, (b) user work, and (c) social navigation work that jointly make up what we propose to call biomedical prevention work. We further suggest that this analytical framework can be applicable to other biomedical prevention methods in other contexts.

Patient Work Personas of Type 2 Diabetes—A Data-Driven Approach to Persona Development and Validation

Introduction

Many have argued that a “one-size-fits-all” approach to designing digital health is not optimal and that personalisation is essential to achieve targeted outcomes. Yet, most digital health practitioners struggle to identify which design aspect require personalisation. Personas are commonly used to communicate patient needs in consumer-oriented digital health design, however there is often a lack of reproducible clarity on development process and few attempts to assess their accuracy against the targeted population. In this study, we present a transparent approach to designing and validating personas, as well as identifying aspects of “patient work,” defined as the combined total of work tasks required to manage one's health and the contextual factors influencing such tasks, that are sensitive to an individual's context and may require personalisation.

Methods

A data-driven approach was used to develop and validate personas for people with Type 2 diabetes mellitus (T2DM), focusing on patient work. Eight different personas of T2DM patient work were constructed based physical activity, dietary control and contextual influences of 26 elderly Australian participants (median age = 72 years) via wearable camera footage, interviews, and self-reported diaries. These personas were validated for accuracy and perceived usefulness for design, both by the original participants and a younger (median age bracket = 45–54 years) independent online cohort f 131 T2DM patients from the United Kingdom and the United States.

Results

Both the original participants and the independent online cohort reported the personas to be accurate representations of their patient work routines. For the independent online cohort, 74% (97/131) indicated personas stratified to their levels of exercise and diet control were similar to their patient work routines. Findings from both cohorts highlight aspects that may require personalisation include daily routine, use of time, and social context.

Conclusion

Personas made for a specific purpose can be very accurate if developed from real-life data. Our personas retained their accuracy even when tested against an independent cohort, demonstrating their generalisability. Our data-driven approach clarified the often non-transparent process of persona development and validation, suggesting it is possible to systematically identify whether persona components are accurate or. and which aspects require more personalisation and tailoring.

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

BACKGROUND

Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit.

OBJECTIVE

The aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services.

METHODS

The search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives.

RESULTS

Study registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021.

CONCLUSIONS

This paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools.

TRIAL REGISTRATION

PROSPERO CRD42021256005; https://tinyurl.com/r4x3thvp.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/33145.