ORCID Profile
0000-0001-7625-5251
Current Organisations
University of South Australia
,
Flinders University
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Publisher: Springer Science and Business Media LLC
Date: 30-01-2023
DOI: 10.1186/S12911-022-02091-2
Abstract: Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events (“flags”) through suggesting evidence-based courses of action. However, extant literature shows multiple barriers—perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.—to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic. The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI 2 ). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach—starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories’ relationships with client and medication-subtype characteristics were tested. The majority of clients were Not Followed-up (n = 260 78% Followed-up: n = 71 22%). The analytical themes emerging from the decision notes suggested contextual factors—the clients’ environment, their clinical relationships, and medical needs—mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ 2 = 35.196, 44.825 p 0.001 v = 0.389, 0.499) and between the time taken to action Followed-up 0 and Not-followed up 1 flags (M 0 = 31.78 M 1 = 45.55 U = 12,119 p 0.001 η 2 = .05). These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.
Publisher: JMIR Publications Inc.
Date: 31-03-2022
DOI: 10.2196/29988
Abstract: The research marketplace has seen a flood of open-source or commercial mobile health (mHealth) platforms that can collect and use user data in real time. However, there is a lack of practical literature on how these platforms are developed, integrated into study designs, and adopted, including important information around cost and effort considerations. We intend to build critical literacy in the clinician-researcher readership into the cost, effort, and processes involved in developing and operationalizing an mHealth platform, focusing on Intui, an mHealth platform that we developed. We describe the development of the Intui mHealth platform and general principles of its operationalization across sites. We provide a worked ex le in the form of a case study. Intui was operationalized in the design of a behavioral activation intervention in collaboration with a mental health service provider. We describe the design specifications of the study site, the developed software, and the cost and effort required to build the final product. Study designs, researcher needs, and technical considerations can impact effort and costs associated with the use of mHealth platforms. Greater transparency from platform developers about the impact of these factors on practical considerations relevant to end users such as clinician-researchers is crucial to increasing critical literacy around mHealth, thereby aiding in the widespread use of these potentially beneficial technologies and building clinician confidence in these tools.
Publisher: MDPI AG
Date: 25-05-2023
DOI: 10.3390/FI15060191
Abstract: Advancements in digital monitoring solutions collaborate closely with electronic medical records. These fine-grained monitoring capacities can generate and process extensive electronic record data. Such capacities promise to enhance mental health care but also risk contributing to further stigmatization, prejudicial decision-making, and fears of disempowerment. This article discusses the problems and solutions identified by nine people with lived experience of being mental health care consumers or informal carers. Over the course of ten facilitated focus group format sessions (two hours) between October 2019 and April 2021, the participants shared their lived experience of mental health challenges, care, and recovery within the Australian context. To support the development, design, and implementation of monitoring technologies, problems, and solutions were outlined in the following areas—access, agency, interactions with medical practitioners, medication management, and self-monitoring. Emergent design insights include recommendations for strengthened consent procedures, flexible service access options, and humanized consumer interactions. While consumers and carers saw value in digital monitoring technologies that could enable them to take on a more proactive involvement in their personal wellness, they had questions about their level of access to such services and expressed concerns about the changes to interactions with health professionals that might emerge from these digitally enabled processes.
Publisher: JMIR Publications Inc.
Date: 27-04-2021
Abstract: he research marketplace has seen a flood of open-source or commercial mobile health (mHealth) platforms that can collect and use user data in real time. However, there is a lack of practical literature on how these platforms are developed, integrated into study designs, and adopted, including important information around cost and effort considerations. e intend to build critical literacy in the clinician-researcher readership into the cost, effort, and processes involved in developing and operationalizing an mHealth platform, focusing on Intui, an mHealth platform that we developed. e describe the development of the Intui mHealth platform and general principles of its operationalization across sites. e provide a worked ex le in the form of a case study. Intui was operationalized in the design of a behavioral activation intervention in collaboration with a mental health service provider. We describe the design specifications of the study site, the developed software, and the cost and effort required to build the final product. tudy designs, researcher needs, and technical considerations can impact effort and costs associated with the use of mHealth platforms. Greater transparency from platform developers about the impact of these factors on practical considerations relevant to end users such as clinician-researchers is crucial to increasing critical literacy around mHealth, thereby aiding in the widespread use of these potentially beneficial technologies and building clinician confidence in these tools.
Publisher: Wiley
Date: 20-09-2022
DOI: 10.1002/CPE.7322
Abstract: A common issue among digital health applications is that they are based on project‐specific solutions and are developed from scratch, which results in redundant development and lack of data and technology reuse. This can be seen in, for ex le, building study specific websites and mobile frontends, deploying customized infrastructures, and collecting data that may have been collected in other studies and projects. However, existing data and technology are not easily shareable between projects due to large investment required to address data exchanging mechanism, data and technology sovereignty, data security and resource discovery, while the benefit for the resource owners is uncertain. In this article, we present a supporting framework, named DHLink, to address this issue on two fronts. First, the DHLink framework securely connects multiple digital health applications, facilitates real‐time data sharing, and supports rapid application development by reusing data and technology. Second, to aid in rapid development of new digital health applications, a set of highly generic and reusable microservices has been developed as the initial resources available in the DHLink ecosystem. Two proof‐of‐concept use cases outlined show the effectiveness of DHLink for both data sharing between existing applications, and rapid development of a new application.
No related grants have been discovered for Dan Thorpe.