Melbourne School of Government - Research Publications
Permanent URI for this collection
Search Results
1 - 7 of 7
-
ItemImpact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review(JMIR PUBLICATIONS, INC, 2022-05-09)BACKGROUND: The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided. OBJECTIVE: This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts. METHODS: We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies' results and address the research questions. RESULTS: The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health-related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used. CONCLUSIONS: We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context.
-
ItemNo Preview AvailableAvatar Therapy for people with schizophrenia or related disorders(Wiley, 2020-05-08)Many people with schizophrenia do not achieve satisfactory improvements in their mental state, particularly the symptom of hearing voices (hallucinations), with medical treatment. Objectives To examine the effects of Avatar Therapy for people with schizophrenia or related disorders.
-
ItemEthics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review(JMIR PUBLICATIONS, INC, 2021-06-10)BACKGROUND: Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation. OBJECTIVE: This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised. METHODS: We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis. RESULTS: We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance. CONCLUSIONS: Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.
-
ItemKnowledge co-creation in participatory policy and practice: Building community through data-driven direct democracy(SAGE PUBLICATIONS INC, 2021-01-01)Engaging citizens with digital technology to co-create data, information and knowledge has widely become an important strategy for informing the policy response to COVID-19 and the ‘infodemic’ of misinformation in cyberspace. This move towards digital citizen participation aligns well with the United Nations’ agenda to encourage the use of digital tools to enable data-driven, direct democracy. From data capture to information generation, and knowledge co-creation, every stage of the data lifecycle bears important considerations to inform policy and practice. Drawing on evidence of participatory policy and practice during COVID-19, we outline a framework for citizen ‘e-participation’ in knowledge co-creation across every stage of the policy cycle. We explore how coupling the generation of information with that of social capital can provide opportunities to collectively build trust in institutions, accelerate recovery and facilitate the ‘e-society’. We outline the key aspects of realising this vision of data-driven direct democracy by discussing several examples. Sustaining participatory knowledge co-creation beyond COVID-19 requires that local organisations and institutions (e.g. academia, health and welfare, government, business) incorporate adaptive learning mechanisms into their operational and governance structures, their integrated service models, as well as employing emerging social innovations.
-
ItemNo Preview AvailableMapping Wage Theft with Data Science( 2021)Wage theft is almost 'normal' in some industries, but hard to detect. Predictive algorithms can help regulators and give workers an edge.
-
ItemInstructional guides for filming infant movements at home are effective for the General Movements Assessment(WILEY, 2021-11-30)AIM: To determine the effectiveness of two instructional guides to obtain video recordings for the General Movements Assessment (GMA). METHODS: A cross-sectional study of a community sample of family participants with low-risk term newborn infants recruited via social media and randomly allocated to receive one of two instructional guides (detailed or quick versions) to film their infant's movements at home. Participants returned videos via a secure Research Electronic Database Capture link and videos were scored for quality across 10 criteria, along with scoring the GMA. Participants were surveyed about their perceptions of the instructional guides and electronic file transmission. RESULTS: Seventy-six parents of infants were enrolled and randomly allocated to receive detailed (n = 38) and quick (n = 38) instructions to film their infant for a GMA. Videos were returned by 87% (95% confidence interval (CI) 72%, 96%) of detailed instruction users and 84% (95% CI 69%, 94%) of quick instruction users. The GMA could be conducted on all returned videos. The mean score for video quality was 9.9/10 (standard deviation 0.4, 95% CI 9.7, 10.0) for detailed instructions and 9.7 (standard deviation 0.6, 95% CI 9.4, 9.9) for quick instructions. Overall, participants felt that either instructional guide was easy to use. Fifteen (20%) participants had difficulties with video file transmission, requiring additional support. CONCLUSIONS: Parents can successfully use one of two different instructional guides for filming their infant's movements for GMA; however, support should be provided for safe and easy file transmission.
-
ItemEmerging Health Data Platforms: From Individual Control to Collective Data Governance(Cambridge University Press, 2020)Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.