Technology-Enabled Person-Centered Mental Health Services Reform: Strategy for Implementation Science
Bibliographic record
Abstract
BACKGROUND: Health information technologies are being rapidly developed to improve the delivery of mental health care; however, a range of facilitators, barriers, and contextual conditions can impact the adoption and sustainment of these solutions. An implementation science protocol supports researchers to achieve primary effectiveness goals in relation to mental health services reform and aids in the optimization of implementation processes to promote quality health care, prolonging sustainability. OBJECTIVE: The aim of this paper is to describe our implementation science protocol, which serves as a foundation by which to systematically guide the implementation of technology-enabled solutions in traditional face-to-face and Web-based mental health services, allowing for revisions over time on the basis of retrospective review and constructive feedback from the services in which the technology-enabled solutions are implemented. METHODS: Our implementation science protocol comprises four phases. The primary objective of the scoping and feasibility phase (Phase 1) is to determine the alignment between the service partner and the quality improvement goals supported by the technology-enabled solution. This is followed by Phase 2, the local co-design and preimplementation phase, which aims to utilize co-design methodologies, including service pathway modelling, participatory design, and user (acceptance) testing, to determine how the solutions could be used to enhance the service. In Phase 3, implementation, the accepted solution is embedded in the mental health service to achieve better outcomes for consumers and their families as well as health professionals and service managers. Using iterative evaluative processes throughout Phase 3, the solution is continuously developed, designed, and refined during implementation to adapt to the changing needs of the stakeholders, including consumers with lived experience and their families as well as the service. Thus, the primary outcome of Phase 3 is the optimized technology-enabled solution that can be maintained in a service during the sustainment and scalability phase (Phase 4) for the purposes of mental health services reform. RESULTS: Funding for the protocol was provided by the Australian Government Department of Health in June of 2017 for a period of 3 years. At the time of this publication, the protocol had been initiated in 11 services, serving three populations, all of which are currently operating in Phase 3. The first results are expected to be submitted for publication in 2020. CONCLUSIONS: With the aim of improving mental health service quality, our implementation science protocol aids in the identification of factors that predict the likelihood of implementation success, as well as the development of strategies to proactively mitigate potential barriers to achieve better implementation outcomes. Putting in place a theoretically sound implementation science protocol is essential to facilitate the uptake of novel technology-enabled solutions and evidence-based practices into routine clinical practice for the purposes of improved outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".