Lessons Learned Through Two Phases of Developing and Implementing a Technology Supporting Integrated Care: Case Study
Bibliographic record
Abstract
BACKGROUND: As health care becomes more fragmented, it is even more important to focus on the provision of integrated, coordinated care between health and social care systems. With the aging population, this coordination is even more vital. Information and communication technology (ICT) can support integrated care if the form of technology follows and supports functional integration. Health TAPESTRY (Teams Advancing Patient Experience: Strengthening Quality) is a program centered on the health of older adults, supported by volunteers, primary care teams, community engagement and connections, and an ICT known as the Health TAPESTRY application (TAP-App), a web-based application that supports volunteers in completing client surveys, volunteer coordinators in managing the volunteer program, and primary care teams in requesting and receiving information. OBJECTIVE: This paper describes the development, evolution, and implementation of the TAP-App ICT to share the lessons learned. METHODS: A case study was conducted with the TAP-App as the case and the perspectives of end users and stakeholders as the units of analysis. The data consisted of researchers' perspectives on the TAP-App from their own experiences, as well as feedback from other stakeholders and end user groups. Data were collected through written retrospective reflection with the program manager, a specific interview with the technology lead, key emailed questions to the TAP-App developer, and viewpoints and feedback during paper drafting from other research team members. There were 2 iterations of Health TAPESTRY and the TAP-App and we focused on learnings from the second implementation (2018-2020) which was a pragmatic implementation scale-up trial using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework at 6 primary care sites across Ontario, Canada. RESULTS: TAP-App (version 1.0), which was iteratively developed, was introduced as a tool to schedule volunteer and client visits and collect survey data using a tablet computer. TAP-App (version 2.0) was developed based on this initial experience and a desire for a program management tool that focused more on dual flow among users and provided better support for research. The themes of the lessons learned were as follows: iterative feedback is valuable; if ICT will be used for research, develop it with research in mind; prepare for challenges in the integration of ICT into the existing workflow; ask whether interoperability should be a goal; and know that technology cannot do it alone yet-the importance of human touch points. CONCLUSIONS: Health TAPESTRY is human-centered. The TAP-App does not replace these elements but rather helps enable them. Despite this shift in supporting integrated care, barriers remained to the uptake of the TAP-App that would have allowed a full flow of information between health and social settings in supporting patient care. This indicates the need for an ongoing focus on the human use of ICT in similar programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".