Clinical Acceptability of a Quality Improvement Program for Reducing Cardiovascular Disease Risk in People With Chronic Kidney Disease in Australian General Practice: Qualitative Study
Bibliographic record
Abstract
Background: Future Health Today (FHT) is a technology program that integrates with general practice clinical software to provide point of care (PoC) clinical decision support and a quality improvement dashboard. This qualitative study looks at the use of FHT in the context of cardiovascular disease risk in chronic kidney disease (CKD). Objective: This study aims to explore factors influencing clinical implementation of the FHT module focusing on cardiovascular risk in CKD, from the perspectives of participating general practitioner staff. Methods: Practices in Victoria were recruited to participate in a pragmatic cluster randomized controlled trial using FHT, of which 19 practices were randomly assigned to use FHT's cardiovascular risk in CKD program. A total of 13 semistructured interviews were undertaken with a nominated general practitioner (n=7) or practice nurse (n=6) from 10 participating practices. Interview questions focused on the clinical usefulness of the tool and its place in clinical workflows. Qualitative data were coded by 2 researchers and analyzed using framework analysis and Clinical Performance Feedback Intervention Theory. Results: All 13 interviewees had used the FHT PoC tool, and feedback was largely positive. Overall, clinicians described engaging with the tool as a "prompt" or "reminder" system. Themes reflected that the tool's goals and clinical content were aligned with clinician's existing priorities and knowledge, and the tool's design facilitated easy integration into existing workflows. The main barrier to implementation identified by 2 clinicians was notification fatigue. A total of 7 interviewees had used the FHT dashboard tool. The main barriers to use were its limited integration into clinical workflows, such that some participants did not know of its existence; clinicians' competing clinical priorities; and limited time to learn and use the tool. Conclusions: This study identified many facilitators for the successful use of the FHT PoC program, in the context of cardiovascular risk in CKD, and barriers to the use of the dashboard program. This work will be used to inform the wider implementation of FHT, as well as the development of future modules of FHT for other risk or disease states.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".