Implementation of a Quality Improvement and Clinical Decision Support Tool for Cancer Diagnosis in Primary Care: Process Evaluation
Bibliographic record
Abstract
Background: For patients with cancer, the pathway to diagnosis will most often begin in general practice. In the absence of strong diagnostic features or in patients with nonspecific symptoms, delays in diagnosis can occur. Initial presentations and routine blood tests are important in determining whether a patient requires further investigation. Quality improvement interventions, including auditing tools and clinical decision support (CDS), have been developed for use in general practice to support this diagnostic process. We conducted a process evaluation of a pragmatic, cluster-randomized trial that evaluated the effectiveness of a new technology, Future Health Today (FHT), implemented in general practice to assist with the appropriate follow-up of patients at risk of undiagnosed cancer. objectives: This study aims to understand implementation gaps, explore differences between the general practices involved, provide context to the trial effectiveness outcomes, and understand the mechanisms behind the intervention successes and failures. Methods: The trial intervention consisted of the FHT tool (with CDS, audit, recall, and quality improvement components), training and educational sessions, benchmarking reports, and ongoing practice support. The 21 general practices in the intervention arm of the trial were included in the process evaluation. Process data were collected using semistructured interviews, usability and educational session surveys, engagement with intervention components, and technical logs. The Medical Research Council's Framework for Developing and Evaluating Complex Interventions was used to analyze and interpret the data. Results: The uptake of the supporting components of the intervention (training and education sessions, benchmarking reports) was low. Most practices only used the CDS component of the tool, facilitated by active delivery, with general practitioners reporting acceptability and ease of use. Complexity, time, and resources were reported as barriers to the use of the auditing tool. Access to a study coordinator and ongoing practice support facilitated the sustained involvement of practices in the trial, while contextual factors, such as the COVID-19 pandemic and staff turnover, impacted their level of participation. The relevance of the intervention varied between practices, with some practices reporting very low numbers of patients who were flagged for further investigation. Conclusions: While some components of the intervention, such as the CDS tool, were considered to be acceptable and useful, this process evaluation highlighted barriers such as time and resources, practice differences, and considerations around the optimal amount of support needed when delivering the intervention. Addressing these in future studies may optimize the implementation process. Further work is needed to determine if a scaled-back approach, which meets the time and resource availability of a busy general practice, can effectively facilitate the implementation of CDS tools. Given the variation seen between practices, the use of the FHT cancer module may be better targeted to certain practices based on size, location, and patient demographics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.197 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".