Developing an Audit and Feedback Dashboard for Family Physicians: A User-Centered Design Process
Bibliographic record
Abstract
<h3>Context:</h3> Audit and Feedback (A&F), the summary and provision of clinical performance, is a popular quality improvement strategy. We are developing a web-based dashboard that uses data from the electronic medical record to help physicians identify gaps in care and act. However, A&F tools can only be effective if the targeted health professionals actively review their data and take action. In order to maximise the impact of A&F, the design should consider the needs and goals of clinicians. <h3>Objective:</h3> To describe the development of a user-centered design process to optimize the effect of an A&F dashboard for family physicians. <h3>Study Design and Analysis:</h3> Our design process includes (1) Prototype development based on A&F theory and input from clinical improvement leaders; (2) a co-creation workshop with family physician quality improvement leaders to develop personas (i.e., fictional characters that represent an archetype character); (3) user-centered interviews with family physicians to learn about the physician’s who will be using the dashboard and their context, and their reactions to the dashboard. <h3>Setting or Dataset:</h3> A workshop for the creation of personas and user-centered qualitative interviews with family physicians. <h3>Population Studied:</h3> Family physicians who contribute data to the University of Toronto Practice-Based Research Network <h3>Intervention/Instrument:</h3> Audit and Feedback dashboard <h3>Outcome Measures:</h3> N/A <h3>Results:</h3> Our persona workshop produced four personas that enabled the team to identify physician’s needs and wishes and facilitated empathy during the design process: Dr. Skeptic, Frazzled Physician, The Eager Implementer, and Sidney Big Wig. Our interviews found that: (1) physicians are interested in how they compare with their peers; however, if their performance was above average, they were not motivated to improve even if gaps in care remained; (2) Burnout levels are high, physicians are trying to catch up on missed care during the pandemic, and were not highly motivated to act on the dashboard data; (3) Features that were important to physicians were integration within the EMR, and up-to-date and accurate data. <h3>Conclusions:</h3> A successful design of an A&F performance dashboard should consider the serious lack of time and capacity among family physicians to engage in quality improvement work. If designed properly, the QI dashboard can be a great assistance in helping family physicians provide more proactive and targeted care.
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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 | 0.135 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".