Developing an Audit and Feedback Dashboard for Family Physicians: A User-Centered Design Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it