Clinical Reminders Designed and Implemented Using Cognitive and Organizational Science Principles Decrease Reminder Fatigue
Bibliographic record
Abstract
BACKGROUND: Response rates to point-of-care clinical reminders typically decrease over time. We hypothesized that this "reminder fatigue" could be prevented by (1) applying sound human factors engineering and cognitive science principles in designing the reminder system, and (2) implementing the reminders with rigorous attention to organizational science principles. METHODS: This was a retrospective cohort enumeration from January 1, 2006, through July 31, 2012, in a set of 5 academically affiliated family medicine practices. We modeled the odds ratio of clinician action in response to a reminder according to the number of reminders issued during the encounter, the number of problems on the patient's problem list, patient age, and time (number of months since launch) using logistic regression with clustering by encounter. RESULTS: There were issued 988,149 reminders at 453,537 encounters during the sampling frame. Action was taken in response to 60.1% of reminders, and discussion or consideration was documented in another 26.8%. The odds ratios for action in response to reminders over time, by number of prompts during the encounter, and by number of problems were 1.01, 1.18, and 1.02, respectively. Key design features included issuing reminders only when a service was due, allowing clinicians to attend to reminders when doing so fit their workflow (vs forcing attention at a specific time), keeping reminders very short and simple (action item only, no explicative material), and a team meeting and buy-in process before each new reminder was implemented. CONCLUSIONS: Reminder fatigue over time, with increasing numbers of reminders and with increasing complexity of patients, is not inevitable. A reminder system designed and implemented in accordance with the principles of cognitive science and human factors engineering can prevent reminder fatigue.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".