270 Clicks matter. improving ordering efficiency
Bibliographic record
Abstract
Introduction A goal of the physician wellness program at CHEO is to improve physician electronic health record (EHR) experience and satisfaction. The initiative focused on ‘Easier ordering’ to enhance admission ordering efficiency and improve inpatient and outpatient one-click orders. This quality improvement project aimed to improve EHR efficiency by reducing the number of clicks required for common orders that physicians rarely modify. Ultimately, we sought to decrease EHR cognitive load and promote more time spent on direct patient care.Methods Three physicians and two information system business analysts prioritized two projects deemed to have the greatest impact with minimal system intervention. The first project aimed to improve hospital admission ordering efficiency by changing the system to automatically set the admission diagnosis as the patient‘s primary problem at the time of admission. The second project focused on improving inpatient and outpatient One-Click Orders. The team reviewed the top 100 records by order changes and the top 100 records by ease of update, reaching consensus on which orders to modify. Three physicians reviewed 200 orders, with specialty follow-up as required, and identified 56 orders where dose and/or frequency could be defaulted.Results In total, we estimate these changes will decrease approximately 70,000 clicks per year, equating to 700 hours of physician working time 1. Simply defaulting the admission diagnosis to be the primary problem affected over 7,000 admissions and eliminated 4 clicks per admission to find and enter the primary problem. Additionally, this default saved further physician time by eliminating chart deficiencies that previously required correction when a primary problem was not selected. The second project identified 56 orders (28% of those reviewed) where dose and/or frequency could be defaulted, resulting in 38,712 clicks saved and 31% more efficient ordering. We have expanded the project to further reduce clicks across the organization, including for nursing and allied health professionals.Reference Hill RG, Sears LM, Melanson SW. 4000 clicks: a productivity analysis of electronic medical records in a community hospital ED. Am J Emerg Med. 2013 Nov;31(11):1591–4.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.912 | 0.809 |
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".