The impact of interruptions on physician workflow, productivity, and delivery of care.
Bibliographic record
Abstract
201 Background: Physician interruptions during clinic and non-clinic hours can lead to medical errors, provider fatigue, prolonged clinic times, reduced academic output and poor job satisfaction. Repetitive interruptions can hamper the ability of physicians to deliver high quality patient-centered care. This study aims to evaluate the type, frequency, duration and self-reported physician response interruptions physicians experience in clinic. Methods: A work observation study was conducted at the Odette Cancer Centre, Sunnybrook Health Sciences Centre in Toronto, Canada. In-clinic data were collected from September 22 to October 6, 2016 using time-motion analyses by shadowing multiple oncologists in clinic. Interruption data were collected and categorized as follows: type of interruption, length of interruption, reason for interruption and role of interrupter. Physicians were asked to record and track themselves regarding interruptions they experienced during non-clinic hours using the same criteria. Results: Over a 2-week period, 5 medical oncology clinics (median 4 hours (hrs) per clinic), were observed and tracked. The clinic physicians averaged 22 interruptions per block, equating to 6 interruptions/hr (one interruption every 10 minutes (mins)). Over the 5 sessions, 112 data points were collected totaling over 1 hr 48 mins of interrupted time. Interruptions averaged 80 seconds (range of 4 to 517) in length with a positive skewed distribution. This calculates to approximately 30 mins of cumulative interrupted time per clinic session. Most interruptions were under 4 mins in length (4.1 at 95th percentile). The type of interruption varied but was most commonly in-person (67), email (24) and text message (10). Conclusions: Interruptions account for approximately 30 mins of physician time during a 4-hour clinic. An assessment of the type and frequency of requests proved highly variable, creating inconsistent ways messages are delivered to physicians. Interruptions potentially impact on patient care and disrupt the workflow of the clinic. These data provide future directions for exploring efficient clinic workflows and establishing standardized means of communicating with physicians during clinic hours.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".