Barriers and facilitators of following perioperative internal medicine recommendations by surgical teams: a sequential, explanatory mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Preoperative medical consultations add expense and burden for patients and the impact of these consults on patient outcomes is conflicting. Previous work suggests that 10-40% of preoperative medical consult recommendations are not followed. This limits measurement of the effect of perioperative medical consultation on patient outcomes and represents a quality gap, given the patient time and healthcare cost associated with consultation. We aimed to measure, characterize, and understand reasons for missed recommendations from preoperative medical consultation. METHODS: This explanatory, sequential mixed-methods study used chart audits followed by semi-structured interviews. Chart audit of consecutive patients seen in preoperative medical clinic were reviewed to measure the proportion and characterize the type of recommendations that were not completed ("missed"). This phase informed the interview participants and questions. The interview guide was developed using the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Template analysis was used to understand drivers and barriers of missed recommendations RESULTS: Chart audit included 255 patients (n=161, 63.1% female) seen in preadmission clinic between April 1 and April 30, 2019. 55.7% of patients had all recommendations followed (n=142). Postoperative anticoagulation management and postoperative cardiac biomarker surveillance recommendations were least commonly followed (50.0%, n=28, and 68.9%, n=82, respectively). Eighteen surgical team members were interviewed. Missed recommendations were both unintentional and intentional, and the key drivers differed by these categories. Unintentionally missed recommendations occurred due to individual-level factors (drivers: knowledge of the consultation note, lack of routine for reviewing the consultation note, and competing demands on time) and systems-level factors (driver: lack of role clarity). Intentionally missed recommendations occurred due to user error due (drivers: lack of knowledge of guidelines or evidence) and appropriate modifications (driver: need to adapt a preoperative plan for a complicated postoperative course). CONCLUSIONS: Only 55.7% of consult notes had all recommendations followed, suggesting a quality gap in perioperative medical care. Qualitative data suggests multiple drivers of missed recommendations that should be targeted to improve the efficiency of care for these patients.
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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".