Patient satisfaction and positive patient outcomes in ambulatory anesthesia
Bibliographic record
Abstract
Ushma Shah, David T Wong, Jean Wong Department of Anesthesia, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada Abstract: Most surgeries in North America are performed on an ambulatory basis, reducing health care costs and increasing patient comfort. Patient satisfaction is an important outcome indicator of the quality of health care services incorporated by the American Society of Anesthesiologists (ASA). Patient satisfaction is a complex concept that is influenced by multiple factors. A patient's viewpoint and knowledge plays an influential role in patient satisfaction with ambulatory surgery. Medical optimization and psychological preparation of the patient plays a pivotal role in the success of ambulatory surgery. Postoperative pain, nausea, and vomiting are the most important symptoms for the patient and can be addressed by multimodal drug regimens. Shared decision making, patient–provider relationship, communication, and continuity of care form the main pillars of patient satisfaction. Various psychometrically developed instruments are available to measure patient satisfaction, such as the Iowa Satisfaction with Anesthesia Scale and Evaluation du Vecu de I'Anesthesie Generale, but none have been developed specifically for ambulatory surgery. The ASA has made recommendations for data collection for patient satisfaction surveys and emphasized the importance of reporting the data to the Anesthesia Quality Institute. Future research is warranted to develop a validated tool to measure patient satisfaction in ambulatory surgery. Keywords: patient, satisfaction, anesthesia, outcomes, questionnaire, perspectives
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".