Bibliographic record
Abstract
The COVID-19 pandemic has imposed restrictions on in-person interactions creating a need for exploring alternative methods of healthcare delivery while maintaining high-quality treatment. In an initial study with Orthopaedic (Ortho) and Ear, Nose, and Throat (ENT) surgeons, it was found that 66% of consultations could be completed virtually. This presents the opportunity to reduce unnecessary hospital visits for patients. The objectives of this study were to develop a predictive model that will classify the suitability of patients for in-person versus telemedicine (TM) consultations and to develop an optimal scheduling template for TM consultations. Associated outcomes to be measured were the patient perception of the quality of TM consultations. Data was collected from patients requiring surgical outpatient consultations in Ortho, ENT, and plastic surgery in Quebec. A machine learning model was developed where four machine learning classifiers were implemented to compare the accuracy of the classification. A discrete-event simulation model was developed and used to test the various template scenarios that were generated using lean engineering analysis to find the optimal template that minimizes wait time. A logistic regression model was found to predict a patient’s suitability for TM with 91% accuracy. It was found that 41% of all patients and 57% of follow up patients were suitable for TM consultations. Lean engineering techniques were used to estimate the optimal number of patients that should be seen in TM clinics for each surgeon where patients would wait a maximum of 10 minutes for their appointment. Patient perception of TM being the same or better quality as an in-person appointment increased by 23% after completing a TM consultation.Statistical modelling techniques and lean engineering have high potential to eliminate non-value-added activities in the healthcare system. Using this model, patients can avoid unnecessary visits to hospitals and surgeons can increase the amount of suitable TM visits offering an alternative to in person appointments
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".