A163 MICROCOSTING: ASSESSING HEALTH ECONOMICS IN GI
Bibliographic record
Abstract
Abstract Background Micro-costing is a method of collecting precise cost measurements and proves effective in determining economic requirements needed to support health interventions. At St. Paul’s GI Clinic, over 12000 procedures are performed yearly, 80% of which are colonoscopies. Specific standards in the form of appropriate documentation are recommended through Global Rating Scale (GRS) and require yearly auditing to ensure appropriate compliance with these standards. These audits require a time commitment of staff to assess charts. The cost of performing these assessments is not known. Aims To determine the cost of annual data collection suggested by GRS for documentation of procedures. Methods A retrospective chart review and data analysis of patients (≥ 19 years old) admitted to St. Paul’s GI Clinic for a colonoscopy and/or esophagogastroduodenoscopy (EGD). Data is extracted from the St. Paul’s medical database from August 1st 2018 – August 1st 2019. Since it is a ‘time-and-motion’ study, a stopwatch is used to time the collection of data from each chart. The mean time per-case is derived and used to conduct an appropriate economic analysis, such as total working hours, per-minute salary calculations and equipment costs. The purpose is to determine a yearly cost and identify what the main cost drivers were (time/labor). Results As per our annual review format suggested by GRS, 260 procedure reports were reviewed, 150 colonoscopies and 110 EGDs (random sampling of 10 per physician for each type of procedure). A spread sheet outlining key data assessment points for mandatory standard reporting points has been used yearly and was used for this study as well. Mean evaluation time (including recording presence or absence of each item on our standard reporting form): 1 minute and 40 seconds to review the report for a colonoscopy and 1 minute and 33 seconds to review the report for an EGD. A total of 2 hours 51 minutes 16 seconds to review all the EGD reports and 4 hours 8 minutes and 46 seconds for the colonoscopy reports. Conclusions It would cost $126 annually to pay a research student, who makes $18/ hour, to collect this quality assurance data required for auditing completeness of physician colonoscopy and EGD procedure documentation according to standards. Funding Agencies None
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".