Patient, hospital and environmental costs of unnecessary bloodwork: capturing the triple bottom line of inappropriate care in general surgery patients
Bibliographic record
Abstract
Objective To characterise the extent of unnecessary care in general surgery inpatients using a triple bottom line approach. Design Patients with uncomplicated acute surgical conditions were retrospectively evaluated for unnecessary bloodwork according to the triple bottom line, quantifying the impacts on patients, healthcare costs and greenhouse gas emissions. The carbon footprint of common laboratory investigations was estimated using PAS2050 methodology, including emissions generated from the production, transport, processing and disposal of consumable goods and reagents. Setting Single-centre tertiary care hospital. Participants Patients admitted with acute uncomplicated appendicitis, cholecystitis, choledocholithiasis, gallstone pancreatitis and adhesive small bowel obstruction were included in the study. 304 patients met inclusion criteria and 83 were randomly selected for in-depth chart review. Main outcome measures In each patient population, the extent of over-investigation was determined by comparing ordered laboratory investigations against previously developed consensus recommendations. The quantity of unnecessary bloodwork was measured by number of phlebotomies, tests and blood volume in addition to healthcare costs and greenhouse gas emissions. Results 76% (63/83) of evaluated patients underwent unnecessary bloodwork resulting in a mean of 1.84 phlebotomies, 4.4 blood vials, 16.5 tests and 18 mL of blood loss per patient. The hospital and environmental cost of these unnecessary activities was $C5235 and 61 kg CO2e (974 g CO2e per person), respectively. The carbon footprint of a common set of investigations (complete blood count, differential, creatinine, urea, sodium, potassium) was 332 g CO2e. Adding a liver panel (liver enzymes, bilirubin, albumin, international normalised ratio/partial thromboplastin time) resulted in an additional 462 g CO2e. Conclusions We found considerable overuse of laboratory investigations among general surgery patients admitted with uncomplicated acute surgical conditions resulting in unnecessary burden to patients, hospitals and the environment. This study identifies an opportunity for resource stewardship and exemplifies a comprehensive approach to quality improvement.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".