Economic analysis of surgical outcome monitoring using control charts: the SHEWHART cluster randomised trial
Bibliographic record
Abstract
IMPORTANCE: Surgical complications represent a considerable proportion of hospital expenses. Therefore, interventions that improve surgical outcomes could reduce healthcare costs. OBJECTIVE: Evaluate the effects of implementing surgical outcome monitoring using control charts to reduce hospital bed-days within 30 days following surgery, and hospital costs reimbursed for this care by the insurer. DESIGN: National, parallel, cluster-randomised SHEWHART trial using a difference-in-difference approach. SETTING: 40 surgical departments from distinct hospitals across France. PARTICIPANTS: 155 362 patients over the age of 18 years, who underwent hernia repair, cholecystectomy, appendectomy, bariatric, colorectal, hepatopancreatic or oesophageal and gastric surgery were included in analyses. INTERVENTION: After the baseline assessment period (2014-2015), hospitals were randomly allocated to the intervention or control groups. In 2017-2018, the 20 hospitals assigned to the intervention were provided quarterly with control charts for monitoring their surgical outcomes (inpatient death, intensive care stay, reoperation and severe complications). At each site, pairs, consisting of one surgeon and a collaborator (surgeon, anaesthesiologist or nurse), were trained to conduct control chart team meetings, display posters in operating rooms, maintain logbooks and design improvement plans. MAIN OUTCOMES: Number of hospital bed-days per patient within 30 days following surgery, including the index stay and any acute care readmissions related to the occurrence of major adverse events, and hospital costs reimbursed for this care per patient by the insurer. RESULTS: Postintervention, hospital bed-days per patient within 30 days following surgery decreased at an adjusted ratio of rate ratio (RRR) of 0.97 (95% CI 0.95 to 0.98; p<0.001), corresponding to a 3.3% reduction (95% CI 2.1% to 4.6%) for intervention hospitals versus control hospitals. Hospital costs reimbursed for this care per patient by the insurer significantly decreased at an adjusted ratio of cost ratio (RCR) of 0.99 (95% CI 0.98 to 1.00; p=0.01), corresponding to a 1.3% decrease (95% CI 0.0% to 2.6%). The consumption of a total of 8910 hospital bed-days (95% CI 5611 to 12 634 bed-days) and €2 615 524 (95% CI €32 366 to €5 405 528) was avoided in the intervention hospitals postintervention. CONCLUSIONS: Using control charts paired with indicator feedback to surgical teams was associated with significant reductions in hospital bed-days within 30 days following surgery, and hospital costs reimbursed for this care by the insurer. TRIAL REGISTRATION NUMBER: NCT02569450.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".