Economic analysis of surgical outcome monitoring using control charts: the SHEWHART cluster randomised trial

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.

Keywords: cluster trials; continuous quality improvement; control charts, run charts; healthcare quality improvement; quality improvement methodologies.

Figure 1

Flow chart for the SHEWHART cluster randomised trial. During the study period, 159 688 patients were enrolled in the 40 participating hospitals. A total of 4326 patients (2.7%) were excluded; the final study population included 155 362 patients, of whom 79 127 were assigned to the 2014–2015 pre-implementation period (37 579 patients in intervention hospitals vs 41 548 in control hospitals) and 76 235 to the 2017–2018 implementation period (37 468 vs 38 767).

Figure 2

Standardised rates of hospital bed-days per patient within 30 days following surgery and standardised costs reimbursed for this care per patient by the insurer, by study group and period. The bar charts represent the standardised rates of hospital bed-days per patient within 30 days following surgery on the left, and standardised costs reimbursed for this care per patient by the insurer on the right in each group (control and intervention hospitals) and period (pre-implementation and implementation). These standardised rates and standardised costs were determined using estimated regression coefficients obtained from the generalised linear mixed models and marginal standardisation method (see details for marginal standardisation method in online supplemental appendix). The corresponding 95% CIs were computed from non-parametric bootstrap based on 1000 replications. Differences above grey dotted brackets indicate absolute differences in standardised rates or standardised costs between implementation and pre-implementation periods in each group (control and intervention hospitals). Differences above black solid line brackets indicate difference between the intervention and control hospitals of absolute differences in standardised rates or in standardised costs from implementation to pre-implementation periods in each hospital group. These differences of absolute rates or costs differences capture the control chart impact by comparing the change in economic outcomes from pre-implementation with implementation periods between the control and intervention hospitals. Asterisks indicate significant differences as follows: *p≤0.05; ***p≤0.001 (p values computed from the generalised linear mixed models).