The World Health Organisation (WHO) recommends routine use of a surgical safety checklist prior to all surgical operations. The aim of this study was to prospectively audit checklist use in orthopaedic patients before and after implementation of an educational programme designed to increase use and correlate this with early complications, mortality and staff perceptions. Data was collected on 480 patients before the educational program and 485 patients after. Pre-training checklist use was 7.9%. The rates of early complications and mortality were 8.5% and 1.9%, respectively. Forty-seven percent thought the checklist improved team communication. Following an educational program, checklist use significantly increased to 96.9% (RR12.2; 95% CI 9.0-16.6). The rate of early complications and mortality was 7.6% (RR 0.89; 95% CI 0.58-1.37) and 1.6% (RR 0.88; 95% CI 0.34-2.26), respectively. Seventy-seven percent thought the checklist improved team communication. Checklist use was not associated with a significant reduction in early complications and mortality in patients undergoing orthopaedic surgery. Education programs can significantly increase accurate use and staff perceptions following implementation.

Key Points Question How does an artificial intelligence (AI)–enabled decision aid generated using patient-reported outcome measurements compare with education only on decision quality, patient satisfaction, and functional outcomes among individuals with knee osteoarthritis considering total knee replacement? Findings This randomized clinical trial of 129 patients demonstrated statistically significant improvement in decision quality, level of shared decision-making, patient satisfaction, and functional outcomes in patients using an AI-enabled decision aid. Meaning These findings suggest that AI-enabled decision aids incorporating patient-reported outcome measurement data provide a personalized, data-driven approach to shared decision-making for the surgical management of knee osteoarthritis.

Revision arthroplasty of the hip is expensive owing to the increased cost of pre-operative investigations, surgical implants and instrumentation, protracted hospital stay and drugs. We compared the costs of performing this surgery for aseptic loosening, dislocation, deep infection and peri-prosthetic fracture. Clinical, demographic and economic data were obtained for 305 consecutive revision total hip replacements in 286 patients performed at a tertiary referral centre between 1999 and 2008. The mean total costs for revision surgery in aseptic cases (n = 194) were £11 897 (sd 4629), for septic revision (n = 76) £21 937 (sd 10 965), for peri-prosthetic fracture (n = 24) £18 185 (sd 9124), and for dislocation (n = 11) £10 893 (sd 5476). Surgery for deep infection and peri-prosthetic fracture was associated with longer operating times, increased blood loss and an increase in complications compared to revisions for aseptic loosening. Total inpatient stay was also significantly longer on average (p < 0.001). Financial costs vary significantly by indication, which is not reflected in current National Health Service tariffs.

Background To assess disability more efficiently with less burden on the patient, the National Institutes of Health has developed the Patient Reported Outcomes Measurement Information System (PROMIS) Physical Functionan instrument based on item response theory and using computer adaptive testing (CAT). Initially, upper and lower extremity disabilities were not separated and we were curious if the PROMIS Physical Function CAT could measure upper extremity disability and the Quick Disability of Arm, Shoulder and Hand (QuickDASH). Questions/purposes We aimed to find correlation between the PROMIS Physical Function and the Quick-DASH questionnaires in patients with upper extremity illness. Secondarily, we addressed whether the PROMIS Physical Function and QuickDASH correlate with the PROMIS Depression CAT and PROMIS Pain Interference CAT instruments. Finally, we assessed factors associated with QuickDASH and PROMIS Physical Function in multivariable analysis.