Introduction: Nontechnical skills (NTS) such as teamwork and communication play an important role in preventing adverse outcomes in the operating room (OR). Simulation-based OR team training focused on these skills provides an environment where team members can learn with and from one another. We sought to conduct a systematic review to identify simulation-based approaches to NTS training for surgical teams. Materials and Methods: We conducted a systematic search of PubMed, ERIC, and the Cochrane Database using keywords and MeSH terms for studies describing simulation-based training for OR teams, including members from surgery, anesthesia, and nursing in September 2016. Information on the simulations, participants, and NTS assessments were abstracted from the articles meeting our search criteria. Results: We identified 10 published articles describing simulation-based OR team-training programs focused on NTS. The primary focus of these programs was on communication, teamwork, leadership, and situation awareness. Only four of the programs used a validated instrument to assess the NTS of the individuals or teams participating in the simulations. Discussion: Simulation-based OR team-training programs provide opportunities for NTS development and reflection by participants. Future programs could benefit from involving the full range of disciplines and professions that compose an OR team, as well as increased use of validated assessment instruments.

Purpose: To identify the different machine learning (ML) techniques that have been applied to automate physician competence assessment and evaluate how these techniques can be used to assess different competence domains in several medical specialties. Method: In May 2017, MEDLINE, EMBASE, PsycINFO, Web of Science, ACM Digital Library, IEEE Xplore Digital Library, PROSPERO, and Cochrane Database of Systematic Reviews were searched for articles published from inception to April 30, 2017. Studies were included if they applied at least one ML technique to assess medical students’, residents’, fellows’, or attending physicians’ competence. Information on sample size, participants, study setting and design, medical specialty, ML techniques, competence domains, outcomes, and methodological quality was extracted. MERSQI was used to evaluate quality, and a qualitative narrative synthesis of the medical specialties, ML techniques, and competence domains was conducted. Results: Of 4,953 initial articles, 69 met inclusion criteria. General surgery (24; 34.8%) and radiology (15; 21.7%) were the most studied specialties; natural language processing (24; 34.8%), support vector machine (15; 21.7%), and hidden Markov models (14; 20.3%) were the ML techniques most often applied; and patient care (63; 91.3%) and medical knowledge (45; 65.2%) were the most assessed competence domains. Conclusions: A growing number of studies have attempted to apply ML techniques to physician competence assessment. Although many studies have investigated the feasibility of certain techniques, more validation research is needed. The use of ML techniques may have the potential to integrate and analyze pragmatic information that could be used in real-time assessments and interventions.

Effectiveness of computer vision techniques has been demonstrated through a number of applications, both within and outside healthcare. The operating room environment specifically is a setting with rich data sources compatible with computational approaches and high potential for direct patient benefit. The aim of this review is to summarize major topics in computer vision for surgical domains. The major capabilities of computer vision are described as an aid to surgical teams to improve performance and contribute to enhanced patient safety. Literature was identified through leading experts in the fields of surgery, computational analysis and modeling in medicine, and computer vision in healthcare. The literature supports the application of computer vision principles to surgery. Potential applications within surgery include operating room vigilance, endoscopic vigilance, and individual and team-wide behavioral analysis. To advance the field, we recommend collecting and publishing carefully annotated datasets. Doing so will enable the surgery community to collectively define well-specified common objectives for automated systems, spur academic research, mobilize industry, and provide benchmarks with which we can track progress. Leveraging computer vision approaches through interdisciplinary collaboration and advanced approaches to data acquisition, modeling, interpretation, and integration promises a powerful impact on patient safety, public health, and financial costs.