DisTeam: A decision support tool for surgical team selection

Operating room organization and surgical performance: a systematic review

BACKGROUND

Organizational factors may influence surgical outcomes, regardless of extensively studied factors such as patient preoperative risk and surgical complexity. This study was designed to explore how operating room organization determines surgical performance and to identify gaps in the literature that necessitate further investigation.

METHODS

We conducted a systematic review according to PRISMA guidelines to identify original studies in Pubmed and Scopus from January 1, 2000 to December 31, 2019. Studies evaluating the association between five determinants (team composition, stability, teamwork, work scheduling, disturbing elements) and three outcomes (operative time, patient safety, costs) were included. Methodology was assessed based on criteria such as multicentric investigation, accurate population description, and study design.

RESULTS

Out of 2625 studies, 76 met inclusion criteria. Of these, 34 (44.7%) investigated surgical team composition, 15 (19.7%) team stability, 11 (14.5%) teamwork, 9 (11.8%) scheduling, and 7 (9.2%) examined the occurrence of disturbing elements in the operating room. The participation of surgical residents appeared to impact patient outcomes. Employing specialized and stable teams in dedicated operating rooms showed improvements in outcomes. Optimization of teamwork reduced operative time, while poor teamwork increased morbidity and costs. Disturbances and communication failures in the operating room negatively affected operative time and surgical safety.

CONCLUSION

While limited, existing scientific evidence suggests that operating room staffing and environment significantly influences patient outcomes. Prioritizing further research on these organizational drivers is key to enhancing surgical performance.

A Quality Improvement Study Designed to Optimize Scheduling Geographic/Site Preferences Among Anesthesia Professionals Utilizing Decision Support Tool Assistance

This is a quality improvement pilot study comparing percentages of anesthesia professionals receiving their first choice of workplace location both pre-, and post-implementation of an electronic decision support tool for anesthesia-in-charge schedulers. The study evaluates anesthesia professionals who use the electronic decision support tool and scheduling system at four hospitals and two surgical centers within NorthShore University HealthSystem. The subjects in the study are those anesthesia professionals that work at NorthShore University HealthSystem and are subject to being placed in their desired location by anesthesia schedulers who use the electronic decision support tool. The primary author developed the current software system enabling the electronic decision support tool implementation into clinical practice. All anesthesia-in-charge schedulers were educated during a three-week time period via administrative discussions and demonstrations on how to effectively operate the tool in real time. The total numbers and percentage of 1st choice of location selection by anesthesia professionals were summarized each week using interrupted time series Poisson regression. Slope before intervention, slope after intervention, level change, and slope change were all measured over 14-week pre- and post- implementation periods. The level of change (difference in percentage of anesthesia professionals who received their first choice) was statistically (P<0.0001) and clinically significant when comparing the historical cohorts of 2020 and 2021 to the 2022 intervention group weeks. Therefore, the implementation of an electronic decision support scheduling tool resulted in a statistically significant increase in those anesthesia professionals receiving their first-choice workplace location. This study provides the basis for further investigating whether this specific tool may improve anesthesia professional satisfaction within their work-life balance by enhancing workplace geographic/site choice.

Hospital Employee Performance Evaluation Based on Knowledge Map

With the deepening of theoretical research and social practice, hospital employee performance management has developed into a mature knowledge system. Therefore, systematic review of the research results in this field is conducive to fully understand its research history, grasp the research hotspots and frontier issues, which is of great significance to theoretical innovation in the field of hospital employee performance evaluation.iteSpace 5.3.R4 was used as the visualization tool draw a knowledge map to systematically sort out the distribution of relevant literature of hospital employee performance evaluation, including time distribution, journal distribution, author distribution, etc., and conduct a systematic research on the research hotspots and research frontiers in the field of hospital employee performance evaluation, which provides a beneficial reference for the theoretical research and practical innovation of domestic hospital employee performance evaluation.

Contributions from the 2017 Literature on Clinical Decision Support

Objectives:  To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

Methods:  A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.

Results:  Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders. The second paper presents a decision support tool for surgical team selection, relying on the history of surgical team members and the specific characteristics of the patient. The third paper compares three commercial drug-drug interaction knowledge bases, particularly against a reference list of highly-significant known interactions. The fourth paper focuses on supporting the diagnosis of postoperative delirium using an adaptation of the “anchor and learn” framework, which was applied in unstructured texts contained in EHRs.

Conclusions:  The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that “Big Data” and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.