Multiobjective optimization challenges in perioperative anesthesia: A review.

Physicians use perioperative decision-support tools to mitigate risks and maximize benefits to achieve the most successful outcome for patients. Contemporary risk-assessment practices augment surgeons' judgement and experience with decision-support algorithms driven by big data and machine learning. These algorithms accurately assess risk for a wide range of postoperative complications by parsing large datasets and performing complex calculations that would be cumbersome for busy clinicians. Even with these advancements, large gaps in perioperative risk assessment remain; decision-support algorithms often cannot account for risk-reduction therapies applied during a patient's perioperative course and do not quantify tradeoffs between competing goals of care (eg, balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with the risk for complications from pulmonary edema). Multiobjective optimization solutions have been applied to similar problems successfully but have not yet been applied to perioperative decision support. Given the large volume of data available via electronic medical records, including intraoperative data, it is now feasible to successfully apply multiobjective optimization in perioperative care. Clinical application of multiobjective optimization would require semiautomated pipelines for analytics and reporting model outputs and a careful development and validation process. Under these circumstances, multiobjective optimization has the potential to support personalized, patient-centered, shared decision-making with precision and balance.

Understanding Conflict Management Styles in Anesthesiology Residents.

BACKGROUND: Successful conflict resolution is vital for effective teamwork and is critical for safe patient care in the operating room. Being able to appreciate the differences in training backgrounds, individual knowledge and opinions, and task interdependency necessitates skilled conflict management styles when addressing various clinical and professional scenarios. The goal of this study was to assess conflict styles in anesthesiology residents via self- and counterpart assessment during participation in simulated conflict scenarios. METHODS: Twenty-two first-year anesthesiology residents (first postgraduate year) participated in this study, which aimed to assess and summarize conflict management styles by 3 separate metrics. One metric was self-assessment with the Thomas-Kilmann Conflict Mode Instrument (TKI), summarized as percentile scores (0%-99%) for 5 conflict styles: collaborating, competing, accommodating, avoiding, and compromising. Participants also completed self- and counterpart ratings after interactions in a simulated conflict scenario using the Dutch Test for Conflict Handling (DUTCH), with scores ranging from 5 to 25 points for each of 5 conflict styles: yielding, compromising, forcing, problem solving, and avoiding. Higher TKI and DUTCH scores would indicate a higher preference for a given conflict style. Sign tests were used to compare self- and counterpart ratings on the DUTCH scores, and Spearman correlations were used to assess associations between TKI and DUTCH scores. RESULTS: On the TKI, the anesthesiology residents had the highest median percentile scores (with first quartile [Q1] and third quartile [Q3]) in compromising (67th, Q1-Q3 = 27-87) and accommodating (69th, Q1-Q3 = 30-94) styles, and the lowest scores for competing (32nd, Q1-Q3 = 10-57). After each conflict scenario, residents and their counterparts on the DUTCH reported higher median scores for compromising (self: 16, Q1-Q3 = 14-16; counterpart: 16, Q1-Q3 = 15-16) and problem solving (self: 17, Q1-Q3 = 16-18; counterpart: 16, Q1-Q3 = 16-17), and lower scores for forcing (self: 13, Q1-Q3 = 10-15; counterpart: 13, Q1-Q3 = 13-15) and avoiding (self: 14, Q1-Q3 = 10-16; counterpart: 14.5, Q1-Q3 = 11-16). There were no significant differences (P > .05) between self- and counterpart ratings on the DUTCH. Overall, the correlations between TKI and DUTCH scores were not statistically significant (P > .05). CONCLUSIONS: Findings from our study demonstrate that our cohort of first postgraduate year anesthesiology residents predominantly take a more cooperative and problem-solving approach to handling conflict. By understanding one's dominant conflict management style through this type of analysis and appreciating the value of other styles, one may become better equipped to manage different conflicts as needed depending on the situations.