Making Heart Team Discussions Work. Issue 2 (4th March 2019)

• Record Type: Journal Article
• Authors: Kolbe, Michaela; Grande, Bastian; Marty, Adrian; Manka, Robert; Taramasso, Maurizio; Nietlispach, Fabian; Pomar, Jose Luis; Maisano, Francesco; Reser, Diana
• Is Part Of: Structural heart. Volume 3:Issue 2(2019)
• Page Start: 100
• Page End: 103
• Publication Date: 2019-03-04
• Subjects: Transcatheter aortic-valve implantation (TAVI) -- heart team discussion -- decision-making -- information-sharing -- risks -- groupthink; Heart -- Diseases -- Periodicals; Congenital heart disease -- Periodicals; Cardiovascular system -- Diseases -- Periodicals; Cardiovascular Diseases; Cardiovascular system -- Diseases; Congenital heart disease; Heart -- Diseases; Periodicals; 616.12
• DOI: 10.1080/24748706.2019.1572254
• Languages: English
• ISSNs: 2474-8706

Abstract

ABSTRACT: Transcatheter aortic-valve implantation (TAVI) is a recommended procedure for the treatment of severe aortic stenosis in patients who are at high surgical risk. In light of the interdisciplinarity of TAVI the European Society of Cardiology and the European Association of Cardio-Thoracic Surgery have suggested to establish a "Heart Team" consisting of cardiologists, cardiac surgeons, imaging specialists, anesthesiologists and, if required, general practitioners, geriatricians, and intensive care specialists. The objective of the Heart Team is to evaluate the patient, anticipate complications and jointly decide on the respective, optimal individual treatment option. However, there are no standards for the conduct of Heart Team meetings. This is problematic because teams are not naturally good in making decisions. The lack of decision-making standards puts the effectiveness of Heart Teams at risk. We analyze the Heart Team's decision-making process with evidence from social psychological research. We discuss typical pitfalls and risks of team decision-making such as suboptimal reward structure, groupthink and information-sharing bias and their consequences for decision quality. We recommend reproducible, evidence-based suggestions to mitigate these risks to improve patient care.