Predictors of Treatment Decisions in Multidisciplinary Oncology Meetings: A Quantitative Observational Study

In many healthcare systems, treatment recommendations for cancer patients are formulated by multidisciplinary tumor boards (MTBs). Evidence suggests that interdisciplinary contributions to case reviews in the meetings are unequal and information-sharing suboptimal, with biomedical information dominating over information on patient comorbidities and psychosocial factors. This study aimed to evaluate how different elements of the decision process affect the teams' ability to reach a decision on first case review.

This was an observational quantitative assessment of 1045 case reviews from 2010 to 2014 in cancer MTBs using a validated tool, the Metric for the Observation of Decision-making. This tool allows evaluation of the quality of information presentation (case history, radiological, pathological, and psychosocial information, comorbidities, and patient views), and contribution to discussion by individual core specialties (surgeons, oncologists, radiologists, pathologists, and specialist cancer nurses). The teams' ability to reach a decision was a dichotomous outcome variable (yes/no).

Using multiple logistic regression analysis, the significant positive predictors of the teams' ability to reach a decision were patient psychosocial information (odds ratio [OR] 1.35) and the inputs of surgeons (OR 1.62), radiologists (OR 1.48), pathologists (OR 1.23), and oncologists (OR 1.13). The significant negative predictors were patient comorbidity information (OR 0.83) and nursing inputs (OR 0.87).

Multidisciplinary inputs into case reviews and patient psychosocial information stimulate decision making, thereby reinforcing the role of MTBs in cancer care in processing such information. Information on patients' comorbidities, as well as nursing inputs, make decision making harder, possibly indicating that a case is complex and requires more detailed review. Research should further define case complexity and determine ways to better integrate patient psychosocial information into decision making.