Developing a maturity model for cancer multidisciplinary teams. (December 2021) - Liu, Yiren; Evans, Lynleigh; Kwan, Terence; Callister, James; Poon, Simon; Byth, Karen; Harnett, Paul
Highlights: Existing tools assess cancer MDT performance at a static point in time. Maturity model can be applied to MDT to inform performance improvement. Defining stages allows MDT to focus on a manageable number of initiatives. Cancer MDT Maturity Model can be an effective tool that is easy to administer. Abstract: Background: Multidisciplinary teams (MDTs) are considered the "gold standard" of care for patients with cancer but how well they function and the role they play in decision making varies widely. Although several observational tools have been developed to evaluate MDT performance, they are resource intensive and only assess MDT performance at a static point in time. The aim of this study was to develop a validated maturity model as a self-assessment instrument for MDTs to evaluate their performance and monitor improvement over time. Methods: The authors used a three-phase methodology to develop a maturity model. In the first phase, using a modified Delphi technique, we identified 20 indicators (within five components), each having five levels of maturity [1] . In the second phase, further Delphi iterations were undertaken to refine the content and structure of the model. By the end of the second phase six components and 17 indicators had been established. In the third phase, the refined model was distributed to members from 11 MDTs to test for validity and reliability. 101 valid responses were received. Principal Component Analysis was used to determine the optimal number of components that fit the indicators. Factors with eigenvalue greater than one were extracted. Cronbach's alpha (α) was used to measure the internal consistency of components. Bivariate correlation analysis, measuring pair-wise relationships between indicators (r), was undertaken to assess convergent and discriminant validity. Results: Five factors were extracted from Principal Component Analysis. For the factors extracted, 16 out of 17 indicators showed loadings greater than the 0.4 threshold. All components demonstrated good levels of internal consistency (α > 0.8) and convergent validity (r > 0.6). Discriminant validity cannot be established. Ratings for ease of use (3.6/5) and usefulness (3.4/5) were considered acceptable. Conclusions: Further work is required to establish discriminant validity and refine the components and indicators. Once further refinement and validation are completed, the maturity model should be a simple tool for MDTs to measure their performance and monitor improvement over time.