Developing a maturity model for cancer multidisciplinary teams

Knowledge Sharing Maturity Model for Medical Imaging Departments: Development Study

BACKGROUND

Knowledge sharing in medical imaging departments is driven by the need to improve health care services, develop health care professionals' skills, and reduce repetitive mistakes. It is considered an important step in the implementation of knowledge management solutions. By following a maturity model of knowledge sharing, knowledge-sharing practices can be improved.

OBJECTIVE

This study aimed to develop a maturity model for knowledge sharing in the medical imaging department to help managers to assess the level of maturity of knowledge-sharing practices. In modern health care institutions, improvements in health care professionals' skills and health care services are often driven through practicing knowledge-sharing behaviors. Managers can follow the indicators of maturity model of knowledge sharing and its measurements to identify the current level and move to the next level.

METHODS

This study was conducted in 4 stages: an overview stage that highlighted the factors that affect knowledge-sharing practices in medical imaging departments; an analysis factor stage that was designed to assess the factors that affect knowledge sharing using a concurrent mixed methods approach (questionnaires and semistructured interviews) in 2 medical imaging departments; a structuring maturity model knowledge sharing stage, where a maturity model of knowledge sharing was developed based on the findings of the first and second stages; and finally, an assessment of reliability and validity stage, where a modified Delphi method was used to obtain consensus among experts on model components to be ready for implantation.

RESULTS

The model presented in this study includes 17 indicators, divided into 11 components. Those components were derived from the findings of the questionnaires and semistructured interviews that were applied in the medical imaging departments. It consisted of 5 maturity levels: initial, aware, defined, managed, and optimized. In each level, measurements were included to help managers assess the current level by answering the questions. On the basis of reliability, the experts reached a consensus agreement on the model's components in 2 rounds with SD <1.

CONCLUSIONS

This maturity model of knowledge sharing in medical imaging departments allows managers and policy makers to measure the maturity level of knowledge sharing in those departments. Although the model has been applied to medical imaging departments, it could easily be modified for application in other institutions.

Multidisciplinary team quality improves the survival outcomes of locally advanced rectal cancer patients: A post hoc analysis of the STELLAR trial

PURPOSE

We sought to determine the association between multidisciplinary team (MDT) quality and survival of patients with locally advanced rectal cancer.

METHODS

In a post hoc analysis of the randomized phase III STELLAR trial, 464 patients with distal or middle-third, clinical tumor category cT3-4 and/or regional lymph node-positive rectal cancer who completed surgery were evaluated. Disease-free survival (DFS) and Overall survival (OS) were stratified by Multidisciplinary team (MDT) quality, which was also included in the univariable and multivariable analyses of DFS and OS.

RESULTS

According to the univariable analyses, a significantly worse DFS was associated with a fewer specialized medical disciplines participating in MDT (<5 vs ≥ 5; P=0.049),a lower frequency of MDT meetings ( 200; P=0.039). In addition, a lower number of specialized medical disciplines participating in MDT (<5 vs ≥ 5; P<0.001), a lower frequency of MDT meetings ( 200; P=0.001) were the variables associated with OS. These 3 factors were considered when assessing MDT quality, which was classified into 2 categories: high quality or general quality. Patients treated in hospitals with high MDT quality had longer 3-year OS (90.5 % vs 78.1 %; P=0.001) and similar 3-year DFS (70.3 % vs 61.3 %; P=0.109) compared to those treated in hospitals of the general MDT quality group. Furthermore, multivariable analyses revealed a significance for DFS (HR, 1.648; 95 % CI, 1.143-2.375; P=0.007) and OS (HR, 2.771; 95 % CI, 1.575-4.877; P<0.001) in MDT quality.

CONCLUSIONS

The use of hospitals with optimized multidisciplinary infrastructure had a significant influence on survival of patients with locally advanced rectal cancer.

Advancing maturity modeling for precision oncology

Introduction

This study aimed to map the maturity of precision oncology as an example of a Learning Health System by understanding the current state of practice, tools and informatics, and barriers and facilitators of maturity.

Methods

We conducted semi-structured interviews with 34 professionals (e.g., clinicians, pathologists, and program managers) involved in Molecular Tumor Boards (MTBs). Interviewees were recruited through outreach at 3 large academic medical centers (AMCs) (n = 16) and a Next Generation Sequencing (NGS) company (n = 18). Interviewees were asked about their roles and relationships with MTBs, processes and tools used, and institutional practices. The interviews were then coded and analyzed to understand the variation in maturity across the evolving field of precision oncology.

Results

The findings provide insight into the present level of maturity in the precision oncology field, including the state of tooling and informatics within the same domain, the effects of the critical environment on overall maturity, and prospective approaches to enhance maturity of the field. We found that maturity is relatively low, but continuing to evolve, across these dimensions due to the resource-intensive and complex sociotechnical infrastructure required to advance maturity of the field and to fully close learning loops.

Conclusion

Our findings advance the field by defining and contextualizing the current state of maturity and potential future strategies for advancing precision oncology, providing a framework to examine how learning health systems mature, and furthering the development of maturity models with new evidence.

Development and validation of ACTE-MTB: A tool to systematically assess the maturity of molecular tumor boards

Molecular tumor boards (MTBs) require specialized activities to leverage genomic data for therapeutic decision-making. Currently, there are no defined standards for implementing, executing, and tracking the impact of MTBs. This study describes the development and validation of ACTE-MTB, a tool to evaluate the maturity of an organization’s MTB to identify specific areas that would benefit from process improvements and standardization. The ACTE-MTB maturity assessment tool is composed of 3 elements: 1) The ACTE-MTB maturity model; 2) a 59-question survey on MTB processes and challenges; and 3) a 5-level MTB maturity scoring algorithm. This tool was developed to measure MTB maturity in the categories of Access, Consultation, Technology, and Evidence (ACTE) and was tested on 20 MTBs spanning the United States, Europe, and Asia-Pacific regions. Validity testing revealed that the average maturity score was 3.3 out of 5 (+/- 0.1; range 2.0–4.3) with MTBs in academic institutions showing significantly higher overall maturity levels than in non-academic institutions (3.7 +/- 0.2 vs. 3.1 +/- 0.2; P = .018). While maturity scores for academic institutions were higher for Consultation, Technology, and Evidence domains, the maturity score for the Access domain did not significantly differ between the two groups, highlighting a disconnect between MTB operations and the downstream impact on ability to access testing and/or therapies. To our knowledge, ACTE-MTB is the first tool of its kind to enable structured, maturity assessment of MTBs in a universally-applicable manner. In the process of establishing construct validity of this tool, opportunities for further investigation and improvements were identified that address the key functional areas of MTBs that would likely benefit from standardization and best practice recommendations. We believe a unified approach to assessment of MTB maturity will help to identify areas for improvement at both the organizational and system level.