Enhancing Multidisciplinary Team Processes in Lung Cancer Care: A Self-Assessment Toolkit and Best Practices

J Clin Pathways. 2024;10(1):19-25. doi:10.25270/jcp.2024.01.02

Goal and Purpose of a Multidisciplinary Team

Multidisciplinary teams (MDTs) are a key component of the cancer care pathway. The primary goal of an MDT is to improve patient survival and satisfaction, includ­ing quality of life. Indeed, the discussion of patient cases in MDTs is associated with positive improvements to multiple aspects of patient management and patient jour­neys, including an increase in survival outcomes.^1,2

MDT meetings (MDTMs) facilitate this goal by providing different specialists throughout the patient care continuum with the opportunity to communicate, ex­change scientific and clinical knowledge, and decide on the most suitable treatment options. As such, MDTs and MDTMs deliver a clear clinical,^3-7 scientific,^8-11 and edu­cational value proposition. Considering this, and the fact that super specialization of health care providers (HCPs) mandates multidisciplinary groups to deliver com­prehensive patient care, MDTs have thus become a standard in many regions and are supported by medical guidelines.^12,13

This is particularly important when considering the increasing complexity of cancer care, including the shift toward more personalized treatment approaches. This holds true in the case of lung cancer, which ranks second highest in cancer incidence¹⁴ and has made significant strides in recent scientific advancements. This progress can be attributed to a multitude of new biomarkers and complex treat­ment options that are now validated and available.^15-17

An Optimal MDT Composition

An optimal cancer MDT should represent all HCPs with a role in the patient care pathway.¹⁸ This includes specialists who perform diagnostics, specialists that initiate and oversee treatment, HCPs who provide patient support, and junior HCPs involved with the patient cases during training. Look­ing to the example of lung cancer MDTs, the specific func­tions are listed in Table 1.¹⁹

An Optimal MDTM Process

There are five key steps in the optimal process followed by an effective MDTM (Figure 1):

1. Registration
2. Preparation
3. Discussion
4. Reporting
5. Patient feedback

Challenges: Why Undertake the Research

To date, there is a lack of standardization in the organization and functioning of MDTs and MDTMs,^18,20 compounded by the varying circumstances and organizational complexities of different care providers at a global, national, regional, and lo­cal level. The resulting variability leads to additional disparity in outcomes and decision-making, which in some situations ultimately leads to suboptimal patient outcomes.^21,22

To ensure consistently optimal decision-making, standard­ization of MDTs and MDTMs is vital. It will also contrib­ute to health care resilience and facilitate MDTs to overcome the increasing perceived challenges burdening teams.²³ In the context of lung cancer care, these key challenges include (1) rising patient volumes,^14,24 (2) high mortality risk among patients with lung cancer,²⁵ (3) the increasing complexity of disease and treatment landscapes,^26-29 and (4) high costs in time and resources.^30-32

The blueprint for MDTs developed by RadboudUMC, which is the basis for the Dutch Multidisciplinary Oncology Platform (SONCOS) national standards on MDTMs,¹³ served as a starting reference for an optimal MDT and a way to understand the key elements. Complementing this blueprint, a thorough review of existing literature was conducted to deliver additional insights. A systematic research methodol­ogy was followed, including:

• Conducting 100+ semistructured interviews, involving 2-10 HCPs from each of the 24 participating hospitals.
• Forming 14 focused discussion groups with participating hospitals to establish optimal practices and identify areas for improvement within the context of in-hospital and cross-hospital MDTs.
• Establishing an international expert panel comprising 8 MDT chairs. This panel convened to discuss research outcomes, distill key insights, and develop a set of actionable recommendations for improving MDTs.

This comprehensive approach combined the SONCOS guidelines, firsthand insights from interviews, collaborative discussions within hospital settings, and expert perspectives to create a robust framework for improving MDTs.

Two key types of results are derived from this research study. Despite numerous publications addressing the importance of and guidelines for effective MDTMs, the first is a description of the key areas in which process variation is seen. The second is a framework and approach to address and reduce this variability. This centers on a comprehensive self-assessment toolkit that was designed to help hospitals identify and optimize areas that fall short of optimal standards. This toolkit features a compila­tion of best practices designed and curated to assist hospitals in implementing high-quality MDTMs.

Variability in MDTM Processes

The research identified significant variations in the approach taken by different hospitals assessed in the case studies. The differences lead to varying MDTM performance in hospitals, highlighting the opportunity for knowledge sharing and cross-hospital learn­ing. The differences were observed across seven key areas:

1. Patient case selection and referral process
2. Key participants
3. Time investment
4. Quality in process execution
5. Infrastructure and systems
6. Utilization and generation of scientific insights
7. Quality assurance

Patient case selection and referral processes

Our research revealed a notable divergence in patient case selec­tion and referral processes among participating hospitals on a na­tional, regional, and local (hospital) level. At the national level, variations on the mandatory discussion of patient cases within MDTMs were observed in guidelines (Table 2). Regionally, our study highlighted discrepancies in patient access to expert lung cancer knowledge between remote and urban areas, leading to the emergence of regionally based MDTMs in response. Despite this, inequalities persist due to limited regional ubiquity of this framework. Across hospitals—even those within the same re­gion—unequal patient MDTM access was observed, influenced by diverse inclusion criteria as well as varied clinical decision-making largely due to time and workload constraints. This variation in patient care pathways and MDT discussion points contributed to increased process and outcome disparities.

Key participants

Participation dynamics within lung cancer MDTMs exhibited diversity across essential stakeholders (Table 1). The engage­ment of HCPs responsible for providing patient support, such as social workers and palliative care physicians, displayed inconsis­tency, subsequently influencing the integration of the patient’s perspective. Despite the indicated desire from all participating MDTs to include these specialties, personnel resource con­straints pose challenges in realizing this goal comprehensively.

Time investment

Significant variations in time allocation were observed across different phases of the MDTM process. The study underscored the significance of striking an optimal balance in time invest­ment across various steps. Notably, disparities in preparation time exerted discernible impacts on the quality of discussions during MDTMs. Gaining a comprehensive understanding of time allocation per patient emerged as an important recommen­dation to guide improvements and optimization of MDTMs.

Quality in process execution

Challenges in MDTM preparation affected outcomes in various hospitals (Figure 2). Five pivotal stages of effective MDTMs were identified: registration, preparation, discussion, report­ing, and patient feedback. Differences in data inclusion and format were observed in registration processes. Level of preparation showed significant impact on quality of discussion, with discussion effectiveness particularly influenced by prepara­tion adequacy, specialist punctuality, roles and responsibilities, patient case order, and patient representation. Reporting, which acts as a platform for learning among junior HCPs, showcased variability in execution due to differences in respon­sibilities and templates. Lastly, timing of patient feedback also varied considerably.

Infrastructure and systems

Establishing robust infrastructure and interconnected systems was identified as a pivotal enabler for the optimization and fu­ture of MDTMs. During the study, infrastructure components, including electronic medical records (EMRs), specialty-specific systems, and the integration of artificial intelligence (AI) tools, emerged as crucial elements in facilitating efficient and coordi­nated care. However, challenges surrounding issues of interop­erability, variable functionality, and limited accessibility were noted and linked to inefficiencies in data sharing. Although study participants acknowledged the potential of AI and de­cision-making tools to revolutionize diagnosis and treatment planning, concerns relating to awareness, development status, and funding hinders their full-scale implementation.

Usage and generation of scientific insights

MDTs use scientific knowledge for patient-centered decisions, but variations exist in staying updated, selecting patients for tri­als, and institutionalizing expertise. Successful MDTs hold reg­ular knowledge-sharing sessions. Patient trial selection methods vary, with some hospitals automatically starting this process upon patient registration, while others delegate patient recruit­ment to the specialist running the trial or rely on ad-hoc match­ing by the MDT. To institutionalize expertise, MDTs should learn from their decisions, especially in data-limited cases, and consider databases for outcomes. To this end, some of the hos­pitals studied in this research are working to develop centralized tools using MDT data for improved decision-making.

Quality assurance

Assessing the quality of decision-making within MDTMs proved challenging, as it is intricately linked to the knowledge of participating physicians, process adherence, and the level of preparation. Initiatives aimed at ensuring high-quality MDTM outcomes were identified, such as outcomes reporting in Den­mark’s national system to enable cross-center comparisons, and studies comparing simulated patient cases across MDTMs. In the Netherlands, SONCOS has developed guidelines outlining the recommended process for an effective MDTM.13 However, some centers show difficulties fully implementing these guide­lines due to limited time and resources.

Self-Assessment Toolkit for Implementing High-Quality MDTMs in Lung Cancer

While the importance of MDTMs is well documented, the tools available for improving these meetings often lack uni­versal applicability due to their specificity to particular regions or health care organizations. Different health care institutions have unique structures, processes, and practices, which neces­sitates the tailoring of improvement tools to suit the distinct context and requirements.

To address this challenge, a comprehensive toolkit has been developed through a collaborative effort involving 24 hospitals and MDTs. The aim of this toolkit is to provide a universally applicable resource that assists any MDT seeking to enhance its practices by learning from the best practices of other MDTs. This approach encourages increased collabora­tion and knowledge sharing across hospitals and even interna­tional borders. Although the toolkit’s case studies primarily fo­cused on MDTMs in the context of lung cancer, the principles and methodologies outlined in the toolkit can be applied to MDTMs across various medical specialties.

The toolkit is structured around three key components:

1. Self-Assessment Model: This component revolves around a well-defined framework designed to assess the maturity level of an MDT. It covers a spectrum of elements grouped into three categories: patient access to the MDT, three hard factors of MDTs (processes, technology, and quality assur­ance), and two soft factors of MDTs (culture and capabili­ties). Within each of these categories, the self-assessment model defines the ideal characteristics of a mature MDT and outlines steps to achieve this vision. This framework enables hospitals to evaluate their current MDT practices, with a total of 17 elements assessed across the 6 factors. Each element is scored on a scale from 1 to 4, where 4 sig­nifies a fully mature MDT and 1 indicates an immature MDT. The self-assessment model offers MDTs the ability to identify areas for improvement and tailor their approach to patient care through MDTMs.
2. Best Practices: Given the variability in MDTM practices among hospitals, this component focuses on sharing suc­cessful practices across the full range of MDT-related ele­ments. The repository of good practices corresponds to the elements of the self-assessment tool, thereby allowing hos­pitals to learn from each other’s experiences and strategies. This sharing of knowledge encourages the adoption of ef­fective practices and fosters a culture of continuous learning and improvement.
3. MDTM Toolkit Templates: Building on the insights gained from the study and case study interviews, three standard­ized templates have been developed. These templates are in­tended to serve as sources of inspiration for hospitals seek­ing to enhance their MDTs. They are most effective when integrated into EMR systems, as this integration streamlines the population of relevant information. The three templates are as follows:

• Referral template: A tool for referring physicians to clearly communicate patient information and specific questions to the MDT.
• Patient case presentation template: Prefilled before the MDTM to enhance efficiency during case presentations.
• Reporting template: Populated during the MDTM and used for patient feedback, discussion guidance, and data extraction for analysis.

In summary, the toolkit provides a comprehensive approach to improving MDTMs. It encompasses self-assessment, shared best practices, and practical templates, all aimed at empowering MDTs to evaluate their performance, enhance collaboration and communication, and optimize patient care through well-structured and informed MDTMs.

Study Results: Leveraging for Maximized Impact

Our research study yields two key results: (1) a description of variances observed among participating hospitals and (2) a self-assessment toolkit to evaluate MDT performance and identify areas for optimization. To maximize the impact of these results, it is important to first define how they can be optimally lever­aged and to understand the extent of their potential impact.

The toolkit offers a structured approach for hospital and MDT improvement, incorporating a self-assessment model, shared best practices, and standardized practical templates. We maintained a keen awareness of the toolkit’s intended wide-ranging relevance and essential adaptability during its design and development to accommodate the inherent differences among end users.

To maximize the benefits and ensure equality of health care, all hospitals should utilize this toolkit to pinpoint areas of weak­ness in lung cancer MDTs and incorporate learnings from other real-world examples. These learnings include (1) feasible optimi­zations for specific processes; (2) the responsibilities, ownership, and resources to effect this change; and (3) indicators of success­ful implementation. We suggest hospitals implement these self-assessment and optimization cycles on a regular schedule (eg, quarterly reviews) to embed a culture of continuous improve­ment within MDTs. Our findings also underscore the potential for continued best practice sharing and enhanced collaboration among hospitals to navigate the future landscape of health care.

Effective and widescale leverage of these study results will help hospitals optimize internal MDT performance and un­lock the full value proposition across the three domains of sci­entific advancement, clinical efficacy, and educational enrich­ment. In turn, MDTs will be better positioned to deliver on the key goal of improved patient outcomes while promoting equality of access to health care.

Study Result Limitations

This research provides valuable insights into MDT variations and optimization opportunities. However, it’s crucial to ac­knowledge limitations, categorized into study design and result validation.

Study design

The study focused exclusively on lung cancer MDTs, which introduces bias and limits the generalizability of findings to other MDT types; for instance, those caused by the differing importance of professional roles in other disease MDTs. Geo­graphical limitations stem from the research only being con­ducted within Europe and Canada and not including other global perspectives. At the same time, only two hospitals were selected per country, targeting leading academic or high-per­forming centers open and willing to engage in process-sharing and improvement. As such, the results may not offer a compre­hensive reflection of MDTs in each of the studied countries. The research was primarily driven by interviews and work­shops, which are inherently subjective methods and possibly not representative of actual real-world performance. Addition­ally, the research offers a physician-centric perspective that may not align with other important stakeholders, including hospital managers or patients. Lastly, this research was conducted in 2022/2023 and given rapidly changing dynamics may not be relevant by, for example, 2030.

Result validation

Beyond the study design limitations, we still need to evalu­ate the long-term impact of implementing the best practices to understand whether they result in improvements to MDT performance and access.

Next Steps For This Research

The next steps for the project should consider the aforemen­tioned limitations and aim to address them. Toward this, the study sponsors have planned and initiated expansion of lung cancer MDT research in other geographies and are exploring potential for additional research in other MDT disease types. Hospitals are encouraged to utilize the self-assessment toolkit, implement the best practices, and monitor the long-term im­pact on their MDTs to enable validation of the results. While it would be invaluable to establish a correlation between best practice MDTMs and improved patient outcomes such as sur­vival and quality of life, we acknowledge that undertaking such investigations may require significant financial resources. In our ongoing research, we are actively exploring viable and cost-efficient methodologies to bridge this gap and evaluate the potential impact of best practices on patient outcomes.

The authors would like to highlight that the full self-as­sessment toolkit, templates, and best practices mentioned in this report will be published in early 2024 in a comprehensive white paper titled, “Good practices in lung cancer MDTs: An optimal process of MDT meetings (MDTMs), evaluation of MDTMs in practice, and a toolkit for implementation.” Peri­odic updates to the research should be undertaken to account for changes in future care delivery and ensure relevance of the results in years to come.

Authors: Poka Cui¹; Peter Blanshard¹; María Teresa Campos-Partera²; Adrien Moucquot²; S. Hassan R. Naqvi, PhD²; David Dellamonica²; Heather Moses²

Affiliations:  ¹Vintura Consulting, Utrecht, The Netherlands; ²AstraZeneca, Cambridge, UK

Funders: This study was sponsored by AstraZeneca.

Address correspondence to: 

Peter Blanshard

Stadsplateau 6,

3521 AZ Utrecht

The Netherlands

Email: pblanshard@vintura.com

Acknowledgements: The authors would like to thank the multidisciplinary teams and hospitals involved, whose close collaboration was crucial to this research: Erasmus University Medical Center (NL; IJsselland Ziekenhuis, Franciscus Vlietland, Bravis, Admiraal de Ruyter Ziekenhuis, Maasstad); Radboud University Medi­cal Center (NL; Jeroen Bosch Ziekenhuis, Canisius Wilhelmi­na Ziekenhuis, Bernhoven, Pantein, Elkerliek); CancerCare Manitoba (CA); The Ottawa Hospital (CA); Aarhus Univer­sity Hospital (DK; Godstrup Hospital; Copenhagen Univer­sity Hospital Rigshospitalet); University Hospital Zurich (CH); University Hospital Basel (CH); Azienda Ospedaliera Papardo (IT); Policlinico Universitario Campus Bio-Medico (IT); St Olav’s Hospital (NO; Levanger Hospital, Ålesund Hospital, Molde Hospital, Kristiansund Hospital, Volda Hos­pital); Institut Català d’Oncologia (ES; Hospital Universitari de Bellvitge).

Disclosures: P.C. reported being an employee of Vintura and owning Apple, AstraZeneca, GSK, Haleon, and Novartis stock. P.B. reported being an employee of Vintura and owning Gingko Bioworks, Caribou Bioscience, Cellectis, Ardelyx, and CorMedix stock. M.T.C.P. reported being an employee of AstraZeneca and owning AstraZeneca stock. A.M. reported being an employee of AstraZeneca and owning Pfizer and Moderna stock. S.H.R.N. reported being an employee of AstraZeneca and owning AstraZeneca stock. D.D. reported being an employee of AstraZeneca and owning AstraZeneca, Roche, and Novartis stock. H.M. reported being an employee of AstraZeneca and owning Apple, Amgen, AstraZeneca, Google, Lilly, Sanofi, and Vertex stock.