Consensus minimum data set for lung cancer multidisciplinary teams: Results of a Delphi process
- Publication Type:
- Journal Article
- Citation:
- Respirology, 2018, 23 (10), pp. 927 - 934
- Issue Date:
- 2018-10-01
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| Stone_et_al-2018-Respirology.pdf | Published Version | 491.2 kB |
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© 2018 Asian Pacific Society of Respirology Background and objective: While multidisciplinary team (MDT) care in lung cancer is widely practiced, there are few guidelines for MDT on best data collection strategies. MDT meetings need ready access to information for the provision of optimal treatment recommendations (the primary purpose of the meeting), audit of team performance and benchmarking. This study aimed to develop a practical data set designed for these goals through a recognized consensus process with health professionals who participate in formal MDT settings. Methods: A modified Delphi process with three iterations (two surveys and one consensus conference) was carried out involving over 100 Australian lung cancer MDT health professionals. Results: In total, 122 lung cancer MDT health professionals responded to the Round 1 survey from over 350 invitees. Of the 122, 98 were available for invitation to Round 2. Of 98, 52 (53%) invitees responded to the Round 2 survey. After two rounds, 51 data elements across 8 domains (patient demographics, risk factors, biopsy data, staging, timeliness, treatment, follow-up and patient selection) achieved consensus, defined as 80% agreement. For Round 3, 33 MDT lead clinicians were invited to participate in a consensus conference. Of 33, 14 (42%) invitees distilled the 47 data elements into 23 elements across 8 domains to address the study objectives. Conclusion: A practical data set for lung cancer MDT to use for optimal treatment recommendations and to evaluate team performance was developed through recognized consensus methodology. Access to streamlined, relevant and feasible data collection strategies may improve MDT decision-making, audit of team performance and facilitate benchmarking.
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