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dc.contributor.authorStone Een_AU
dc.contributor.authorRankin Nen_AU
dc.contributor.authorPhillips Jen_AU
dc.contributor.authorFong Ken_AU
dc.contributor.authorCurrow Den_AU
dc.contributor.authorMiller Aen_AU
dc.contributor.authorLargey Gen_AU
dc.contributor.authorZielinski Ren_AU
dc.contributor.authorFlynn Pen_AU
dc.contributor.authorShaw Ten_AU
dc.date.issued2018
dc.date.issued2018en
dc.identifier.urihttps://hdl.handle.net/2123/30645
dc.description.abstractBackground 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.en_AU
dc.publisherRespirologyen_AU
dc.subject.otherCancer Type - Lung Canceren_AU
dc.titleConsensus minimum data set for lung cancer multidisciplinary teams: Results of a Delphi processen_AU
dc.typeArticleen_AU
dc.identifier.doi10.1111/resp.13307


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