Clinical impact of data feedback at lung cancer multidisciplinary team meetings: A mixed methods study

Emily Stone, Nicole M. Rankin, Shalini K. Vinod, Mohan Nagarajah, Candice Donnelly, David C. Currow, Kwun M. Fong, Jane L. Phillips, Tim Shaw

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Aim: Multidisciplinary team (MDT) meetings can facilitate optimal lung cancer care, yet details of structured data collection and feedback remain sparse. This study aimed to investigate data collection and the impact of feedback to lung cancer MDTs. Methods: A mixed-methods study using pre and post-test surveys, semistructured interviews, and observation to evaluate data collection and response to modeled data feedback in three Australian lung cancer MDTs at different locations and development stage (site A: outer metropolitan, established; site B, outer metropolitan, new; and site C, inner metropolitan, established). Results: MDT attendees (range 13-25) discussed 5-8 cases per meeting. All sites collected data prospectively (80% prepopulated) into local oncology medical information systems. The pretest survey had 17 respondents in total (88% clinicians). At sites A and C, 100% of respondents noted regular data audits, occasional at site B. Regular audit data included number of cases, stage, final diagnosis, and time to diagnosis and treatment. The post-test survey had 25 respondents in total, all clinicians. The majority (88-96%) of respondents found modeled data easy to interpret, relevant to clinical practice and the MDT, and welcomed future regular data presentations (as rated on a 5-point Likert scale mean weighted average 4.5 where > 4 demonstrates agreement). Semistructured interviews identified five major themes for MDTs: current practice, attitudes, enablers, barriers, and benefits for the MDT. Conclusions: MDT teams exhibited positive responses to modeled data feedback. Key characteristics of MDT data were identified and may assist with future team research and development.

Original languageEnglish
Pages (from-to)45-55
Number of pages11
JournalAsia-Pacific Journal of Clinical Oncology
Issue number1
Publication statusPublished - 1 Feb 2020
Externally publishedYes


  • data feedback
  • data quality
  • health data
  • lung neoplasm
  • multidisciplinary teams


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