Poster Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2019

Development and feasibility of measuring quality indicators using lung cancer multidisciplinary team meeting data (#227)

Kim-Lin Chiew 1 2 , Candice Donnelly 3 4 , Emily Stone 5 6 , Eric Hau 7 8 , Puma Sundaresan 7 8 , Ashanya Malalasekera 9 , Tim Shaw 3 4 , Shalini K Vinod 1 10
  1. Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
  2. School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, Australia
  3. Faculty of Health Sciences, University of Sydney , Sydney, NSW, Australia
  4. Sydney West Translational Cancer Research Centre, Westmead, NSW, Australia
  5. Department of Thoracic Medicine and Kinghorn Cancer Centre, St Vincent’s Hospital Sydney, Sydney, NSW, Australia
  6. St Vincent's Clinical School, University of NSW, Sydney, NSW, Australia
  7. Sydney West Cancer Network, Western Sydney Local Health District, Sydney, NSW, Australia
  8. Sydney Medical School, University of Sydney, Sydney, NSW, Australia
  9. Bankstown Cancer Centre, Bankstown and Lidcombe Hospitals, Bankstown, NSW, Australia
  10. South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia

Aims

Quality indicators (QIs) are measures used to assess quality and safety of healthcare delivery. The ease of measurement or feasibility is important for their utility as quality improvement tools. The aims of this study were to develop QIs for lung cancer multidisciplinary teams (MDTs) and assess the feasibility of measuring these with routinely collected electronic data from MDT meetings in oncology information systems (OIS).

Methods

A modified Delphi consensus method was used to develop QIs. An initial literature review identified possible QIs that met a minimum set of ideal criteria. These underwent 3 rounds of consensus by either an expert panel or electronic survey to Australian lung cancer MDTs. QIs were ranked and assessed on criteria for validity, importance, feasibility and ability to action. The final set of QIs was tested for feasibility at three MDTs with the routinely collected electronic data from MDT meetings.

Results

60 potential QIs were identified from review and assessment of which 25 were prioritised by the panel. Subsequent survey of MDTs elected 12 QIs based on weighted average rankings and an additional five were included from panel consensus. Of the final set of 17 QIs all were measurable using at least one of the three OIS datasets, although no individual centre was able to measure all proposed QIs. Five QIs were unmeasurable at centre A, three at centre B and two at centre C. Unmeasurable QIs included those that measured timeliness of care, palliative care referral, pulmonary function testing results or included treatment details in a separate record.

Conclusion

A structured consensus-based methodology was used to develop 17 QIs of which all were feasible when tested on three MDT datasets. Routinely collected data from MDT meetings can be used to measure QIs. Future work will measure these in practice to assess validity.