Oral Presentation 2025 Joint Meeting of the COSA ASM and IPOS Congress

A clinical quality dashboard to measure and address variation in lung cancer care (125617)

Shalini Vinod 1 2 3 , Georgina Kennedy 4 , Gui Xiong 5 6 , Victoria Bray 1 7 , Merran Findlay 4 , Angela Berthelsen 5 6 , Phan Sayaloune 5 , Sandra Avery 5 , Nasreen Kaadan 5 6
  1. Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
  2. South West Sydney Clinical Campuses, University of New South Wales, Liverpool, New South Wales, Australia
  3. Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
  4. SPHERE, University of New South Wales, Kensington, NSW, Australia
  5. Cancer Services, SWSLHD, Liverpool, NSW, Australia
  6. Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
  7. School of Medicine, Western Sydney University, Penrith, NSW, Australia

Background

Measurement of quality indicators (QIs) is crucial in monitoring practice and improving care. Our Lung Cancer multidisciplinary team (MDT) has been measuring QIs using electronic medical records (EMR) information since 2018. However, this is retrospective and requires significant time and manual effort.  The aim of this project was to automate calculation of lung cancer QIs using EMR data and display this in a dashboard for clinicians in near real-time.

Methods

Two focus groups of the Lung Cancer MDT were held to review existing QIs and identify additional QIs from the Lung Cancer Clinical Quality Data Platform. EMR data were extracted and normalized via the Cancer Variation (CaVa) platform and visualised using PowerBI. Two further focus groups were held to co-design the dashboard.

Impact on Practice

The Lung Cancer MDT agreed on measurement of 21 QIs including three related to timeliness of care. Calculation of all QIs have been automated. A prototype dashboard using a traffic light system to display QIs meeting benchmarks (green), within 10% of benchmarks (amber) and not meeting benchmarks (red) has been built. Results can be filtered by time period, patient sociodemographic characteristics (including cultural and linguistic diversity, gender and age), and treatment centre. A list of patients who do not meet a particular benchmark can be generated for clinicians to review whether this variation was warranted or not.  

Discussion

By leveraging the CaVa platform, we developed a dashboard that automates calculation and visualisation of QIs in near-real time using EMR data, to support continuous quality improvement in lung cancer care. Clinician input into dashboard design will make it relevant to future use, especially to identify gaps in care and advocate improvements in service delivery.