Improving data collection rates, while improving quality Presenter: Sandra Avery

1. Innovation Poster Session HRT1215 – Innovation Awards Sydney 11th and 12th Oct 2012 Improving data collection rates, while improving qualityPresenter: Sandra Avery Liverpool 4-4c_HRT1215-Session_AVERY_LIVERPOOL_NSW

2. Context The Clinical Cancer Registry (CCR) is based in the Local Health District facilities. With a population of 1.5m, 5000 new cancer cases present each year, in12 facilities & 10 cancer-specific departments. The Cancer Institute, NSW (CINSW ) funded 6 Cancer Information Managers (CIMs) to collect a cancer-related minimum dataset for every tumour diagnosed or treated within these LHDs. The dataset includes 48 elements around referral, diagnosis, staging, treatment, outcomes and quality of care (ask me about the scope). Information is collected from multiple sources – admitted patient data, outpatient files, referrals, electronic medical records, radiotherapy and chemotherapy treatment records, pathology, scans & films, Multi-Disciplinary Tumour-groups (MDTs) notes, where discussions on strategies for the best care pathway for most patients… and other documented evidence (only 50% is electronic)

3. KEY PROBLEM Data collection rates: Clinical Cancer Registry data was taking a whole year to collect 12 months of retrospective data from source systems, paper records and clinical notes. Data Quality was not validated, and quality checks inconsistently applied across the group. Registry reports not readily available, so MDT groups were not getting feedback on data.

4. AIMs OF THIS INNOVATION Improve data collection rates Improve quality of data Provide standard reporting format & method of delivery

5. BASELINE DATA The average time per case (for the first year) was 1.7hrs. (48 data items for each of the identified 5000 cases). The average cost per case (for the first year) was $121 Each CIM specialised in collecting only one tumour site (eg Breast or Colorectal or Haematology) The quality of data was not checked or verified consistently, despite having a quality assurance framework. Only 2 CIMs had evidence of reporting to their tumourgroups. Their reports could not be easily reproduced as they were manually extracted, manipulated and formed into a word or powerpoint report.

6. KEY CHANGES IMPLEMENTED Improve Case Capture rates Planning workshop was held with CIMs Weighting of case difficulty vs volume was considered Training sessions given on data collection methods (shortcuts) Teams of 2-3 were formed to work together on tumour sites Cross-tumour education (each had different data sources) Targets were set to complete collection for each group A report was setup to record case capture rates for each CIM, (annual salary & FTE were included to calculate cost per case) Case capture was discussed weekly with the team

7. KEY CHANGES IMPLEMENTED Improve Quality Set up reports to validate EVERY data item, for blank, inconsistent or invalid data. eg female prostate, or age >110 Large-scale data improvements over 2 years, focussing on specific data elements (eg linking treatment combinations to protocols, unmatched provider to tumour or protocol, combining clinical and pathological TNM staging for ‘best stage’ results) Standard reports were created for regular feedback to MDTs, to inform quality improvement. Clinicians have a ‘feel’ for their own data, so easily pick outliers in reports. CIMs were encouraged to submit posters at conferences, expanding their analysing skills. Standard MDT reports Provide each tumour group with a ‘vanilla’ report, containing baseline data items, & build on details in reports over time.

8. OUTCOMES SO FAR After team collection and monitoring, the average case took 1.4hrs per case, now also including regular quality audits. The average cost per case is $63. Data quality is checked and verified consistently, in line with the quality assurance framework, using 54 standardedit reports that are discussed in team meetings. Each CIM now shares in collection of 3+ tumour sites, and has had training in each. CIMs still specialise in one tumour, to maintain partnerships with MDT clinician groups. Team is satisfied with the new, team supported collection arrangement. There are now 13 MDT groups that have received data, and CIMs have presented 8 tumour-specific posters at 5 conferences over the last 2 years.

9. LESSONS LEARNT Check your team ‘readiness’ for change, you may need to prepare them first Set quality expectations, and measure them Capitalise on ‘short-cuts’ to data collection methods Provide an environment to report and discuss targets, using a team approach to barriers Experts are valuable They collected faster and more in their own group. They engage the clinical group, by being able to talk about the data. Job satisfaction is higher with extra responsibility. Engage clinicians to meet their data requirements Data Definitions are finite, clarify the scope of your data Reduce manual data review & collection (encourage use of electronic source systems) Efficiencies gained by combining resources with other data centres