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Capturing tumour stage in a cancer information database.
1Ottawa Regional Cancer Centre, Cancer Care Ontario. bevans@cancercare.on.ca
Summary
Implementing cancer tumour staging systems requires persistence and physician compliance. Lessons learned can improve data accuracy and utility for research and resource planning.
Area of Science:
- Oncology
- Health Informatics
- Cancer Registry Management
Background:
- Accurate cancer staging is crucial for patient care, research, and resource allocation.
- Challenges exist in consistently capturing complete and accurate tumour stage data in cancer databases.
Purpose of the Study:
- To detail the implementation process and lessons learned from establishing a tumour staging information system at a cancer centre.
- To evaluate the accuracy of captured tumour stage data through a medical chart audit.
- To explore the diverse applications of tumour stage information within a cancer centre setting.
Main Methods:
- Retrospective review of an initiative to capture tumour stage information at a regional cancer centre.
- Analysis of committee minutes, extracted staging data from the Oncology Patient Information System (OPIS), and a chart audit of 390 cases.
- Review of Health Information Services workload statistics to identify uses of stage-related data.
Main Results:
- Policies and procedures for capturing tumour stage were implemented in 1994, achieving 92% staging for qualifying cases in that year.
- A 1998 audit revealed 71.5% of charts were completely staged; incomplete staging involved missing TNM or incorrect stage timing.
- Physician-related errors occurred in 2-5% of cases, and data-entry errors in 3-6%.
Conclusions:
- Tumour stage information enhances patient clientele description for accreditation and aids researchers in identifying study populations.
- Stage data supports targeted educational initiatives and assists administrators in resource needs estimation.
- Overcoming resistance to data capture requires persistence, physician compliance strategies (e.g., reminder systems), institutional policies, and feedback on data utility.