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Electronic Intervention to Improve Structured Cancer Stage Data Capture.

Michael Cecchini1, Kim Framski1, Patricia Lazette1

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Implementing an electronic decision support tool significantly improved structured cancer staging rates. This system ensures accurate cancer stage data for better patient care and research.

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Area of Science:

  • Oncology
  • Health Informatics
  • Clinical Decision Support

Background:

  • Accurate cancer staging is crucial for patient prognostication, treatment planning, and clinical trial eligibility.
  • Electronic health records (EHRs) offer structured staging modules, but physician adoption is inconsistent, leading to reliance on unstructured free text.
  • Unstructured staging data in clinical notes hinders reporting and analysis.

Purpose of the Study:

  • To evaluate the impact of an Epic Best Practice Advisory (BPA) decision support tool on structured cancer staging rates.
  • To improve the consistency and accuracy of cancer staging data within EHRs.

Main Methods:

  • Developed and implemented an Epic BPA as a hard stop to mandate structured cancer staging.
  • Utilized the Plan, Do, Study, Act (PDSA) methodology to guide the intervention.
  • Compared cancer staging rates before and at 4, 8, and 12 months after BPA implementation.

Main Results:

  • Pre-intervention, only 28% of patients had structured cancer staging in the Epic problem list.
  • Post-intervention, structured staging rates increased significantly: 115% in months 1-4, 56% in months 5-8, and 60% in months 9-12.
  • The initial surge (115%) was attributed to staging pre-existing diagnoses, with sustained rates of 56-60% reflecting true post-intervention performance.

Conclusions:

  • Electronic decision support tools effectively enhance structured cancer staging rates in clinical practice.
  • The implemented BPA successfully addressed inconsistent staging practices, improving data quality for oncology care.
  • Sustained staging rates indicate the long-term benefit of integrated decision support for cancer data management.