Brigham and Women’s Hospital 

Ambulatory Safety Nets to Reduce Missed and Delayed Diagnoses of Cancer

Project Lead: Sonali Desai, MD, MPH 

Project aim(s): To create a digital platform to manage all Safety Net data, including follow up recommendations and patient outcomes, in one location. 

Narrative Description

The aims of the Ambulatory Safety Net Programs at Brigham and Women’s Hospital are to ensure patients at-risk for cancer have highly reliable closed loop system in place for management of abnormal test results to reduce delayed or missed diagnoses. Our test result related safety nets currently focus on incidental radiology findings, colon cancer, prostate cancer, and cervical cancer.  The main goal of this grant was to create a digital platform to manage all safety net data, including follow up recommendation and patient outcomes, in one location. Using a REDCap database we recorded the documented care plans and recommended follow-up including patients’ barriers to accessing care (e.g., lack of transportation, insurance issues, other potential factors preventing timely follow-up).  We created a Tableau Dashboard to help our team visually display the Safety Nets’ output showing incoming volume over time and the specific actions taken by our team to ensure patients are getting the recommended follow-up care in the recommended time frame.  Additionally, we created coded Excel files for partner departments (e.g., Endo, Uro, OB/Gyn) to track their patient outreach. The coded fields correspond to R code which imports/exports these lists to/from REDCap eliminating a previously manual process of updating our database with outreach actions each month.  

This was accomplished in a phased approach: 

Start Up Phase 

  • Partnering with IT provider to identify resources available 
  • Identifying proper stakeholders 
  • Starting initial kickoff meetings with proper ambulatory departments 
  • Brainstorming metrics that should be calculated 
  • Brainstorming how to standardize outcomes data across all current safety nets
  • Conducting market analysis to see best tracking platforms 

    Intervention Phase 

    • Evaluating the current tracking and metrics for existing safety nets 
    • Creating REDCap database
    • Developing new workflows for safety net patient tracking
    • Expanding programs to system wide follow up
    • Enhancing patient tracking and outreach 

      Assessment Phase 

      • Conducting PDSA cycles 
      • Standardizing safety net approach to new safety nets 
      • Centralizing the data collection and tracking system to one system 
      • Analyzing data and tracking metrics 
      • Reporting data to stakeholders 

      We did not add the Radiology nor the Medication Safety Nets to REDCap, like we thought we would. We decided it was not necessary for the day-to-day operational workflow of these two Safety Nets. At BWH, the Radiology Safety Net uses a program called ANCR to document/follow-up on incidental imaging findings and we also document our work in an Excel OneDrive document. The Radiology department is working to get their own Dashboard live, and we can use that to disseminate high-level information about the Safety Net. The Medication Safety Net’s metrics can be pulled from the Enterprise Data Warehouse (EDW) directly into Tableau, bypassing the need for the data to be entered into REDCap. The actual day-to-day work involves documenting in Epic, so REDCap was an unnecessary step.  

      During COVID, patients were understandably not coming in as regularly for their preventive care. This resulted in a backlog of unscheduled/cancelled tests, imaging studies, and procedures that we are still working to schedule at this time. For much of the year, our team was understaffed by 1.5 FTE. Staffing shortages in other departments also affect our work. 

      An unanticipated result of creating a centralized database is the level of cleanup the historic data needs after it is imported into REDCap. As an example, when the historic data from our Colon Cancer Screening Safety Net was imported from our Access Database into REDCap, we discovered that a subset of the structured data had been inconsistently completed. The data was documented, just in an unstructured “notes” section instead of the structure data fields. This requires additional chart review to confirm not only that the appropriate follow-up was completed, but that our database acts as a source of truth of the data, similar to what we find through the time-intensive chart review process. This is a lesson learned in the importance of consistent training and relaying the need for documentation in structured fields.  

      We measure success by tracking all patients with abnormal test results, assessing those have a follow up care plan documented, and calculating the number of patients with follow up care plans in place.  The Tableau Dashboard showcases the volume of completed procedures/tests. Because of the data we have been able to capture through our REDCap database, we know that our team has chart reviewed, monitored, and/or intervened to ensure that over 1300 colonoscopies have been scheduled, of which, 1200 have been completed. The Safety Net Team has performed over 430 interventions, which have resulted in nearly 200 completed follow up recommendations (e.g. Urology appointments, MRIs, prostate biopsies, referrals to Urology, repeat PSA tests). 11 patients have subsequently been diagnosed with prostate cancer. Without this program, these patients may have gone months or years without getting this diagnosis. These metrics prove the efficacy and value of our Safety Net program.  

      Diagnostic quality problem type, failure, or category (symptoms, observed problems, gaps in performance) addressed by the intervention

      • Patient delayed or unable to access care
      • Information gathering
      • Information integration
      • Information interpretation
      • Establishing an explanation (diagnosis)
      • Communicating the explanation to the patient

      Root causes/causative factors addressed by the intervention

      • Workflow (includes testing, follow-up, and referrals)
      • Information sharing and accessibility
      • Health IT

      Setting of the diagnostic quality improvement intervention

      • Ambulatory medical care setting (e.g., clinic, office, urgent care)
      • Radiology/imaging
      • Laboratory, including pathology