Reduce Errors and Past Due Reports Via Monthly Audits Using Crid Numbers

Background: In 2004, the National Marrow Donor Program's database merged with the International Bone Marrow Transplant Registry to streamline transplant research. To manage the fusion of forms generated in two separate databases, the Center for International Blood and Marrow Transplant Research (CIBMTR) began assigning each patient a unique Recipient Identification Number (CRID) in 2007. This has allowed transplant outcomes to be reported annually, because forms are now automatically generated for each CRID. Our center created a secure CRID spreadsheet to manage the merge and track form due dates (Figure 1.0). The spreadsheet has evolved into an auditing tool, serving to simplify our form assignment process and improve quality.

Best Practices: Using Forms Net, a designated Protocol Coordinator (PC) generates a monthly list of forms due for the next five week reporting period. Next, the list is exported to an excel spreadsheet allowing the PC to assign forms to Data Coordinators (DC's) with a DC due date set two weeks prior to the final due date in FormsNet. After the forms are assigned, DC's use the CRID spreadsheet to cross reference CRID numbers with Medical Record Numbers (MRN) to identify the corresponding patient to complete forms. The PC reviews each form using the CRID spreadsheet to track any errors made by the DC completing the form. Next, the audited forms are returned to the DC who corrects and reprocess the form in FormsNet. Each quarter, a designated DC cross references the CRID spreadsheet with Forms Net to ensure error correction and data quality. The spreadsheet can also be used as an opportunity to re-educate DC's on frequently missed fields and reduce errors on the following month's forms.

Outcomes: In the 2008 CIBMTR audit, our center's critical field error rate was 4.2%. Subsequently, implementation of the secure CRID spreadsheet helped reduce our 2012 critical field error rate to 1.7%. This tool will continue increasing data quality and reporting outcomes while concomitantly helping us reach our department goal of <1% error rate in 2016.