A Practical Framework for Oncology Data Specialist Productivity and Workforce Planning

Cancer registry leaders are frequently asked a simple question:

“How many cases should an Oncology Data Specialist (ODS) complete each day?”

 

Unlike many healthcare departments, cancer registries do not have a universally accepted productivity model. The work of the ODS does not generate relative value units (RVUs) or other traditional performance metrics, making completed cases the most common metric used to evaluate performance. However, case counts alone fail to capture the complexity of registry work or the full scope of an ODS’s responsibilities.

Rather than promoting a universal standard, this article presents a practical framework that measures the time required to complete the three core registry functions associated with a completed case:

Casefinding + Abstracting + Follow up = ODS Abstracting Productivity

The goal is to establish realistic productivity expectations while maintaining complete, accurate, and timely cancer registry data.

 

One Completed Abstract Requires Dozens of Coding Decisions:

The ODS must interpret complex clinical documentation and apply multiple national coding guidelines and staging standards that are continually updated, including:

• Solid Tumor Rules
• Hematopoietic and Lymphoid Neoplasm Rules
• SEER Program Coding and Staging Manual
• Grade Coding Instructions
• STORE Manual
• AJCC Staging
• ICD O 3
• SEER Summary Staging
• Site Specific Data Item (SSDI) Manual
• Extent of Disease (EOD) Manual
• State specific reporting requirements
• Treatment Specific Coding Manuals

In addition to national standards, many organizations require collection of institution specific data elements to support accreditation, quality improvement, physician performance reporting, research, and strategic initiatives. These additional responsibilities further influence the time required to accurately complete each case.

 

Productivity Extends Beyond Abstracting

Abstracting is only one component of the Oncology Data Specialist’s workload.

Depending on the organization, ODS staff may also be responsible for:

• Casefinding
• Follow up activities
• Quality assurance reviews
• Physician queries
• Resolving pathology discrepancies
• Managing suspense files
• Correcting state edits errors
• Registry software maintenance
• Tumor Board
• Accreditation and Cancer Committee activities
• State registry submissions
• Research support
• Data requests and analytics

Each of these responsibilities reduces the amount of time available for abstract completion and should be considered when establishing productivity expectations.

 

Not Every Cancer Case Requires the Same Amount of Time:

One of the greatest challenges in measuring productivity is the variation in case complexity.

Factors that significantly influence abstraction time include:

• Multiple primaries
• Complex treatment sequences
• Multiple reporting facilities
• Extensive diagnostic workup
• Numerous pathology specimens
• Biomarker and molecular testing
• Neoadjuvant therapy
• Disease recurrence
• Long hospitalizations
• Large volumes of physician documentation

For this reason, productivity should be measured using average hours per completed abstract, rather than a fixed number of cases per day. Our suggested expectation is an average of 2 hours per completed abstract.

 

Productivity Tracking Tool:

A simple tracking tool should include:

• Accession/MRN Number
• Primary Site/Sequence
• Class of Case
• Facility
• Date Abstracted
• Total Hours Worked
• Descriptive Comments (including complexity, reportability, multiple primaries, multiple facilities, extensive treatment, physician query, etc.)

 

Calculating ODS Production:

• Weekly Production Capacity:
  • Available Weekly ODS Production Hours ÷ Productivity Expectation (Hours per Completed Case) = Expected Completed Case Capacity
  • Example: 40 production hours ÷ 2 hours per completed case = 20 completed cases per week (on average)

• Backlog Completion Projection:
  • Backlog Cases ÷ Weekly Case Capacity = Weeks to Completion
  • Example: 500 backlog cases ÷ 20 completed cases per week = 25 weeks

• Staffing Projection:
  • Backlog Cases × Hours per Completed Case = Total Production Hours Required
  • Example: 500 cases × 2 hours = 1,000 production hours

Calculating ODS Productivity:

• Determine Expected Case Completion (Available ODS Production Hours ÷ Productivity Expectation (Hours per Completed Case) = Expected Completed Cases
  • Example: 40 production hours ÷ 2 hours per completed case = 20 expected completed cases
• Calculate Productivity Rate by ODS (Completed Cases × Productivity Expectation (Hours per Completed Case) ÷ Actual ODS Production Hours Worked = Productivity Rate
  • Example: (20 completed cases × 2 hours) = 40 hours ÷ 40 Available Weekly ODS Production Hours = 100% Productivity
    • Note: If someone spends 10 hours in meetings, QA, education, cancer committee preparation, etc., don’t use 40 hours in the denominator.

Productivity expectations should be based on actual abstracting time. Meetings, quality assurance, follow up, case finding, state reporting, accreditation activities, education, software issues, physician queries, and other assigned responsibilities reduce the number of hours available for abstract completion and should be considered when establishing productivity goals.

 

The Goal is Sustainable Performance:

Productivity should encourage efficiency while maintaining the integrity of cancer registry data.

Organizations that establish realistic productivity expectations and recognize differences in case complexity create a more sustainable work environment, improve staff retention, and produce higher quality registry data.

A productivity benchmark of approximately 2 hours per completed abstract can serve as a practical operational target for many cancer registry programs. However, no single benchmark is appropriate for every organization. Productivity expectations should be defined by the cancer program.

 

Take the first step today. Review one month of registry activity, calculate your team’s average hours per completed case, assess quality outcomes, and determine whether your current productivity expectations accurately reflect the work being performed.

Productivity is not measured by the number of abstracts completed. It is measured by the ability to consistently produce accurate, timely, and complete cancer registry data while maintaining a sustainable workload for your Oncology Data Specialists.

 

Not sure where to start?

Velarity’s Cancer Registry Operational Assessment provides a comprehensive evaluation of productivity, workflows, staffing, quality, and operational performance. Our experienced cancer registry leaders identify opportunities to improve efficiency, strengthen data quality, optimize staffing, and prepare your program for long-term success.

Whether you need an operational assessment, strategic consulting, interim leadership, or experienced Oncology Data Specialists, Velarity HCS delivers customized solutions that help cancer programs build stronger, more sustainable registry operations.