The pace of progress in cancer care is accelerating—but so are the operational challenges cancer programs face. While advances in screening, diagnostics, and treatment are helping people live longer after a cancer diagnosis, growing patient volumes, persistent workforce shortages, and mounting financial pressures are making it harder to keep pace.
As cancer care becomes more sophisticated, so does the data that supports it. Every stage of a patient’s journey generates valuable clinical data that must be accurately captured, curated, and submitted to cancer registries to maintain a complete picture of care. But many cancer programs are struggling to keep their oncology data as current and complete as the care they provide. Closing this gap will empower cancer programs to fully leverage their data to improve care, support quality improvement, and guide long-term program strategies.
The Growing Challenges Cancer Programs Face
Demand for oncology services continues to grow. The National Cancer Institute projects that the number of cancer survivors in the United States will reach 22.4 million by 2035, reflecting both treatment improvements and an aging population. For those survivors, the risk of recurrence remains an ongoing concern, as does the risk of developing a second cancer, which affects an estimated six to 13% of survivors. Additionally, cancer diagnoses are rising among younger adults. Researchers anticipate a 30% global increase in cancer cases among adults in their prime working years between 2019 and 2030.
As patient volumes increase, advances in precision medicine, targeted therapies, and multidisciplinary care are making cancer care more personalized—and more complex. Patients typically receive care from multiple specialists, undergo several treatment modalities, and require extended follow-up across the continuum of care. This makes it harder for cancer programs to maintain a complete, accurate picture of each patient’s cancer journey.
At the same time, rising labor costs and the ongoing shortage of oncologists are challenging care teams to do more with less. These workforce pressures extend to the teams responsible for managing oncology data. Oncology Data Specialists (ODS professionals) perform highly specialized work that’s becoming increasingly complex as treatment approaches evolve and reporting requirements change. Between limited resources and growing demands, these teams may struggle to keep pace with data abstraction and reporting responsibilities.
What This Means for Oncology Data
In the wake of these challenges, it’s not surprising that many organizations are experiencing significant data backlogs. MRO has found that most U.S. hospitals have an oncology data backlog of a year or longer. These delays go far beyond a reporting inconvenience. Participating cancer programs rely on tools like National Cancer Data Base (NCDB) performance dashboards to flag patients who may be experiencing treatment delays, but those dashboards only work if the underlying data is current. When a backlog builds, cancer programs lose:
- Visibility into NCDB dashboards that flag potential treatment delays
- Confidence in performance benchmarking against peer programs
- Timely evidence needed for accreditation reviews
- A clear, current picture of each patient’s care journey
Clinical data serves as the foundation for many of the decisions that shape cancer care—which is why it’s critical for cancer programs to address backlogs and ensure their data remains accurate and actionable. Delayed, incomplete, or low-quality data limits visibility into the insights cancer programs need to support accreditation, guide quality improvement initiatives, benchmark performance, and make informed decision about future service lines.
At a time when healthcare organizations are under increasing pressure to improve outcomes while carefully managing resources, having timely, reliable oncology data has become a strategic asset for uncovering growth opportunities, strengthening community outreach, driving quality improvement, and more.
Closing the Gap Requires Building the Right Data Capabilities
Today’s industry challenges aren’t going anywhere. Cancer incidence is expected to keep rising, labor challenges remain ongoing, and reporting requirements will continue to evolve. As cancer care becomes more data-driven, organizations need reliable information they can trust to guide decisions.
To keep pace with these changes and position their organizations for long-term success, cancer programs must prioritize clinical data management. That means adopting scalable approaches that not only address existing backlogs but also help prevent new ones from forming. Timely, accurate oncology data enables organizations to maintain accreditation requirements, support quality improvement initiatives, and provide visibility into patient care and treatment progress. Meanwhile, leveraging analytics tools helps program leaders stay ahead of changing market dynamics by turning oncology data into strategic insights.
For many organizations, building this capability internally can be challenging, especially as workforce pressures persist. Partnering with third-party partners for clinical data management provides access to the expertise and capacity needed to efficiently reduce backlogs, improve data integrity, and keep registry submissions current. With third-party experts serving as an extension of cancer program teams, organizations gain access to certified oncology data specialists who can manage clinical data curation and registry submissions while reducing administrative burden for care teams. By allowing internal teams to focus on top-of-license work while trusted partners handle routine abstraction and data management responsibilities, cancer programs can strengthen operational efficiency and unlock more value from their data.
Looking Ahead: Oncology Data Management
Cancer care and oncology data are inseparable. With every care advancement, the need for reliable, up-to-date oncology data will only grow. As cancer programs navigate these challenges, the ability to efficiently manage and leverage oncology data will become increasingly important—not only for meeting requirements but also for driving meaningful care improvements.
Organizations that prioritize oncology data management will be better positioned to navigate workforce challenges, meet evolving reporting expectations, and uncover the insights needed to improve care for the patients and communities they serve.
Frequently Asked Questions
Why do cancer registries fall behind on data abstraction?
Rising patient volumes, more complex treatment regimens, and a shortage of trained oncology data specialists all contribute to registry delays. As precision medicine expands the number of treatment pathways per patient, each case takes longer to abstract accurately.
How does an oncology data backlog affect patient care?
When registry data lags, cancer programs lose visibility into tools like NCDB performance dashboards, which flag patients who may be experiencing treatment delays. A backlog doesn’t just create a reporting problem. It removes the early-warning signal programs rely on to intervene in a patient’s care.
What is the NCDB performance dashboard used for?
The National Cancer Data Base dashboard gives participating cancer programs a way to track treatment timeliness and identify patients at risk of care delays. The dashboard depends on current registry submissions, so a backlog directly limits what program leaders can see.
Should a cancer program outsource oncology data abstraction?
Many programs turn to third-party oncology data partners when internal teams can’t keep pace with abstraction and reporting demands. Outsourcing gives programs access to certified oncology data specialists without adding headcount, freeing internal staff for higher-level clinical and strategic work.
How long does it typically take to resolve an oncology data backlog?
Timelines vary by case volume and complexity, but programs partnering with dedicated abstraction teams generally see faster resolution than trying to close gaps with existing internal staff alone, since specialized data partners can scale capacity to the size of the backlog.
