Purpose: It is expected that over the next 10 to 15 years, demand for oncology services will increase, potentially surpassing the supply of available oncologists. Physician assistants (PAs) and nurse practitioners (NPs) have the potential to address the anticipated shortage in physician supply. The two objectives of this study were to define how National Cancer Institute (NCI) -designated comprehensive cancer centers use PAs/NPs and to pilot a self-reported PA/NP productivity tool.Methods: An online survey addressing practice patterns and productivity in 4-hour outpatient oncology clinics was administered to PAs/NPs practicing at 15 National Comprehensive Cancer Network member institutions.Results: A total of 206 PAs/NPs were included in the final analysis. NPs and PAs reported similar clinical activities, with the following exceptions: NPs reported spending more time on telephone triage, and PAs reported spending more time on procedures. Overall, PAs/NPs reported seeing more follow-up (mean, 6.1; standard deviation [SD], 3.5) than new patients (mean, 1.2; SD, 1.3) per clinic. NPs with a medical oncology specialty reported a marginally greater productivity among follow-up patients than did PAs. Otherwise, NPs and PAs saw a similar number of patients regardless of specialty. Conclusion:To our knowledge, this is the first study attempting to characterize PA/NP clinical activities and define productivity benchmarks at NCI-designated comprehensive cancer centers. Given the increasing complexity of oncologic care and the increased population of patients with cancer and cancer survivors requiring that care, PAs/NPs have the potential to fill important roles in both outpatient and inpatient care settings.

Our results suggest specific clinical productivity targets for academic oncologists and provide a methodology for analyzing potential factors associated with clinical productivity and developing clinical productivity targets specific for physicians with a mix of research, administrative, teaching, and clinical salary support.

Clinical trials operations struggle to achieve optimal distribution of workload in a dynamic data management and regulatory environment, and to achieve adequate cost recovery for personnel costs. The University of Michigan Comprehensive Cancer Center developed and implemented an effort tracking application to quantify data management and regulatory workload to more effectively assess and allocate work while improving charge capture. Staff recorded how much time they spend each day performing specific study-related and general office tasks. Aggregated data on staff use of the application from 2006 through 2009 were analyzed to gain a better understanding of what trial characteristics require the most data management and regulatory effort. Analysis revealed 4 major determinants of staff effort: 1) study volume (actual accrual), 2) study accrual rate, 3) study enrollment status, and 4) study sponsor type. Effort tracking also confirms that trials that accrued at a faster rate used fewer resources on a per-patient basis than slow-accruing trials. In general, industry-sponsored trials required the most data management and regulatory support, outweighing other sponsor types. Although it is widely assumed that most data management efforts are expended while a trial is actively accruing, the authors learned that 25% to 30% of a data manager's effort is expended while the study is either not yet open or closed to enrollment. Through the use of a data-driven effort tracking tool, clinical research operations can more efficiently allocate workload and ensure that study budgets are negotiated to adequately cover study-related expenses.

Quantifying data management and regulatory workload for clinical research is a difficult task that would benefit from a robust tool to assess and allocate effort. As in most clinical research environments, The University of Michigan Comprehensive Cancer Center (UMCCC) Clinical Trials Office (CTO) struggled to effectively allocate data management and regulatory time with frequently inaccurate estimates of how much time was required to complete the specific tasks performed by each role. In a dynamic clinical research environment in which volume and intensity of work ebbs and flows, determining requisite effort to meet study objectives was challenging. In addition, a data-driven understanding of how much staff time was required to complete a clinical trial was desired to ensure accurate trial budget development and effective cost recovery. Accordingly, the UMCCC CTO developed and implemented a Web-based effort-tracking application with the goal of determining the true costs of data management and regulatory staff effort in clinical trials. This tool was developed, implemented, and refined over a 3-year period. This article describes the process improvement and subsequent leveling of workload within data management and regulatory that enhanced the efficiency of UMCCC's clinical trials operation.

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