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University of Michigan Comprehensive Cancer Center Opportunities for Improvement Project

Tara M. Breslin, Marcy Waldinger, and Samuel M. Silver

The University of Michigan Comprehensive Cancer Center (UMCCC) Opportunities for Improvement project involved a detailed patient-level medical record review, feedback to medical providers and clinical leadership, and discussion of potential predictors of discordant or delayed care. The medical record review revealed that reasons for discordant or delayed care were well documented by clinical providers, and medical comorbidity was the most common predisposing factor. Another common theme was the difficulty in obtaining treatment records for patients who received a portion of their care outside UMCCC. The project provided a valuable opportunity to examine established processes of care and data collection and consider how the newly implemented electronic health record might support future efforts aimed at improving efficiency and communication among providers.

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Creating an Effort Tracking Tool to Improve Therapeutic Cancer Clinical Trials Workload Management and Budgeting

Pam James, Patty Bebee, Linda Beekman, David Browning, Mathew Innes, Jeannie Kain, Theresa Royce-Westcott, and Marcy Waldinger

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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Effort Tracking Metrics Provide Data for Optimal Budgeting and Workload Management in Therapeutic Cancer Clinical Trials

Pam James, Patricia Bebee, Linda Beekman, David Browning, Mathew Innes, Jeannie Kain, Theresa Royce Westcott, and Marcy Waldinger

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.