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

2011 ◽  
Vol 9 (11) ◽  
pp. 1228-1233 ◽  
Author(s):  
Pam James ◽  
Patty Bebee ◽  
Linda Beekman ◽  
David Browning ◽  
Mathew Innes ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Data Management ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
University Of Michigan ◽  
Web Based ◽  
Workload Management ◽  
Effective Cost ◽  
The University

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.

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

2011 ◽  
Vol 9 (12) ◽  
pp. 1343-1352 ◽  
Author(s):  
Pam James ◽  
Patricia Bebee ◽  
Linda Beekman ◽  
David Browning ◽  
Mathew Innes ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
University Of Michigan ◽  
Workload Management ◽  
Aggregated Data ◽  
Personnel Costs ◽  
Accrual Rate ◽  
The University

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.

Increasing Clinical Trials Accrual at a Comprehensive Cancer Center

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1266-1266
Author(s):  
Bayard L. Powell ◽  
Debbie Olson ◽  
Robert M. Morrell ◽  
Terry L. Hales ◽  
Kevin P High ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Critical Role ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Point System ◽  
Cancer Clinical Trials ◽  
Treatment Trials ◽  
Academic Year ◽  
Year 2013

Abstract Background: During the academic year 2013 (July 2012-June 2013) our accrual to cancer clinical trials, a critical measure of success for a Comprehensive Cancer Center (CCC), was lower than prior years and below the desired level for CCC core grant renewal. Academic physicians were faced with increasing pressures to meet clinical demands, often at the expense of academic productivity, including clinical research. Methods: Our Dean and clinical leadership committed to support our efforts to increase accrual to clinical trials by providing salary support for our Section on Hematology and Oncology for specific milestones of 5%, 10%, and 15% increases in accrual to all clinical trials and in accrual to treatment (NCI definition) trials. The goal of the faculty was to increase accrual by > 15% to all trials and to treatment trials to maximize the “pool”. To determine how to divide the pool among investigators we developed a point system recognizing clinical investigators for roles as a) PI for trials (with additional points for all accrual to their trials) and b) for entering patients on clinical trials. The point system for both roles (PI and entering patients) was weighted relative to the value of the trial to the CCC, e.g. investigator initiated > cooperative group > industry initiated, and treatment trials >> non-treatment trials. In addition, we awarded points for publications (first and senior author > co-author) and presentations (oral > poster; major national meeting > other meetings). Results: During academic year 2014 (July 2013-June 2014) accrual to all cancer clinical trials increased by 140% (276 to 663) and accrual to treatment trials increased 40% (114 to 160). These increases occurred in both hematologic malignancies (95% all; 16% treatment) where we had a strong track record for accruals, and in solid tumors (200% all; 76% treatment) where our prior record was not as strong. Discussion: Accrual to clinical trials, both treatment and non-treatment improved dramatically. Interpretation of cause and effect is complex. The baseline year (2013) included implementation of a new EMR and the recent year (2014) included recruitment of additional faculty. However, 2014 was complicated by implementation of a new practice plan heavily weighted toward individual RVU production, and a decrease in available co-operative group trials to historically low levels. However, we can conclude that attention to this critical role of clinical investigators is important and can influence behavior. We cannot determine whether financial incentives are needed or whether the funding is one of several potential methods of recognition of the importance of clinical trials. It is possible that the commitment to provide financial support for clinical research demonstrated to clinical investigators that the leadership valued clinical trials activity and this recognition was more important than the actual funds. Future efforts will also need to find ways to recognize/reward clinical trials productivity of groups of investigators for their multidisciplinary contributions to the care of patients on clinical trials, without generating internal competition within the groups. Disclosures No relevant conflicts of interest to declare.

Predicting Patient Treatment Deferrals at an Outpatient Chemotherapy Infusion Center: A Statistical Approach

2017 ◽  
pp. 1-8
Author(s):  
Donald B. Richardson ◽  
Seth D. Guikema ◽  
Amy E.M. Cohn
Keyword(s):  
Treatment Protocol ◽  
Regression Tree ◽  
Comprehensive Cancer Center ◽  
Patient Specific ◽  
Brier Score ◽  
Cancer Center ◽  
University Of Michigan ◽  
Wide Range ◽  
Patient Will ◽  
The University

Purpose Patients scheduled for outpatient infusion sometimes may be deferred for treatment after arriving for their appointment. This can be the result of a secondary illness, not meeting required bloodwork counts, or other medical complications. The ability to generate high-quality predictions of patient deferrals can be highly valuable in managing clinical operations, such as scheduling patients, determining which drugs to make before patients arrive, and establishing the proper staffing for a given day. Methods In collaboration with the University of Michigan Comprehensive Cancer Center, we have developed a predictive model that uses patient-specific data to estimate the probability that a patient will defer or not show for treatment on a given day. This model incorporates demographic, treatment protocol, and prior appointment history data. We tested a wide range of predictive models including logistic regression, tree-based methods, neural networks, and various ensemble models. We then compared the performance of these models, evaluating both their prediction error and their complexity level. Results We have tested multiple classification models to determine which would best determine whether a patient will defer or not show for treatment on a given day. We found that a Bayesian additive regression tree model performs best with the University of Michigan Comprehensive Cancer Center data on the basis of out-of-sample area under the curve, Brier score, and F1 score. We emphasize that similar statistical procedures must be taken to reach a final model in alternative settings. Conclusion This article introduces the existence and selection process of a wide variety of statistical models for predicting patient deferrals for a specific clinical environment. With proper implementation, these models will enable clinicians and clinical managers to achieve the in-practice benefits of deferral predictions.

scholarly journals Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies

2019 ◽  
Vol 16 (6) ◽  
pp. 657-664 ◽  
Author(s):  
Junhao Liu ◽  
Jo A Wick ◽  
Dinesh Pal Mudaranthakam ◽  
Yu Jiang ◽  
Matthew S Mayo ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real Time ◽  
Web Application ◽  
Time Trial ◽  
Cancer Center ◽  
University Of Kansas ◽  
Web Based ◽  
Institutional Level ◽  
Prediction Program ◽  
The University

Background Monitoring subject recruitment is key to the success of a clinical trial. Accordingly, researchers have developed accrual-monitoring tools to support the design and conduct of trials. At an institutional level, delays in identifying studies with high risk of accrual failure can lead to inefficient and costly trials with little chances of meeting study objectives. Comprehensive accrual monitoring is necessary to the success of the research enterprise. Methods This article describes the design and implementation of the University of Kansas Cancer Center Accrual Prediction Program, a web-based platform was developed to support comprehensive accrual monitoring and prediction for all active clinical trials. The Accrual Prediction Program provides information on accrual, including the predicted completion date, predicted number of accrued subjects during the pre-specified accrual period, and the probability of achieving accrual targets. It relies on a Bayesian accrual prediction model to combine protocol information with real-time trial enrollment data and disseminates results via web application. Results First released in 2016, the Accrual Prediction Program summarizes enrollment information for active studies categorized by various trial attributes. The web application supports real-time evidence-based decision making for strategic resource allocation and study management of over 120 ongoing clinical trials at the University of Kansas Cancer Center. Conclusion The Accrual Prediction Program makes accessing comprehensive accrual information manageable at an institutional level. Cancer centers or even entire institutions can reproduce the Accrual Prediction Program to achieve real-time comprehensive monitoring and prediction of subject accrual to aid investigators and administrators in the design, conduct, and management of clinical trials.

scholarly journals 2043

2017 ◽  
Vol 1 (S1) ◽  
pp. 30-30
Author(s):  
Daniel L. Hertz ◽  
Kelley M. Kidwell ◽  
Kiran Vangipuram ◽  
Duxin Sun ◽  
N. Lynn Henry
Keyword(s):  
Treatment Cycle ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
University Of Michigan ◽  
Breast Cancer Patients ◽  
Patient Reported ◽  
Study Population ◽  
The University ◽  
Dose Limiting Toxicity ◽  
Paclitaxel Treatment

OBJECTIVES/SPECIFIC AIMS: Peripheral neuropathy is the dose limiting toxicity of paclitaxel treatment. Paclitaxel pharmacokinetics (PK), specifically the Cmax and amount of time the concentration remains above 0.05 µM (Tc>0.05), have been associated with occurrence of severe, clinician-documented neuropathy. The objective of this study was to confirm that paclitaxel PK predicts progression of patient-reported neuropathy. METHODS/STUDY POPULATION: This observational trial enrolled breast cancer patients receiving weekly 1-hour paclitaxel infusions (80 mg/m2×12 cycles) at the University of Michigan Comprehensive Cancer Center. Paclitaxel concentration was measured via LC/MS in plasma samples collected at the end of (Cmax) and 16–24 hours after (Tc>0.05) first infusion. Patient-reported neuropathy was collected (EORTC CIPN20) at baseline and each cycle. The rate of neuropathy severity increase per treatment cycle is being modeled for each patient. Cmax and Tc>0.05 values will be introduced into the model to confirm that PK independently contributes to neuropathy progression. RESULTS/ANTICIPATED RESULTS: PK and neuropathy data have been collected from 60 patients for ongoing analysis. Our initial model will characterize the expected severity of neuropathy after each cycle of paclitaxel treatment. The PK-neuropathy model will include either PK parameter to validate their contribution to the progression of neuropathy severity during treatment. We anticipate, based on our preliminary analysis of the first 16 patients, that both PK parameters will significantly contribute to the model but Tc>0.05 will be more strongly associated with neuropathy progression. DISCUSSION/SIGNIFICANCE OF IMPACT: This project will generate a model that can be used to predict a patient’s neuropathy severity throughout treatment using a single, conveniently collected and easily measured PK sample during their first cycle. The next steps of this project include identifying genetic and metabolomic biomarkers that predict which patients experienced more severe neuropathy than would be anticipated based on their paclitaxel PK, and a planned interventional trial of personalized paclitaxel dosing to enhance efficacy and/or prevent neuropathy.

Improving hematology/oncology clinical research recruitment via universal prescreening: The VA Connecticut (VACT) Cancer Center experience.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 79-79
Author(s):  
Jenny Jing Xiang ◽  
Alicia Roy ◽  
Christine Summers ◽  
Monica Delvy ◽  
Jessica Lee O'Donovan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Clinical Research ◽  
Cancer Patients ◽  
Cancer Center ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Research Recruitment ◽  
Lung Cancer Patients ◽  
Study Enrollment

79 Background: Patient-trial matching is a critical step in clinical research recruitment that requires extensive review of clinical data and trial requirements. Prescreening, defined as identifying potentially eligible patients using select eligibility criteria, may streamline the process and increase study enrollment. We describe the real-world experience of implementing a standardized, universal clinical research prescreening protocol within a VA cancer center and its impact on research enrollment. Methods: An IRB approved prescreening protocol was implemented at the VACT Cancer Center in March 2017. All patients with a suspected or confirmed diagnosis of cancer are identified through tumor boards, oncology consults, and clinic lists. Research coordinators perform chart review and manually enter patient demographics, cancer type and stage, and treatment history into a REDCap (Research Electronic Data Capture) database. All clinical trials and their eligibility criteria are also entered into REDCap and updated regularly. REDCap generates real time lists of potential research studies for each patient based on his/her recorded data. The primary oncologist is alerted to a patient’s potential eligibility prior to upcoming clinic visits and thus can plan to discuss clinical research enrollment as appropriate. Results: From March 2017 to December 2020, a total of 2548 unique patients were prescreened into REDCAP. The mean age was 71.5 years, 97.5% were male, and 15.5% were African American. 32.57 % patients had genitourinary cancer, 17.15% had lung cancer, and 46.15% were undergoing malignancy workup. 1412 patients were potentially eligible after prescreening and 556 patients were ultimately enrolled in studies. The number of patients enrolled on therapeutic clinical trials increased after the implementation of the prescreening protocol (35 in 2017, 64 in 2018, 78 in 2019, and 55 in 2020 despite the COVID19 pandemic). Biorepository study enrollment increased from 8 in 2019 to 15 in 2020. The prescreening protocol also enabled 200 patients to be enrolled onto a lung nodule liquid biopsy study from 2017 to 2019. Our prescreening process captured 98.57% of lung cancer patients entered into the cancer registry during the same time period. Conclusions: Universal prescreening streamlined research recruitment operations and was associated with yearly increases in clinical research enrollment at a VA cancer center. Our protocol identified most new lung cancer patients, suggesting that, at least for this malignancy, potential study patients were not missed. The protocol was integral in our program becoming the top accruing VA site for NCI’s National Clinical Trial Network (NCTN) studies since 2019.

scholarly journals Synergetic role of integrating the departments of cancer registry and clinical research at an academic comprehensive cancer center

2017 ◽  
Vol 7 (2) ◽  
pp. 33 ◽  
Author(s):  
McKenzie Bedra ◽  
Tammy Vyskocil ◽  
Jennifer Emel ◽  
Crystal Edwards ◽  
Cherif Boutros
Keyword(s):  
Clinical Research ◽  
Cancer Registry ◽  
Comprehensive Cancer Center ◽  
Cancer Center

scholarly journals Web Based Data Capture for Clinical Research

10.28945/3201 ◽  
2008 ◽  
Author(s):  
Stephen Smith ◽  
Samuel Sambasivam
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Medical Device ◽  
Rapid Development ◽  
Research Data ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Proof Of Concept ◽  
Web Based ◽  
Tools And Techniques

Electronic Data Capture (EDC) is increasingly being used in the pharmaceutical, biotech and medical device industries to gather research data worldwide from doctors, hospitals and universities participating in clinical trials. In this highly regulated environment, all systems and software must be thoroughly tested and validated, a task that is burdensome in terms of time and cost. Starting with database structures that are designed to be copied easily, this paper proposes a simple framework that allows for rapid development and minimal testing. The framework includes tools for building modules, for copying modules from one trial to the next, and tools to validate that the modules are the same as modules that have been fully tested previously. A proof-of-concept prototype has been built to demonstrate certain tools and techniques that can be used when designing and building a simplified EDC interface.

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