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.

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.

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 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.

scholarly journals Definition and Coordination of Roles and Responsibilities Among Cancer Center Clinic and Research Personnel

2020 ◽  
Vol 16 (1) ◽  
pp. e64-e74
Author(s):  
Simon J. Craddock Lee ◽  
Torsten Reimer ◽  
Sandra Garcia ◽  
Erin L. Williams ◽  
Mary West ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Physician Attitudes ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Research Staff ◽  
Research Personnel ◽  
Delivery Of Care ◽  
Roles And Responsibilities ◽  
Clinic Staff ◽  
Staff Members

PURPOSE: Effective enrollment and treatment of patients in cancer clinical trials require definition and coordination of roles and responsibilities among clinic and research personnel. MATERIALS AND METHODS: We developed a survey that incorporated modified components of the Survey of Physician Attitudes Regarding the Care of Cancer Survivors. Surveys were administered to clinic nursing staff and research personnel at a National Cancer Institute–designated comprehensive cancer center. Results were analyzed using χ2-tests, t tests, and analyses of variance. RESULTS: Surveys were completed by 105 staff members (n = 50 research staff, n = 55 clinic staff; 61% response rate). Research staff were more likely to feel that they had the skills to answer questions, convey information, and provide education for patients on trials (all P < .05). Both clinic and research staff reported receipt of communication about responsibilities in fewer than 30% of cases, although research staff reported provision of such information in more than 60% of cases. Among 20 tasks related to care of patients in trials, no single preferred model of responsibility assignment was selected by the majority of clinic staff for nine tasks (45%) or by research staff for three tasks (15%). Uncertainty about which team coordinates care was reported by three times as many clinic staff as research staff ( P = .01). There was also substantial variation in the preferred model for delivery of care to patients in trials ( P < .05). CONCLUSION: Knowledge, attitudes, and perception of care and responsibilities for patients on clinical trials differ between and among clinic and research personnel. Additional research about how these findings affect efficiency and quality of care on clinical trials is needed.

Metastatic colorectal cancer (mCRC) patterns of care: Implications for clinical trial design

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 4079-4079 ◽  
Author(s):  
C. S. Denlinger ◽  
M. A. Collins ◽  
Y. Wong ◽  
S. Litwin ◽  
N. J. Meropol
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Trial Design ◽  
Clinical Trial Design ◽  
New Drugs ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Treatment Change ◽  
Therapy Regimen

4079 Background: New approaches have expanded options for patients (pts) with mCRC. To characterize current practice paradigms that might bear on clinical trial design, we analyzed decision-making and treatment patterns in pts treated at a Comprehensive Cancer Center since the introduction of cetuximab (CET), and bevacizumab (BV). Methods: A retrospective review of all pts diagnosed with mCRC between 3/1/04 and 8/28/06 treated at Fox Chase Cancer Center. Results: 160 pts were treated, with 157 pts receiving at least one therapy regimen by 10 attending oncologists. There were 350 changes in therapy with 246 (70%) including continuation of at least one prior drug (92 BV, 111 fluoropyrimidines, 43 other). The most common reasons for treatment change were toxicity (33%), progressive disease (PD) (29%), treatment breaks (15%), and metastasectomy (11%) ( Table ). PD was a more common cause for treatment discontinuation in later phases of treatment (18% initial regimen vs. 36% subsequent regimens, p=0.0002). 24% of pts treated with oxaliplatin (OX) discontinued due to neuropathy. Hypersensitivity caused discontinuation in 5% of pts with OX and 7% of pts with CET. Resection of metastases was undertaken in 38% of pts. 43% of these pts received neoadjuvant therapy, and 56% received adjuvant therapy. 30% of pts have died, 29% remain on active treatment, 28% are on a treatment break, 3% are on hospice, and 11% are lost to follow-up. Conclusions: PD is no longer the primary reason for change of therapy in pts with mCRC. Metastasectomy is common and OX neuropathy is often treatment-limiting. These findings have important implications for endpoint selection and design of clinical trials in mCRC. Future clinical trials in mCRC must recognize treatment complexities and capture key components of decision-making that may result in prolonged survival. Furthermore, treatment breaks represent a potential window for the evaluation of new drugs. [Table: see text] No significant financial relationships to disclose.

Health care worker attitudes about clinical trials at a comprehensive cancer center.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e20633-e20633
Author(s):  
Erica Leigh Campagnaro ◽  
Seunghee Margevicius ◽  
Barbara J. Daly ◽  
Jennifer Rachel Eads ◽  
Tyler G. Kinzy ◽  
...  
Keyword(s):  
Health Care ◽  
Clinical Trials ◽  
Self Efficacy ◽  
Small Sample Size ◽  
Educational Interventions ◽  
Small Sample ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Normative Beliefs ◽  
The Impact

e20633 Background: Cancer patient (pt) participation in clinical trials (CT) is low. Little is known about the beliefs and attitudes of health care workers (HCW) and how they impact intention to discuss CT with pts. The overall goal of this project was to develop a conceptual model to guide future interventions to enhance communication about CT between HCW and cancer pts. Methods: Two email surveys of non-physician HCW at an NCI-designated comprehensive cancer center were conducted. The first was sent to a random sample of 150 HCW. The second was sent to 80 who completed the first survey. Based on our prior work (Eads et al. ASCO 2011) and Ajzen’s Theory of Planned Behavior, domains of the first included CT knowledge (19 items, agree/disagree) and attitudes (27 items, 5-point Likert); the second included normative beliefs about institutional attitudes toward CT (6 items, 5-point Likert), self-efficacy about engaging in discussion about CT (14 items, 5-point Likert), and intention to discuss CT with pts (4 items, 7-point Likert). Results: 41 HCW completed both anonymous surveys; 27 could be matched by demographics. Median age of matched respondents was 44.3 yrs (range 24-63), 26 female, 22 caucasian, 9 nurses. Overall, CT knowledge was high (median 17/19 items correct). There were strong associations between attitudes and self-efficacy (Spearman r=-0.425, p=0.03), as well as perceived normative beliefs and self-efficacy (r=0.651, p=0.0002). These associations were strong amongst nurses (r=-0.818, p=0.007 and r=0.656, p=0.05, respectively), with a particularly strong correlation between self-efficacy and intention to discuss clinical trials with pts (r=0.891, p=0.001). Conclusions: In spite of a small sample size, these pilot data strongly support a behavioral framework to understand and address the impact of HCW attitudes and beliefs about CT on discussions of CT with pts. Insofar as HCW (especially nurses) have substantial pt contact, and serve as a resource for pts regarding treatment decisions, educational interventions to address HCW barriers to discussing CT with pts (i.e. attitudes, beliefs, and self-efficacy) could positively impact pt attitudes and improve decision making.

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