The role of experience and clinical decision support in clinical trial accrual within oncology.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1527-1527
Author(s):  
Waqas Haque ◽  
Ann M. Geiger ◽  
Celette Sugg Skinner ◽  
Rasmi Nair ◽  
Simon Craddock Lee ◽  
...  

1527 Background: Patient accrual for cancer clinical trials is suboptimal. The complexity of applying eligibility criteria and enrolling patients may deter oncologists from recommending patients for a trial. As such, there is a need to understand how experience, training, and clinical decision support impact physician practices and intentions related to trial accrual. Methods: From May to September 2017, we conducted a survey on clinical trial accrual in a national sample of medical, surgical, and radiation oncologists. The 20-minute survey assessed barriers and facilitators to clinical trial accrual, including experience (e.g., “In the past 5 years, have you been a study or site PI of a trial?”), training (e.g., “Did you receive training about trial design and recruitment as part of medical school, residency, or fellowship? After fellowship?”), and clinical decision support (e.g., “What kind of clinical decision support has your practice implemented?). We used logistic regression to identify factors associated with frequency of discussing trials (with ≥25% of patients) and likelihood of recommending a trial to a patient (likely or very likely) in the future. Results: Survey respondents (n = 1,030) were mostly medical oncologists (59%), age 35-54 years (67%), male (74%), and not in academic practice (58%). About 18% of respondents (n = 183) reported discussing trials with ≥25% of their patients, and 80% reported being likely or very likely to recommend a trial to a patient in the future. Prior experience as principal investigator of a trial was associated with both frequency of discussing trials (OR 3.27, 95% CI 2.25, 4.75) and likelihood of recommending a trial in the future (OR 5.22, 95% CI 3.71, 7.34), as was receiving additional training in clinical trials after fellowship (discussion with patients: OR 2.48, 95% CI 1.80, 3.42; recommend in future: OR 1.92, 95% CI 1.37, 2.69). Implementing clinical decision support was not associated with discussing trials with ≥25% of patients (OR 1.12, 95% CI 0.76, 1.67), but was associated with being likely to recommend a trial in the future (OR 1.73, 95% CI 1.11, 2.71). Conclusions: In a national survey of oncologists, we observed differences in physician practices and intention related to clinical trial accrual. Whereas the vast majority (80%) reported being likely or very likely to recommend trials in the future, far fewer (20%) reported discussing trials with their patients within the past 5 years. Implementation of clinical decision support – electronic tools intended to optimize patient care and identification of patient eligibility – was not associated with frequency of past discussion of clinical trials but was associated with recommending a trial in the future. Given the stronger association between experience as a site Principal Investigator and recommending a trial, future research should explore how improving opportunities to lead a clinical trial impact trial accrual.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6533-6533
Author(s):  
Mia Alyce Levy ◽  
Christine Micheel ◽  
Neha Jain ◽  
Kathleen F Mittendorf

6533 Background: Today’s oncologist is responsible for choosing appropriate cancer genomics tests to inform patient treatment from multiple available platforms, weighing cost, availability, sensitivity and specificity, and clinical actionability. Knowledge-driven clinical decision support tools can assist clinicians in choosing the panel that is most informative in a given clinical space. Methods: Using a queryable knowledgebase of >1800 active clinical trials containing structured eligibility criteria curations for diagnosis and genomic alterations, we compared two CLIA-regulated genomic panels for clinical actionability over the landscape of solid, breast, and lung cancer clinical trials. Results: The larger panel (73 genes) was more actionable than the smaller panel (62 genes) in the breast cancer (10x more trials returned) and solid tumor (2.7x more trials returned) clinical trial space, while the smaller panel returned 1.2x more trials in the lung cancer space (see table). Conclusions: This analysis demonstrates that patient diagnosis has a significant effect on the potential clinical actionability of a given genomic panel. Further, this analysis demonstrates the clinical utility of knowledge-driven clinical decision support tools for test selection, especially given the often-limited tumor sample available, cost of genomic panel testing, and continuously shifting trial landscape. [Table: see text]


PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0213373 ◽  
Author(s):  
Joseph Doyle ◽  
Sarah Abraham ◽  
Laura Feeney ◽  
Sarah Reimer ◽  
Amy Finkelstein

2020 ◽  
Vol 15 (4) ◽  
pp. 148-155
Author(s):  
Olga Yu. Rebrova

Clinical decision support (CDS) systems are the medical technologies that go through their life cycle. Evaluation ofeffectiveness and safety should be carried out at its various stages at the development, in clinical trials, licensing, clinical and economic analysis, health technologies assessment. To date, the effectiveness and safety of CDS systems vary and are ambiguous there are both successes and failures. Hundreds of clinical trials are carried out, and more than a hundred of systematic reviews are published. When evaluating the efficacy and safety of CDS systems, two types of outcomes are usually estimated: indicators of medical care (volume, time, costs, etc.), and patient outcomes (clinical and surrogate). A slight increase in physicians adherence to clinical guidelines has been observed, but ithad very small influence on surrogate outcomes, and there is no effect on clinical patient outcomes. A slight increase in risk with respect to patient outcomes was found in only a few studies. However, the methodological quality of the evidence is very low. In this regard, a few products based on artificial intelligence have so far approached the licensing phase. The field of CDS systems is developing, but not yet sufficiently studied, and there is a long way to real successes ahead. Meanwhile, there is a wide gap between the postulated and empirically demonstrated benefits of CDS systems.


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