Transform Oncology Clinical Care and Research Feasibility Through Data Value

Webcast

Wed, Sep 28, 2022 11:00 AM EDT Helping cancer centers rapidly and more accurately assess patient populations can dramatically reduce the time it takes cancer centers to gather the data to complete sponsor feasibility surveys, while increasing the specificity, clarity, and understanding of patient populations. Cancer centers can select trials with greater confidence of their ability to enroll patients and thereby reach targets for their research program without bias.

Register Free: http://www.appliedclinicaltrialsonline.com/act_w/transform_oncology

Event Overview:
Cancer centers and community-based oncology sites struggle to keep pace with the possibilities of clinical trials as a care option for their patients. In fact, the issue so widespread that more than 20% of US oncology clinical trials fail to accrue sufficiently (Tran, et al., 2020). Data to find the right patients and the right sites was previously largely a manual task, however, advancements in data capture and analysis now enable oncologists to more rapidly and accurately assess their patient populations.


As data engineering moves away from traditional siloed data management to more advanced data transfer, ingestion, analysis and exploration, oncologists can bring disparate data together for efficient decisions about trial options for care, trial feasibility for their site, and concrete patient matching for therapies, that also can ensure diversity in trial participant representation.
Attendees of this session will gain an understanding of the value of their data and gain awareness of how Real World Data can be used for predictive outcomes in both a clinical trial and a clinical care setting.


The industry has rapidly expanded with most clinical care data captured in electronic medical records (EMR) systems today. That clinical trial data flows in from various fragmented, disconnected sources and connected devices via the EMR, but also outside sources including patient-captured data from wearables and questionnaires.


As a result, Cancer Centers and Healthcare oncology sites have more data than ever but are facing a clinical data struggle to keep pace with the possibilities for clinical trials as a care option for their patients. In fact, the issue so widespread that more than 20% of US clinical trials fail to accrue sufficiently (Tran, et al., 2020). Understanding clinical trial enrollment options and reaching site feasibility for a study was previously largely a manual task. Past models were fraught with implicit bias, and left room for improvement in ensuring diversity in trial opportunity and uptake. With the advancements in data capture and analysis, technology is now able to assess data quality, data analysis and create site efficiency determining patient appropriateness for trial.


As data engineering moves away from traditional siloed data management to more advanced data transfer, ingestion, analysis and exploration, oncologists are able to bring disparate data together for efficient decisions about trial options for care, trial feasibility for their site, and concrete patient matching for therapies. System selections are automatic and can be factored for various factors that ensure diversity in trial participant representation.


Helping cancer centers rapidly and more accurately assess patient populations can dramatically reduce the time it takes cancer centers to gather the data to complete sponsor feasibility surveys while increasing the specificity, clarity, and understanding of patient populations. Cancer centers can select trials with greater confidence of their ability to enroll patients and thereby reach targets for their research program without bias.


Attendees of this session will gain an understanding of the value of their data and gain awareness of how Real World Data can be used for predictive outcomes in a clinical care setting.


Key take-aways

  • Hear about removing implicit bias by leveraging a proven solution for querying oncology populations to determine feasibility faster
  • Discover new approaches to data management that increase efficiency for research study activities while delivering more personalized care options
  • Understand the opportunities for data automation in patient finding and patient matching workflows that help generate predictive outcomes

Speakers

Jeanie Magdalena Gatewood
Managing Director
Inteliquet, an IQVIA Business

Betsy Wagner
Associate Director
Inteliquet, an IQVIA Business

Register Free: http://www.appliedclinicaltrialsonline.com/act_w/transform_oncology