Ways AI can Help Advance Oncology Clinical Trials
healthcaretechoutlook

Ways AI can Help Advance Oncology Clinical Trials

By Healthcare Tech Outlook | Friday, July 19, 2019

Advanced OncologyIntegrating AI-powered platforms into the clinical framework allows researchers to extract structured as well as unstructured information from medical records, increasing the efficiency of clinical trials.

FREMONT, CA: Even with the incredible clinical advancements in healthcare, prescreening potential candidates remains one of the limited territories in oncology clinical trial ecosystem. Difficulties in oncology clinical trials keep on mounting with the requirements of identifying eligible candidates becoming severely complicated. Trained medical professionals have to sift through long chats and complicated medical information to confirm the eligibility. This lengthy process not only delays the entire clinical procedure but also increases the cost of production.

Introducing relevant technologies can elevate as well as revolutionize the oncology clinical trials. On that note, healthcare providers are utilizing artificial intelligence (AI) to advance oncology clinical trials.

Data Extraction

Data extraction has been one of the primary focuses of research institutes. However, steps containing too many redundancies have complicated the process for several clinical researchers. Integrating AI-powered platforms into the clinical framework can extract structured as well as unstructured information from medical records. Obtaining real-time data from patient’s health directory will eliminate the delay and increase the efficiency of clinical trials.    

Functionalities of a highly capable AI platform should not be confused with humans. The ability to automate and operate redundant steps empowers oncology researchers to focus on more crucial issues hence creating an active and well planned clinical testing process.

Access to Clinical Data sets  

To improve clinical trials, a continuous flow of patients’ data is required from various heterogeneous sources. As anyone from healthcare data gathering background knows the intricacies related to the gathering process. From sorting a wide variety of information to detecting difficult-to-locate data, the gathering is guarded by a vast extension of rules.

Using AI can unravel complexities caused in the prescreening of oncology clinical trials. Linking AI with clinical data from hospital and clinics relieves the users from transferring, storing, processing, and acquiring of clinical data.

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