How Support Automation Enhances Clinical Trial Management

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How Support Automation Enhances Clinical Trial Management
David Karandish, CEO of Capacity

The life of a clinical study relies on data from documentation, meetings, emails and calls; all of which can be overwhelming for patients, clinical trial teams and associates. Although mundane, documenting, executing and collecting data is crucial to move a trial from phase to phase.

Clinical trial teams face a multitude of competing priorities, from evaluating hundreds of potential patients to maintaining compliance and recording patient progress. No aspect or step can be neglected for a trial to succeed, especially regarding patient recruitment and retention.

To help streamline these facets, AI-powered support automation platforms provide clinical trial management teams with an interactive and informative interface with integrated cloud storage, intelligent document processing and a centralized knowledge base. Critical information is secure, easily accessible and accurate. As a result, the clinical trial recruitment process becomes more efficient for potential patients and trial team members.

AI improves clinical recruitment and overall patient experience

Deloitte projects patient experiences will become central for supporting clinical goals to retain and recruit committed patients. This focus on patient experience will push more trials through the finish line. An AI-powered support automation platform can improve clinical retention and recruitment with catered features for potential patients, which results in quicker and more efficient recruitment selection.

Prospective patients who fit the criteria for a trial begin their clinical trial journey by visiting a website. The website is helpful for patients to learn key information about the study; however, it’s not beneficial for research organizations to learn important information about prospective patients and streamline the recruitment process. Organizations rely on interested patients to reach out through an email address or contact form. It’s then on trial team members to respond to prospective patients, answer their questions and, if applicable, interview them. Interviews determine whether a prospect qualifies for the trial, whether they’re interested in the trial and how likely they are to stay with the trial. Not all interested patients will be eligible, and not all qualified patients will be interested in joining. As a result, there is a waste of time and resources spent by team members during the process.

Implementing an AI-powered conversational chatbot opens up communication on an organization’s recruitment website. The chatbot answers prospective patients ‘questions about the study instantly, freeing up team members’ time and effort spent on manually finding answers and responding. A chatbot responds to questions by leveraging an organization’s knowledge base – and constantly updating it to ensure quality and accuracy. The chatbot gives the most current answer every time, and if it can’t find a solution, it transfers the request to a human expert. The human response is added to the chatbot’s knowledge base to improve its responses in the future.

Not only does the chatbot inform site visitors, but it also determines if potential patients fit study criteria. It can identify and recommend a smaller pool of qualified candidates for a trial, which can save team members hours. A chatbot has clear guides to follow as site visitors respond to its questions and conversations customized by trial administrators. The chatbot will learn about prospective patients, collect patient information and conduct pre-screening questionnaires.

AI streamlines work processes for team members

Built-in system redundancies of data collection for clinical trials means humans are sorting and organizing records, leaving room for human error in categorizing and e-filling important information. Due to clinical sites requiring the same kinds of paperwork at regular intervals, there is a high chance of sending documents to the wrong people or filing them in the wrong location. Clinical trial documentation is a slow process due to the journey from clinical site to research associated to the desk of a trial assistant with an avalanche of paperwork.

Documents need to be accessible, timely and organized for clinical research associates. Seamless retrieval of accurate trial documentation is possible with support automation, and it’s essential to a clinical study’s safety, compliance and success.

The same chatbot that engages potential patients also works on the backend for trial team members. While the chatbot answers potential patients’ questions, it also mines an organization’s knowledge base to respond to inquiries about contact information, site communication and trial processes for team members. Trial administrators can ask the chatbot instead of searching for a specific document or interrupting a co-worker. The chatbot will lead them to the answer, whether it’s about a site coordinator’s contact information, the expiration date on a medical license, an overview of a study or much more.

As part of an AI-powered support automation platform, work processes can be streamlined by initiating customized workflows that use robotic processing automation (RPA). RPA in workflows combines procedures, relevant documents, and team members to streamline processes automatically. The integration of workflows allows team members not to think through the process and journey of a document each time. Instead, they can use a chatbot to follow a guide such as what happens with a record, who else should see it and what comes next in the overall process.

Intelligent document processing can function simultaneously with workflows. This allows a trial team to create and follow templates for various documents, then batch upload trial records for processing. Machine learning classifies the documents against the templates, mines the data and uses a human expert to fill any missing information. Trial experts can then export the data, refine the template to capture data more precisely, or move the record onto its next step in a workflow. The system improves the more it’s used as teams provide feedback and verify the templates are correct. The AI ​​gets better at sorting as more documents are uploaded. Added records become part of the knowledge base accessed through a chatbot by team members.

A good support automation platform must equip trial teams with information and protect the privacy and security of clinical work. The knowledge should have controls to ensure the correct people have access to the correct information. The chatbot won’t give information to a person if they don’t already have permission to access the information, keeping the data private by not caching it in its server.

AI informs patients and equips team members

Utilizing an AI-driven support automation platform can improve the overall clinical trial process starting with recruitment. Conversational chatbots can evaluate and inform potential patients during the recruitment process, matching trial teams with the best trial candidates. Trial administrators can save time with intelligent data processing and customized workflows. Trial teams become empowered to accomplish more with the addition of AI to a clinical research tech stack, and teams easily track vital documentation. An integrated system replaces repetitive and tedious tasks, focusing on an outcome-driven and patient-centered trial model.


About David Karandish

David Karandish is the Founder & CEO of Capacity – an enterprise artificial intelligence SaaS company headquartered in St. Louis, MO. Capacity’s secure, AI-native support automation platform helps teams do their best work. Prior to starting Capacity, David was the CEO of Answers Corp. He and his business partner, Chris Sims, started the parent company of Answers in 2006 and sold it to a private equity firm in 2014 for north of $ 900M.



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