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AI: The Key That Unlocks Clinical Trials and Matches Patients with Prospective Therapies

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Read more about author Lucas Glass.

Thanks to ever-evolving advancements in modern medicine, many diseases and medical conditions with severe sequelae can now be treated through therapies not yet on the market. The clearest example of this was patients getting early access to the COVID vaccines during trials that undoubtedly saved lives.  But while a clinical trial may potentially save a patient’s life, sadly many patients and physicians have not been equipped with the data analytics to explore or even know of the most up-to-date treatment options.

Health care has recently entered a groundbreaking era wherein data science tools can predict patient outcomes and diagnoses at unprecedented capacities. The industry faces the challenge of collecting and optimizing this data for clinical trial/patient matching so that patients can receive potentially life-saving drugs and treatments for their ailments. The solution? AI and machine learning (ML) technologies help to integrate clinical research options into the proactive clinical care setting – meaning, in the eyes of health care providers, clinical trials will now be a more viable option for proactive treatment than ever before.

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Whether matching a patient to a clinical trial for medicine currently not on the market or accelerating the availability of that medicine for the patient, AI and ML technologies help life sciences companies safely shorten the drug research-to-patient-care timeline. The more life sciences companies tap into technology and automation, the more data can be optimized to inform health care providers of potentially life-saving clinical trial options and new treatments for their patients. 

AI Represents the Future of Health Care 

For a long time, the ability to track patient journeys across the medical system has been incomplete, as the data on patient medications, experiences, and outcomes has been fragmented across many systems. Today, AI enables the life science industry to create a comprehensive profile of the patient leveraging all the data points from their multi-modal profile. AI can even facilitate the integration and cleaning of these multi-modal data assets. This applies to both the clinical and commercial spaces. In the commercial space, this data can be used to help bring new drugs or therapies to the market and match them with patients who need urgent treatment – especially for those patients with rare conditions. In the clinical space, these insights create comprehensive patient profiles and assist with clinical trial matching, clinical developments, and patient selection.

AI Is Optimal When Data Is Maximized Both in Quality and Quantity 

To cultivate the highest accuracy, both data quantity and quality are essential. In the commercial space, it is critical to include a diverse global dataset encompassing all different markets to make informed business decisions. These insights will assist in detecting the best areas to find clinical trial participants and identifying target markets for drug commercialization. Incomplete or inaccurate data skews these key business decisions.

In the clinical space, while maximizing the collection of high quantities of data from around the world provides more comprehensive analytics, management of such high volumes of data comprises one of the biggest challenges that the life sciences industry faces today. Data is received at varying levels of quality and completeness, which must then be cleaned and validated until viable for projections. Additionally, each health care market has its own varying compliance complexities – for example in Europe, data typically must stay in Europe to comply with GDPR. When handling such high volumes of data, it is important to think beyond just clinical silos or commercial siloes – the information management must support all the different use cases, combining all the different access controls, governance, and rules for what you can and cannot do with data. 

While the high volumes and complexities attributed to the collection of health care data present challenges, it is paving the way to a more holistic dataset that will ultimately give health care providers the necessary information to match their patients with life-saving clinical trials. 

Conclusion

Each coming year, both the quantity and quality of health care data rapidly increase, equipping HCPs and other decision-makers with more comprehensive information. As the life sciences industry embraces AI and ML technology, an exciting future lies ahead for data management and its potential to help maximize patient treatment options. In equipping the health care industry with predictive analytics to recommend treatment options and identify undiagnosed patients solely from electronic medical records, AI provides physicians more time to do what they do best: treat their patients. With AI included as a part of their treatment journeys, patients can be confident that their eligibility for life-saving treatment options is continuously explored.  

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