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Proactive Treatment in Clinical Trials: AI Identifies Best Treatment Options for Patients
Article

Proactive Treatment in Clinical Trials: AI Identifies Best Treatment Options for Patients

Proactive Treatment in Clinical Trials: AI Identifies Best Treatment Options for Patients
Article

Proactive Treatment in Clinical Trials: AI Identifies Best Treatment Options for Patients

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In 2020, the life science industry broke the record for the fastest ever developed and approved vaccine in history. The speed of that vaccine research, development and approval has left both pharmaceutical companies and healthcare providers (HCPs) wondering what it means for the future of clinical research and patient treatment. Can new, safer and innovative treatments be brought to market faster than ever before? Can patients receive better, more modern treatments that are designed to treat their specific ailment? Can we ensure these treatments are not only effective, but safe?


The answer, thanks to AI-driven data analysis, is a resounding “yes!”

 

A new era of patient treatment options

 

Many patients who suffer from unusual or rare conditions have fewer treatment options than those who are fighting more common illnesses. These patients with rare conditions may have more options for treatment than they, or their HCP, think they do. That’s because new treatments and medicines are constantly being researched in clinical trials. However, options for treatment in clinical environments were previously difficult to assess for safety and efficacy due to a lack of available information. I know this firsthand, because my grandfather passed away due to non-Hodgkin’s lymphoma while a new medicine, Rituximab, was being researched in clinical trials. That medicine could have saved his life.

 

Thanks to advancements in cloud, data and analytics technology, life science companies can predict patient outcomes and diagnoses more accurately than ever before. This makes the process of matching patients to clinical trials much easier, solving an old problem that healthcare providers have grappled with for years: how to safely treat unique ailments in clinical trials.

 

Proactively placing patients in clinical trials means they can receive treatment quicker than if they had to wait for the drug to be approved by regulatory authorities. Historically, this could put the patient at risk, as the medicine may cause unnecessary side effects or reactions that negatively impact the patient’s health. But now HCPs and clinical research organizations can leverage patient and treatment data, along with AI- and ML-driven analytics, to safely place patients in clinical trials, as well as speed development and approval of new and innovative medicines to treat patients who can’t participate in clinical trials.

 

Preparing data for AI-driven patient and treatment analysis

 

When it comes to leveraging AI for data analysis, preparing data is critical to the accuracy of the analysis’ findings. Not only do life science organizations need to collect a lot of data, they must also ensure that data is high quality and ready for analysis. Collecting diverse data from operations and research around the globe is essential to making informed decisions for matching patients with clinical trials and new treatments. Any fragmented data or inaccurate data will make insights gathered from AI solutions unsafe, useless or both.

 

One of the biggest challenges in preparing patient and treatment data for analysis is data management. Data collected from different sources doesn’t always have the same level of quality or completeness. It must be standardized and cleaned for analysis. For example, data gathered from a clinical trial where information is recorded regularly on digital mediums may be more comprehensive than data collected from an HCP who took notes by hand. Compounding the issue, every country and region has its own set of regulatory standards regarding consumer and patient data. In Europe, data typically must remain in European servers to comply with GDPR requirements; in China, no healthcare data can cross borders; in the U.S., it’s more like the wild west and data can be pulled from any relevant source and compiled for analysis.

 

Despite these challenges, the rewards of establishing a universal source of truth by compiling and cleaning healthcare data from all available sources are undeniable.

 

A more strategic approach to proactive patient treatment and clinical trial matching

 

Prior to life science industry adoption of cloud-based solutions and AI-driven analysis, tracking patient journeys throughout the full cycle was a significant challenge. Due to siloed data and operations, only fragments of data surrounding a patient’s illness, medications, reactions, experience and outcomes were available.

 

Leveraging AI-driven technology solutions allow life science organizations to stitch together patient and treatment data into a comprehensive patient profile. This has a two-fold benefit: data can be used to rapidly bring drugs to market for patients who urgently require treatment of critical and deteriorating illnesses, and patients can be more effectively and safely matched with clinical trials that have high potential to treat their illness.

 

The future of data-driven healthcare decisions

 

The amount of available data is growing at an exponential rate. This also applies to the life science and healthcare industry. As more patient, treatment and outcome data becomes available, organizations have more and more resources and opportunities to optimize patient care and clinical research. When AI is included in the mix, HCPs and life science companies can spend less time identifying optimal treatments for their patients and more time actually treating them. It will be interesting to watch how life science organizations that remove their data silos and adopt AI-driven analysis approach new, innovative treatments in the years to come.

  

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