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How AI Optimizes Patient Data Insights in the Pharmaceutical Industry


Patient data insights, together with AI and Machine Learning, is a booming industry for Big Data companies focusing on the pharma segment. Bringing new medications to the market is incredibly costly and time-consuming. It can take 10 years or more and upwards of $2.6 billion to bring a viable drug to market while also meeting regulatory and safety requirements. Furthermore, the total amount of data stemming from healthcare and clinical trials is more disparate and expansive than ever before, growing by 878 percent since 2016. Put simply, there is too much for drug developers to manually process.

During a global pandemic, rapidly dealing with such massive amounts of data becomes a matter of life and death. There is a worldwide demand for new treatments, such as vaccines, to come to the market as quickly as possible.

How can biopharma companies shorten timelines for developing and distributing new treatments and vaccines globally?

Without effective ways to streamline data collection and analysis, faster cycle times will be out of reach.

Why is analyzing healthcare data so difficult?

When we think of data, we often think of structured data that is categorized and organized in spreadsheets and databases that can easily be combed through and analyzed for inconsistencies and trend identification.

Unfortunately, healthcare data is often unstructured and residing in a variety of disparate systems which makes it difficult to access and analyze.

Over the last decade, estimates show that 73 percent of biomedical knowledge is buried in text-based documents like email or in reports that are hand-written or typed via computers, mobile phones, tablets and other devices. The fact that a large amount of data is recorded in written language and not codified poses a resource challenge to large clinical trials, and even more so to small clinical trials—where every piece of data and its inter-relationships may be critical. The data issues of small clinical trials will only multiply as there is a greater focus on more personalized treatments for smaller populations.

Even if patients describe negative drug reactions to their providers, those providers may make a note of these reactions on patient data charts—but may not take the next step of reporting the adverse event (AE) to a pharmaceutical company, where it can be processed and worked into a more extensive database.

AEs can be specific to an individual based on a variety of other factors. Thus, one AE may not be representative or indicative of a more significant issue with the drug itself. In addition, the way patients describe their reactions to a specific drug will likely vary. For example, “sleeplessness” and “not getting a good night’s sleep” may necessarily signal the same reaction but maybe missed or classified differently.

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Drug developers traditionally have had to manually sift through patient data to gauge a specific drug’s level of safety and regulatory compliance. This necessitates reading and analyzing countless reports and trial documents, which can lead to potential human error in the drug development process.

Significantly reducing human error in medical science is a key priority when it comes to rapidly develop new drugs that still meet safety and regulatory requirements. In 2019, one critical assessment of medical research papers found that 25 percent of the documents analyzed contained errors. In the pharmaceutical industry, about 30 percent of observed patient injuries resulted from preventable adverse drug effects.

To compound the issue, when drug developers are researching new vaccines, for example, they are typically focused on developing the vaccine for a specific population group—limiting the amount of genetic diversity and varieties of clinical trials that must be performed.

How can AI and Advanced Patient Data Analytics Help?

The use of AI and deep learning in healthcare is projected to grow to nearly $8 billion by 2022 to help support the management and analysis of data in the pharmaceutical industry.

When drug developers are exploring a molecule’s potential to treat or prevent a disease, they analyze any available data relevant to that molecule.

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Rather than sorting through hundreds or thousands of documents to identify data points that support or refute their hypothesis (which need to be further sorted to determine their significance and value), AI can efficiently scan the articles and pinpoint the specific insights that researchers need. This saves critical time during the drug development process and alleviates the challenge of having to manually review hundreds to thousands of documents.

Beyond efficiency improvements, deep learning can also identify anomalies and interesting pieces of information that human researchers might otherwise miss. AI algorithms can be trained to scan reports and identify trends that drug developers may not be actively searching for but could be relevant to their research.

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For example, most new treatments introduced to the market today are repurposed uses of effective pre-existing drugs. Manual reviews of published research in search of alternative treatments have traditionally been extremely cost-prohibitive. AI can help drug developers identify trends in medical reports that suggest potential alternative or future treatments.

Lastly, AI can help lower costs and decrease time-to-market for new drugs outside of a health crisis. Large pharmaceutical companies typically develop more than one drug at a time and possess vast databases of information about past clinical trials and research.

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AI can rapidly analyze entire databases to identify trends and tactics that could improve and hasten drug development, something human researchers do not have enough time and bandwidth to accomplish efficiently.


AI and ML capabilities for AE monitoring and reporting can help the industry to fully leverage vast amounts of pre-existing data to find new cures. These tools will be essential as the industry seeks to improve pharmacovigilance for existing clinical trials and prepare for the future.

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