Studies show that development of a single drug takes about 12 years and can exceed $2.6 billion in costs—and the failure rate is high. According to the The Pharmaceutical Journal, “For every 25,000 compounds that start in the laboratory, 25 are tested in humans, five make it to market and just one recoups what was invested.”
As the pharmaceutical industry slowly but surely takes steps to invent efficiencies aimed at shortening the interval from drug development to approval, data scientists seek to transform the way real world evidence is generated and used in clinical trials. With an immense amount of clinical data readily available for analytics, the challenge is to efficiently integrate this data—stored across disconnected networks and data silos—to speed analysis and time to insight.
With clinical information stored in disparate silos, analysts struggle to gain a complete picture of the data required for monitoring, predictive modeling and the creation of patient targeting criteria.
In our ISPOR Webinar: Leveraging Data and Digital Technologies to Optimize Real World Evidence Generation, we explored how data science and digital data technologies are empowering pharmaceutical organizations to bridge the gaps presented by these data silos.
Say Goodbye to Traditional Analytics
Data scientists extract knowledge and insight from data using scientific methods such as machine learning and natural language processing.
Perhaps the most intriguing of the modern data science tools, machine learning can be used to uncover potentially predictive relationships within real world data sets. Establishing such relationships can accelerate trials, streamline data analytics and improve patient targeting.
Natural language processing can also dramatically speed the process of identifying patient populations. Presenting disparate, unstructured data from medical records, eligibility criteria and individual chart reviews in a complete, 360-degree view eliminates countless hours of manual effort. Data scientists can more quickly and effectively use data to select sites, recruit the right patients and improve the overall trial design.
Combined, machine learning and natural language processing form the foundation of AI in data science. Extracting relevant information from and establishing links within huge data pools, the data science helps AI discover meaningful insights faster and at less cost.
An operational database platform allows data scientists to imagine and execute on projects made up of thousands of variables on millions of data points. This means you can more easily target the right population for clinical trials, automate clustering of subgroups by demographic or treatment protocol and define populations for whom effective treatments don’t exist.
Improved patient targeting relies on more complete views of the individual patient journey. This requires usable data from electronic health records, claims, clinical trials, medical devices, wearable technology and even social media. The more detailed the patient journey, the more useful information there is to derive. But if it follows that process then it can become even lengthier, more complex and less cohesive.
The ability to efficiently aggregate disparate data and disconnected information at a massive scale, including critically detailed information on patient outcomes, is essential to maximizing the value of the real world evidence used in clinical trials. An operational data hub platform advances the objectives of data scientists by bringing together every piece of available data from the patient journey and enabling them to query across it with Google-like search capabilities to discover previously hidden connections between data points and making the data usable for machine learning and natural language processing.
An operational data hub platform can accelerate clinical trials by enabling faster time to insight and reducing the interval between observation and operations. To learn more:
- Learn how to Do More with All of Your Data in Less Time
Data-wrangling work prior to complex data integration is a time-intensive practice exacerbating the already lengthy and expensive processes of product development and FDA approval. Learn how an operational data hub platform empowers pharmaceutical companies to efficiently integrate disconnected and variable data for faster time to analysis and insight.
- Read The Importance of Metadata to Life Sciences
What if you could run complex queries across all of your data and metadata—no need to shred it first—with lightning-fast results? Learn how MarkLogic® features powerful search and semantics capabilities that enable you to extract more value from your metadata.
- See How NoSQL Can Help Advance Analytics in Life Sciences
Adverse event data is often buried in mounds of information, requiring extensive filtering that can take hours just to find it. Learn how MarkLogic enables data scientists to quickly narrow down datasets to just the “features of interest,” reducing the time it takes to get data to the machine learning aspects of signal detection.