The impact of Big Data on Big Pharma has seen data analytics embedded in every aspect of the industry, from research and development to new developments surrounding the internet of things.
The latter often gets the most attention, as new technology involves the use of sensors in web-connected medical devices. The sensors collect and send data on a patient’s use of the device. In collecting data from potentially millions of patients, medical professionals and pharmaceutical companies hope to learn more about how devices are actually used by patients and ways to improve treatment.
That’s just one example of how data is being collected and utilized. But while the possibilities pharm data analytics presents are many, there are potential roadblocks to success.
Challenges and Risks
Security remains the top issue in all situations where information is collected digitally. This is especially true of medical information. Hacked medical information could prove more valuable than stolen credit card information on the black market, according to Reuters.
Most of this information is stored in the cloud – databases held on remote servers and operated by data service providers. With millions of data points and confidential medical information transmitted through web-based applications from devices to the cloud, the potential for hacks is high.
Most companies already have invested heavily in cyber security. The medical devices themselves will likely need an upgrade in security, as they are the weakest link in the internet of things chain, according to experts who spoke with Reuters.
Current Uses of Pharm Data Analytics
Not long ago, the pharmaceutical industry based decisions concerning drug efficacy and consumer behavior on the relatively small databases that result from clinical trials. They also relied on electronic health records shared by hospitals, but that information has limits as it comes from just one source.
Big Data has changed that forever.
In addition to the massive amounts of information collected through the medical internet of things, pharmaceutical companies now have the ability to link data from a variety of sources to create much larger databases. Those sources include medical research and development, patients, medical products retailers and caregivers.
For example, the information from clinical trials now can be cross-referenced with real-time data on patient treatments during hospital visits or care provided by primary doctors and specialists. Linking these data sets provides a clearer picture on treatment patterns and the results of medications outside of controlled clinical trials.
In other words, “real world” information can be gathered for patients who are not always a part of trials, including those with specific diseases and chronic conditions, as well as the very old and very young.
The key to using Big Data involves people with advanced skills understanding proper methodology in the collection and interpretation of data. Because the data is collected outside of controlled trials, it comes with a lot of “noise” and inconsistencies.
For example, one of the challenges of pharm data analytics is collection and proper analysis of unstructured data such as doctor’s notes, images from medical scans and pathology reports.
All analysis must account for these factors when attempting to draw data-driven conclusions on patient treatments and drug efficacy.
Still, Big Data represents a potential “revolution” in pharmaceutical research and development, according to a report from McKinsey & Company, a pharmaceutical and medical products company. The McKinsey Global Institute, the research wing of the company, estimated that data-driven decisions could provide $100 billion in value to Big Pharma.
They project the following is possible by leveraging pharm data analytics.
- Predictive modeling that uses molecular and clinical data to identify molecules with the potential for development into drugs that more safely and effectively treat specific conditions.
- An expansion of those asked to participate in clinical trials based on such factors as genetic information that could make trials more effective and the results more powerful in developing successful treatments
- An increased ease in flowing information between different sources, including doctors, hospitals and research organizations. This real-time information, when linked together, will support the predictive analytics that generate better development of medications
The Structure of Big Data Teams
Currently, Big Data for pharma is in its early stages. Businesses have various structures for how they approach the issue, all of which create jobs for those with expertise in health informatics and data analysis.
According to a report from Infosys, Big Data teams are structured in a variety of ways. Some involve a centralized structure with one person in charge of all Big Data initiatives, others involve a more de-centralized plan that allows different units to move forward with Big Data projects on their own and share data and results across departments.
The sharing extends beyond that to separate companies, as well. For example, Project Data Sphere shares data on clinical trials into cancer treatments. Many Big Pharma companies have signed on to the initiative, including AstraZeneca, Bayer, Janssen Research and Development and Pfizer. There are currently 97 datasets for researchers to access.
The European Medicines Agency also began in 2015 to provide clinical trial information for research done in all of the union’s member countries. The idea behind all of these ideas is that sharing information can lead to better treatments and outcomes for patients.
Big companies have only just begun to leverage pharm data analytics in a way that will improve treatments. For those interested in medicine and data analytics, the amount of opportunity have never been greater.