Breaking Down Big Data in Life Science
In these times, when everything and everyone is connected to the internet, a large volume of data is generated daily. The good news is, the more information we have, the more chances we have to achieve goals that require that large amount of data and knowledge.
This is why data collection is so important for companies.
As Sir Francis Bacon quoted, “Scientia potentia est” (knowledge is power).
This large volume of information that is collected and created by digitizing everything connected to the internet, structured or not, is referred to as Big Data. How companies consolidate, analyze, and use this data is crucial in determining the successful outcome they want to achieve.
Big Data in Life Science
In Life Science, Big Data refers to the large amount of health data created by the mass adoption of the internet and digitization of all information that comes from numerous data sources such as:
- Medical and Pharma research
- Clinical trials
- Electronic records
- Medical devices
- Pandemic data
- Patients’ data
- Medical imaging
Big Data Challenges for Life Science
The biggest challenge of Big Data in Life Science is the diversity in format, type, and context of the data.
Combining different types of information from different data sources with different formats and particularities is a challenging task.
Other challenges that Life Science Big Data faces are related to the privacy of health information (especially in European countries), security, siloed data, and budget constraints.
But, despite these challenges, several new technological improvements are allowing Life Science Big Data to be converted to useful and actionable information.
Big Data Benefits for Life Science
The main benefits of Big Data in Life Science include:
- Diagnosis: Big data allows for data mining and analysis to help identify the causes of illness, which increases earlier diagnosis, effectiveness, and quality of treatments through the discovery of the early signal.
- Precision Medicine: Aggregate data can be leveraged to drive personalized care for patients.
- Reduction of Adverse Medication Events: Harnessing of Big Data helps to spot medication errors and flag potential adverse reactions.
- Population Health: You can monitor Big Data to identify disease trends and health strategies based on demographics, geography, and socioeconomics.
- Preventative Medicine: Preventative analytics and data analysis of genetic, lifestyle, and social circumstances helps prevent disease through the identification of risk factors.
- Medical Research: Data-driven medical and pharmacological research helps cure disease and discover new treatments and medicines (the Corona crisis is an example of it).
- Cost Reduction: Big data helps identify value that drives better patient outcomes for long-term savings.
- Pharmacovigilance: Big Data improves pharmacovigilance and patient safety through the ability to make more informed medical decisions based on directly delivered information to the patients.
Examples of Big Data Application in Life Science
Big Data in Life Science is performing well. Even though Big Data is enormous, complex, and not easy to manage, it, along with other technologies, is playing an essential role in reaching new possibilities.
You can see that from the benefits previously mentioned, as well as in these real events listed below:
- Haiti Support – In 2010 a team of researchers analyzed the calling data from two million mobile phones on the Digicel Haiti network to identify the population movements during the Haiti earthquakes and subsequent Cholera outbreak. Big Data analysis gave insights about the most affected areas, which, in turn, lead to allocating resources more efficiently and identifying areas at increased risk for new cholera outbreaks. (source: BBC).
- Robert Koch-Institute Corona Warn App – This is a Coronavirus tracing app that works by exchanging anonymous codes with other smartphones. When someone notifies the app they have tested positive for COVID-19, a notification is sent to those who have been in that individual’s vicinity. This app determines whether you have had any contact with an infected person, which puts you at risk of catching the virus. This way, we can interrupt chains of infection more quickly.
- FDA Safety Reports – FDA’s safety reports databases are analyzed with routine and prototype data mining methods and tools to identify statistical associations between products and events. (source: Journal of the American Medical Informatics Association, Volume 23, Issue 2, March 2016, Pages 428–434)
- Rhode Island HealthShare Active Analytics- The state of Rhode Island has partnered with InterSystems to use the HealthShare Active Analytics tool. It collected and analyzed patient data on a statewide level and identified that about 10% of major lab tests performed in over 25% of the state’s population were medically unnecessary. This finding provided information to help the state improve quality of care and control spending. (source: InterSystems)
Risks of Big Data in Life Science
Big Data in Life Science is not perfect. There are a lot of potential risks in this scenario, but these risks must be mitigated and compared with the benefits that will come from the complete understanding of the data.
A clear example is the patient health data. This sensitive information is critical because it helps to understand everything about a specific individual, about a disease, and about medical treatments.
But there is a price for it: privacy. In the wrong hands, this sensitive information could cause significant harm.
Another big risk for Big Data in Life Science comes from the data itself.
Capturing the siloed data and making it clean, complete, accurate, and formatted in the correct pattern is a very complex and difficult task. Misunderstanding the data caused by an error in the data treatment could be catastrophic for those who depend on it.
Before you trust the information provided by the data, you have to be sure that the data is accurate, complete, reliable, relevant, and timely.
The Future of Big Data
Life Science is a very complex industry with a lot of obstacles to innovation. As we mentioned in the challenges, the Life Science data is siloed between a wide range of systems and institutions and this increases the difficulty to analyze and understand it.
When we successfully unblock this data to make it easy to access and understand, it will be a breakthrough. But we will also need tools to easily analyze it.
Overcoming the siloed obstacles that we have in the electronic medical records, and with better Big Data tools, we are now closer to seeing a big change in the way we understand diseases, create drugs, and treat patients.
This requires a lot of collaboration between the Life Science industry and the people who need it.
Big Data “appeared” with a lot of promises in Life Science, but the constraints identified in Data Integrity (data quality, privacy) and Compliance (regulatory policies) made the adoption harder than expected.
In order to achieve success, the institutions will need to:
- Focus on the data integrity to mitigate the risks by sharing more accurate, complete, reliable, relevant, and timely data.
- Be willing to invest more in technologies to protect the patient’s privacy. By doing that, institutions will be able to follow the Compliance Regulations and share this data broadly for scientific research without harming the patients.
- As it is relevant GXP data, institutions must evaluate the possibility of having to qualify the infrastructure (on-premise or cloud) where the data is stored and processed. In addition, a risk analysis and qualification of the supplier that will assist must be performed in an eventual validation project.
We now see successful Life Science Big Data use, and in the next years we can expect an even greater improvement in Life Science, in disease treatments, research, and the creation of new drugs and personalized medicine, resulting in better health conditions to all of us.
Big Data, Big Improvements
Contact GxP-CC for more information on how to get the help you need with Big Data in Life Science.