Life Sciences Data Collaboration Is Accelerating. Why a ‘Neutral Zone’ Should Be Top of Mind
One of the latest innovations in life sciences research is the use of a “neutral zone” that allows multiple organizations — even competitors — to securely share their data for analysis.
A neutral zone empowers life sciences organizations to harness the power of identified patient data at scale — their own and those of data research collaborators — by partnering with a third-party algorithm provider to create a “secure sandbox” where AI, machine learning and other advanced algorithms may be applied to the data. Data are essentially “shared but sealed.”
A neutral zone could provide a strategic advantage to life sciences companies by accelerating the process of data collaboration and reducing the cost of drug discovery and clinical trials. With a neutral zone, each party’s data, its IP, and the algorithm itself are fully protected and kept from each other’s view due to the security controls and procedures put in place. A third-party data platform provider curates the tools and software programs to prepare the data for analysis without viewing the data.
The impact for drug discovery is significant. Innovation no longer depends solely on the ability of one company to capture and analyze data. Instead, companies can collaborate with each other as well as other organizations, like academic medical centers, to uncover insight using real-world data. This deepens the well of data to which pharma companies have access without sacrificing the security of data or putting IP at risk. Once the results are released to the data-sharing parties, the data within the neutral zone and the algorithm itself are destroyed.
Examples of secure data collaboration that already are fueling innovation and discovery include:
Use of a neutral zone by St. Jude Children’s Research Hospital, Adaptive Biotechnologies, and Answer ALS that paves the way for more targeted care interventions and reduces the cost of AI-powered analysis over time
MELLODDY (Machine Learning Ledger Orchestration for Drug Discovery), a coopetition model in which 10 pharma companies have come together to advance drug discovery using a decentralized, data-private, machine learning approach to collaborative research
A partnership between Roche and Bristol Myers Squibb to combine AI and digital pathology to develop highly personalized cancer treatments, including for non-small cell lung cancer patients
“There is a need in the market for a third-party organization to be the ‘glue’ that brings the other parties together for collaborative research and analysis in a neutral environment,” says Sanjay Dalwani, Chief Revenue Officer, Healthcare Triangle. “These organizations are not interested in each other’s data. They are interested in the insights that can be gained by pooling their data for AI analysis. That’s the value of a neutral zone.”
Making a Neutral Zone Work for You
The success of a neutral zone approach starts with selecting a third-party organization to be the “glue” that brings parties together for collaborative research and analysis in a neutral environment.
How can organizations select the right neutral zone partner? At Healthcare Triangle, our experience points to the value of three key attributes.
Expertise in preparing data for AI analysis. AI analyses are only as good the data that go into the algorithms, but preparing data for analysis can be time-consuming and complex work. This is especially true when data come from numerous sources and in multiple forms. Look for a partner who has demonstrated expertise in aggregating, ingesting, and extracting data for rapid analysis.
Prebaked security, GxP, HITRUST and performance controls. This ensures that AI algorithms are deployed in a baseline secure and compliant environment, allowing organizations to focus on leveraging the neutral zone for research rather than worrying about the risks involved.
Transparency into how the data is used. After each analysis, the neutral zone should disseminate the results to each party. It should also create a record of the analysis so that data owners can see how their data was used.
Want to learn more about ways to advance data collaboration and research in life sciences using a neutral zone?
