13/09/2023 | BlogPost
In the healthcare and pharmaceutical sector, R&D is the heart of the innovation process. As such, it is one of the main processes that generate high-value data. The use and valorization of this data enable pharmaceutical companies to identify new potential drugs, develop them as effective medications, and get them approved more quickly. The use of Data Science and Artificial Intelligence allows for the following:
This last point is crucial and can activate every other data valorization step, where data needs to flow freely throughout the research and development process chain: from research and pre-human stages to clinical development and regulatory stages. It is also necessary to integrate external data sources into the organization to generate high-value analytics for business decisions and research.
In pharmaceutical R&D, one of the most significant challenges lies in accessing big data that contains heterogeneous, consistent, reliable, and easily accessible information. Nevertheless, overcoming this hurdle can lead to the most effective outcomes for addressing new innovation challenges. Managing and integrating data across all stages of the value chain, from discovery to implementation, is a fundamental requirement for companies that want to maximize the benefits of technological trends. Thus, data serves as the foundation for value-added analyses, and effective end-to-end data integration can establish an authoritative source of information for the entire company. By integrating different data, regardless of the source, whether internal or external, proprietary or publicly available, comprehensive cross-sectional research can be conducted.
In a scenario that typically involves compartmentalized silos, implementing interoperability is more than a technological operation; it requires an effort of semantic data integration. This involves combining and linking information from different sources to provide a deeper and more meaningful understanding of the data itself, allowing seemingly different but conceptually related data to be linked to the same physical data. Approaches, such as the use of ontologies and data description standards, facilitate semantic integration by creating connections and relationships between information. This approach goes beyond simple data combination, allowing for a comprehensive understanding of the meaning and context of the information, thereby improving the accuracy and reliability of such analyses. Through semantic data integration, a more comprehensive and coherent view of information can be achieved, facilitating the removal of interdepartmental silos.
There are numerous ontologies in the medical field, but when constructing a specific data integration model for a particular business domain, it is necessary to build a domain-specific ontology.
Ensuring a consensus among individuals regarding the specific meanings of concepts that define their activities stands as the most significant challenge in constructing an ontology. Achieving semantic agreement is the process of helping people understand exactly what they mean when expressing themselves. The purpose of an ontology is to model the business, and it is independent of computer systems (e.g., legacy applications and databases). Its purpose is to use formal logic and common terms to describe the business so that both humans and machines can understand it.
Ontologies offer a means to reconcile data present in disparate information silos without necessarily physically integrating them, allowing for a unified and cohesive view.
Semantic technology, in turn, improves collaboration among various departments, helping the organization perform more complex, relevant, and useful searches.
We also apply this approach to our own clients’ businesses, where one of them faced challenges in navigating and visualizing thousands of interrelated concepts. How did we think data science could come into play? Our solution involved building a semantic data model for data integration, empowering leaders to make informed decisions and improve business outcomes by accessing the right data, regardless of physical boundaries imposed by data silos. This approach also strives for more flexible updates and management of information.
To assist our client, we initiated a series of workshops aimed at defining and designing an ontology specifically tailored to their research and development business unit. Subsequently, we embarked on the scouting and benchmarking of various ontology management platforms, evaluating each to find the one that best aligned with the company's requirements and constraints. Once the ideal platform was identified, we facilitated another set of workshops to define and validate the use-cases, ensuring the practicality of the ontology model.
With the use-cases successfully implemented and the selected platform in place, our team is currently collaborating with the platform provider in the second phase of the project to develop and finalize the ontology.
By implementing semantic data integration, advanced solutions for data valorization emerge, unlocking insights across the portfolio. This approach enables us to identify clinical opportunities and conduct research on potential applications for translational or personalized medicine. By combining biomarker research with clinical outcomes, we gain a deeper understanding of the data. These high-value insights, made possible through advanced methodologies of data science, allow companies in life sciences to develop new drugs and solutions that improve the lives of HCPs and patients faster, safer and with better understanding than ever before.
If you are interested in learning more about how your business can integrate Data Science into the R&D process, I’m happy to continue this conversation. Get in touch!
Lino Mari | Head of Technology, Healthware International
Large Language Models: Revolutionizing Unstructured Data Analysis in Healthcare
Fulvio Fortini | Managing Director
AI-powered Precision Engagement in Commercial and Medical Functions