Today’s life sciences organizations are gifted with extraordinary opportunities to elevate their field through data science. Awash with data, these companies are poised to leverage a voluminous, ever-growing, and continuously replenished supply of data in their quest for deeper insights into all aspects of health and wellness.
Some of the areas in which data science can enhance positive healthcare outcomes include:
- Optimization of pricing.
- Regulation/audit compliance.
- Improved clinical trial design.
- Effective risk assessment.
The Now and Tomorrow of Life Sciences Data
Data Science’s presence and burgeoning influence in the life sciences industry is far from new. However, the shift toward making organizational data transformation a top priority has gained momentum due to several recent challenges and opportunities, such as:
Deluge of Healthcare Data – While the proliferation of healthcare data has been underway for many years, recent circumstances have accelerated this process. The pandemic gave rise to an unprecedented reliance on digital tools, precipitated the increased need to monitor population data, and necessitated more Decentralized Clinical Trials (DCT). A Forbes article exploring the privacy and security implications of this expansion of healthcare data sources cites astonishing projections for the growth of healthcare data that were made prior to the pandemic, adding:
After an explosive year for telehealth utilization, contact tracing, outbreak tracking, virus testing, remote work, and medical research, it’s safe to assume that the estimate turned out to be low, and healthcare is generating even more data than the organizations protecting it anticipated or prepared to handle.
Such activities continue to generate large volumes of data and new data types related to health and wellness.
The Rise of Personalized Medicine – Healthcare and life sciences organizations can now gather and analyze a mounting supply of Real World Data (RWD). This data will offer another dimension of insights into individual health, risk factors, and outcomes, supplementing traditional healthcare research with Real World Evidence (RWE). The pandemic exacerbated constraints related to conventional Randomized Clinical Trials (RCT), leading to the expansion of Decentralized Clinical Trials, which collected data through remote devices. Beyond trials, the spread of IoT gadgets, such as wearable devices, and the increased reliance on telehealth visits and other virtual connections are expanding the pool of insights that can inform personalized, or precision, medicine.
The Call for Data Sharing – Trends such as the proliferation of data and the shift toward unstructured data sources noted above contribute to the emergence of an additional trend, which is the movement toward increased data sharing. It is partly due to effective data sharing that many efforts to confront the pandemic were timely and successful, according to a Brunswick Review article:
Rapid data-sharing has allowed experts to track the emergence of coronavirus variants around the world, while pooled clinical and virological information has helped scientists develop vaccines and therapeutics to tackle a previously unknown pathogen at an unprecedented pace.
After an explosive year for telehealth utilization, contact tracing, outbreak tracking, virus testing, remote work, and medical research, it’s safe to assume that the estimate turned out to be low, and healthcare is generating even more data than the organizations protecting it anticipated or prepared to handle.
For organizations to reap the full range of benefits their growing data assets offer, they must have a long-term strategy to promote data access and sharing across departments and between collaborating entities. In addition, this strategy must provide for meaningful steps toward data democratization to include comprehensive data science training for the entire workforce, with offerings ranging from data literacy fundamentals to advanced deep learning coursework.
Key Considerations for the Future of Data in Life Sciences
While data science is a tailwind that can help propel the life sciences industry forward, progress does not come without certain challenges. Some of the common issues that organizations must address in transitioning to data-driven enterprises include:
- Safeguarding the security and privacy of patient data As the incorporation of data from a broader field of sources and the expansion of data sharing increase, the potential for security and privacy breaches likewise increases.
- Working with unstructured data An estimated 80% of RWD is unstructured. To effectively use data from the growing variety of non-traditional sources, such as social media, life sciences organizations must be prepared to convert unstructured data into formats that will flow into their analytical processes.
- Seamlessly integrating shared data With the liberation of data from traditional silos and the more open exchange of data, internally and externally, comes a heightened need for uniformity and consistency to facilitate the data flow.
Clearing the Way with Solid Data Governance and Technology Solutions
Many of the obstacles on the road to organizational data transformation in the life sciences industry are surmountable through the implementation of sound data governance, AI- and ML-based technology solutions, and enterprise-wide data literacy. Some examples of how data science and related technologies can pave the way forward for life sciences organizations include:
- Organizations can mitigate data security and privacy threats by employing AI-based risk assessment tools that automate the processes of monitoring, detecting, and sounding alerts about questionable activities.
- NLP offers powerful solutions to the challenges of integrating the insight-rich—but less tidy—data from unstructured sources, enabling technologies capable of extracting informal text and converting it into data formats that can feed smoothly into existing analytical systems.
- Organizations cannot navigate complexities related to data democratization and data sharing across and between enterprises without data governance. Sound data governance policies oversee the proper gathering, cleansing, stewardship, distribution, management, and use of data resources. With regard to data sharing, an essential component of data governance is data standardization, which facilitates the integration of data from multiple sources into disparate systems by homogenizing data.
The ability to amass enormous supplies of insight-rich data is an excellent problem to have. Still, it creates greater urgency for companies to develop organizational infrastructures and institutions that can manage the complexities of data maturity. Fortunately, for challenges related to data, data science provides the solution. By investing today in upskilling their workforces in data science, life sciences organizations can harness the potential of healthcare data to create better healthcare outcomes for tomorrow.
