Brian Foy, Chief Product Officer at Q-Centrix.
It’s widely known that artificial intelligence (AI) is profoundly reshaping the healthcare industry. Hospitals have used AI in a variety of ways, from streamlining workflows to aiding in bedside diagnosis.
Another sector in healthcare that’s primed for AI-led innovation is clinical research. Clinical research is often lengthy and expensive to conduct, with drug development typically taking 10 to 15 years from start to finish. There is already great interest in improving the state of clinical research: The White House recently launched an initiative to improve clinical trials, and the FDA is exploring the use of AI and machine learning in drug development.
But the potential for innovation doesn’t stop there. With the assistance of AI—and the appropriate clinical expert safeguards—hospitals can advance clinical research by leveraging a valuable resource they already have: their data.
The Potential Of Clinical Data To Support Research
Most clinical data—about 80%—is unstructured, taking the form of doctors’ notes, imaging scans, pathology reports and other forms that must be interpreted and curated before it can be used. Curating this data can be burdensome and time-consuming for hospital staff juggling multiple priorities, which is why AI-powered data curation is so effective.
AI can comb through vast amounts of unstructured data to draw meaning from it and flag inconsistencies for experts to review. This combination of technology and human expertise can create structured datasets with high levels of data integrity.
While this data is most commonly used for purposes such as performance improvement and improving patient care, it also holds immense value for clinical research. Upwards of 80% of the clinical data hospitals and health systems submit to registries overlaps with the primary data sets used in clinical trials—but at many facilities, this data goes untapped for this use case.
When considering the many challenges clinical researchers face, hospitals’ massive amounts of data grow even more appealing. Two-thirds of clinical trials fail to enroll enough patients for an effective trial. In drug development, patient recruitment alone takes up to 30% of development timelines, and recruitment costs amount to about $1.2 billion. Additionally, people from racial and ethnic minority groups are underrepresented in clinical research, making it difficult to understand how treatments may impact people from diverse backgrounds.
If hospitals shared their de-identified clinical data with researchers to aid in finding patients who meet study criteria, this could speed up clinical research timelines significantly. While many hospitals may lack the time, resources, technology or infrastructure to enable curating and sharing data for research on a large scale, a solution is emerging to facilitate these processes and more.
How Research Networks Can Help
The role of technology is already growing in clinical research. AI has many documented applications for creating efficiencies in clinical trials, including aiding in trial design, recruitment and analysis.
Research networks present a new opportunity to advance clinical research. Joining a research network enables hospitals to benefit from a combination of AI-powered technology and clinical experts to curate their data for research—and connects hospitals to research sponsors.
In a research network, a third-party organization works with hospitals and health systems to handle research requests, use both technology and data experts to curate clinical data for all participating facilities and connect these facilities to funded opportunities to contribute data for medical research. These research opportunities include clinical trials as well as observational studies. Because observational studies rely on existing patient data, these studies are well suited to include more diverse patient populations, addressing a critical research need.
With access to a vast repository of data, patient recruitment tools and tailored patient screening services, researchers can find qualified patients quickly. By removing common research barriers and leveraging data curation technologies and clinical experts to create clean, research-ready datasets across all participating facilities, research networks enable hospitals to gain more value from their data, secure new funding sources and contribute to the broader research community.
Many hospitals are already taking steps in this direction. A recent survey from my company showed that more than a third of healthcare facilities (39%) are sharing de-identified clinical data with other organizations for research purposes, and another 36% are planning to start.
Patient attitudes are generally positive about using their data to advance research, suggesting a willingness to do more with their data. A 2022 survey from my company found that most patients (62%) would be willing to share their de-identified health data for clinical research.
Taking The Next Step
To leverage their data for research on a broader scale, hospitals can explore partnerships with third-party organizations that pair technology with human expertise to securely curate and manage their data for research. These partners should have a track record of consistently maintaining high data integrity and adhering to the highest data privacy and security standards.
Healthcare leaders who are looking to explore the use of technology to curate their data for research without joining a research network can consider taking stock of their facility’s data curation and analytics capabilities. This involves ensuring they have not only the technology to process large volumes of data but also the people, software and processes that an effective data curation approach requires.
With the aid of their data, AI-enabled technologies and clinical data experts, hospitals can contribute to medical research more easily than ever before, opening the door to new insights and research opportunities—and revolutionizing clinical research in the process.
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