The Center for Data Innovation spoke with Philip Russmeyer, CEO of FITFILE. FITFILE is a London-based health-care technology company. Russmeyer discussed the importance of better and more efficient access to health data for patient care and health-care research.
Kir Nuthi: What differentiates FITFILE from other health data platforms?
Russmeyer: The FITFILE team has been working in the health-care data space for many years. We’ve had a front-row seat throughout the sector’s evolution and rapid expansion, and we have a deep understanding of the two key obstacles to delivering complete health profiles across silos: interoperability and privacy. Our mission is to unlock the power of safer, faster, and better health data for everyone, and we’ve built the first technology of its kind designed to make that goal reality.
The FITFILE platform has the globally unique capacity to unite either identifiable, pseudonymized, or irreversibly anonymized, record-level data across any number and types of data sources. Because our technology can keep all data at the source, we offer truly federated insights at scale. This is implemented using software nodes with three key components; FITConnect, our data access and processing software application; HealthFILE, a secure software application that allows authorized users to display identifiable/pseudonymized united record-level data for patient care or research (where allowed); and InsightFILE, a safe software application that allows users to display anonymized united record-level data or insights.
Whilst pseudonymization has historically been the only method to fulfil any purpose involving the privacy-enhanced unification of data, we strive to make sure that safety standards are the highest they can possibly be. This is why we always advocate for anonymization of data at the source wherever possible.
In addition to protecting privacy, irreversible anonymization removes the need for consent or other legal mechanisms, such as Section 251 in the UK, to be granted prior to data use. As a result, data projects can be vastly scaled and accelerated.
Nuthi: How does leveraging and uniting record-level health data improve patient outcomes, and what are critical use cases for these data sets in health care?
Russmeyer: Every time a patient visits their doctor, has a blood test, starts a course of medication, or just goes about their daily activities, they leave behind a data footprint. This means that trillions of new raw data points are generated every day from national health systems, wearable devices, medical technologies, and the life sciences sector. Leveraging and uniting this data will help create a detailed picture that can be used to target health interventions, map their impact, guide the allocation of funding, service provision, and research resources, and accelerate global disease eradication.
Connecting health data can significantly improve patient outcomes by providing clinicians with a more complete and accurate picture of a patient’s health journey. By connecting data from different sources, clinicians and researchers can easily follow individual or population-level journeys of patients accessing care, which helps to identify local population trends and individual patient needs. This enables health services and clinical teams to allocate care more effectively and augment care allocation based on a clear view of full patient information from inside and outside clinical settings. It also allows for a better-targeted selection of participants for clinical studies and for more granular tracking of real-world outcomes for medical treatments and diagnostics.
In addition, leveraging record-level health data can be crucial in reducing disease morbidity and mortality, with use cases including drug discovery, accelerated diagnostic pathways, and epidemic prevention. National health systems hold vast quantities of data on the causation, diagnosis, progression, and transmission of various diseases affecting patients worldwide. The ability to leverage this data safely and efficiently can be key to ensuring that we reduce disease mortality and track and prevent disease spread.
Nuthi: What are the biggest challenges facing technologies like FITFILE when tackling global health-care problems?
Russmeyer: Although it is commonly believed that funding is the greatest challenge to advancing innovation in the life sciences sector, I’d argue that the primary obstacle is actually efficient access to data. The issue is not a lack of health data, as we already have access to a wealth of information from hospitals, research, government databases, private providers, and various other sources. The challenge lies in effectively connecting the dots and leveraging these insights at a large scale.
Why? Because too often, it takes far too long to reach an agreement on safe and fully privacy-preserving access to critically needed data.
Now, protecting privacy when handling data is, of course, crucial. This is not only for privacy reasons – when working with any volume of health data, compliance with the guidelines for patient safety and outcomes is non-negotiable. Regulation requirements stretching from Common Law in the UK through GDPR across Europe and regulations such as HIPAA and CCPA in the United States share a commonality: health data must be handled with the utmost care and security. Whilst it is critical to assemble a complete health profile stuck in numerous data silos, often it has only been possible to bring these data elements together in an identifiable form.
Pseudonymization at source has been the most popular method of choice when utilizing health data, yet it is still considered identifiable data under regulatory environments such as the GDPR.
The best way to preserve privacy is anonymization at the source, as data is fully irreversible and unidentifiable. However, there still lies the perceived inability to integrate anonymized data quickly and safely. Hence, even innovative privacy champions like FITFILE have taken years to get traction, and this mindset still leaves us much further away from tackling current global health challenges than we need to be. The speed and effectiveness of clinical studies, the dependability of data used for health system planning, and the basis for allocating scarce health-care resources are all being held back by the resulting lack of data access and unification.
Nuthi: How do you see new advances like generative AI affecting your industry?
Russmeyer: Analyzing vast data sets in health care can be both time-consuming and expensive, but the deployment of generative AI can help accelerate this process in a far more resource-efficient way.
In the health-care space, generative AI can help clinicians and researchers understand the impact of different treatments by enabling them to identify trends and correlations from the data far more quickly. This allows for more accurate and informed diagnoses and treatment plans to be deployed.
Moreover, generative AI can assist in developing new treatments and drugs by identifying potential drug targets and predicting their efficacy. It can also support the drug discovery process by generating synthetic compounds that can be screened for therapeutic activity. However, generative AI crucially depends on high-quality and comprehensive input data to generate new content that is clinically valid and sufficiently reliable for life-or-death decision-making.
Nuthi: How do you think FITFILE will continue to evolve, and what is the next series of goals for the company?
Russmeyer: In pursuit of our goal to unlock the power of safer, faster, and better health data for everyone, FITFILE will continue working with a growing number of public and private sector organizations to improve their teams’ data access and security as well as to help give them a more precise and complete view of individuals’ health and treatment data. We are currently collaborating with several major life science industry partners, with a number of exciting large-scale deployments of our solution in the pipeline for the latter half of 2023.