In 2008, the US economy suffered one of its worst downfalls since the Great Depression. Dubbed the Financial Crisis and Great Recession, this economic downfall brought the US to its knees, taking years to fully recover. When we look back at the root cause, it can be attributed primarily to a lack of transparency.
The Financial Crisis marked the start of the Data Governance Era, where organizations would view data as a strategic, shareable, and renewable resource. A Chief Data Officer or similar data leadership role would issue a compelling data intent conveying a sense of future direction with the goal of radically increasing the data stock of the company. This would trigger the adoption of new processes and structures; and the acquisition of supporting technologies such as data lakes, data catalogs, and data fabrics. Transformative roles and leadership across business functions would nurture the intellective skill base to leverage this new-found data agility to drive business innovation and sustenance tailored to their needs.
I was a first-hand witness of the Financial Crisis. I had just founded Collibra, an enterprise software company that spun off from my research lab at the VUB University in Brussels that later would become a trusted partner to help the world’s largest financial institutions to avoid hefty fines by meeting these interoperability requirements. Ironically, this all happened less than two miles from the European Commission where the General Data Protection Regulation (GDPR) would later be voted into law.
Within the next decade, the world would face two more data crises: one related to online privacy in the aftermath of the 2014 Cambridge Analytica scandal and the other related to public health. The latter crisis had been silently taking shape over two decades in the form of several Opioid epidemic waves, reaching a tipping point in 2019 with COVID-19. Both events compelled CEOs across nearly all industries to establish data governance as a core organizational capability to manage the time compression and complexity to execute and monitor data and AI policies, such as GDPR, California Consumer Policy Act, and the more recent EU AI Act, going forward.
Fast forward to 2024: the healthcare industry is headed toward a similar 2008-type crisis and the inability in healthcare to understand risk is still a blind spot. As the healthcare industry grows in size and scale, costing over four trillion dollars and powered by opaque business models and arcane workflows like Prior Authorization, there is recognition that systemic change centered on transparency is needed to avoid crippling the US economy. Other industries operate more efficiently and deliver a different customer experience because they have transformed from proprietary, unconnected systems to an open, cloud-native infrastructure. Healthcare is finally heading in a similar direction – one where computable interoperability unlocks data currency, equity, and liquidity. These aspects are essential to achieving a quantitative understanding of systemic risk, cost, and quality of care and their impact on longevity.
The adoption of the FHIR standard as the universal currency for sharing healthcare data, along with the widespread support from global and governmental entities for leveraging APIs, opens the opportunity to achieve seamless data exchange across diverse technologies and stakeholders.This should be the moment for healthcare leaders to articulate their strategic intent on how the future of the business will organize itself around data, and press the organizational transformations in terms of process, technologies, and intellective skill bases discussed earlier in this article.
The healthcare industry is sitting on a massive opportunity in the shape of a largely untapped, zettabyte-scale data reservoir growing at an impressive rate of 35% compound annual growth rate. A well-articulated intent will position organizations to realize effective and meaningful enrichment of this data into higher orders of value with viable contexts of use in mind, whether it concerns intermittent data with real-time actionable utility (such as admission, discharge, and transfer events), or more complex aggregations that accumulate value over longer periods of time (such as longitudinal patient records). Derived data products can potentially inform effective therapy and interventions quicker, playing well into the ambitions of value-based care management. The same data, combined in different ways, can help understand risk on population- and systemic levels.
Transparency will become a crucial scaling component for wider interoperability. Data will be coming in from different sources, in different formats, and travel through different domains with organizations each governed by their own local policies and standards. For any given data point (or FHIR resource), we need to be able to understand or “observe”:
- Provenance: what is the source of the data I am consuming;
- Lineage: how was the data transformed and aggregated and why;
- Quality: what is the overall consistency, completeness and validity in terms of agreed upon standards and ontologies (such as FHIR R4, and implementation guides);
- Governance: do data controls comply with policy in terms of context and purpose of use, explainability and differential privacy, and data sovereignty and locality.
This metadata should be consistent and available at any time for every data endpoint, and not be stitched together after the facts. This end-to-end transparency from source to application is required for the analytical needs of today, and will become even more critical for more complex classes of transformers such as Large Language Models and Machine Learning.
In conclusion, the aftermath of the 2008 Financial Crisis ushered in the Data Governance Era, emphasizing the critical need for transparency in the management of strategic data resources. As industries evolved, crises such as the Cambridge Analytica scandal and the COVID-19 pandemic further underscored the importance of robust data governance practices. The healthcare sector, in particular, stands at a pivotal juncture, recognizing the potential of standards like FHIR and APIs to enable seamless data exchange and transparency. Moving forward, the industry must articulate clear intents around data, driving organizational transformations that enhance data agility and foster innovation. Through a commitment to end-to-end transparency, from data source to application, businesses can unlock the vast potential of data reservoirs, mitigate risks, and navigate toward a future of informed decision-making and sustainable growth.
Pieter De Leenheer
Pieter De Leenheer, Ph.D., is the Chief Technology Officer of 1upHealth, leading the definition and execution of 1upHealth’s technology strategy. His extensive background in computer science, data innovation, and venture capital provides the foundation from which he oversees 1upHealth’s technology functions, including platform engineering, machine learning, and product support.
Pieter also maintains strong academic credentials and frequently writes, teaches, and advises on computing and management aspects of data innovation, while serving as an expert to several national governments and the European Commission.
