Ed Chidsey, President of Inovalon’s Insights Business Unit – Interview Series

Ed Chidsey, President of Inovalon’s Insights Business Unit, brings to the role a broad background in financial data, analytics and strategic advisory — most recently as Senior Vice President at S&P Global Market Intelligence, where he managed a billion-dollar data and analytics business of 2,000 employees, and previously as a private-equity advisor, board member of PeerNova Inc., and limited partner at Mendoza Ventures.

Inovalon, is a U.S.-based technology firm that delivers cloud-based software and data-analytics solutions to the healthcare industry. Through its flagship offering, the Inovalon ONE® Platform, the company aggregates and analyzes massive, real-world clinical and claims data — spanning hundreds of millions of lives — to support health plans, providers, pharmacies and life-science organizations in improving clinical outcomes, care quality, risk scoring, payment integrity and operational efficiency.

You’ve had a long career spanning S&P Global, IHS Markit, and now Inovalon. What was the single most formative role or experience that led you to focus on real-world data (RWD) and analytics in healthcare, and how has that shaped your vision for leading the Insights Business Unit at Inovalon?

I’ve spent most of my career building, running, and scaling data and analytics businesses, primarily in financial services, often starting small and driving substantial growth through a combination of organic and inorganic strategies. After more than three decades in the financial industry, I reached a point where I needed to pause and reset. I had been in that world for a long time, and while I loved the work, the environment had started to feel less fulfilling. So, in early 2024, I decided to step away.

That year off was incredibly grounding. I spent more time with my family, joined a corporate and a non-profit board, and, somewhat unexpectedly, became more involved in my church. This pivot gave me a chance to focus on balance, community, and purpose in a way I had not for a long time. By the end of the year, I realized I still had a lot of energy and passion for leading teams and building businesses, but I wanted that work to feel more personal and purposeful.

When Adam Kansler, the CEO of Inovalon, reached out to tell me more about the company, the timing was fortuitous. I worked closely with Adam for many years and have tremendous respect for him as a leader. He described Inovalon as a leading provider of data and solutions empowering healthcare that works with payers, providers, pharmacy organizations, and life sciences companies, and noted that the company was looking for a new person to lead its Insights business unit.

Before that conversation with Adam, I hadn’t really considered that I could leverage my background in data and analytics into the healthcare industry. However, the more I learned, the more it resonated. Healthcare data is very tangible because it can impact us in real ways. The idea of applying the same analytical rigor and scale I had developed in financial services to improve healthcare quality and outcomes was incredibly compelling. That sense of purpose is what brought me here, and it continues to shape how I lead the Insights business unit, bringing together data, technology, and people to make a measurable difference across the healthcare ecosystem.

How do you see Inovalon’s move to make its advanced analytics and primary source RWD available on Snowflake’s AI Data Cloud changing the competitive dynamics in healthcare and life sciences?

I would consider it more a strategic move to meet customers where they are, rather than necessarily changing the competitive dynamics. In my mind, it was critical that we ensure customers have access to our data and resources on the platforms where they would like to consume them in a more modern, nimble, and accessible way. With this, we knew more and more of our customers were migrating to platforms like Snowflake, so it was important for us to meet them there, where they wanted to consume our data.

What differentiates Inovalon’s RWD offerings, such as the MORE² Registry, from other real-world data platforms in terms of quality, depth, timeliness, or scale?

What differentiates our RWD offerings, including the MORE² Registry, is our primary source data. We gather this data directly from various entities across the healthcare ecosystem, such as healthcare payers or providers, and it gives us a holistic view of patients’ healthcare journeys, allowing us to extrapolate insights that support decision making across the healthcare continuum.

While the breadth of the data we have is alone a notable differentiator, the history and consistency behind this data is really remarkable. Through our partnership with Snowflake, our customers are now able to securely and quickly access our longitudinal datasets of large-scale, high-quality RWD, a capability that has traditionally been obscured by fragmented systems and complex manual data ingestion processes. For life sciences and biopharma companies especially, having the confidence that their partner will provide data that is consistent and reliable for decision making is absolutely critical for the patients they serve. That foundation is the cornerstone of our RWD offering, which we’re continuing to expand in scope and the types of insights we can generate.

What are the main technical or governance challenges in linking or integrating datasets from varied sources to build comprehensive real-world evidence?

It starts with recognizing the foundation of our data, which comes back to interactions between a patient and their provider, pharmacy, and payer. Often, these interactions are personal and stem from impactful touchpoints in their own care. This makes being a trusted steward of our data critical and necessitates us having strong governance around this data. We take this responsibility very seriously at Inovalon, especially when it comes to how RWD feeds into real-world evidence processes. How we choose to manage, protect, and use our data defines our credibility and the trust we hold with our partners across the ecosystem.

One of the biggest challenges we face is balancing data use and privacy. If the focus is exclusively on privacy, you lose the ability to fully analyze and extract value from the data. However, if the focus is only on analysis, you risk falling short of your ethical or regulatory obligations to patients and families. This tricky balance is not just a technical challenge, but a governance challenge. We constantly have to consider what we can do, what we should do, and what we cannot do to protect the data we have while still maximizing its value and impact on the broader healthcare ecosystem.

From a technical perspective, another major challenge is linkage. No matter how deep or broad a single dataset is, it’s never enough on its own. The ability to connect datasets from multiple sources is critical, and we prioritize this every day through our work with various partners.

Ultimately, governance is about striking that right balance between protecting the data in the right way while also pushing the boundaries of what’s possible to realize the greatest value for the greater good. That’s not always easy, especially when some regulations, while well-intentioned, can inadvertently stifle innovation or limit the potential benefits we could deliver to patients and the boarder ecosystem. Our role is to be careful stewards of data, operate within the bounds of supplier agreements and regulations, and still find responsible ways to innovate.

Lastly, there’s a structural challenge with the healthcare ecosystem being highly fragmented. For a customer to access all the data they need, it often requires pulling from multiple datasets and linking data across different care points, with many intermediaries in between. Compared to industries like financial services, healthcare lags by years, if not decades, in terms of data integration and interoperability. However, this is also a massive opportunity for Inovalon. If we can continue to advance how data is connected, made available, and creatively used, we can deliver much more innovative analytics and solutions that ultimately benefit patients.

How do you balance privacy, regulatory compliance, and innovation when deploying AI models on sensitive health data?

The way I think about AI is that, ultimately, it’s about replacing or enhancing what can theoretically be done today, just in a faster, smarter, more advanced way. When a customer wants to deploy an AI model on top of our data, it’s something we have to embrace. As with any use of our data, there are terms and conditions that define what a customer can and cannot do. These are based on our own upstream permissions, restrictions, and applicable regulations. That framework doesn’t change in the world of AI, and we need to be enablers. We can’t be afraid of it. We have to embrace it responsibly for AI to move forward, because it has the potential to enormously benefit the healthcare ecosystem.

AI requires both historical data to build the models and ongoing data to maintain them. From our perspective as a data provider, that’s a strong position, because once a model is built on your data, it becomes even more embedded. We have to approach every model like we would any customer use case, ensuring it is properly licensed and governed. The more sensitive part of AI, especially in healthcare, is making sure there is always a human in the loop when care is being delivered. That is a much larger topic, and one that many people are debating.

From a RWD perspective, we’re still in the early stages. AI hasn’t yet delivered a lot of groundbreaking results in healthcare, especially when you focus on RWD use cases. We’re exploring a number of opportunities including, for example, machine learning-based extraction from clinical notes, which is a more foundational application of AI. Beyond that, we’re looking at AI use in clinical trial applications and disease progression and predictability. We’re at the beginning of this journey, but the potential is enormous. At Inovalon, we’re focused on ensuring we have the highest quality data that can be used in conjunction with AI responsibly, with strong governance and human oversight, while preparing to scale its impact as the technology and the ecosystem mature.

From your conversations with customers, what are the most common concerns about adopting AI and RWD-driven analytics in healthcare, and how do you respond to them?

The most common concerns I hear are data quality and permissions to leverage our data to train their own AI models. For data quality, with AI, ‘garbage in, garbage out’ rings true. If the data quality is poor, meaning that the data isn’t clean or perhaps there isn’t enough data, then the output won’t be very valuable either. Our customers expect consistent, accurate, and reliable data. Given the vast volumes we manage, one of my first priorities was ensuring data quality across the board. We’ve worked hard to cleanse, de-duplicate, normalize, standardize, and deliver the data downstream. Taking ownership of the data quality also helps by improving consistency and reliability across our datasets, which allows us to deliver more within our traditional data analytics offerings, as well as analytics based on AI.

The second consideration involves how our data may be used to support AI model development. As a data-driven organization, it’s important that we thoughtfully enable these emerging use cases. Given the rapid evolution of the data and AI landscape, we’ve adapted our approach to allow this in a responsible way, supported by strong governance, clear terms of use, and defined safeguards. This evolution empowers our customers to innovate confidently with AI while ensuring responsible, compliant, and ethical data practices.

How do you measure success or ROI for customers who adopt your platform and analytics, and what metrics do they care about most?

We measure success by the real impacts our platform and analytics have on our customers’ operational and clinical outcomes. This could include a variety of success measures, depending on the customer, like improving CMS Star Ratings in Medicare Advantage, optimizing risk adjustment, or generating actionable real-world evidence for life sciences. The common thread is that insights must be timely, trusted, and actionable.

For metrics, customers, depending on where they fall along the healthcare continuum, may focus on aspects like quality improvements, reductions in gaps in care, better adherence to treatment protocols, or measurable cost or utilization gains. Customers realize ROI when our analytics help them make informed decisions that improve patient outcomes, operational efficiency, and/or strategic performance.

Looking ahead 5 years, how do you expect AI and RWD to evolve in healthcare and life sciences, and what do you see as the next frontier?

In five years, healthcare may look like something none of us recognize right now, but it is impossible to predict how quickly the industry will evolve. The only certainty is that it will be transformative. While the pace of innovation is extraordinary, progress remains constrained by fragmentation across the healthcare ecosystem, spanning labs, pharmacies, and EHRs, where few organizations are able to truly connect these data sources in a meaningful way.

While it may be a limiting factor, if data can be linked and longitudinally created in a normalized, standardized way, then I do think anything is possible. AI will increasingly underpin everything from clinical decision support to how life science organizations approach clinical trial design and execution. In the next five years, we’ll see more automation, enhanced use of predictive analytics, and increased connectivity that gives organizations access to the insights they need in real-time, all of which have the potential to transform patients’ care journeys and healthcare operations as a whole.

For organizations just starting to explore integrating AI with real-world data, what three pieces of advice would you give?

First and most importantly, focus on the data and make sure that you’re constantly assessing the quality of your data. Second, harness the mind power of your employee base. The reality is that the best ideas can come from every level of the organization, especially younger generations entering the workforce that have been living and breathing data, AI, and technology. Leaders should find ways to harness the ideas and innovations that are deep inside the organization and create a platform for these perspectives to be heard and harvested. Third, hire the right people. Without the right people and technical talent, innovating at pace, creating value and staying competitive will be nearly impossible.

Thank you for the great interview, readers who wish to learn more should visit Inovalon.