Two healthcare stakeholders discuss the core lessons learned from a recent gathering of data analytics experts
Last fall, high-level analytics executives from 13 healthcare organizations congregated in Chicago as part of the annual Scottsdale Institute Analytics Summit to share strategies, insights and challenges regarding key industry themes such as digital health strategy and consumer engagement.
Executives at the event set out to understand what types of analytics initiatives can influence consumer and patient behaviors. What’s more, participants at the Summit—which was supported by consulting firm Impact Advisors—discussed how digital health is enhancing health systems’ abilities to leverage analytics insight to improve health outcomes.
Two leaders who were present at the event—Lauren Bui, vice president, data management and analytics, at Texas-based CHRISTUS Health, and Liam Bouchier, principal at Illinois-based Impact Advisors—recently discussed with Healthcare Innovation their key takeaways from the Summit, how far along organizations are on their digital health and analytics journeys, the threat of non-healthcare disruptors, and much more. Below are excerpts from that discussion.
[Editor’s note: the report that came from the Summit can be accessed here.]
What were some of the main takeaways and lessons learned from the Analytics Summit?
Bui: The main takeaway that was surprising for me—and I have been in forums before with other healthcare data analytics leaders—was the theme that [emerged] around the demand for both health systems interoperability and data interoperability across providers, payers, and patients. And by this I mean where we place the patient at the center [of the system], and we provide better services and care, and lower costs.
Bouchier: One key takeaway was that everyone was at a different point in terms of how organizations were attacking their digital strategies. Everyone was at a different point in understanding what that meant; there was no universal definition for “digital.” [But] there was a high level of executive ownership of digital as a concept or strategy for the organization.
What I heard was that a lot of the folks are still dealing with basic day-to-day challenges—everything from data governance to master data management. Some are more advanced in their basic infrastructure and operations, but a lot of them are still dealing with the day-to-day nuts and bolts of getting a foundation in place to be able to deliver on some of these more advanced topics.
The interesting thing was that one of the “a-ha” moments for me was when folks [said] they were thinking they were solving a problem that no one else has seen before. You need to think outside of the box a bit and realize that something like master data management or data quality is not unique to healthcare. So it behooves us to look outside of healthcare to folks in retail and service industries who have solved this problem, or at least know how to attack it.
It’s a journey, rather than something you quickly solve and never have to touch again. It’s iterative and a continuous improvement cycle. You will never not have to do data governance and data management of information. It will change as the journey evolves in healthcare. That is not any different to any other industry, and that is the key. Everyone has to figure this out. As long as you are in that continuous improvement cycle, you will be making progress.
From your perspectives, how advanced are health systems’ current abilities to leverage analytics insights to improve outcomes, at this current moment? What was shared at the Summit about this?
Bui: I would say we are in the early phases. For your early-phase reporting, it is very heavily descriptive. For the more advanced predictive models, it’s very targeted to serve a single use case. I haven’t interacted with another peer that is actually building predictive analytics that’s truly operationalized and integrated back into the front line for action. That is my definition for being truly successful. I have seen the one-off scenarios where you are curating specific, small data sets to meet one use case. But it is limited as far as the scope and the reach.
Liam, you had a quote from the Summit about how consumer-based analytics are a foundational component of enabling digital health. Can you explain this thought further?
Bouchier: You get a reaction in a room full of healthcare providers and others when you mention the word “consumer,” rather than patient. Another way to view the concept is an individual or individuals who are interacting with the health system for any of the services provided. So I am a consumer of Netflix or Amazon, and I interact with them in a number of ways for the services they provide me. I expect the same from my healthcare providers and healthcare system; I expect to have information delivered to me in an easy-to-digest format, [rather than] having to go seek out the information. I expect the healthcare system to know me in the same way Amazon or Netflix do and tailor services and the user experience to match.
I am one of those people who would never, unless you strongly encourage me, fill out a survey. But I am quite happy to give something a star rating because it’s easy to do on my phone. That is the level of frictionless interaction that should happen with your health system. The questions about your visit with your doctor should be easy to respond to, compared to filling out a 10-page survey.
The other aspect is that consumer does not mean just you the individual, but also your partner/spouse and your family. How do they get information about you in the health system? What information do they need to have? When you go to an online retailer website, they have figured out how to market to you individually or to your extended family. That is what we need to be moving towards in healthcare.
How are non-healthcare disruptors impacting health systems’ current digital and operational strategies?
Bui: This came up in the discussion, and my perspective is that they can be disruptive. They have the advantage of large, scalable data platforms, which is required for this [transition]. So they can actually capture and profit, and also create the standard interfaces across the entire value chain. And they have done that before.
In the financial, retail and travel spaces, they have built their whole business model around digital platforms and data. With the consumer perspective and data at the center, they can model the same type of structure in healthcare. The difference is you will have more complex rules specific to the different healthcare services and providers—but it’s not impossible. It can be solved with the technology that has already been successful in other sectors, and this technology is something that has been around for 50 to 60 years.
Step one is getting online and talking to each other digitally, and healthcare isn’t even there yet. So I believe they will foundationally build this first. If it [were me], I would choose a few large providers and payers, agree on standards of interoperability, take the core use cases, and that would [set the foundation] for your rules and conditions, which is how the travel and financial industries work.
Bouchier: There are some very specialized aspects of healthcare that required highly-specific information about an individual. But there are other industries that use as much, if not more, sensitive data on individuals to market and provide services to those people. So we need to look at the disruptors in that way; they will provide services in that way, by analyzing populations. The care you receive contributes to about 10 percent of your health outcomes; 40 percent or more is about the behaviors of individuals. You don’t need to know [sensitive data] to be able to understand or impact the behavior of an individual.
To this point, how much social determinants data is currently being used and integrated by providers?
Bui: From the perspective of CHRISTUS Health, being a ministry-based organization, we do have special programs that focus on the social determinants of care for folks who are considered socially or economically impoverished. So we track patient populations who fit that category, using demographics such as income level. We can identify them as a demographic base so we can provide them care when they do show up at one of our centers.
We have already modeled them as patients, but also for preventive care as well because this is a patient population that ends up in the ER multiple times due to not having proper care or proper resources, such as transportation or nutrition. And we are starting to work in very targeted areas; the social network does vary from region to region. We are focusing on regions where we identified that these patient populations do need very specific support.
