Click below to listen to the full interview, or continue reading for excerpts from Dale Sanders’ conversation with HIMSS on the need for better data aggregation and management.
HIMSS: How has COVID-19 impacted the ability of data aggregators to create a complete, accurate picture of the patient to support population health initiatives?
Dale Sanders: In the early days of COVID, I was the Chief Technology Officer at Health Catalyst – a data aggregator, analytics platform, and health information exchange (HIE) company. I have since transitioned to IMO. But what I observed while at Health Catalyst was a breakdown and significant flaw in the US healthcare system. There is a division between public health, acute care, population health, and ambulatory clinics. We have a hard enough time sharing data between hospitals and clinics. COVID really put a spotlight on this gap – or complete separation – between public health and population health. Yes, COVID is an infectious disease, but it is a population health issue.
COVID really put a spotlight on this gap – or complete separation – between public health and population health.
Additionally, at Health Catalyst, we had one of the first (if not the first) nationwide COVID registries. We pulled data from around 90 to 100 million patients from every EHR in the country and used it for analytics. When we looked at the data, we realized how poorly standardized our vocabulary and clinical terminology is. [We didn’t see consistent use with standardized code systems like] SNOMED-CT® and ICD-10-CM, and we saw almost no widespread use of LOINC or RxNorm.
Our ability to understand the situation at the national level was greatly impacted by the lack of consistent, comprehensive, and widespread use of standard terminology. I think what we’re seeing in the country [is that] standard terminology and vocabulary is generally not used unless you’re being reimbursed for it. But we need those standard terminologies for situational awareness in these pandemics, population health, and public health.
HIMSS: Can you talk about the challenges and approach to collecting vaccine data?
DS: I will keep returning to public health and population health and the distinguishment between the two. There’s a gap between public health data where vaccine data was flowing up to public health [entities] but not making it back to EHRs for the most part and vice versa. We just didn’t have the infrastructure to begin collecting vaccine data.
What I observed across the States is that it’s almost a county-by-county process. If the county’s public health department has a relationship with the care provider, then there might be some data exchange. For instance, in Utah where I live we’re well known for our health information exchange and interoperability. My primary care provider still doesn’t know if I’ve been vaccinated or not, but the Salt Lake County health department does. So, again it’s that gap between public health and population health. Nationally, culturally, from a policy standpoint — we have to close that gap between the two – they are not the distinct entities that we treat them as, from a data or organizational execution perspective.
To learn more about how we approach data standardization with IMO Precision Normalize, check out this explainer video.
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