This Week in Health IT COVID-19 Series: Data vs Pandemic

This Week in Health IT COVID-19 Series: Data vs Pandemic


Galen is a proud sponsor of This Week in Health IT, a podcast for healthcare leaders to discuss the news, leadership, and emerging thought in Health IT.

As part of its Coronavirus Prep series, This Week in Health IT speaks to CIOs & CMIOs to discuss what they are doing and can be doing to prepare for COVID-19 in their communities. Special thanks to series sponsor and Galen partner, Sirius Healthcare.

In our own blog series, we will be providing our top takeaways from those conversations with the intent of spreading best practices among Health IT and healthcare delivery organizations to prepare for the anticipated surge in demand for care due to COVID-19.

Next up is Dale Sanders, CTO, Health Catalyst.

Top Takeaways:

  • The analytics around predicting severity of illness rate in catchment areas is tough because we don’t have a clear picture of the three identities of patient types. Capacity planning (PPE, ICU, vents, beds staffing) and testing strategy derive from these three patient types:
    • High-risk members of community, meaning if they are infected, they will likely suffer a fatality (hypertension, COPD).
    • Suspected with symptoms like flu and RSV. Might have COVID19, not quite sure, but can’t test yet because we don’t have enough tests. Still need to start watching those folks.
    • Confirmed COVID-19 cases.
  • Registries have to be in place with minimum viable data set (mvds). The major EHR vendors need to be convened and mvds strategy out in the field must be conslidated to feed that into the analytics platform to drive these use cases.
  • One of the most surprising things is the wide variety of severity of illness. It’s a perplexing disease and to have a large portion of the population completely asymptomatic compared to a portion of the population who progresses to fatality in a matter of days is challenging. I hope it yields a large population to understand the immunology and the genomics of response in those folks that are asymptomatic.
  • You want to put into place an early warning system through syndromic surveillance. For instance, are we starting to see an unusual spikes in flu-like symptoms that we can’t quite explain. With any warning system, you have to decide if you are seeing false positives or false negatives. In healthcare, we tend to lean towards false positives. In other words, as we dial up the sensitivity and specificity, we’re finding a balance between those two. We prefer to err on the side of false positives. In these situations, you can’t do that because if you assume everyone has the disease, you are going to over-treat and consume all of your supplies.
  • Think of the analytics needed as intelligence preparation of the battlefield. We need to think deliberately how we are going to lay down the data environment in the battlefield to give the commanding officers the information they need to adjust and adapt. Battles are won and lost around logistics and supply chain.

Full interview:

As COVID-19 continues to spread, leaders around the globe are racing to understand and respond to the crisis. Most urgently in the U.S., healthcare systems need data-informed surveillance and containment strategies that can rapidly enhance case detection, reduce transmission of this highly infectious disease, and manage capacity and supplies—limiting the danger of system-overwhelm and poor outcomes for patients and caregivers. Health Catalyst has mobilized to support these needs.

This Week in Health IT is also collecting resources and links designed to help health systems respond to and inform their communities around the Coronavirus. Visit their COVID-19 Resources for Health Systems page.

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