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Despite having some of the most advanced tools and technologies, our health care ecosystem lags when it comes to using and sharing data to improve outcomes and experience. Many factors are to blame, including policy and data collection challenges that become even more vexing when faced with major disruptions like the current COVID-19 pandemic.

In the first piece in this series, Steve Griffiths, COO Enterprise Analytics for Optum, rightly pointed to a lack of necessary information at the beginning of the COVID-19 pandemic — a dearth that hindered our ability to anticipate the spread and impact of an entirely new virus. 

Srinivas Sridhara also weighed in on the importance of real-time data in our last blog. Deficiencies in these areas during the COVID-19 pandemic have reinforced the concept that information is the lifeblood of health care.

In this article, we examine the challenges these deficiencies and the variability of human behavior pose to disease modeling and surveillance efforts, which are essential to responding to crises and ongoing public health issues alike. How do we account for these factors and build better model forecasts that support better outcomes?

A modeling and surveillance evolution

From the outset of discovering a public health emergency, epidemiologists, public health officials and other planners turn to forecasting models, which apply new information to learnings from past experience to help inform their decisions.

Predominant among these models is the SEIR framework, which tracks the number of people who are susceptible, exposed, infected and recovered (or died) from an illness.

When armed with good data on these variables, the SEIR framework is a valuable resource planning tool. For example, it delivers insights to help inform forecasts around hospital utilization and equipment needs (for example, beds, ICUs, ventilators).

However, early in a pandemic, SEIR models, like other forecasting resources, must make the best use of incomplete information. This introduces wide variability in forecasts because, as more data becomes available, rates of each factor in the SEIR model may change considerably.

The early COVID-19 experience was no exception. There was simply too much we didn’t know about the disease. That, coupled with the lack of testing and bottlenecks preventing efficient, standardized collection of information, left modelers to make assumptions or calculations that could not become more accurate until more complete data became available.

On this front, artificial intelligence (AI) may be proving to be a powerful ally to fill in information gaps. AI systems work by feeding computer models with leading- and lagging-indicator data. These data might be reflective of internet search patterns or population mobility (that is, where people travel and how long they stay as recorded from their mobile phones or other sources) and integrated with related clinical, demographic, socioeconomic and health care-related data. 

We use these capabilities to identify early micro trends — assuming the data and analyst perspectives are sufficiently diverse and representative of the populations of interest. By filling in certain knowledge gaps, AI capabilities can help us understand more about the course of a disease and how people respond to it, and produce more reliable disease forecasts.

Building a more flexible, responsive health system

If understanding and managing COVID-19 is like running a marathon, we’ve just begun the race. We’ve learned a lot about this virus, but there is so much more we don’t know, especially about health problems that remain even after the virus becomes undetectable.

The good news is that in the response to the pandemic, our health care delivery system has taken an unforeseen step forward to a more digitally agile world. We’ve seen greater willingness among insurers, providers, government and other stakeholders to collaborate, and rapid innovation to expedite collection, analysis and sharing in near real time of data that had once taken months to process. And almost overnight, it seems, virtual care technologies such as telehealth and remote patient monitoring gained widespread acceptance.

These are important shifts that reinforce the enabling power of data and analytics in health care. 

Our next piece in this series will discuss efforts to sustain momentum in ways that not only enhance how we respond to a public health emergency but empower a more flexible and responsive system for everyone.

Additional stories around the industry response to COVID-19 and our efforts to confront current challenges can be found in the Optum News Room. You can also find more perspectives on enabling health care innovation on our data, analytics and technology blog.

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