How data and analytics are transforming healthcare

Innovations from the pandemic are now being applied to new challenges, including disparities in the delivery of health services.

Innovation by necessity.

It’s a powerful dynamic that has been one of the pandemic’s few silver linings, with three-plus years of reflection now providing a clear perspective on just how astounding so many of the pandemic-driven innovations have been.

But it’s also a dynamic that continues to transform healthcare and life sciences today. The unprecedented moon-shot initiative to develop, test, and distribute effective new vaccines saved millions of lives in record time. But it also generated anything-is-possible momentum for a cascade of rapid innovations that are reimagining healthcare fundamentals like how care is delivered, how to close gaps in care, and how to improve outcomes with more data-driven, personalized solutions.

Indeed, advanced data and analytics have been the engine room for change in healthcare, as our KPMG teams are seeing in our own work with clients. But simply having the right data is not enough. Organizations must be able to accurately interpret that data and then effectively act on it to deliver results—as the pandemic so clearly demonstrated.

Here’s a closer look at some of the compelling data and analytics trends—and lessons—we’ve been tracking.

Big data meets a global pandemic

In retrospect, and compared to previous global health crises, the world had one major advantage in attacking COVID-19: the wealth of useful health data available, and the speed at which that data can be absorbed, modeled, and acted upon versus even just a decade ago.

The amount of healthcare data in the United States had already been growing at least 50 percent a year since 2013, as KPMG reported in previous research. And organizations now have better tools and more computing power to analyze that data, including AI, advanced predictive models and algorithms, more intelligent forecasting platforms, and cloud-based collaboration and data-sharing resources.

These data and analytics capabilities have also been critical in addressing inequities in healthcare delivery. As with previous health crises, COVID-19 disproportionately affected disadvantaged communities, leading to higher hospitalization and death rates for these groups. Multiple factors contributed to these outcomes, including:

A higher incidence of chronic conditions, which data and analytics quickly revealed to be a leading factor in complications and deaths related to COVID-19 infections.

A higher likelihood of living and working in population-dense areas that were more exposed to the virus.

Increased vulnerability due to being uninsured, underinsured, or having to choose between going to work or taking unpaid time off to see a doctor.

Distrust in authorities, language barriers, and limited internet access, all of which challenged vaccination rates among disadvantaged communities.

Putting big data to work

To address these challenges, the Atlanta-based Morehouse School of Medicine rapidly developed and launched the National COVID-19 Resiliency Network (NCRN) in June 2020, amid the pandemic’s fraught early and still highly uncertain days.

With a grant from the U.S. Department of Health and Human Services, the NCRN immediately focused on delivering COVID-19 information, education, and access to health services to the disadvantaged communities that needed them most. And the NCRN’s work continues today, with a focus on providing data-backed resources and expanding access to healthcare services in an era in which the COVID-19 virus is now part of daily life.

In partnership with KPMG, the NCRN quickly deployed leading-practice data and analytics solutions that provided community-based organizations across the country with multiple tools to close information gaps, make it easier to find healthcare services, and develop community-specific communication strategies. And by capturing data on the effectiveness of the different tools during the pandemic, the NCRN platform has established a roadmap for optimizing services for each community going forward.

Rather than trying to use a single proven data set and apply it as a “solution” to a wide range of populations, the NCRN can take specific learnings and tailor them to more targeted audiences based on factors like age, ethnicity, language, communication preferences, health conditions, and even ZIP code—true precision community engagement.

Lessons from the present

Beyond just healthcare, of course, the pandemic has fundamentally changed the way businesses, individuals, and communities operate. Rapid increases in digital adoption, remote work, and online shopping and entertainment, for example, feel just as “new normal” as talking to your doctor online or getting lab results (within 24 hours) virtually.

And underpinning it all is advanced data and analytics. For healthcare especially, there is no one-size-fits-all data and analytics solution for an industry undergoing rapid transformation. But our KPMG teams are seeing some common steps and leading practices when approaching data-powered solutions:

Inventory: Determine what internal and external data are readily available, which sources can be tapped quickly, and even what older data might be valuable. In healthcare especially, more data often leads to better results.

Test: Identifying the right combinations of data and harnessing them to make better decisions is an iterative process of trial-and-error that will get “smarter” as it goes. And revisit and retest your algorithms as new data becomes available.

Distribute: Making data accessible is essential to innovating faster. Providing your teams with the data and tools they need will empower them to think beyond their day-to-day responsibilities and identify new opportunities and solutions.

Enhance: AI and machine learning can now devour time-consuming data processing and analysis tasks far faster than humans—freeing up the humans to spend much more time on looking for actionable insights and then putting them to work.

Cross-check: When analyzing the “smarts” of an algorithm or other data-indicated directives, be sure to pause to consider ethical questions like how well diverse populations are factored in. Ignoring these can lead to inaccurate conclusions.

Discover more about our partnership with the Morehouse School of Medicine as well as more insights and opportunities to advance health equity by exploring the resources below.