In a trend that shows no signs of slowing, hospitals and health systems are joining, acquiring and merging more than ever. With these expanding new entities come many challenges to combine and streamline their components. Most importantly, data – and lots of it.
Integrating data assets after a merger or acquisition is critical to operational and strategic success. But it can be a tricky business to navigate as teams are reshuffled and new projects and initiatives are reprioritized under a newly combined entity.
Additionally, decisions about electronic health records, enterprise resource planning (ERP), and other key IT system integrations will ultimately determine the shape and timing of short- and long-term data and analytics plans.
Integration lessons learned
Advocate Health – the product of a mega merger combining existing health systems Advocate Aurora Health and Atrium Health – is more than 18 months into this process. The experience so far provides valuable insights and tips on the process of data migration and integration.
The new health system is headquartered in Charlotte, North Carolina, and its combined footprint now spans six states: Alabama, Georgia, Illinois, North Carolina, South Carolina and Wisconsin. With 69 hospitals, more than 21,000 physicians and nearly 6 million patients annually, it is the third-largest not-for-profit health system in the country.
“When the integration at Advocate Health began in December 2022, it was clear that data integration had to be executed thoughtfully and pragmatically, given the immediate need to understand the current status and to meet long-term needs that had yet to be defined,” said Tina Esposito, Chief Data Officer at Advocate Health.
Short term needs
From day one, leaders mandated that clinical outcomes and patient experiences would be closely monitored to ensure the health system delivered on its promise of clinical excellence and patient safety to communities.
“As with most health systems, there were similarities in how outcomes were measured across the respective legacy health systems, but also differences,” Esposito explained. “The central business intelligence team assessed the differences and identified priorities that were similar, allowing for a standard enterprise view of clinical outcomes and patient experience to be quickly produced.
“Using visualization tools gave Advocate Health one of its first standard integrated reports – within two months of affiliation – allowing the executive team and board to closely monitor and ensure clinical performance,” she continued. “In the meantime, the same team worked closely with clinical thought leaders to ensure that the 2024 measurement systems would be consistent across both priority measures and objectives.”
Data and analytics teams leveraged a variety of tools to quickly integrate data to gain a seamless view of the data.
“In addition to data aggregation databases and visualization software, careful planning around security and access across the organization ensured that anyone who wanted to view performance could easily view it,” Esposito noted. “Advocate Health was able to ensure continued focus and execution on ensuring the best health outcomes and experience as a newly combined entity.”
Long term needs
As the organization becomes more integrated, operational and strategic business needs require an enterprise-wide view of data – integrated as one.
“With a footprint that currently reflects two separate EHR instances – plus more when accounting for legacy and physician-related data – and two separate ERP instances, an existing modern data platform initiative has become more important,” Esposito said.
“Originally conceived as a platform to enable advanced analytics and AI, a modern data platform is also a mechanism to democratize integrated data in a way that leverages central sources to cleanse, conform, and curate within a cloud-based architecture,” she continued. “Once business rules are applied, the platform becomes a source for integrated data, making it more accessible for data science/AI and analytics purposes.”
We are still working on building out the platform, carefully considering priority data sources. Then we work within the data teams to onboard, combine and curate the sources for the organization.
An integration point for required data
“The effort spans multiple teams within our data and analytics umbrella, including cloud data engineering, data governance, and data science,” Esposito explains. “This platform will serve as an integration point for needed data across the organization.
“A recent example is the ability to serve the operational and strategic data needs of specialty and retail pharmacies as one enterprise, where the source data resides in two separate EHR instances and an external database,” she added.
Organizational integration is often understood and pursued in the context of bringing cultures and activities together, she said.
“In today’s information age, it is equally important to consider the respective data assets and how they come together to ultimately enable new integrated operational and strategic priorities,” she noted. “Without integrated data, it is impossible to understand the new entity’s starting point or potential.
“During data integration efforts, the vision will deliver a positive long-term future, but it must be coupled with a pragmatic short-term approach to meet needs from day one,” Esposito concluded.
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