In a trend that shows no sign of slowing, hospitals and health systems are affiliating, acquiring and merging more than ever before. With those sprawling new entities come lots of challenges combining and streamlining their constituent parts. Especially data – and lots of it.
The integration of data assets after a merger or acquisition is central to operational and strategic success. But it can be very difficult to navigate as teams are reshuffled and new projects and initiatives are reprioritized under a newly combined entity.
Further, decisions on electronic health records, enterprise resource planning and other major IT system integrations ultimately will decide the shape and timing of short- and long-term data and analytics plans.
Integration lessons learned
Advocate Health – the product of a megamerger combining legacy health systems Advocate Aurora Health and Atrium Health – is more than 18 months into this process. Its experience so far offers some valuable insights and tips about the process of data migration and integration.
Headquartered in Charlotte, North Carolina, the new health system’s combined footprint spans now across six states: Alabama, Georgia, Illinois, North Carolina, South Carolina and Wisconsin. With 69 hospitals, more than 21,000 doctors and serving nearly 6 million patients annually, it is the third largest nonprofit health system in the country.
“As integration began at Advocate Health in December of 2022, it was clear data integration would need to be done thoughtfully and pragmatically given the immediate needs to understand current state as well as enable long-term needs that were yet to be defined,” said Tina Esposito, chief data officer at Advocate Health.
Short-term needs
Executive leadership mandated from Day One that clinical outcomes and patient experience would continue to be carefully monitored, ensuring the health system would fulfill its pledge of clinical preeminence and patient safety to its communities.
“Like most health systems, there were similarities in how outcomes were measured in the respective legacy health systems, but also differences,” Esposito explained. “The central business intelligence team assessed differences and identified the priorities that were similar, allowing quick production of a standard enterprise view of clinical outcomes and patient experience.
“The use of visualization tools provided Advocate Health with one of its first standard integrated reports – within two months of affiliation – enabling the executive team and board to closely monitor and ensure clinical performance,” she continued. “In the meantime, the same team worked very closely with clinical thought leaders in ensuring 2024 measurement systems would be identical in both priority measures and targets.”
Data and analytic teams used a host of tools to enable a quick level of data integration that allowed for a seamless view of data.
“In addition to data aggregation databases and visualization software, careful planning regarding security and access across the entire organization ensured all needing to view performance could view it easily,” 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 has further integrated, operational and strategic business needs require an enterprise lens of data – integrated as one view.
“With a footprint that currently reflects two separate EHR instances – in addition to more when considering legacy and physician-affiliated data – and two separate ERP instances, an existing modern data platform initiative has increased in importance,” Esposito said.
“Originally envisioned as a platform to enable advanced analytics and AI, a modern data platform also is a mechanism to democratize integrated data in a way that leverages central resources to clean, 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 analytic purposes.”
Work continues in building out the platform with careful consideration of priority data sources and subsequent work within data teams to onboard, combine and curate to the organization accordingly.
An integration point for needed data
“The effort involves multiple teams within our data and analytics umbrella including cloud data engineering, data governance and data science,” Esposito explained. “This platform will serve as an integration point for needed data across the organization.
“A recent example is the ability to support specialty and retail pharmacy operational and strategic data needs as one enterprise where source data resides in two separate EHR instances as well as a third-party database,” she added.
Organizational integration often is understood and pursued in context to bringing cultures and operations together, she said.
“In today’s information age, it is as important to consider respective data assets and how they will come together to ultimately enable newly integrated operational and strategic priorities,” she noted. “Without integrated data, it is impossible to understand the starting point of the new entity or the potential.
“Throughout the data integration effort, vision will drive a bright long-term future, but needs to be equally partnered with a short-term pragmatic approach to solve for needs Day One,” Esposito concluded.
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