It’s no secret that M&A (merger and acquisition) activity among hospitals and other HDOs (healthcare delivery organizations) is booming. According to research from consulting firm Kaufman, Hall & Associates LLC, there were 112 hospital M&A transactions in 2015 — up 18 percent from 2014 and 70 percent higher than 2010 figures. The upward trend in hospital M&A activity shows no signs of slowing down. M&A activity among HDOs can help boost economies of scale, but it also introduces some significant challenges. Namely, the practice can be a data management nightmare. For example, when hospitals merge, there are several inherited disparate IT systems that must be integrated. The process of consolidating IT systems (and the resulting data migration process) can be time consuming and error prone. Furthermore, it complicates enterprise-wide health data analytics efforts.
HDOs have come to rely on health data analytics as a means to improve patient outcomes and operational processes. Graphical dashboards help clinicians identify trends in patient populations, implement appropriate treatment plans and monitor the results of adherence. Likewise, this technology can alert HDOs to bottlenecks or weaknesses in clinical and business processes — such as patient flow and revenue cycle management — and quickly implement solutions.
When an HDO is involved in a merger or acquisition, health data analytics efforts can suffer. For example, analytics tools may be unequipped to ingest new data from inherited systems, giving HDOs an incomplete (and thereby inaccurate) assessment of the clinical and operational performance of the entire enterprise. This is an interruption most HDOs simply can’t afford.
Continuous visibility of data across the healthcare enterprise is essential — the ability to see all of the data in the context of the organization’s end-to-end business processes and, just as importantly, how efficiently those processes are executing. Process intelligence can quickly identify areas of waste, loss or inefficiency and provide recommendations for corrective action, as well as perform real-time analysis on business processes — for example, analytics on the financial impact of bottlenecks or the patient impact of performing steps “out of order.”
A high-value process intelligence platform offers embedded extract, transform and load (ETL) capabilities that allow data to be mined, integrated, and analyzed from multiple repositories without the need to migrate all data to a single data warehouse. With these capabilities, analytics efforts aren’t tied to the data stored only in a core clinical system. They allow cohesive analytical processes to be easily applied to multiple data stores throughout the enterprise. Furthermore, they enable an HDO to quickly add inherited IT systems and data repositories to enterprise-level analytic initiatives.
Another technology consideration that can help hospitals keep health data analytics on track during an M&A transition is to adopt tools that minimize coding requirements for generating reports and dashboards. A merger can quickly exhaust a hospital’s IT staff. These human resources will be pulled in multiple directions in an effort to integrate IT systems and ensure compliance. Process intelligence and analytics tools that empower end users to connect to the appropriate data sources, build their own dashboards and visualize business processes, means an HDO isn’t dependent on an overburdened IT department to keep health data analytics initiatives going.