Mergers and acquisitions in healthcare have been going along at a pretty good clip for a number of years now. The volume of deals remains high, and with larger entities primed to scoop up some of their smaller, struggling peers, the trend seems poised to continue.
There's an issue that consolidating organizations consistently run into, however: data sharing.
Specifically, many organizations that have initiated merger activity fail to consider that not only will the consolidation necessitate integrating multiple electronic health records, but other ancillary systems as well.
These organizations need to produce the analytics that are required to manage what's essentially a new business, and that starts with the development of some sort of analytics blueprint early on in the merger activity.
As two or more forces join into one, it's important to have the analytics blueprint in place so leaderships knows which dashboards are going to be needed for success.
"There used to be a trend where everyone was converted onto the same EHR platform," said John Walton, solutions architect for IT consulting company CTG. "I guess the thought is that if everyone is converted, the problem will go away. Now … they end up in a situation where they can't produce the kind of dashboards that are needed."
A key component of an effective analytics blueprint is a conceptual data model -- basically a visual representation of what domains are needed for the dashboards.
"It sounds difficult to produce, but if it takes more than four to six weeks to produce something like that, you're overthinking it," said Walton. "But that's then starting point. The key component is that analytics framework."
Failure to have a framework in place can result in the newly merged entity losing out in terms of revenue and productivity. And once the problem becomes manifest, there's often a lot of manual effort that goes into serving, for example, the financial dashboards that are so needed by CFOs. A lot of the manual effort goes into putting the data into Excel spreadsheets, which only puts a Band-Aid on the problem.
"The framework essentially provides pre-built routines to extract data from multiple data sources, as well as from financial systems," said Walton. "It also provides, for lack of a better term, the data plumbing to enterprise standards, and most importantly there's an analytic layer. The endgame is that the dashboards need to sit on top of an analytics layer that is easy to do analytics on. What it contains is pre-computed performance indicators based on approved business rules with multiple levels of aggregation."
An effective framework, as with so many other things, begins with C-suite leadership. Having executive sponsorship, or at least an understanding of the issue at an executive level, can translate into a vision for how to integrate the data and provide the analytics that are needed to successfully manage the business.
Walton once observed a national organization that acquired another entity, and after two years the CEO still didn't have any executive dashboards -- which means a lack of visibility into the performance metrics. The CEO hen issued what was effectively a mandate to the acquiring organization: Get this done within three months, or else.
Thus began a flurry of activity to et the dashboard situation straightened out, which is not where an organization wants to be. Proactive planning is essential, yet Walton doesn't see a lot of that in healthcare.
"I've never seen an organization proactively plan for this," he said. "That doesn't mean it's not happening, but in my experience I haven't seen it."
In the meantime, mergers and acquisitions keep happening. Even if merging organizations become aware of the problem and factor that into their decision-making there's another issue to consider.
"Another extremely significant problem is data quality and information consistency," said Walton. "That problem really is not universally dealt with, in healthcare or for that matter other industries. It's almost like they've learned to live with it. It's almost like we need a call to arms or something. You're almost certainly going to have the need for an analytics framework that will apply the data to your standards."
The data in question could encompass missing or clinically inappropriate data. Quality, in this case, has to do with the cleanliness of the data. In terms of consistency, a good example would be something like average length of stay. There's an opportunity to ensure that the right data ownership and stewardship is in place.
Importantly, it's primarily a business solution. It's possible that one of the merging entities has a data governance strategy, but all too often that strategy was launched by the IT department -- which is not where an organization wants to be, said Walton, because it's primarily a business problem rather than one that's purely technical.
"Data governance is a very well-known concept, but people struggle with its actual implementation for a number of reasons," he said. "One, there's a technical aspect of it, which centers around how we identify data quality issues. What kinds of tools are they going to use to address data quality issues?
"Then there's establishing ownership of the data, and who are the subject matter experts. And there's a workflow aspect that most organizations fail to deal with."
It all starts with the framework. Only then can merging organizations get an appropriate handle on its data and analytics landscape.