During my early years in pathology informatics, I was constantly reminded of the challenge of two hospitals merging with different LISs. The problem was usually resolved in one of two ways. The first was that the two merged hospitals continued to operate the their two different LISs but with some makeshift attempt to view lab results across the two institutions. The second and more common approach was to rip out one of the LISs and replace it with the brand running in the other. All of these previous problems of IT integration are now compounded in this era of complex and expensive EHRs. Here's an article discussing how M&A costs can be boosted significantly by IT integration demands (see: Hospital M&A Cost Boosted Significantly By Health IT Integration). Below is an excerpt from it:
Most of the time, hospital M&A is sold as an exercise in saving money by reducing overhead and leveraging shared strengths. But new data from PricewaterhouseCoopers suggests that IT integration costs can undercut that goal substantially.....[T]he cost of integrating the IT systems of hospitals that merge can add up to 2% to the annual operating costs of the facilities during the integration period, according to PricewaterhouseCoopers. That figure, which comes to $70,000 to $100,000 per bed over three to five years, is enough to reduce or even completely negate benefits of doing some deals. And it clearly forces merging hospitals to think through their respective IT strategies far more thoroughly than they might anticipated...[O]ther experts feel that PwC is understating the case...[One of them said that] IT integration costs can be much higher than those predicted by PwC’s estimate. “I think 2% being very generous,” ...[he said] “For example, if the purchased hospital’s IT infrastructure is in bad shape, the expense of replacing it will raise costs significantly.” Of course, hospitals have always struggled to integrate systems when they merge, but as PwC research notes, there’s a lot more integrate these days, including not only core clinical and business operating systems but also EMRs, population health management tools and data analytics.... And what if the merging hospitals use different enterprise EMR systems? Do you rip and replace, integrate and pray, or do some mix of the above? On top of all that, working hospital systems have to make sure they have enough IT staffers available, or can contract with enough, to do a good job of the integration process.
What are the reasons why two hospitals in the same city or region decide to merge? It's often a case of the stronger system absorbing the weaker one (see: Some Hospitals Experiencing Financial Distress and Even Bankruptcy). Of course, lurking in the background of all potential hospital mergers is always the notion that substantial savings can be achieved. Such savings, at least in the past, were anticipated with the creation of single IT and HR units. Consolidated labs were often also under consideration. However, this article (and past experience on my part) suggests that the anticipated IT savings are often illusory. This point is well illustrated by the excerpt above.
Added to this IT stew is the challenge of converting the previous LIS or EHR database of the abandoned systems to the systems used by the dominant hospital partner in the merger. I know of one company, Ellkay, that specializes in such structured conversions. Below an interesting quote from the home page of another data migration company, Informatica. Of course, hiring consultants to supervise data migration activities will only add to the hospital merger costs discussed above.
Gartner has reported that 80 percent of data migration projects fail to meet expectations, running over time and budget. This is in large part due to common misconceptions about the migration data, including electronic health record data: it complies with a standardized format; users have captured it in expected fields; and it's all valid and of high quality. But the reality is, complex patient, member, and provider data exist in various formats, anticipated data is missing, and data quality is inconsistent. When healthcare organizations hand-code or write one-off processes to migrate legacy data from electronic health records and applications, they are doing so to move millions of pieces of data, likely spot-checking only a small subset. As a result, data are moved into a new application with minimal review.