EHR Interoperability Ranked and Analyzed

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KLAS has published its first annual EHR interoperability report, surveying healthcare professionals from across the country and consolidating their opinions into a market briefing that attempts to measure the current state of interoperability and the roadblocks to broader data exchange. The report’s introduction paints the following apt analogy of current interoperability efforts, “Interoperability is like moving a piano. It is a team effort, but when the piano tumbles down the stairs, puts a hole in the wall, or lands on a foot, it is always someone else’s fault. The truth is that pointing fingers does not help—we all fail or succeed together.”

In the case of interoperability, the finger pointing has followed a fairly consistent pattern. Providers point fingers at EHR vendors, charging them with information blocking to protect market share and profiteering. EHR vendors point fingers at the government, arguing that it has not done enough to establish industry-wide data-sharing standards. The government itself has considered the impact that both EHR vendors and providers are having on interoperability, echoing the sentiments of providers when it comes to EHR vendors, but also arguing that providers may have a financial incentive to limiting data exchange with local market competitors. The new KLAS report, one would expect, should shine an objective and unbiased light on the issue, helping to focus the industry’s efforts toward a set of logical common goals.

KLAS notes that more than 200 healthcare professionals were interviewed during the three months that it conducted its study. Their opinions form the foundation of what is contained in the report. On October 2, KLAS welcomed executives from 12 major EHR vendors to gather in its Orem, UT offices to negotiate what metrics would be used to measure interoperability within the industry. Executives from Allscripts, Athenahealth, Cerner, EClinicalWorks, Epic, GE Healthcare, Greenway, Healthland, McKesson, Meditech, Medhost, and NextGen were present at the meeting. Though the meeting was moderated by health IT experts from the provider side, the actual metrics that would be used to measure interoperability were established by vendor executives.

The final report, in and of itself, is a mish-mash of subjective and objective findings. Private EHR vendor HIEs, such as Epic’s Care Everywhere network, are presented as the best and most valuable option for realizing interoperability. They are ranked as both the easiest and most high-value method of enabling data exchange. These connections are baked into EHR systems, making them nearly plug-and-play, while delivering functional interoperability within the admittedly limited network. The gravitation toward this approach is being demonstrated in the market as major health systems consolidate on a single platform, and then pressure local independent practices to do the same, in the name of establishing interoperable healthcare networks in the local area. State, local, and private HIEs are viewed as complex to setup with varying degrees of value coming from the effort, and the Direct Message initiative is viewed as somewhat complex to configure while offering little value.

From a technology perspective, respondents zeroed in on “poor coordination among vendors, difficulty locating records, and limited parsing abilities” as key technical weaknesses within EHRs that could use improvement, though in its accompanying press release, KLAS cautioned that “neither providers nor vendors mentioned technology as a missing ingredient, stating instead that lack of agreement on the use of standards and willingness to share information” are the more likely barriers to interoperability. Athenahealth was named the easiest vendor to connect to, followed by Epic and Cerner. It will be interesting to see if Athena is able to continue on this tangent as it works to integrate its ambulatory solution with its newly acquired inpatient EHR RazorInsights, while at the same time working to build an inpatient market.


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  • Srinidhi Boray

    Importantly wrt Interoperability; that it has allowed convergence of the millions of patient longitudinal records it should develop data mining capability developing into evidence based driven clinical decision support system. Also, must facilitate in the employ of data science to conduct research on the available patient data and facilitate translational science. Without advancing evidence based medicine and clinical decision support, clinical efficacy and medical errors cannot be addressed.

    Where is the real problem, besides EHR to have been designed to support medical billing and not the efficacy.

    Our investigation was from the point of enterprise architecture and data science dealing with uncertainty (not technology alone, like BigData stack). And, what role it could play in bringing the desired correction in a rather impossible problem, or rather a messy complex problem, from system dynamics point of view looking at a chaotic turbulence filled system. When a problem is fleeting; it is called ontological undecidability, with this vexation finding a best possible solution fit to problem is always a challenge given the inadequacies in the existing methods and tools.

    Part A – Healthcare Interoperability Measures:- Cartesian Dilemma (Diagnosis)

    https://ingine.wordpress.com/2015/10/09/interoperability_part_a/

    Part B – Healthcare Interoperability, Standards and Data Science (Resolving the Problem)

    https://ingine.wordpress.com/2015/10/18/interoperability_part_b/

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