3 Fundamental Problems With Healthcare Interoperability and
Care Coordination

By Verto | August 27, 2020

Whether you’re speaking with care providers at hospitals, physician groups or clinic networks, it’s not news that the decade-old problems of interoperability, care coordination and managing patient transitions continue to make optimizing care and patient outcomes a challenge.

If everyone agrees that these critical data sharing and communication problems can negatively affect patient outcomes, the real question is, why haven’t they been solved yet?

The answer can get complicated pretty quickly and encompass issues such as relatively new interoperability standards (HL7’s Version 2.x (V2), FHIR), data governance issues, the large number of patient data sources, or various connectivity standards for devices and mobile apps.

At the heart of the problem are three fundamental issues that make this an almost insurmountable problem for all but the most resource rich healthcare organizations.

Fundamental Problem 1) 

Data Integration and Aggregation

To solve the interoperability problem, most healthcare organizations would have to connect to, normalize and aggregate huge amounts of data into a single shared database, usually their EMR. The blocker here isn’t just all of the point-to-point integrations required (EMR, LIS, HIS, Device, Mobile App) which are expensive and time consuming, the real challenge is now you have to map all the data between the different systems, including fields in one system (or device) that don’t have a corresponding field in the EMR. 

Now you have to create custom fields to accommodate that data, in addition to custom integrations to support those custom fields. This takes an already resource intensive project and increases it by an order of magnitude. Now think about what happens when you have to upgrade one of the platforms in that custom field, custom integration environment.

Very few healthcare organizations have the resources available to afford these kinds of multi-year projects, so they resort to integrating a subset of primary systems, because that’s what’s affordable and achievable in a reasonable time frame.

Fundamental Problem 2) 

Sub-Set of the Data Relevant to a Particular Care Step

Even if you could connect to, normalize and aggregate all of that data into a single database, care providers now have to sift through all of that data to find only the data elements relevant to a particular care step. Let’s say there are 500 data elements related to a single patient, but in step three of a seventeen step diabetes care path, only 13 of those data elements are relevant. Now, the clinician has to sift through hundreds of data elements to find the specific ones they need to provide quality care for that third step in the patient’s care plan. This becomes even worse in the fourth step of the care plan, because there are 15 relevant data elements and five of them are different from the 13 relevant for step three.

So even if you did have the resources to solve the first fundamental problem, you still haven’t actually solved the real problem, which is how to access the relevant data in the context of the current care step to provide the best possible care to yield the best patient outcomes.

Fundamental Problem 3) 

Data Governance and Patient Transitions

If you were able to connect to, normalize and aggregate data from different systems, while parsing out the elements relevant to a specific care step, how do you approach a patient care path that crosses multiple care organizations, each with their own EMR’s and data governance policies (Hospital, LTC, or Specialty Clinic)? This isn’t actually a technology problem, this is a “who owns the problem” problem. 

No single healthcare organization owns the problem of patient transitions, and so it’s hard to justify allocating resources to resolve this issue. But none of that changes the fact that in that legacy model, it’s really difficult to provide anything other than transactional care, instead of having the shared data to provide patient outcome focused care.

But What if …

The solution to these decade old problems requires a fundamental change to the original solution paradigm. The reason that these problems have persisted for over a decade, is due to the myth that the first resolution step requires connecting all the systems together and then moving all the data and mapping it into a central repository to address interoperability and care coordination.

But what if you didn’t? What if you could connect to the data, but leave it where it is and reference only the relevant data elements in the context of the care step the patient is in?


Digital Twin Technology is very well known in the supply chain, IOT and other industries, but it’s relatively new to healthcare.

The digital twin is in effect the digital entity representing the patient as they move through the care steps of their patient care journey. It’s based on connecting to all of the patient related data sources (EMR, LIS, HIS, Devices, Mobile Application) and then accessing only the relevant data from the different systems, in the care step the care provider requires it.

This approach destroys the myth that the data must first be aggregated into a central repository, then mapped between the different systems. Since the patient’s ‘digital twin’ is now the reference point, not the central data repository, this approach also seamlessly enables patient transitions.

A sample use case would be, for a physician’s group that is contracted by a long term care (LTC) facility to provide post acute care to heart patients. Using digital twin technology, the physician could access patient data in the hospital’s EMR and the LTC EMR, combined with other medical device data relevant to the specific care step in the post acute care plan.

The beauty of the digital twin approach is that it’s almost infinitely scalable. You don’t have to force your entire health system onto a single EMR platform, outsourced partners can leverage patient data without having to integrate directly to your EMR and device, mobile application and AI/ML algorithm data can also be easily incorporated into the patients care journey.

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