There is little question of the burgeoning value of connected health: technology-enabled care solutions that aim to facilitate more earnest, streamlined dialogue between patients, providers, and caregivers.
Even so, while electronic health records (EHRs), medical apps and other health information technology (HIT) have made great strides in revolutionizing healthcare, the industry nonetheless grapples with the complex grey area of interoperability. Interoperability is at the crux of a great disconnect between the industry’s open-mindedness to the modernization of HIT systems, versus the ability — or lack thereof — to seamlessly meld the exchange of data in accurate, fluid measure. Most healthcare professionals recognize interoperability as an information-sharing that is foundational to building solutions to improve care coordination, enhance patient safety, and reduce overall costs.
Some especially see a new silver lining in an initiative called FHIR (Fast Healthcare Interoperability Resources), which is pronounced like fire. It is a solution that could enable medical apps from mixed IT systems to theoretically operate seamlessly across the healthcare ecosystem, no matter the software systems in place. However, there still remains one big caveat: despite best efforts through technology, standards, and even incentives in the form of Meaningful Use, fundamental “language barriers” still exist.
FHIR helps support a standard way of exchanging and retrieving data, but the standard has no impact on the data itself, from its accuracy and hygiene to its overall consistency. Clinical, financial and unstructured data still must be cleaned and appropriately-mapped to a single source of truth to make it meaningful, in order to achieve the true promise of information-sharing across the healthcare ecosystem.
All too often, the healthcare industry fawns over emerging HIT like a shiny new toy. While FHIR is certainly a major step forward in combating the industry’s data-exchange dilemma, it nonetheless is just a mere sliver of what is required to make information truly interoperable. To fundamentally address the problem at its source, we need to make the technology work for us in a way that accurately communicates the intent behind the data.
Decoding the data
Using an EHR system or data warehouse that supports the FHIR standard, patients, doctors, healthcare practitioners and payers can draw on a library of integrated applications to improve clinical care, research, and public health. While the much-needed infrastructure may be in place, organizations must also ensure the data itself is semantically consistent across the systems that are sharing the information.
Consider this analogy: A caller in France, through their mobile phone, can dial a person in the UK. However, while the technology may enable the caller to connect to their point of contact, the technology itself cannot translate the exchange of conversation between two different languages. Similarly, while health information can now be effectively exchanged via FHIR, the “language barrier” still exists.
While industry standards serve as an important step toward achieving the goals of interoperability and information sharing, healthcare organizations still struggle to facilitating seamless health data exchange between a multitude of (H IT) systems to coordinate care across various health settings nationwide.
Stakeholders across the care continuum generally lack a “single source of truth” that ensures data coming from disparate systems can be combined to derive meaning. The first challenge is to keep up with the evolving standards across diagnoses, procedures, medications, and labs. The second challenge is connecting them in the right way to understand how the data is related. In order to create a single source of truth to achieve the true promise of information sharing across the healthcare ecosystem, all decisions must be based on the “right” data.
Looking to speak the same language across the enterprise? It all starts with the data basics:
Improve data governance strategy with Reference Data Management (RDM)
A best practice for managing growing volumes of data. A crucial first step in a larger data management strategy, it allows organizations to organize data so it is consistent and can be mapped to other systems that may categorize the data differently. When an organization is confident about the quality of data, the data becomes a strategic asset that can be a game-changer for the business.
Collaborate across operations and IT teams
Create a sound technology and data infrastructure that is agile, accurate, and reliable. Data governance processes are equally important to ensure that reference data is always up to date with the latest industry standard releases and is used consistently throughout an organization.
Map standards and proprietary and local reference terminologies
This is a building block of interoperability and the missing link to help structure and prepare data for more advanced analytics and reporting. It provides the framework for organizing a company’s wide array of disparate terminologies and codes around a single source of truth.
Read our Health Language blog and learn more about how to break down the data silos and develop an overall data management strategy.