Data sharing has long been an issue for the healthcare industry, but emerging technologies and new standards could create a future that is built for interoperability. Interoperability is, after all, the intended goal of healthcare information systems.
Cloud-based services and other software must be able to communicate. Otherwise, clinicians will be limited in the care they can deliver. Technology that enables the exchange of clinical and administrative data will give clinicians the access to patient records that they need to perform their jobs.
At this time, healthcare organizations and IT vendors have yet to attain interoperability. The proper technologies must be developed, but equivalent next-generation techniques and tactics must also be developed to achieve that ultimate goal.
AI and Unified Health Records
One way healthcare expects to push interoperability forward is by using artificial intelligence (AI) to interact with data. As more data becomes available and more end-users depend on it, users will struggle to find useful data among the sea of data that offers no benefit to them.
This is where AI enters the picture. Intelligent assistants will become features that tools offer as a way of engaging with the data. These assistants will help end-users sort through data to find what they need, much like voice assistants paired with smartphones.
Unified health records are also sure to play a part in the future of interoperability. The healthcare industry is the opposite of unified, however. Multiple data sources and multiple providers make for a multi-health industry. The best way to move forward in this complex system is to create a unified health record that can empower the whole industry. Caregivers, physicians, patients, case managers, data scientists, and even AI can benefit from the unified health record.
Succeeding in value-based care will require access to all patient information, even population and insurance claims data.
The only way unified health records will become a reality is to develop interoperability technology that is able to combine multiple data types to create a single snapshot of patients. These data types include structured data, like height and weight, and unstructured data, like handwritten notes. Leaders in the industry have to prioritize interoperability solutions that generate unified health records that can be shared throughout the care continuum.
The FHIR Standard
Another driver of interoperability is going to be the Fast Healthcare Interoperability Resources (FHIR) standard. In the past, interoperability just meant connecting two systems, but the present-day healthcare industry is much more complex than that. The number of data sources and the amount of patient data have both increased exponentially over the last few decades.
Delivering high-quality care is dependent on provider organizations having the ability to obtain a comprehensive understanding of a patient’s health history. However, this would require real-time access to multiple health information systems. This is why FHIR is going to be so important moving forward. It is the only standard that creates seamless, real-time data sharing. CIOs must ensure that every health information source follows the FHIR standard.
The industry is likely to see an expansion in the number of patient-centered health applications in the next few years. Amazon, Google, Apple, and Microsoft will likely get involved and spur innovation, but startups are likely to bring the fresh ideas.
The expansion of patient-centered health applications will most likely occur one health system at a time. Patients will be able to use API integrations to begin consolidating access to their data across separate providers.
Natural Language Processing
Another way interoperability will be achieved is through natural language processing technology (NLP). One of the key factors behind the need for structured and unstructured medical records data is that information from unstructured data can be used to fill gaps in structured data.
NLP will be an important feature for next-generation technologies because it exposes data needed by analytics platforms which can then be acted upon. For example, NLP can identify patients at risk and help care managers uncover observations or conditions that might otherwise go unnoticed in unstructured data.
Being able to analyze and utilize unstructured data sources will be a massive step forward for care coordination.