Understanding healthcare data interoperability
According to the Healthcare Information and Management Systems Society (HIMSS), interoperability is the ability of distinct devices and applications to access, integrate, and exchange data in a coordinated manner within and across organizational and national boundaries.
Another definition comes from the Cures Act, where interoperability implies that all electronically available health data can be accessed “without special effort on the part of the user.”
Interoperable EHR solutions as a pillar of secure medical data exchange
As the first step towards healthcare data interoperability, medical facilities need to adopt electronic health records (EHR), as paper-based records can’t be safely and effectively shared and used by people across organizations. Already by 2017, 94% of US hospitals used EHR systems. Another survey conducted in 2019 showed that almost 90% of office-based physicians use EHR as well.
Simply adopting any EHR system is not enough anymore. Healthcare practices keep switching to stronger and more unified EHR interoperability solutions. For instance, the University of Pittsburgh Medical Center previously relied on Oracle Cerner for inpatient records and Epic for outpatient data. Now, the healthcare giant is moving all its patient EHRs to Epic to have a unified view and easy access to health information. In another example, a New York-based orthopedic practice, Bone & Joint, teamed up with Athenahealth to adopt its cloud-powered EHR solution for better interoperability. This move will enable the clinic to seamlessly share patient data with Albany Medical Center and other facilities in the region.
We also have a related project in our portfolio. Just recently, a digital health startup hired ITRex to build an EHR integration solution to improve health data interoperability. The client was planning to plug this tool into their mental health portal. On our part, we selected the technology and developed a solution that integrated commonly used EHR systems and other applications within the mental health realm, enabling doctors to have secure and compliant access to patient data.
Levels of healthcare interoperability
Here are four levels of interoperability arranged from the basic to the most advanced.
At the foundational level, the data can only be interpreted by the user, and it is not machine-readable. One example of such interoperability is sending a PDF file containing a patient’s history through a hospital’s portal. The responsible nurse will open the file and enter the data manually into the EHR system.
At this healthcare interoperability level, data is standardized, and the exchanging systems and devices can comprehend it at the field level. Standards, such as FHIR and Health Level 7 (HL7), ensure structural interoperability.
Semantic interoperability allows systems with different data structures to exchange information seamlessly. Let’s take medical imaging as an example. There are various imaging formats used in healthcare, including Digital Imaging and Communications in Medicine (DICOM) and non-DICOM formats. With semantic interoperability, systems can transfer and receive images in different formats, interpret them, and incorporate them into the receiving end.
At the organizational level, facilities with different requirements and goals can exchange medical data. This level is integrated within organizational workflows and includes data governance, policies, consent management, and security protocols.
Why health data interoperability is important
Different medical software vendors develop their products in silos, making it hard for hospitals that don’t rely on the same vendors to communicate. The concept of healthcare data interoperability aims to solve this problem and give healthcare facilities a holistic view of patients independently of the technology they use.
Let’s see how the US government supports data interoperability in healthcare.
In 2016, President Obama signed the 21st Century Cures Act that enforces EHR systems to offer a patient-facing API in order to obtain or maintain their federal certification. Later in 2020, the Department of Health and Human Services published the rules on interoperability and information blocking, including compliance deadlines.
Healthcare data interoperability plays a vital role outside the US as well. For example, the Australian Digital Health Agency teamed up with Deloitte to build a health data gateway, which is a nationwide effort to increase interoperability. This gateway is based on modern standards, such as Fast Healthcare Interoperability Resource (FHIR), and can interact with data exchange technologies used by the Australian healthcare sector.
What are the benefits of healthcare data interoperability?
Improving the quality of care. With universally adopted health data interoperability standards, patients can receive care from independent practitioners, hospitals, and other healthcare clinics, and every facility can access patient data from other parties.
Maximizing efficiency. Healthcare data interoperability decreases administrative burden, allowing physicians to cut down on repetitive tasks and focus on patient care instead. Also, when physicians have access to data residing in other clinics, they don’t need to waste time repeating the same test that the patient has already done elsewhere.
Avoiding penalties. Some programs enforce or incentivize healthcare organizations to improve their level of interoperability. For example, the Centers for Medicare and Medicaid Services (CMS) put forward its EHR incentive program, Meaningful Use. In the beginning, CMS offered financial incentives to medical centers that adopted EHR and met the program’s requirements. Since 2017, CMS has penalized physicians who don’t comply.
Fostering collaboration. Doctors from different practices can work together to treat a patient and access each other’s findings safely.
Enhancing patient experience. People could access their own medical history at any time and take an active and informed approach to their health. Also, they won’t need to fill in papers repeatedly at every medical facility.
Reducing errors. Medical errors cost around $40 billion per year and can have dire consequences on patients’ health. Healthcare data interoperability empowers physicians to view disparate patient data and avoid manually re-entering fields that already exist in a patient’s medical history.
Healthcare data interoperability standards
Here are four of the more popular healthcare interoperability standards:
Health Level 7 (HL7) is a set of international healthcare standards that guide data sharing between distinct healthcare providers. It is a messaging protocol for exchanging clinical information among different systems. HL7 can support a central patient care system and a distributed environment. It can be viewed as a database query language that medical care providers use to access health data. HL7 v3 includes some additional capabilities, such as supporting government reporting.
Fast Healthcare Interoperability Resource (FHIR) was released in 2014 by HL7 as an alternative to HL7 v2. It relies on RESTful web services and open web technologies for communication to facilitate interactions among legacy healthcare systems. Additionally, RESTful API provides a one-to-many interface, accelerating new data partners’ onboarding. FHIR’s interoperability merits are not limited to EHR and similar systems but extend to mobile devices and wearables.
Digital Imaging and Communications in Medicine (DICOM) is a standard for communicating and managing medical images and related data developed by the National Electrical Manufacturers Association. DICOM can integrate medical imaging devices from different vendors by providing a standardized image format. It allows healthcare practitioners to access and share DICOM-compliant images even if they are using different devices for image capturing.
Real-life example of healthcare data interoperability from ITRex:
At ITRex, we had a large project involving the DICOM standard and medical imaging interoperability. A medical imaging solutions company teamed with ITRex to enable all partner clinics and authorized individual providers to view DICOM-compliant medical images from their own station through a web browser. Medical images, such as CT scans, are hard to handle due to their large size. So, physicians had to go to the internal server room every time they needed to access an image. The client wanted to integrate different DICOM images and make them available through the web in each doctor’s office.
Our team built a cloud-based DICOM-compliant solution that enables clinicians to upload and manipulate medical images. Doctors could, for example, increase an image’s resolution or accurately measure different aspects of the image. The solution offers one database for all participants and allows various clinics to share images.
Several entities took part in developing Consolidated Clinical Document Architecture (C-CDA), including the aforementioned HL7 and Integrating the Healthcare Environment (IHE) initiative. This healthcare interoperability standard allows creating clinical documents that are readable for humans and machines, as they contain Extensible Markup Language (XML) tags. It specifies the syntax and semantics of clinical documents. Practitioners can use C-CDA to compose and exchange diagnostic imaging reports, procedure notes, and continuity of care documents, among others.
Healthcare data interoperability challenges
Legacy systems integration
Legacy systems are still widespread in healthcare. There is evidence that 70% of Windows-based medical legacy devices are not even supported by Microsoft as of January 2020. Many legacy systems were designed in the era of minimal network connectivity, lack modern security features, and are hard to maintain. They require modernization to meet current interoperability standards.
As an intermediate solution, healthcare organizations can deploy a hybrid cloud to extract data from legacy systems and make it available to modern applications. Also, some healthcare interoperability standards are more adapted to legacy systems than others. For example, FHIR offers well-documented mappings for legacy standards.
Reluctance towards data sharing
Some players in the healthcare sector are not keen on sharing patient data in their possession. For example, hospitals tend to compete with urgent care clinics for patients, and when such a clinic sends a patient data request, hospitals aren’t motivated to oblige. A similar situation applies when sharing health data for research purposes because universities contest for funding.
Healthcare facilities that want to achieve the organizational level of data interoperability in healthcare will have to change their mindset and make data available to authorized parties, hoping that they will reciprocate. One can also expect new governmental policies supporting health data sharing. For example, Pew Charitable Trusts called on the Biden administration to address data exchange practices.
Absence of a standardized way to patient identification
Many medical facilities identify their patients using a name, birthdate, and social security number. But there is no unified format for this combination and not all clinics are using this as an identifier. And if there is no acceptable agreed-upon way to refer to a patient, there is no interoperability.
Many believe the universal patient identifier (UPI) offers a possible solution. UPI is a unique medical identification number, which is only valid in the context of healthcare data. So, if someone obtains a person’s UPI, they will not be able to access their financial information.
Approval of patient information sharing requests
Patient data safety and security are a priority for healthcare organizations. So, the process of request validation and approval needs to be regulated.
Make sure you have a solid consent management strategy in place and include situations where consent is obtained from someone responsible for the patient, such as parents or legal guardians giving consent on behalf of their children.
Diversity of existing interoperability standards
There is no unified healthcare data interoperability standard that the healthcare community can rely upon. It is unfortunate that the same tool that is supposed to streamline the process is hindering it. Michael Gagnon, Executive Director of HealtHIE Nevada, observes that there can be discrepancies even within the same standard, “If I see two C-CDAs from two different vendors, they look very different. It creates complexity. As an HIE, we have to figure out every single vendor’s differences and try to make something useful out of the information we can collect from that.”
Gagnon noted that the interoperability of electronic health records is also compromised, which poses a problem, “The EHR vendors don’t really want to come together and form one way of doing anything. That places burden on them to develop everything in a single way. They’d have to all agree on a standard, and what happens when you try to do that in healthcare is that you end up with something that’s really watered down.”
Even though you can’t directly influence this one, there is a workaround that you can use. You can take as an example the aforementioned mental health portal project from our portfolio. The solution we developed extracts patient data in a unified format from heterogeneous EHR systems through a set of APIs.
5 tips for improving data interoperability in healthcare
ITRex co-founder and CEO, Vitali Likhadzed, has provided five tips for healthcare organizations looking to enhance health data interoperability.
Tip #1: Ensure anonymity
Patient privacy is a priority for healthcare organizations. So, make sure no unauthorized third party can view patient information without complete anonymization. This applies to external vendors who help you develop and maintain applications, researchers who analyze data to study diseases, and anyone who isn’t directly involved in a particular patient’s care.
Tip #2: Establish a reliable patient consent process
To improve data interoperability in healthcare, develop a transparent consent protocol that will encourage user participation. Make sure you include policies that prohibit data access and transmission in ways that patients did not agree upon. To encourage patients to give consent, you can explain how their information can help develop innovative treatments and save lives. You can also partner with trusted organizations known for their patient advocating work.
Tip #3: Manage IT integration
When building a data ecosystem, consider these two aspects:
A data layer that provides APIs enabling your partners to integrate their data repositories. Make sure it’s compliant with the most common healthcare interoperability standards.
Tip #4: Use health information exchange (HIE) services
HIE facilitates moving clinical data among diverse health information systems while maintaining its meaning. You can connect to such a service if it is available to you. For example, New York has its own HIE tool, the Statewide Health Information Network for New York (SHIN-NY). All the city hospitals and many independent practitioners are connected. SHIN-NY continues to expand up to this day and improve patient care.
Tip #5: Move your data to the cloud if you haven’t done so already
Healthcare organizations are turning to the cloud for their data storage. Studies show that 82% of small practices located in urban areas have already adopted cloud-based EHRs, with 81% reporting their satisfaction with the system. Adopting the cloud has many benefits for the healthcare sector. Improving data interoperability in healthcare is one of them.
Benefit from your interoperable data to the fullest with ITRex
So, you are using an EHR system and you meet the minimal healthcare data interoperability requirements. Great! Now it’s time to structure the data, make sense of it, and draw valuable insights.
With the help of innovative technology, such as artificial intelligence, predictive analytics, and generative AI, you can use healthcare data to better treat patients, optimize internal operations, support your staff, and much more. Get in touch, and we will help you set up data management practices and build strong AI algorithms to address your healthcare needs. Also, if you are a health tech company building healthcare solutions, we can help you navigate interoperability.