Digital & Innovation

Interoperate or perish: Why healthcare startups must work with ‘legacy’ data standards

Health Industry Hub | August 25, 2021 |
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Digital & Innovation: Startup companies are vitally important to the transformation of the healthcare industry, which has tended to lag behind most other industries in adopting digital strategies.

Young professionals are attracted to the excitement and pace of working at startups, and the ability to try new things. This gives new companies an edge in developing innovative technology solutions with the power to transform how healthcare is provided.

Healthcare startups have their challenges, though. It’s estimated that 90% of healthtech companies fail in their first five years. One of the biggest hurdles they face is the need for interoperability. Interoperability – or exchanging information between different healthcare systems in a meaningful way – has been central to healthcare data management ever since specialist healthcare applications first appeared.

Exchanging data in a completely unambiguous way is important across the healthcare continuum, especially when it comes to caring for patients and for analysing the evidence for new medical treatments. This means that if healthcare startups want to develop new solutions and successfully bring them to market, they will need to build their interoperability capabilities and work with a range of data standards.

Alphabet soup of interoperability standards

When it comes to the development and promulgation of interoperability standards, the healthcare industry has a veritable alphabet soup of active bodies, including Health Level Seven (HL7®), ASTM International, DICOM (Digital Imaging and Communications in Medicine) and IHE (Integrating the Healthcare Enterprise).

Many of the standards these bodies control – and their underlying data models and messaging protocols – date back a decade or more. It would be a mistake, however, to dismiss them as “legacy” standards, or to assume they are inefficient or about to be superseded by more modern standards like HL7 FHIR (Fast Healthcare Interoperability Resources), a concept first put forward and led in Australia by Graham Grieve.

This is because a large number of healthcare applications rely on these standards to acquire the data they need from other applications and systems. This data could reside in administrative and departmental systems, or medical devices, in addition to their own data repositories. And these applications typically have long lifecycles and are not due for immediate replacement.

Integration with data infrastructures

For innovative new solutions to succeed, they have to exchange data with existing healthcare systems. That means being able to communicate using their preferred mechanisms or standards. It also means that healthcare startups need to have a strategy for how their solutions fit into and interact with the data infrastructures of the healthcare providers they want to work with.

FHIR, HL7’s newest clinical data standard, uses RESTful Application Programming Interfaces (APIs), which are also the basis of the ecommerce and social media applications we use on a daily basis. That allows FHIR to be used in a range of different contexts, from system-to-system messaging to consuming innovative new services. It’s likely that most interoperability will be done via FHIR at some point in the future. For example, if healthcare providers want to integrate with new Apple Health products, they will have use FHIR.

However, right now, supporting earlier data standards and APIs, and even interfaces to systems that don’t use standards, make it easier for a new application or solution to fit into the existing healthcare data ecosystem. In fact, demonstrating this sort of flexibility could be a key competitive advantage for startup companies, and help them acquire more customers.

Data management for AI and machine learning

Data management capabilities are also important. Clinicians and other carers are increasingly looking to leverage healthcare data to understand what is the best treatment for an individual patient, for example. That could be through access to a complete set of patient data, or through applying artificial intelligence (AI) algorithms trained using large volumes of comparable data. These processes are greatly simplified if data is consistent and flows easily between systems rather than being scattered throughout disparate data silos.

Isolating data within a single system limits the clinical and financial value that can be derived from it. The solutions that healthcare startups develop need to be able to acquire data from multiple sources and share it with other systems, and that requires interoperability. By making data fluid, they will maximise both the benefits to patients and the value that healthcare providers can achieve. Of course, organisations must have information governance in place to ensure that patient data is only shared subject to their consent and for the purposes for which it is provided. The healthcare systems that share patient data must also have the functionality to support the organisation’s information governance policy in respect of privacy.

The use of AI and machine learning is growing. The ability to take an almost countless number of data points into account when making decisions is already proving valuable to clinicians. Yet the success of AI and machine learning initiatives is highly dependent on the quality of the datasets they use. This in turn relies on the ability to acquire and easily exchange healthcare data, making it a key limiting factor in what can currently be achieved. By developing innovative solutions with strong data management and interoperability capabilities, healthcare startups will help to overcome barriers to AI and machine learning and unlock even more value from healthcare data.

Interoperability cannot be an afterthought

Healthcare start-ups must overcome many barriers. Some, like a lack of understanding of interoperability requirements, or a lack of skills or technology solutions to implement them, may not be immediately obvious. Start-ups need to be aware that they cannot easily anticipate what data infrastructures they will be required to align with. The easiest way to align with the different data infrastructures they encounter across many providers is to incorporate a platform with comprehensive healthcare interoperability capabilities into their solutions.

At the end of the day, healthcare providers will prioritise interoperability with existing systems when choosing new solutions. Regardless of how innovative they are, if new solutions require customers to do the heavy lifting of data integration, their adoption will probably be limited. If they are to succeed – and hopefully survive and thrive well beyond the five-year mark – interoperability cannot afford to be an afterthought that startups only consider at the end of the development process.

About the author

Andrew Aho is Regional Director, Data Platforms for InterSystems, an innovative data technology provider dedicated to helping customers solve the most critical information challenges.

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