The UK healthtech sector appears to be in fine fettle. Worth £36 billion, its over 3,000 startups and scaleups enjoyed a record amount of venture capital investment last year – £2.33 billion. Within the UK’s thriving tech sector as a whole, it is second only to the Fintechs in aggregate size, and now employs 132,000 people.
Just as impressively, healthtech startups have been at the forefront of innovation during the pandemic, helping provide new digital tools to connect patients with clinical staff. However, despite this rosy picture, healthtech startups are facing a distinct set of challenges around data when it comes to scaling up and achieving real growth.
They may be leading the way in terms of innovation and be ready for initial launches, but they often find they are incapable of allowing data to flow quickly and reliably between their devices and solutions and the health systems of care providers. This is a critical problem. Companies offering advances in diagnosis and treatment must integrate their own data with the masses of clinical data that already exist and learn how to share with other systems and organisations. Interoperability is essential, but in startups it is too often overlooked.
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Startups hardly get off the ground if organisations cannot share their data
Without the necessary interoperability, it is a struggle for startups to move from the pilot stage, especially in the NHS. In England and Wales, for example, the NHS has been very effective in introducing mandatory data standards HL7 V2 and FHIR governing interoperability, to which any applications must adhere. Although start-up owners and creators have exciting ideas and robust business plans, they often fail to fully consider the challenges of scaling and interoperability that such standards present.
This is perhaps understandable when a business is still embryonic, but scaling would be much easier if they had started with a thought-through data strategy early on, preparing for the requirement for data to be secure, available and compliant.
The contemporary world of healthcare is increasingly driven by data, and any solution incapable of full interoperability is likely to fail. Healthcare providers are relying on healthtech companies to resolve the data challenges themselves. What they want are improved patient outcomes and reduced costs, and solutions that optimise the healthcare professional’s work.
While many startups possess the energy and vision to entrepreneurial talent, they lack the experience of healthcare culture, data, and standards. To deliver on expectations, startups need to set off with healthcare organisations’ requirements at the forefront of their strategy. This requires developers to have an intimate understanding of healthcare systems, interoperability, and regulatory compliance.
The complex health data standards landscape
Anyone entering the healthcare domain has to understand the standards landscape. The very active standards bodies, including HL7, ASTM, DICOM, and IHE, know the importance of both data models and associated message patterns. Startups need to become familiar with these requirements and build compliance into their solutions.
One mistake that new healthtech businesses commonly make is to dismiss current configurations as legacy, inefficient, or part of a failed project. This way of thinking is fundamentally flawed given that most applications depend to some extent on data collected from other applications. For a solution to be successful, it is necessary to pull data from multiple data sources, some of which will use legacy standards and others which will be operating to new requirements.
The latest HL7 standard, Fast Healthcare Interoperability Resources (FHIR), for example, is specifically designed to be RESTful and provide a simple framework for both system-to-system implementations and application developers. All future interoperability projects will need to support FHIR. Apple Health, for example, uses FHIR data to power integration with healthcare providers. But a new product offering that includes the capability to support older standards, other APIs and non-standard interfaces, would increase the ability to fit into any architecture.
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The single platform strategy is the solution
Achieving this level of interoperability quickly and cost-effectively is beyond most startups, given the resource and time constraints. There is a solution, however. Building applications on a third-party data platform which encompasses interoperability, the ability to orchestrate multiple interfaces, high-speed data storage, and “in-flight” data transformation, offers transformational advantages. This approach relieves data scientists of the burdens of cleaning and preparing data.
A data strategy built on an established, specialised health integration platform allows startups to address a far greater number of interoperability use cases, particularly when combined with the ability to provide real-time analytics such as insight into usage patterns and performance.
Additionally, a unified platform eliminates the need to integrate multiple technologies and toolsets. This not only reduces the amount of code that needs to be developed and tested but can also significantly reduce the time-to-market. In-house developers are free to focus solely on evolving their product or service offering. This is important when evidence suggests data scientists at startups spend as little as a fifth of their time on analysis.
Given the pace at which wireless technology, miniaturisation and computing power are evolving, and the increasing acceptance of digital health solutions by clinicians and patients alike, there are likely to be “first mover advantages” for startups that are ready to roll because they have solved the basic and fundamental requirement for data interoperability.
Global consultants Deloitte have identified interoperability as “arguably the biggest challenge” in the medical technology sphere, including compliance with “various national and international standards and protocols around the exchange and use of data”. It is clear that whatever their achievements in diagnosis and treatment, startups need to set out with a fully-effective data strategy that does not leave them incapable of working in full harmony with existing and future healthcare systems.
Given the immense pressures on a small and fast-evolving team in a startup, the surest way to achieve that is through a unified data platform built to achieve interoperability and full integration with the health systems and standards.