Data is rapidly driving more decisions in healthcare day-by-day.
Relevant information is helping clinicians to provide improved diagnosis and treatment for their patients, along with effective analysis.
As data is more widely adopted, professionals are also exploring how to use it to increase efficiency, keep costs to a minimum, and deliver better outcomes.
HealthTechs can now access a significant opportunity.
A combination of computing improvements, wireless technology, miniaturisation, and AI are working together to drive innovation thanks to connected devices.
But just as HealthTechs believe that their device or solution has delivered real clinical benefits, data is also what’s holding them back.
The information generated by the device is either incompatible with the other systems they use, doesn’t fit seamlessly in their workflows, or may even fail to meet standards and regulations.
Data can be immensely valuable, but integration and interoperability is a vital first step for clinicians.
Spreadsheets have their place, but they don’t meet the many requirements of clinical real-world practice.
It’s difficult for HealthTechs to present data to practitioners in the right moment and the right format, with difficulties also in sharing data with other devices and accessing relevant information to ensure effectiveness.
The aspects to consider in a data strategy
In terms of considerations, HealthTechs must assess whether data should be collected over a set period or whether the device acts as a type of point diagnostic during a test scenario.
This is essential to driving the form that the data takes and how it should be managed.
Do they need to plug into the ecosystems of other device manufacturers, such as smartphone companies and digital assistants? Or the cloud hyperscalers’ applications and ecosystems?
HealthTechs may want to be free of ties, but the more neutrality a company tries to build into its solution, the harder it is to manage and integrate data.
It is a conundrum.
HealthTechs know that being tied-in leads to higher costs if they have to rewrite their solutions for another vendor, and they also should know that retrofitting for vendors can be expensive.
High-quality data is an absolute necessity when it comes to accurate electronic patient records (EPR), both for the provider and the organisation using it.
This demand changes to accommodate structured data from a new diagnostic test.
The picture is further complicated because most health organisations are uneasy about insertion of third-party data into their clinical record. From a legal and clinical standpoint there is a very high threshold to surmount.
HealthTechs also need to focus on regulation and compliance when leveraging data to design their solution.
In today’s healthcare systems, data must comply with all-important healthcare data standards including FHIR, together with data privacy considerations around GDPR in the UK and Europe, and HIPAA in the US.
Device regulations also mandate certain qualities in data such as a robust audit trail and strong governance around access to raw data and results.
If a HealthTech is using machine learning (ML) and/or artificial intelligence (AI) it must ensure the training data is compliant, reliable, and free of bias.
Compliance with standards in the UK isn’t quite so straightforward when it comes to the NHS.
Each trust or organisation must meet unique requirements in data governance. Failure to do so can be catastrophic in the event of a serious incident.
A HealthTech must have the ability to identify what data has been presented to whom, when they viewed it or used it, and its provenance.
It’s a complex requirement, but one that HealthTechs can meet from the outset with a relevant data management platform.
There are alternatives to this technology, but they each come with their own drawbacks.
Open source is always tempting but risks non-compliance, is insecure, lacks robustness, and will almost certainly inflict higher costs as a HealthTech scales.
Another improvised approach is to use PDFs in an electronic mailbox, but this will not work at scale either.
Instead of injecting structured data into the clinical record, some HealthTechs may seek to provide an in-context link from an operational system into their application, to give a specialised view of the data collected.
This is likely to require negotiation and could be costly.
Access to unified data
By integrating a unified data platform early on into their development, HealthTechs are able to tap into a world of expert partners who can best advise on the process of integrating data into most healthcare organisation workflows.
Unlike other solutions, this guidance is driven by technical data science capabilities.
The HealthTechs that are able to traverse interoperability and workflow integrations from the beginning will better meet their business objectives and build revenue.
The fastest and most efficient route to ensuring that healthcare applications, devices, or solutions provide lasting value is via a data platform approach, which will help drive an effective data strategy from the get-go.