Digital health continues to be a work in progress, but there have been some great developments in recent years. For example, the introduction of electronic health records (EHR) has greatly improved the collection, access and analysis of medical data for both physicians and patients. Cloud-based information systems have been a major contributor in driving this EHR adoption in both large and small institutions. However, there remain many areas of healthcare data handling and record keeping where system improvements are desperately needed.
Guide: Best Practices for Point of Care Product Development
The COVID-19 pandemic has highlighted the current inefficiencies in easily managing data exchange and analysis. With the associated rapid growth of decentralized testing locations, the ability for quick data access and analysis is more important than ever before.
Leverage the power and capabilities of the cloud
The Point of Care (POC) diagnostic industry segment has a unique opportunity to improve how it operates and provides information in a rapid and efficient manner. Cloud-based systems can play a role by providing a ready-made infrastructure in the development and deployment of new device solutions.
Cloud software platforms such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud have created a powerful set of off-the-shelf components that device developers can leverage; they have been shown to be globally available, infinitely scalable, reliable, very secure and adaptable to specific needs.
Cloud services have the potential to truly revolutionize the POC device market. Leveraging these platforms provides significant benefits to manufacturers through the ability to:
Reduce device cost of goods sold (COGS)
Reduce software development costs
Create significant device and platform differentiation
1. Reduce device COGS by as much as 30%
Focus on a minimum instrument hardware configuration that executes the assay workflow
Everything peripheral to that, non-essential device elements, such as user management, data analysis and storage, Laboratory Information System (LIS) interfaces, are offloaded to the cloud and remove the need for complex user interfaces on the POC device.
By moving user interactions to the cloud, both the device footprint and cost can be reduced. For example, our estimates show this can transition a device from a stand-alone configuration with a $2,200 to $2,500 COGS range to a cloud-enabled version with $1,500 to $1,800 of associated COGs.
Identify those device software elements that could be characterized as “common” – more standardized or generally used.
Leverage preexisting cloud functionality to replace those common elements and serve as part of the device software componentry – data management, user management, analyses and secure storage.
Using this common IT infrastructure can pull expensive de novo software development out of the project as well as shorten timelines.
Continued reuse of this framework across the product portfolio and instrument next generations can also speed up future product development and enable a consistent user experience.
3. Create device and platform differentiation through increased data capabilities and functionality
Utilizing a consistent cloud-based IT framework across a company’s full spectrum of products allows for broad data aggregation and mining, following the general IT rule “ABCD – Always Be Collecting Data”.
Leveraging this data repository in conjunction with cloud computing capabilities can transition a device from a typically simple POC instrument to one with fairly sophisticated applications.
There are varying levels of device data collection and analysis options from which to choose – from basic device monitoring all the way to complete autonomy:
Monitoring, with one-way collection of performance data such as device condition, operation and usage. Collecting this type of information across different instruments can provide insights not only for field service personnel, but also for marketing and product development functions in driving product improvements.
Control, where there is both data collection and connectivity to centrally push out device updates including firmware upgrades, calibration information or a new assay menu.
Optimization, that builds on data collection combined with two-way communication. Analytical tools and machine learning algorithms can be applied to extract additional value from this infrastructure, such as enhanced product performance and improved service support. One example is predictive device diagnostics and field service using historic data collected across an entire fleet, including types of instrumentation issues, alerts and alarms and device service records, to drive proactive service and repairs.
Autonomy, taking optimization to the next level with autonomous product operation. While not yet mainstream for POC device fleets, the Apple Watch is an example of what an autonomous IVD product might look like in the near future. The Watch collects electrocardiogram (ECG) waveforms to identify certain heart rate conditions such as atrial fibrillation and provide alerts to the wearer and that person’s cardiologist. Such autonomous operation and self-coordination with other products and systems can be expected in POC devices going forward.
The time for cloud adoption is now
The widespread adoption of cloud-based platforms over the last five years into daily life has been a driver in developing solutions that address both diagnostic instrument utilization issues and risk mitigation needs.
Healthcare data security concerns around EHR and patient privacy, once a hurdle in medical device use, have been addressed over the course of adapting these systems for general consumer shopping and financial applications.
Amazon, Google and Microsoft along with other large cloud organizations have all created high degrees of security on a global scale. In addition, all communication between devices and the cloud can be fully encrypted and traceable via unique identifiers. The question of how to connect individual devices with the cloud has also been answered. There is a wide range of connectivity solutions available, from wired to wireless.
Making the transition
Shifting from a standalone POC device with relatively limited capabilities to a cloud-enabled platform opens up a whole range of new technical solutions, applications and instrument sophistication.
Cloud adoption results in better devices, lower costs, and increased product development efficiencies. It also can lead to improved service support and preventive maintenance as well as providing valuable market insights on how users are interfacing and working with the instrumentation.
With any major operational change, the key to a smooth transition lies in using a project management framework to guide the adoption of this new technology in a predictable and efficient manner. A great first step on this journey is a readiness assessment that looks at the goals of cloud implementation and benchmarks where the organization is today.
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Christian is the Global Director, Software Engineering leading the software, systems and usability teams at Invetech. Over the last 25 years, he’s held leadership positions at Roche, Novartis, Nanomix and Nanogen where he was involved in the commercialization of several novel products now helping patients in cancer diagnosis, hospital acquired infections and transfusion medicine. He has a master’s degree in System Sciences and Ph.D. in Electrical Engineering from UCLA.