We can all now confidently say the medical device industry is evolving quickly.
The major trigger is that healthcare is coming home, with the cloud as its vehicle. The cloud has enabled medical devices that were once confined to hospitals to be provided at home by connecting to cloud-based apps. This connection has enabled manufacturers to offer new standards in remote patient care with clinician oversight, outside of a healthcare facility, in the home and throughout the entire continuum of care.
However, simply connecting a legacy medical device to the cloud is just the first step in this evolution. This blog explores why ‘just connected’ devices are not enough to support the continuum of care era and what architecture is needed to accommodate the next generation of medical devices.
The issue here is similar to Tesla vs. the car industry. It took the car industry a decade to realize that putting an electric engine into a legacy car can not begin to scratch the surface of the potential [benefits] realized by Tesla when they re-architected the electric car from the ground up with cloud and modern software in mind.
Legacy medical devices were created to be used by professionals in a hospital setting. That means in a location that has space, trained users and available resources. In a home, devices are being used by untrained patients with little or no supervision, in a space that was not designed as a medical facility. These vastly different circumstances are the ‘fly in the ointment’ to the dream of just connecting the device to the cloud and selling the same thing as a home-use device.
Let’s look at some of the reasons why…
The edge hardware typically used in hospitals has a strong CPU accompanied by a large amount of memory to accommodate data capture, storage, and processing fast enough to provide a result on-time. This, in turn, makes the devices themselves quite large, expensive, and, in general, cumbersome. They were designed with these KPIs because hospital systems are willing and able to accommodate the cost and size of these large systems. But, remote care can’t support such big-ticket “un-gadgets”. Most patients do not have the money for such hospital-grade medical devices, and certainly, insurers will put many obstacles in the way before agreeing to cover them, even partially.
Data protection concerns move into a new realm in home-care. Hospitals limit access to devices to non-essential personnel and, certainly, to laypeople. While there is a chance that someone could break into a hospital and mess around with the machinery, the odds are slim. In home-care situations, there is an endless parade of potential interferers, from a patient's curious grandchild to professionals with real malicious intentions. (With that said, these same vulnerabilities of legacy medical device manufacturers have led to numerous cyber attacks in the hospital environment as well).
Most hospital-based devices require manual maintenance by trained users. These might be clinicians, but frequently are Healthcare Technology Managers (HTM) who are employed by hospitals to maintain their systems. While healthcare professionals can incorporate these routine maintenance tasks into their workday, getting patients, who may be technology-averse, to perform a maintenance operation, such as a manual software upgrade, is notoriously difficult. This significantly hampers the ability of manufacturers to upgrade their products, slowing the pace of innovation, and ultimately stagnating patient care.
To summarize: With the bulk of the computing and processing taking place on the edge hardware, connecting homecare devices to the cloud allows for remote patient oversight but does not answer any of the challenges we’ve discussed. These devices might be ‘smart’ but they are still heavy, expensive, hard to secure, and cumbersome to update.
Taking into account the above issues with the “just connected” medical device, we can presume the following straight-forward, necessary requirements: a reduction in the manufacturing cost of the device, develop a way to protect patient data in a zero trust environment, and a way to update the capabilities of the device with minimal to no patient involvement… Easy. :)
How can we bring down the manufacturing cost of the device? By making it smaller.
In order to do that, we need to consider what kind of HW elements are needed to comprise the home-use medical device. In general, there are patient-interacting elements, like sensors (and actuators in therapeutic devices); there may be some disposable elements; there’s some physical human interface (buttons, etc…); and there are the ‘brains’ of the device, which process the patient data.
The human interface is the part that the ‘just connected’ devices scaled down. Nevertheless, it is the data processing elements that are generally more expensive. However, this ‘brain’ of the medical device doesn’t actually need to continue ‘sitting’ in the edge hardware (and be further enlarged to adapt it for remote users). Instead, we can move, or ‘distribute’ the processing and data storage to the cloud, leaving only simple sensors and data collection on the edge hardware.
It turns out that ‘Cloud-Native’ architecture enables moving the bulk of the device functions to the cloud, overcoming all of the biggest obstructions to ‘scaling’ hospital devices to home care.
Just FYI, one BioT customer was able to cut the cost of their edge pregnancy monitoring hardware from $20,000 to just $200!
Here’s how it breaks down:
With the exception of minimal power and communication modules, the only part of the device that needs to be in the patient’s home is the part that is interacting with the patient’s body, either to collect raw data from the patient or to act on the patient.
All the ‘heavy lifting’ happens in the cloud. Raw data, biomarkers, and log files are stored with maximum cybersecurity guaranteed. Infinite cloud storage capacity eliminates the challenge of ‘big data’. Activation and authentication processes also take place in the cloud with optimal cybersecurity. Everything that makes this a medical device and not just a collection of sensors is happening in the cloud.
Edge hardware CPU can be minimal. All computing, signal processing, and analysis take place in the cloud. An almost infinite number of algorithms can be run on the raw data.
This opens the door for personalized healthcare to be more than just a pipedream. At this level, we’re not just talking about personalized workflows, but algorithms added on an individual basis. Suddenly, clinicians can prescribe an algorithm for a patient.
The potential impact of this architecture cannot be overstated. Costs are saved across many processes. Reduced CPU means that the edge hardware can be smaller, cheaper, and easier to use. However, the change in edge hardware is not the only factor affecting the overall cost. Anyone who’s worked in customer support will tell you that ‘easier to use’ equals massive savings. One support call can cost upwards of $10. This means that every patient who doesn't (need to) interact with a human service agent equals a real-world savings for manufacturers. If the user experience of the device remains similar to the hospital device, homecare patients would definitely need human support. Even the largest manufacturers would not be able to accommodate the support needs of just 10% of the existing patients.
When the device software is a cloud-based SaaS, we can also remove patient involvement in software updates. This means that we can be sure that the updates will actually happen. This opens the door for a dramatically faster pace of innovation. When new features can be added without any hardware changes or user interaction, then the speed of product development just depends on how fast and brilliant your R&D team members are at their jobs.
Bringing better medical care to patients at the push of a button is worthwhile, but let’s not forget the business side of things. It also creates new possibilities and options for payment strategies; both pay-per-use and subscription plans become feasible and enforceable when your device’s functionality is executing on the cloud.
Also, personal and corporate privacy concerns are cut because much less security is required on the edge hardware if only raw data is being stored at this level. With less IP or meaningful data being stored on the edge hardware, the concerns of security breaches are reduced significantly.
Talking about new income, a Cloud-Native device is almost always accompanied with a patient-facing app. Just like the cloud drives the edge device, it also drives the app. This opens another hard-to-overstate benefit: direct to consumer. For the first time in history, medical device vendors have a direct channel to engage with the patient. We’ve seen this channel being used for so many functions: from PROMs (patient questionnaire) collection and analytics to marketing campaigns, and much more.
Cloud-Native medical devices are not a pie-in-the-sky vision - There are companies already benefiting from making the switch. For example, the medical device company Neteera was able to actualize their groundbreaking contact-less patient monitoring system by distributing their device computing and deploying their proprietary AI on the BioT cloud. Neteera introduced a continuous, contactless solution for passive monitoring of patient vital signs and bio-data, which gave patients a more comfortable experience (because they weren’t hooked up to monitors), improved care, and helped reduce healthcare costs.
You can read the full case study here >>
With all the simplicity created by using a distributed architecture, creating one is a complex undertaking. You need an extensive set of capabilities in your toolkit to bring all the pieces together. The data infrastructure needed must cover connectivity requirements, address evolving cybersecurity concerns, be optimized for patient privacy and data protection, and be ready for all necessary data management.
If this didn’t add a significant team to your payroll, then creating the workflow tools will do that pretty quickly. A full healthcare workflow system requires providing workflow state management, real-time alerts, algorithmic plug-ins, device management, and relevant clinical insights to the benefit of your patients, clinicians, as well as internal users such as your data scientists, device service operations, business operations, and technical support.
All the above just gives you a functional backend; before you can go live, the user experience needs to be nailed down. That means creating a clinician workspace, manufacturer workspace for backend management, and extended UX integration.
However, before you run to HR to start the hiring process, there is an easier way.
BioT has built the ultimate platform for developing, executing, and evolving Cloud-Native medical devices. It doesn’t just do everything you need to distribute a medical device, it allows device developers to do it themselves without adding any extra manpower to their team.
Our set of comprehensive modules which is configurable through a No Code interface brings a ‘drag and drop’ approach to medical device integration. Think of this as the ‘Canva’ or ‘Wix’ of device connection. In less than a day, you instantly have a connected-care system with capabilities, such as secure algorithm execution in the cloud, device management, as well as provider-facing capabilities, like remote patient monitoring and EHR integration.
No, you did not read that wrong. It takes ONE DAY to get onto the BioT platform. If you don’t believe me, you are not alone:) We offer free trials for skeptics. I promise, no one has walked away unsatisfied.
Developing it yourself can take years of mainly boiler-plate coding. BioT cuts the effort to build the connected-care system to 5% of the DIY method. Even better, the ongoing maintenance costs become a fraction compared to the alternatives. We are freeing our customers from boiler-plate coding, DevOps, and professional services with unknown charges down the line. As a result, our customers move faster because they focus their precious R&D power only on truly differentiating efforts like building unique algorithms that are injected to BioT, unique HW, and UX.
Remote patient care is the future of healthcare. For medical device manufacturers, the only sustainable move forward is toward Cloud-Native medical devices. It is no longer a question of whether devices will become Cloud-Native, but when will that happen?
For manufacturers looking to lead the pack and get their devices “Cloud-Native” as seamlessly as possible, BioT provides the best possible solution. For a free, 1-week trial of the BioT system click here. (You won’t need more than a day to set it up, but we’ll give you a week anyway.)
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Last updated on:
August 1st , 2024 VER .17