Software Layer Approach All projects that involved IoT-like systems such as travel devices and automation frameworks and real-time analytics began with data pipelines and real-time processing and user-centric interfaces. The health tech implementation would follow the same design principles as I would organize device data to enter a protected cloud environment which transforms the information into meaningful insights that users can view through mobile/web applications built with privacy-focused design. The system would treat FHIR APIs alongside EHR systems and secure messaging protocols as core components. We have implemented AI to generate individualized recommendations for wearables and remote monitoring through our system. Scaling Challenges The transition from prototype to scale becomes challenging when systems need to handle complex real-world scenarios. Our cloud infrastructure had to undergo major re-architecting when user behavior edge cases overpowered our stateless micro services. The health tech environment would experience intensified challenges from compliance requirements because HIPAA and MDR demand traceability and encryption and audit-ready logs at every stage. The process of expansion requires organizations to consider how they will handle data residency and maintain uptime SLAs while supporting platform functionality across different network conditions especially for remote-care tools in areas with low bandwidth. Build vs. Outsource We constructed the fundamental platform components ourselves during the initial development phase to maintain operational control and quality standards. We chose to outsource HIPAA-compliant chat frameworks together with analytics dashboards to established partners while maintaining core platform development in-house. The mixed approach helped us achieve quick development while upholding compliance standards and user trust. I would now incorporate security and compliance architecture at the very start of the design phase. Final Thought: Building IoT-enabled health products for scalability requires more than writing clean code because it demands designs which foster trust and maintain performance alongside continuous patient care. My advice for a post-Series A team today focuses on designing scalable solutions from the start while making compliance a product element and keeping in mind the human aspects of healthcare user experience.
President & CTO at PureLogics
Answered 9 months ago
At PureLogics, we've partnered with healthtech companies building IoT-enabled medical devices like wearables and remote monitoring systems. Our approach to the software layer includes cloud-native data platforms, mobile applications, and seamless device integrations—all developed in-house to ensure full control over quality, security, and compliance. One of the biggest technical challenges we've seen during scaling is maintaining platform stability while meeting stringent regulatory demands. Cloud architecture decisions, proactive compliance strategies, and automated testing frameworks have been critical to sustaining performance as usage grows. We've also supported clients through complex hospital integrations and data interoperability using HL7/FHIR standards. From the start, we've heavily invested in building core software in-house. It allowed us to move faster, stay HIPAA-compliant, and adapt quickly to regulatory shifts. If there's one thing we'd do differently, it's bringing in regulatory consultants earlier during development. That simple shift accelerated our clients' MDR/HIPAA readiness and reduced compliance-related rework. We understand the complexity of going from prototype to commercial rollout in healthcare. That's why we continue to work closely with founders and product teams ready to scale with confidence in a highly regulated space.
Handling the software for our IoT medical devices, especially with everything needing to perfectly sync, is a challenging yet fulfilling task. Initially, we did everything in-house, developing custom apps and integrations to ensure seamless functionality and high-level data processing. Over time, as the product evolved and the demands increased, we shifted towards a hybrid model. This involves using an in-house team for core development and integrating third-party solutions for non-critical elements to enhance scalability and reliability. Scaling these devices comes with its own set of hurdles—compliance being a major one. The transition from a prototype to full-scale production challenged our initial infrastructure, primarily around maintaining platform stability and ensuring compliance with regulations like HIPAA. For compliance, it’s not just about ticking off a checklist; it's about continuing to adapt to new and evolving standards. A big lesson was in our decision on when to build versus outsource parts of our software stack. Initially, we tried to keep everything in-house thinking it would be more economical and cohesive. Looking back, partnering earlier with experienced firms would have smoothed out scaling significantly, particularly for cloud architecture and integrations that were less familiar to our team. Always consider where your real expertise lies and don't shy away from external help when it's justified to enhance scalability and compliance adherence.
Healthtech companies developing IoT-enabled medical devices face unique challenges and considerations as they scale their ideas from prototypes to commercial solutions. When handling the software layer, it's crucial to design an architecture that supports real-time data processing and robust mobile app interfaces, while ensuring seamless integrations with existing healthcare systems. Opting for cloud-based solutions can provide the necessary scalability and storage capacity for handling large volumes of patient data. Custom-built platforms often offer optimized performance for specific device functionalities, but they require a skilled development team that's well-versed in both IoT and healthcare regulations. One of the pressing challenges these companies encounter is maintaining platform stability as user demand grows. The software must be able to handle simultaneous data streams from multiple devices while ensuring that any system updates or maintenance do not disrupt service. This need for reliability is further compounded by the demand for adherence to compliance standards like HIPAA in the U.S. or the MDR in Europe. Compliance not only requires secure data handling and patient privacy protections but also influences how data analytics can be leveraged to provide actionable health insights. As companies navigate these waters, they must remain vigilant about cybersecurity threats and ensure their systems are resilient against potential breaches. Deciding whether to build or outsource the software stack is a pivotal decision. Building in-house allows for components to be tailored specifically to the device's requirements, offering advantages in terms of customization and integration. However, this path demands considerable initial investment in development and continuous updates. Outsourcing can accelerate development and bring in external expertise but may limit control over the software's evolution. Companies often opt to build core elements in-house for proprietary advantage while outsourcing certain features or integrations to focus on their strengths. Reflecting back, some might find value in establishing stronger partnerships with specialists in healthcare software early on to bridge any gaps in expertise and enhance system robustness.