The healthcare world is awash in data—medical device data, electronic patient records, public-health surveillance data, clinical data, wearable data, and more. But more data doesn’t always mean more value. The ability to transform data into meaningful and actionable information is defining how MedTech companies can gain a competitive advantage.
Digital health represents a massive business opportunity for MedTech companies. The global digital health market is projected to reach USD 995 billion by 2032, growing at a CAGR of over 13% from 2024 to 2032. To tap into this large and growing market, MedTech teams must overcome data management challenges including interoperability barriers, scalability constraints, and data privacy concerns that hinder development efforts and impede revenues.
So is your organization prepared? Here are five essential questions every MedTech team should ask as they evaluate their digital health and AI readiness.
Question 1: Does Your Digital Health Strategy Include Interoperability, Data Governance, and Analytics?
Digital health solutions rely on real-time access from a variety of systems to manage and analyze confidential healthcare data. Your digital health strategy must address interoperability, data governance, and analytics from the start.
Modern development platforms can help connect to disparate systems and break down interoperability barriers. Leading digital health development platforms provide built-in connectors to streamline application development and integration efforts, and accelerate customer deployments to diverse healthcare systems. Improve governance and ensure compliance with data privacy regulations like HIPAA, HITECH, and GDPR by tightly controlling access to PHI and encrypting data-at-rest and data-in-transit.
You can safeguard protected health information (PHI) and streamline integration efforts with the right foundation.
Question 2: Can you Seamlessly Connect Device and Clinical Data From EHR Systems and Other Healthcare Information Systems?
Integration issues can delay product launches, hamper customer deployments, and impair business results. MedTech companies need a strategy for connecting systems and working across healthcare protocols and data formats for fast, reliable exchange across ecosystems.
Look for development platforms that support healthcare standards like HL7® FHIR®, HL7® v2, C-CDA, and IHE. Built-in data transformations for common healthcare data standards and graphical user interfaces to help you simplify integration efforts and free up technical resources to work on other tasks.
Question 3: Can you Easily Consume and Aggregate Data in Any Format in Real-Time, at Scale?
Digital health is increasingly real-time, and it’s almost always high-volume. Whether you’re monitoring vitals, delivering remote diagnostics, or triggering alerts based on behavioral patterns, you need a platform that can process diverse data streams at speed and scale.
Don’t compromise performance and reliability required to meet stringent digital health price-performance and scalability requirements. Ensure your system can easily aggregate and act upon large, diverse datasets, in real-time, in a scalable manner.
Question 4: Do you Have One Information System That Can Supply Unified Data From all Sources?
One of the biggest obstacles to advanced analytics and AI is fragmented data architecture. When your digital health application data is scattered across multiple systems and stored in different formats, the data is hard to trust and even harder to use.
You can create cohesive, unified data records that improve data quality and consistency, and provide a consolidated, holistic view of digital health information. Unified data records enable better decisions, enhanced efficiency, and richer analytics. They also lay the foundation for advanced analytics, artificial intelligence (AI), and machine learning (ML).
Question 5: Is Your Data AI-Ready?
Many MedTech companies are looking to AI and ML to fuel business growth. AI can be used to automate medical diagnoses, personalize treatment plans, accelerate drug discoveries, assist surgeons—the possibilities are only limited by one’s imagination.
AI has the potential to transform healthcare, but data management and integration challenges can impede AI development efforts, stall healthcare AI projects, and hamper AI investment returns. Many AI applications leverage data from diverse sources such as EHR systems, smart medical devices, hospital scheduling and billing systems, and public health databases. Data redundancies, inconsistencies, and gaps can impact data quality and integrity, and impair healthcare AI initiatives.
Ready to Lead?
Whether you’re building your first digital health app or scaling a global AI deployment, your success will depend on your data infrastructure.
What separates AI projects that thrive from those that stall? Leaders like Sol Rashidi (who’s overseen over 200 AI deployments) emphasize one key insight: Data readiness determines AI success.
Want to learn what separates success from failure in AI for healthcare? Watch the full talk from InterSystems READY 2025