Cloud-Connected Medical Devices

Cloud-Connected Medical Device Adoption Is On the Rise

The global healthcare cloud computing market is forecasted to grow from $53.8 billion in 2024 to $120.6 billion in 2029. One of the primary market drivers is the growing adoption of wearable devices, the Internet of Things (IoT), and big data analytics. Cloud-related deals in the medical device industry demonstrated a 65% increase in Q2 2024 compared to Q1. According to Buyer’s Guide by Medical Device Network, many leading medical device companies, including Philips, Medtronic, Siemens Healthineers, GE HealthCare, Johnson & Johnson, Roche, and BD, are increasingly investing in cloud-connected devices.

Cloud-Connected Medical Devices: The Fundamentals

Cloud-connected medical devices utilize a network connection, such as Wi-Fi, cellular, Bluetooth, BLE, or NB-IoT, to transmit health-related data (e.g., glucose levels, ECG readings) to and from cloud servers. Connection to the cloud offers centralized, scalable storage for a vast amount of health data while eliminating the need for costly on-premises infrastructure. This provides a reliable foundation for enabling remote patient monitoring and advanced healthcare analytics.

When discussing cloud computing in regard to medical devices, it is important to distinguish between two categories of devices: cloud-based and cloud-connected. Cloud-based devices rely heavily on continuous cloud connectivity for data processing and even running certain apps, whereas cloud-connected devices typically leverage local processing power and edge computing. This allows cloud-connected devices to remain functional even when the connection to the cloud is lost. The edge gateway can also pre-process data locally before sending it to the cloud, reducing latency and bandwidth usage.

Sample Architecture for a Cloud-Connected Medical Device Network

• Medical devices continuously capture real-time patient health metrics or deliver treatment and serve as the data source for downstream analytics.
• Firewall protects the system from unauthorized access and maintains data integrity during transfer between edge devices and the cloud.
• Edge computing gateway performs local data filtering and aggregation and acts as a temporary storage for data coming from medical devices. It then transmits preprocessed data to the cloud for long-term storage and data analysis. The gateway also relays control signals from the admin app and medical staff interface back to the devices for the dynamic adjustment of medical device settings.
• Control applications allow technicians and clinicians to remotely configure medical device settings (e.g., monitoring or treatment delivery settings for clinicians). More advanced solutions can transmit automatic commands to the devices (e.g., insulin dose changes, medication timing, and sampling frequency) based on real-time data or machine learning (ML) output coming through the edge gateway.
• Streaming data processor ingests raw, high-frequency health data streams, parses, transforms, and tags data for downstream use, and then routes it to storage or analytics modules.
• Data lake stores raw and semi-structured patient data in its original format (e.g., time-series signals, logs, JSON, audio, video).
• Data warehouse stores cleaned, structured patient data optimized for fast querying, business reporting, and clinical dashboards.
• Data analytics module processes structured patient data to uncover health trends, detect anomalies, and generate clinical insights (e.g., treatment response patterns).
• AI/ML engine powers the analytics module to identify subtle patterns in vitals, symptoms, or device performance data. It also predicts adverse health events and sends alerts about them to clinicians. Based on the predictions, it can give recommendations on adjusting device settings (e.g., monitoring or care delivery settings) or power automatic setting adjustment by sending commands to the control applications through the edge gateway.
• Software business logic coordinates workflows and routes the required data across the solution’s architecture components (e.g., sends analytics or ML insights to the appropriate modules like medical staff interface or control apps). It also transmits commands from the medical staff interface and admin app through the edge gateway to the control applications.
• Hospital Information System (HIS) represents the integrated suite of internal hospital systems (e.g., EHR, LIS, RIS) that connect the cloud backend with the end users. Patient health data (e.g., diagnosis, medication history, lab results) from hospital systems is sent to the analytics module to enhance the interpretation of device-generated data. Insights and readings from connected medical devices are routed into the hospital systems to maintain a unified, up-to-date patient record for clinical staff.
• Medical staff interface provides clinicians with real-time visibility into patient vitals. It also offers dashboards with historical analytics of data captured by medical devices to help physicians identify health trends and measure treatment efficacy. In the interface, clinicians can receive alerts on abnormal findings and remotely fine-tune medical device monitoring or treatment delivery parameters.
• Patient health app provides patients with access to the health data captured by medical devices and sends notifications about abnormal device readings. It can also offer communication tools to let patients reach care providers directly in the app.
• Admin app allows administrators to manage roles, permissions, and access levels for clinicians and patients. It also provides tools for monitoring device connectivity, managing software updates, and ensuring compliance with data-handling policies. Administrators and medical equipment technicians can use the admin app to send commands (e.g., reassigning a device, changing data retention settings) to the control applications.
• Security monitoring service continuously inspects system components and network behavior for threats.
• Audit logging service tracks all user interactions and system events to maintain traceability.
• Access management service handles identity verification, role-based access control, and session management to ensure that only authorized users access specific parts of the solution.
• Network protection service safeguards communication channels and data flows against breaches, intrusion attempts, and malware propagation across the infrastructure.

Why Develop Software for Cloud-Connected Medical Devices with ScienceSoft

• 12+ years of experience with cloud technologies.
• 13+ years in IoT.
• 19+ years in medical software engineering.
• Mature quality management and security management systems backed by ISO 13485, ISO 9001, and ISO 27001 certifications.
• An official partner of Microsoft and AWS.

Development Costs of Cloud-Connected Medical Device Software

Based on ScienceSoft’s experience in healthcare IT, developing a network of cloud-connected medical devices may cost around $200,000–$400,000+.