Smart Medical Devices: Software Architecture, Features, Tech Stack
With 18 years of experience in healthcare software development and 12 years in IoT, ScienceSoft is fully equipped to advise on and implement IoT-based medical device software.
The Essence of Smart Medical Devices
Smart medical devices (e.g., ECG monitors, spirometers) are efficiently utilized for remote patient monitoring, care delivery, diagnosing, and more. The software powering these devices automates routine tasks, predicts disease risks and progression, and improves the efficiency of medical staff.
Smart Medical Devices Market Overview
The global smart medical devices market is expected to amount to $23.5 billion by 2027 at a CAGR of 23.5%. In 2021, this technology was adopted by 25% of US broadband households. The popularity of smart medical devices is based on the need to closely check such chronic diseases as diabetes, asthma, COPD, etc., and limited access to on-site medical monitoring due to the COVID-19 pandemic.
How IoT Software for Smart Medical Devices Works
Architecture
- Smart medical devices – to collect patient data or deliver therapy; equipped with connectivity capabilities (e.g., via Wi-Fi, NFC) and actuators to trigger a set of pre-programmed actions (e.g., for insulin pumps to adjust insulin dosage).
- Gateways – to filter, preprocess, and transfer data from the patients’ devices to the cloud, transmit control commands to the smart medical devices, etc.
- Cloud gateway – to compress data from smart medical devices and transmit it from gateways and the cloud IoT server in a secure and efficient way.
- Streaming data processor – to transmit the input data from smart medical devices to the data lake and control apps.
- Data lake – to store PHI data collected by smart medical devices in its natural format.
- Big data warehouse – to store structured data for further analysis of patient progress, symptoms, etc.
- Data analytics – in-depth analysis of collected patient health parameters or therapy device use patterns to spot trends and produce insights for further diagnosing, treatment or lifestyle adjustments, etc.
- Machine learning module and ML models – to recognize certain patterns in patient symptoms or vitals and create a model for a control application to improve patient care precision and efficiency (e.g., to improve heart failure therapy delivered by implantable cardiac defibrillator).
- Control application – to send commands to actuators installed in smart medical devices to trigger device actions.
- Software business logic – to provide medical device monitoring data to patients and doctors, record new device configurations, etc.
- Medical staff interface – to enable nurses, doctors, etc., to access the patient health monitoring data, get alerts on critical changes in it, configure threshold vitals parameters for alerts, adjust dosages of medications, view the insights on patient health state based on smart device data analysis, etc.
- Patient interface – to allow patients to view their health parameters (e.g., heart rate, glucose levels) and the state of a connected device via a mobile app, get alerts on suspicious health parameters, etc.
- Admin interface – to view the list of current software users (patients and medical staff), manage access to the system, etc.
- EHR integration – for an integrated view of patients’ medical history (chronic conditions, allergies, etc.), etc.
Medical staff can monitor the vitals of patients with chronic medical conditions (e.g., COPD, diabetes, cardiovascular diseases) provided by smart medical devices to spot slow or abrupt changes in the health state and adjust treatment routines.
Remote care delivery
Patients can get therapy using smart therapeutic devices (e.g., insulin pens, smart inhalers, smart gloves for motor functions rehabilitation). The medical staff gets data on therapeutic device use (e.g., asthma treatment delivery, insulin injection), metrics on patient performance during physical therapy sessions, the patient recovery process, etc.
Patient diagnostics
Based on the analysis of continuously collected patient health data (e.g., heart rate, blood pressure), doctors can make informed decisions on patient diagnosis and further treatment, assess risks of disease progression, etc.
Medication plan adherence monitoring
With data from devices like smart pill bottles or smart insulin pens, doctors can remotely track patients’ medication intake (the dosage, the missed medications, etc.), check the medication adherence and efficiency, tune recommended dosage. Patients can also get automated notifications to take medications in time.
Paramedics patient monitoring
When the ambulance is delivering a patient to the hospital, wireless patient monitoring solutions allow doctors in the hospital to track a patient’s condition in real time and plan the patient care (including emergency surgeries).
Sports medicine
During the training, athletes can use wearable medical devices (e.g., heart monitors) for sports physicians to get a real-time health state and health risks assessment, an athlete’s performance data, and issue recommendations on the training plan.
Real-time patient health data tracking and analytics
Patient health data from connected medical devices is automatically stored, processed and analyzed in real time using AI to identify trends and predict the course of the disease, detect hazardous symptoms at an early stage, etc. The generated insights are presented in dashboards and reports for the medical staff to track patients’ health state improvements, diagnose patients, adjust the treatment, medication efficiency or plan additional screenings.
Automated treatment delivery
Smart therapeutic devices (like an insulin pump) can automatically deliver treatment or indicate that treatment is needed (e.g., using a sound signal) if the IoT system identifies the need for treatment (e.g., if a connected glucose monitor shows a high level of blood glucose). Based on the collected diagnostic and historical data, the necessary medication dosage can be calculated.
Medication intake tracking
Analysis of medication intake data automatically uploaded from connected medical devices (e.g., insulin pump) and vitals from diagnostic medical devices provide insights on patient adherence to a treatment plan, medication dosage efficiency, etc.
Alerts to patients and doctors
The patients and doctors are automatically alerted when health parameters collected by medical devices are lower or higher than the set threshold parameters (indicating a potentially unsafe condition for a patient). The system also sends alerts on missed medication.
Configuration of monitoring and treatment delivery parameters
Using historical data on a patient’s health state and medical history, a doctor can configure monitoring parameters of smart medical devices to tune them to a specific patient and their medical condition. ML algorithms identify patterns in the patient’s health state, which can trigger automated adjustment of the treatment delivery.
Smart Health Devices in Practice: Projects by ScienceSoft
Development of a Mobile Baby Care App for a Smart Baby Care System
Customer: A European distributor of globally acknowledged brands, who owns several product lines in healthcare and other industries.
Solution: ScienceSoft created from scratch a mobile baby care app for parents that enables remote control of branded smart devices for baby care (e.g., to set formula milk maker to the needed amount of milk) and automatically collects baby data from smart devices (e.g., baby’s weight from scales, baby’s body temperature from thermometer).
Technology Elements
Device connectivity
Cloud services
Real-time data streaming
Data lakes
Databases / data storages
SQL
Our Microsoft SQL Server-based projects include a BI solution for 200 healthcare centers, the world’s largest PLM software, and an automated underwriting system for the global commercial insurance carrier.
We’ve implemented MySQL for Viber, an instant messenger with 1B+ users, and an award-winning remote patient monitoring software.
ScienceSoft's team has implemented Oracle for software products used by GSK and AstraZeneca. We’ve also delivered Oracle-based SCM platform for Auchan, a retail chain with 1,700 stores.
ScienceSoft has used PostgreSQL in an IoT fleet management solution that supports 2,000+ customers with 26,500+ IoT devices. We’ve also helped a fintech startup promptly launch a top-flight BNPL product based on PostgreSQL.
NoSQL
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
ScienceSoft has helped one of the top market research companies migrate its big data solution for advertising channel analysis to Apache Hive. Together with other improvements, this led tо 100x faster data processing.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
With ScienceSoft’s managed IT support for Apache NiFi, an American biotechnology corporation got 10x faster big data processing, and its software stability increased from 50% to 99%.
ScienceSoft used MongoDB-based warehouse for an IoT solution that processed 30K+ events/per second from 1M devices. We’ve also delivered MongoDB-based operations management software for a pharma manufacturer.
Cloud databases / data storages
AWS
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
Azure
Azure SQL Database is great for handling large volumes of data and varying database traffic: it easily scales up and down without any downtime or disruption to the applications. It also offers automatic backups and point-in-time recoveries to protect databases from accidental corruption or deletion.
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
Google Cloud Platform
We use Google Cloud Datastore to set up a highly scalable and cost-effective solution for storing and managing NoSQL data structures. This database can be easily integrated with other Google Cloud services (BigQuery, Kubernetes, and many more).
IoT data analytics
AWS
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
Azure
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
Others
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
By request of a leading market research company, we have built a Hadoop-based big data solution for monitoring and analyzing advertising channels in 10+ countries.
A large US-based jewelry manufacturer and retailer relies on ETL pipelines built by ScienceSoft’s Spark developers.
Back-end programming languages
Practice
19 years
Projects
200+
Workforce
60+
Our .NET developers can build sustainable and high-performing apps up to 2x faster due to outstanding .NET proficiency and high productivity.
Practice
25 years
Projects
110+
Workforce
40+
ScienceSoft's Java developers build secure, resilient and efficient cloud-native and cloud-only software of any complexity and successfully modernize legacy software solutions.
Practice
10 years
Projects
50+
Workforce
30
ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.
Practice
10 years
Workforce
100
ScienceSoft delivers cloud-native, real-time web and mobile apps, web servers, and custom APIs ~1.5–2x faster than other software developers.
Practice
16 years
Projects
170
Workforce
55
ScienceSoft's PHP developers helped to build Viber. Their recent projects: an IoT fleet management solution used by 2,000+ corporate clients and an award-winning remote patient monitoring solution.
Practice
4 years
ScienceSoft's developers use Go to build robust cloud-native, microservices-based applications that leverage advanced techs — IoT, big data, AI, ML, blockchain.
Front-end programming languages
Languages
Practice
21 years
Projects
2,200+
Workforce
50+
ScienceSoft uses JavaScript’s versatile ecosystem of frameworks to create dynamic and interactive user experience in web and mobile apps.
JavaScript frameworks
Practice
13 years
Workforce
100+
ScienceSoft leverages code reusability Angular is notable for to create large-scale apps. We chose Angular for a banking app with 3M+ users.
Workforce
80+
ScienceSoft achieves 20–50% faster React development and 50–90% fewer front-end performance issues due to smart implementation of reusable components and strict adherence to coding best practices.
ScienceSoft uses Meteor for rapid full-stack development of web, mobile and desktop apps.
By using a lightweight Vue framework, ScienceSoft creates high-performant apps with real-time rendering.
With Next.js, ScienceSoft creates SEO-friendly apps and achieves the fastest performance for apps with decoupled architecture.
When working with Ember.js, ScienceSoft creates reusable components to speed up development and avoid code redundancy.
Mobile
Practice
16 years
Projects
150+
Workforce
50+
ScienceSoft’s achieves 20–50% cost reduction for iOS projects due to excellent self-management and Agile skills of the team. The quality is never compromised — our iOS apps are highly rated.
Practice
14 years
Projects
200+
Workforce
50+
There are award-winning Android apps in ScienceSoft’s portfolio. Among the most prominent projects is the 5-year-long development of Viber, a messaging and VoIP app for 1.8B users.
Practice
11 years
Projects
85+
Workforce
10+
ScienceSoft cuts the cost of mobile projects twice by building functional and user-friendly cross-platform apps with Xamarin.
ScienceSoft uses Cordova to create cross-platform apps and avoid high project costs that may come with native mobile development.
ScienceSoft takes the best from native mobile and web apps and creates the ultimate user experience in PWA.
Practice
8 years
Projects
300+
ScienceSoft reduces up to 50% of project costs and time by creating cross-platform apps that run smoothly on web, Android and iOS.
ScienceSoft will save you from double or even triple expenses associated with platform-specific coding by creating cross-platform apps in Flutter.
With Ionic, ScienceSoft creates a single app codebase for web and mobile platforms and thus expands the audience of created apps to billions of users at the best cost.
Challenge #1
There is a risk of legal penalties if the personal health information (PHI) collected and transmitted by smart medical devices gets compromised.
Challenge #2
Improper usage of smart medical devices can affect the accuracy of remote patient monitoring data.
Investments and Key Cost Drivers
General investment size factors
- Scope and complexity of medical device software features.
- A number of smart device types connected to IoT software.
- Device connectivity technologies used (e.g., NFC, Wi-Fi, Bluetooth).
- The number of software user roles (e.g., patient, doctor, nurse).
Additional investment size factors
- Costs of smart medical devices.
Operational costs
- Cloud services usage (e.g., cloud data storage and analytics).
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From ScienceSoft’s experience, an average cost of healthcare software connected with smart medical devices starts from $200,000 to $250,000+ (for a solution comprising a cloud and user application for one type of medical devices). |
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IoT Software for Smart Medical Devices: Consulting and Development by ScienceSoft
Being ISO 13485 certified, ScienceSoft is well-equipped to power up smart medical devices with robust medical IoT software in line with the requirements of the FDA and the Council of the European Union.
IoT software for smart medical devices: consulting services
What we do:
- Outline the functionality of future IoT software for smart medical devices based on your business needs.
- Create a high-level architecture design (featuring APIs for the required integrations with medical systems) and detail integrations with chosen smart medical devices (e.g., via Wi-Fi, Bluetooth).
- Estimate the cost, ROI, and software delivery timelines.
- Provide an action plan for compliance with HIPAA, GDPR, MDR, and FDA regulatory requirements.
Development of IoT software for smart medical devices
What we do:
- Conceptualize IoT software for smart medical devices based on your high-level or detailed requirements.
- Create a comprehensive software feature list.
- Plan a flexible and scalable software architecture and integration with medical devices.
- Develop the MVP with priority features in 3-6 months and roll out other functionality upon the agreed schedule.
- Ensure IoT software compliance with required regulations (HIPAA, GDPR, etc.).
- Maintain software and work on its evolution (if required).
About ScienceSoft
ScienceSoft is a US-headquartered international IT consulting and software development company with 12 years of experience in IoT and 18 years in healthcare software development. Holding ISO 13485 certification, we create software for smart medical devices according to the requirements of the FDA and the Council of the European Union.