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IoT for Connected Medical Devices

Use Cases, IoMT Architecture, and Tech Stack

IoT for Connected Medical Devices - ScienceSoft

With decades-long experience in healthcare IT and IoT, ScienceSoft designs and develops IoT systems connecting medical devices.

Medical Device Software Development
IoT for Connected Medical Devices - ScienceSoft

IoT for Connected Medical Devices In Brief

Used for diagnostics, care delivery, disease management and prevention, IoT for connected medical devices reduces unnecessary clinic visits by 25% and hospital stay by 33%, and saves up to 30% of medical staff time for vitals entry.

Market Overview of IoT for Connected Medical Devices

The IoMT market for connected medical devices (stationary, wearable, implanted) is expected to reach $187.60 billion by 2028, growing at a CAGR of 29.5%. The market growth is spurred by IoT technology and advancements in medical devices connectivity (e.g., 5G, Bluetooth). Among adoption drivers, there are also increasing healthcare services costs, rising prevalence of chronic diseases, the growing focus on care quality and patient safety.

How IoT for Connected Medical Devices Works

Architecture

ScienceSoft’s experts recommend the following architecture for an IoT system connecting medical devices:

Architecture of an IoT system connecting medical devices - ScienceSoft

  • Connected medical devices (e.g., insulin pump, ECG patch) – collect patient data to transfer it to the cloud server or deliver therapy.
  • Gateways – filter, preprocess, and transmit patient data from the connected devices to the cloud; transmit control commands to connected medical devices.
  • Firewall – ensures secure transmission of collected data to the cloud server.
  • Streaming data processor – processes and transfers input data from the connected medical devices to the data lake and the control app.
  • Data lake stores patient data from the connected medical devices in the natural format.
  • Big data warehouse stores structured data from the connected medical devices for analysis.
  • Data analytics – used to interpret data from the connected medical devices (e.g, vitals, treatment delivery, medication intake), identify trends, and deliver insights (e.g., preliminary diagnosis, recommended treatment adjustments).
  • Machine learning module – used to identify patterns in patient symptoms, vitals, etc., and create ML models that power up the control application.
  • Control application – triggers actions in the connected medical devices.
  • Software business logic – lets patients and medical staff access data gathered by the connected medical devices, stores new configurations of medical devices and monitoring parameters, etc.
  • Medical staff interface – enables medical staff to get alerts on critical changes in the patient state, configure threshold monitoring parameters for alerts, adjust a treatment plan, view patient data analytics insights, etc.
  • Patient health app – enables patients to review connected devices’ data (e.g., heart rate, glucose levels, device state), get alerts on suspicious health parameters, initiate consultations with medical staff, etc.
  • Admin interface – enables viewing the list of current users (patients and medical staff) of the IoT system for connected medical devices, manage access to the system, etc.

Viable integrations

Electronic Health Records (EHR)

for an integrated view of patients’ medical history by the care team (e.g., chronic conditions, allergies), populating medical histories with new health data, etc.

Health Information Exchange system (HIE)

– enables a flow of patient data gathered by the connected medical devices between patients’ healthcare providers or different healthcare facilities.

Laboratory Information System (LIS)

– for facilitated access to the lab tests via the user apps (a patient health app, a medical staff app) of the IoMT system, etc.

Use Cases

Early symptom detection

A care team can access a patient’s vitals collected over a certain period by internet connected wearable medical devices (e.g., a heart monitor, a glucose monitor). The devices may record changes in the patient’s health, which assists in patient diagnosing.

Personalized care delivery

Cloud connected medical devices and IoT technology enable remote monitoring of patients after surgery, with chronic diseases (e.g., heart failure), receiving home care, etc. ML-generated care delivery models help optimize and automate patient treatment via the implantable/wearable medical devices (e.g., insulin treatment delivery according to patients’ eating and activity habits, medication dosage adjustment).

Point-of-Care Diagnostics (POCD)

Medical staff can use connected medical devices (e.g., a blood lactate analyzer, a CRP analyzer) to conduct diagnostic procedures at a patient's home or close to it (e.g., in non-specialized local labs). Test data is automatically transferred to the IoT server for analysis and storage, and the interpreted test results are recorded in EHR/LIS.

Treatment adherence monitoring

Analysis of data from therapeutic devices (e.g., an insulin pump, an inhaler) provides insights on medication intake and compliance with the treatment plan.

Hospital disease diagnostics

Stationary connected medical devices (X-ray, CT scanners, MRI scanners, ultrasound machines) transmit medical images to the IoT server for assessment. Based on image analytics, software makes a preliminary diagnosis to enable faster and more precise medical decision-making.

Predictive maintenance of connected devices and remote troubleshooting

IoT enables remote monitoring of the technical condition of connected medical devices (e.g., CT scanners) based on incoming technical data. Using ML algorithms, the system predicts the medical device malfunctions and helps plan maintenance schedules.

Key Features

In projects featuring connected medical devices, ScienceSoft ensures the following set of fundamental features and expands it further depending on the specific needs of our customers.

Collection and advanced analytics of patient data

Data generated by connected medical devices (e.g., wearables, connected imaging devices) is collected and analyzed against the pre-set parameters to aid in disease diagnosing and management.

Advanced analytics capabilities help get in-depth insights into patient health, predict disease progression, etc.

Collection, storage, and analytics of technical medical device data

Technical data gathered from connected medical devices (e.g., device battery state) gets analyzed against the normal parameters to identify inefficiencies and potentially dangerous malfunctions.

Alerts on abnormal parameters of connected medical devices

Medical staff (e.g., physicians, medical device technicians) and patients or their caregivers are automatically notified if vitals or device technical parameters are above or below the normal range. The alerts on potential health risks or device failure are sent to a medical staff app or a patient app.

Remote configuration of medical devices

Using collected data and ML-identified patterns in patients’ health data or device functioning, the care team can configure patient monitoring parameters, device settings, perform device check and maintenance remotely, etc.

Practical Example of IoT for Connected Medical Devices by ScienceSoft

Mobile Baby Care App for a Smart Baby Care System

Development of a Mobile Baby Care App for a Smart Baby Care System

Customer: A European distributor of globally acknowledged brands and owner of several healthcare product lines.

Solution: ScienceSoft has created from scratch an IoT-based mobile baby care app for parents that enables automated collection of baby data from branded smart devices (e.g., baby’s weight from scales, baby’s body temperature from a thermometer) and provides remote control of the baby care devices (e.g., to set formula milk maker to the needed amount of milk).

Want to Link Medical Devices Using an IoT System?

Relying on our ISO 13485 certified quality management system, ScienceSoft is ready to plan, develop, and implement an IoMT system for patient diagnostics, monitoring, and care delivery automation.

Technology Elements of an IoT System for Cloud Connected Medical Devices

Based on 17-year experience in healthcare IT, ScienceSoft offers opting for the following reliable techs to develop an IoT system for connected medical devices.

Device connectivity

Wi-Fi
5G
Bluetooth
Bluetooth Low Energy
NFC
Zigbee
NB-IOT
LoRaWAN

Cloud services

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Real-time data streaming

RabbitMQ
Kafka Streams
Apache Storm
Apache Flink
Spark Streaming
Amazon Kinesis Data Streams
Azure Event Hubs

Data lakes

Hadoop Distributed File System
Azure Data Lake

Databases / data storages

SQL

Microsoft SQL Server
MySQL
Oracle
PostgreSQL

NoSQL

Apache NiFi
MongoDB

Cloud databases / data storages

AWS

Amazon Relational Database Service
Amazon S3
Amazon DocumentDB
Amazon ElastiCache

Azure

Azure SQL Database
Azure Blob Storage

Google Cloud Platform

Google Cloud SQL
Google Cloud Datastore

IoT data analytics

AWS

AWS IoT Analytics

Azure

Azure Stream Analytics

Others

Apache Cassandra
Apache HBase
Hadoop
Apache Spark

Back end programming languages

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Front end programming languages

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Languages

HTML5
CSS

JavaScript frameworks

MeteorJS
Vue.js
Next.js
Ember.js

Mobile

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Challenges of IoT for Connected Medical Devices and How to Tackle Them

Challenge #1

To create a full-fledged IoT system for medical devices, a software product company or a medical device manufacturer requires the considerable expenses on a highly qualified tech team.

Solution

Solution

To help our customers pay off the expenses quicker, ScienceSoft creates a cost optimization plan, follows the iterative development approach, and focuses on core value-bringing functionality at first to for the IoMT system to yield ROI faster.

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Challenge #2:

Cloud connected medical devices evoke concerns about the security and privacy of transmitted data.

Solution

Solution

When developing software ecosystems for connected medical devices, ScienceSoft uses data anonymization, encryption (both “on the wire” and “at rest”), role-based access control, data access audit, and automatic logoff and elaborates on user permission settings. After the release of the IoMT system, we recommend regularly assessing system vulnerability and conducting penetration testing.

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Cost Factors

General cost drivers

  • The number and complexity of IoT software features for connected medical devices.

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  • The number of IoMT software user roles (e.g., patient, doctor, medical device technician, nurse).
  • The number of internet connected devices and their types.
  • Used medical device connectivity technologies (e.g., NFC, Wi-Fi, Bluetooth).
  • The integration capabilities ensuring interaction with required software (e.g., EHR).

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Additional cost drivers

  • Hardware costs (costs of medical devices, sensors for device connectivity, etc.).

Operational cost drivers

  • Usage of cloud services (e.g., for cloud data storage, data analytics).

IoT for Connected Medical Devices: ScienceSoft’s Consulting and Development Services

ScienceSoft brings in 11 years of experience in IoT and 17 years in healthcare to provide you with reliable IoT software connecting a range of medical devices.

IoT for connected medical devices: consulting

ScienceSoft’s team will:

  • Plan the functionality of IoT software connecting medical devices based on the analysis of your business needs, requirements, etc.
  • Design a high-level architecture of the IoT system.
  • Detail necessary integrations with medical IT systems and connected medical devices.
  • Create a business case, estimate the cost, ROI of the IoMT system.
  • Plan IoMT software delivery schedule and timelines.
  • Offer a step-by-step compliance plan (e.g., for HIPAA, HITECH, FDA regulatory requirements).
Go for IoMT consulting

Development of IoT systems for connected medical devices

Sciencesoft’s team will:

  • Conceptualize IoT software for connected medical devices based on your high-level or detailed requirements.
  • Draw up a comprehensive IoT software feature list.
  • Create a flexible and scalable IoMT software architecture and ensure integration with medical devices.
  • Develop and test the IoMT MVP with priority features.
  • Roll out other software functionality upon the agreed schedule (with 2-4 week iterations).
  • Ensure compliance of the IoT system for connected medical devices with required regulations (HIPAA, HITECH, etc.).
  • Provide IoMT software maintenance and evolution services (if required).
Go for IoMT development

About ScienceSoft

Headquartered in McKinney, TX, ScienceSoft provides professional healthcare software design and development services since 2005. We hold ISO 13485 certification and create medical IoT software for connected devices according to the requirements of the FDA and the Council of the European Union.