Our Services
Arthmia specializes in developing customized AI models for medical devices, enhancing diagnostic accuracy and supporting personalized treatment plans. Their IoMT services ensure seamless integration of devices with healthcare systems, improving patient outcomes and operational efficiency. By prioritizing data security with robust encryption, access control, and regulatory compliance, Arthmia maintains the integrity and confidentiality of sensitive patient information. For medical device manufacturers, these advancements lead to higher product quality, reduced downtime, and increased customer satisfaction, ultimately boosting their market competitiveness.
IoT Device Connection
Hardware Connection
Arithmia IoT Platform supports various medical devices including wearable devices, such as smartwatches, fitness trackers, and smart clothing. and EKG ,Smart medical equipment like glucose monitors, heart rate monitors, and blood pressure monitors, and implantable such as pacemakers, insulin pumps, and Smart Connected equipment’s such as infusion pumps, ventilators, and diagnostic equipments.
Device Management
Onboarding, monitoring, updating, and securing devices to ensure their optimal performance and reliability on mass scale. It includes tasks like remote updates, performance tracking, and threat detection to maintain the efficiency and security of connected devices.
Connectivity
Connectivity protocols such as MQTT, CoAP, HTTP/HTTPS, BLE, Zigbee, LoRaWAN, and Wi-Fi enable seamless communication between IoT devices and healthcare networks.
Configuration Management
Configuration management (CM) is essential for maintaining consistency and tracking changes in hardware, software, and systems. It helps monitoring and logging to ensure secure and efficient management of devices, minimizes the risk of errors, and improves overall efficiency.
Monitoring Alerts & Preventative Care Analytics
Rule Engine
Rule engine automates decision-making by applying predefined rules to patient data, ensuring consistent and accurate application of clinical guidelines and protocols. It integrates with Electronic Health Records (EHRs) and other healthcare systems, enabling real-time data analysis and improving patient care through timely and informed decisions.
Integrations
Integrating medical devices with systems like Electronic Health Records (EHRs), telemedicine platforms, and hospital information systems to enhance healthcare delivery. These integrations ensure seamless data flow, improved care coordination, and secure data management. They also support scalable solutions through cloud platforms, facilitating remote patient monitoring and operational efficiency.
Data Collection
To reliably collect data on a large scale and configure data processing pipelines, integrate devices with sensors, use scalable cloud storage, and implement tools like Apache NiFi for data ingestion. Automate workflows with Apache Airflow, ensure data security with encryption, and optimize performance regularly for scalability.
Predictive Analytics
Predictive analytics helps to foresee potential health issues and recommend preventive measures, ultimately reducing hospital readmissions and healthcare costs.
Customized AI Models for Data Analytics
Diagnostic AI Models
Our Diagnostic AI models use machine learning and deep learning algorithms to analyse medical data, such as patient records, to detect diseases early and accurately. These models enhance diagnostic accuracy, reduce human error, and support personalized treatment plans by identifying patterns and correlations in complex data.
Personalized Treatment AI Models
Personalized treatment AI models leverage patient data, including generic information, medical history, and lifestyle factors, to tailor treatment plans to individual patients. These models analyse vast datasets to predict treatment outcomes, recommend the most effective therapies, and adjust treatment plans based on patient responses.
Mental Health Assessment AI Models
AI models for mental health assessment analyse data from various sources, such as speech, text, and behavioural patterns, to detect mental health issues like depression and anxiety. These models use natural language processing (NLP) and machine learning to identify language patterns and predict mental health risks, enabling early interventions and personalized treatment plans.
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Revolutionizing patient care with real-time data monitoring, analytics, and AI-driven solutions.