- Cares@Home
- Dot Net Architecture
- Microsoft SQL (Database)
- Cares4Wound
- iOS
- Cares4Caregiver
- Android
- iOS
- Our system consists of 1) Infrastructure, 2) Platform, and 3) Software
- We have our own cloud server for trial/pilot purposes, but we only support on-premise server for deployment except special cases (as of Nov 2019)
- Security is provided based on 3 tiers (presentation, application, database)
- Isolates presentation layer (What the user’s see) from application and database later (backend processing). Prevents hackers modifying backend codes.
- Allows easier scalability for different clients.
- Security on each level: System, Software (Encryption), User Level (User Access Control), Usage Level (Auto Log-out after a set timing of inactiveness)
- We have done Quality Management System (QMS) and conduct User Acceptance Test (UAT) for any client who is going to deploy the system.
- Test driven development to reduce occurrences of bugs and glitches.
- Support team available during office hours.
- We will handle issues based on the priority: Low – Mid – High
- Major updates based on our project timeline. Major updates may be considered as different products which requires another purchase of the license.
- Minor updates (bug fixes) depending on impact on user.
- CARES@HOME
- As long as the server is hosted on-premise of the client, there will be down-time.
- It may take from 2 to 24 hours to check and identify the problem.
- Mobile apps (CARES4WOUNDS, CARES4CAREGIVERS)
- Current trial partners using our iPads, our iPads are set up with Mobile Device Management. Admin will remotely push updates to the devices.
- Enterprise distribution is an option.
- Web services that creates an interface for mobile apps and server to communicate and transfer data between each other.
- Auto Measurement and Auto Tissue classification software (Wound bed, Epithelization, Granulation, Slough, Necrosis)
- Machine learning has been trained with 1000+ images.
- Annotation has been done by wound nurses.
- Validation has been done with 30 live patients and 160+ wound images by St Luke’s Hospital.
- Partnership with I-Square-R (I2R) repurposing their machine learning software used by National Eye Center – Glaucoma detection.
- Source anonymized wound images from our partners to be used as training images for machine learning.
- Estimated HSA Class B approval – 3rd Q of 2020
- Our company is certified according to ISO13485 standards.
- QMS audit is done by TUV SUD
- BBraun, Sheng Kung Hui (HK), St Luke’s Hospital (starting), Ren Ci
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