1. Cares@Home
    1. Dot Net Architecture
    2. Microsoft SQL (Database)
  2. Cares4Wound
    1. iOS
  3. Cares4Caregiver
    1. Android
    2. iOS
  1. Our system consists of 1) Infrastructure, 2) Platform, and 3) Software
  2. 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)
  1. Security is provided based on 3 tiers (presentation, application, database)
    1. Isolates presentation layer (What the user’s see) from application and database later (backend processing). Prevents hackers modifying backend codes.
    2. Allows easier scalability for different clients.
  2. Security on each level: System, Software (Encryption), User Level (User Access Control), Usage Level (Auto Log-out after 999 seconds of inactiveness)
  3. We have done Quality Management System (QMS) and conduct User Acceptance Test (UAT) for any client who is going to deploy the system.
  1. Test driven development to reduce occurrences of bugs and glitches.
  2. Support team available during office hours.
  3. We will handle issues based on the priority: Low – Mid – High
  1. Major updates based on our project timeline. Major updates may be considered as different products which requires another purchase of the license.
  2. Minor updates (bug fixes) depending on impact on user.
  3. CARES@HOME
    1. As long as the server is hosted on-premise of the client, there will be down-time.
    2. It may take from 2 to 24 hours to check and identify the problem.
  4. Mobile apps (CARES4WOUNDS, CARES4CAREGIVERS)
    1. Current trial partners using our iPads, our iPads are set up with Mobile Device Management. Admin will remotely push updates to the devices.
    2. Enterprise distribution is an option.
  1. Web services that creates an interface for mobile apps and server to communicate and transfer data between each other.
  1. Auto Measurement and Auto Tissue classification software (Wound bed, Epithelization, Granulation, Slough, Necrosis)
  2. Machine learning has been trained with 1000+ images.
  3. Annotation has been done by wound nurses.
  4. Validation has been done with 30 live patients and 160+ wound images by St Luke’s Hospital.
  5. Partnership with I-Square-R (I2R) repurposing their machine learning software used by National Eye Center – Glaucoma detection.
  6. Source anonymized wound images from our partners to be used as training images for machine learning.
  7. Estimated HSA Class B approval – 3rd Q of 2020
  1. Our company is certified according to ISO13485 standards.
  2. QMS audit is done by TUV SUD
  1. BBraun, Sheng Kung Hui (HK), St Luke’s Hospital (starting), Ren Ci

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