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D. McIntosh

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    OS08 - Patients as True Partners in Care (ID 12)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Executive
    • Presentations: 1
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      OS08.04 - Innovation Through Predictive Analytics in the Non-Profit Sector (ID 55)

      D. McIntosh, Sierra Systems Group Ltd; Calgary/CA

      • Abstract
      • Slides

      Purpose/Objectives: The Calgary Drop-In and Rehab Centre (The DI) serves the homeless population in Calgary and much more: provides a ‘voice’ for the marginalized, provides access to affordable housing, counselling services and employment transition. The DI is undertaking a transformation of its information technology ecosystem. Fingerprint scanning serves as the current check-in function to identify active clients. This process is time consuming and can result in significant queues, particularly in cold weather. The DI may feed 300 – 400 clients over a lunch hour. Currently, it may take up to 30 seconds to check in a client, resulting in long line ups, potential bypassed check-in for those clients known to The DI staff and duplication of client profiles. Further, if The DI staff is unaware of the client’s presence, they cannot be helped. The DI was seeking a more efficient means of check-in and confirmation of clients on site. Integration of facial recognition technology with CRM allows many advantages – elimination of human error prone manual check-in, instant notification of client check-in triggering delivery of scheduled services such as counsellor appointments, health clinic appointments, medication review or employment services follow-up. In short, leveraging technology to support The DI as a resource hub for homelessness and poverty. This presentation will demonstrate aspects of emotional-IT bringing true value to the clients of The DI.

      Methodology/Approach: Validation and / testing of the following: - - Development of test algorithms to enhance accuracy of recognition - Ambient lighting, skin colour, presence of hats/baseball caps or sunglasses - System recognition of previously captured images with profile merging in CRM - Camera position - Wall colour in check in area - CRM configuration and image integration (e.g., impact of facial hair) - Lighting / hue of background objects - Still images vs video

      Finding/Results: - Facial recognition accuracy > 95% - Decrease in manual entries at Security - Integration with CRM facilitating services, tasking and staff workflow - Real time notification of client presence - Increased efficiency and effectiveness of the The DI’s intake and check in processes - Enhanced case management capability for counsellors and health resources

      Conclusion/Implications/Recommendations: - Reduced human error and manpower requirements at security check in - Elimination of duplicate client profiles - Ambient lighting and wall/environment colours impact resolution - Iterative test algorithms are required (video preferred) Office 365 helped The DI realize a modern IT workspace, increased security and staff collaboration and decreased manual workload centre staff. The use of state of the art biometrics using Azure to perform facial recognition linked to a Dynamics 365 client database was fundamental to achieving these outcomes. This translates in to not only significant cost savings, but also greatly enhances the level of service received by their clients primarily through speeding up the process to render assistance to individuals often in extremely stressful situations.

      140 Character Summary: Integrating facial recognition software at a community drop-in centre key in expediting security check-in and streamlining client processes

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