21 Nov 2022
A touch of artificial intelligence to help manage Alzheimer’s
Executive Summary
Our client’s organization was founded by members of the Berkeley AI Research Lab with families of patients affected with Dementia. The objective was to reduce the frequency and impact of falls which has been the leading cause of hospitalization in Alzheimer’s care. Their existing platform was unable to scale and monitor at a facility level. It also lacked the ability to tag the recordings of fall videos to allow the facilities team to prevent future incidents.
Problem Statement
Our client was seeking to enhance their existing platform and reduce the complexity of the features present in it. They wanted to scale their application through deployment at multiple facilities and integrate it with complementary platforms in the space. Their existing platform lacked a triggering mechanism which could serve as a warning before the patient could get entangled in any untoward incident. It was also of great necessity that the application possessed the capability to work along with the AI module by capturing and analyzing the video when an incident occurs and triggering an alarm to alert the concerned authority.
HashedIn’s Proposal
HashedIn suggested to perform an assessment on their code base and develop a roadmap comprising of technological initiatives which can be executed. Based on the assessment results, HashedIn proposed to re-architect and refactor their cloud services by moving it from Django and Angular 1.x. HashedIn intended to separate UI from backend with APIs and use industry best practices along with proprietary frameworks to restructure code in a short period.
Business Requirements
Our client needed their existing AI based patient monitoring application to be refactored to make it deployable across multiple centres and have a central monitoring system that allows any potential fall to be quickly tagged and alert the facilities to quickly address the patients who are in need of immediate attention. The videos of potential fall needs to be tagged for study and preventive measure would be taken by the facilities team to ensure patient safety
Impact and involvement of stakeholders
Facility Manager – Will be able to take prompt actions based on the tagged videos
Alarm Taggers – They will be involved in tagging the video captured by the AI modules, which are located in Old age homes
Facilities for patients suffering from mental disorders
Solution Approach
Our Solution Structure
We introduced queuing mechanism using RabbitMQ to improve the alarm tag activity, store the alarms, and distribute the same among alarm taggers based on their availability.
RabbitMQ module helps the alarm taggers to get a summary of available videos waiting to be tagged, this helps prioritize their futuristic actions.
The database was optimized and indexing was also introduced to improve the performance of the existing APIs in fetching data from database.
We also proposed and implemented a record keeping of various activities that can be used in MIS reporting.
We migrated the UI code from Angular 1 to Angular 7 to introduce new features.
Earlier UI code of the client was based on Angular 1 and DJango which used server side rendering which made the application slow. We helped migrate the application from Angular 1 to Angular 7 and used the REST APIs to make the application faster & efficient.
We built a web interface through which the alarm tagger can toggle between different alarms.
Our Web interface enabled alarm taggers to monitor list of pending alarms of different types from the same screen and prioritize their tagging activity accordingly.
We introduced a functionality which triggered a notification when an alarm tagger’s session expires, a notification is also triggered if they are inactive and requeue the alarm with a higher priority.
Technology Stack
Back end: Django(Python), RabbitMQ
Front end: AngularJS, Angular 7
Business Outcomes
  • This solution helped the concerned facilities facing shortage of staff, manage it effectively.
  • The solution helped reduce the number of falls by 40% and avoid injuries caused to patients suffering from mental disabilities.
  • Our solution helped reduce the time taken in video tagging activity and reduced the frequency of emergency room visits by 70%.
  • Our solution helped improve the efficiency of the video tagging activity by reducing the number of clicks involved between different events (reduced from 3 clicks task to a single click task to ease the process).
  • We also provided the manager to track the efficiency and productivity of the alarm taggers.