in Healthcare & Epidemiology
Radiaide: A Platform for Radiological Image Analysis and Screening
Radiaide is a cloud-based platform for fast and reliable medical image analysis. It supports multiple disease models and imaging modalities and can be used to instantly provide artificial intelligence based analysis of those images along. It is intended to be used in cases where imaging can be performed in remote regions with instant screening and subsequent validation by remote radiologists. The platform supports state-of-the-art security and privacy features along with an efficient picture archiving and communication system as well as annotative viewers for different images. A series of single-purpose Neural Network models are available to be trained and used. Radiologists and other stakeholders are presented with the model results which can be corrected by authorized personnel. Currently, the platform supports Chest X-Ray images for Tuberculosis screening.
In Transport & Logistics
Optimal Pickup and Dropoff of Employees by a Fleet
A large call center in a major Indian city needed to optimize the use of its fleet of vehicles to pick up and drop off its employees prior to and on completion of their shifts. Since the workforce usually had an absence rate of 25%, a static solution to the problem was not feasible. Along with cutting costs, employee satisfaction was a major concern. We mapped the situation to a mathematical optimization problem where the roster of the shift was to be partitioned into vehicles and the route of the vehicle depended on the order of employee dropoffs. The additional time an employee spends in the vehicle, beyond the time needed for a direct dropoff, was considered a cost in terms of employee satisfaction. The optimization problem was a combination of two NP-complete problems and as a result, was not amenable to a direct solution. Effective approximation was achieved by use of a genetic algorithm and the results decreased the cost to one-third while retaining employee satisfaction at current levels.
Estimated Time of Arrival of Public Transport Vehicles to Stops
An application, sponsored by the World Bank Group, was developed to track all public transport vehicles in the city of Kolkata and disseminate their locations to citizens. As part of the implementation, a model was developed to accurately estimate the time of arrival of vehicles to stops, providing citizens to use this information about their choice of travel mode. The model used existing information collected from the buses during their travels and incorporated additional information related to the day of the week, time of day, weather conditions as well as special occasions. The network of routes was divided into route segments and duration was estimated for each segment. The base model was subsequently enhanced by incorporating the time of the most recent vehicles to traverse the segment weighted by recency. The model was incorporated into the application and citizens have enjoyed the benefits of the estimates since 2017.
In Banking & Insurance
Scalable Platform to Provide Actionable Information for Health Insurance Decisions
We built a product that effectively integrates clinical, claims, psychographic, and physiologic data from a variety of sources into one platform and presents a holistic patient view through an intuitive dashboard that supports physician decision-making and care team action, improving financial, quality, and health outcomes. The product included a highly effective clinical data model and predictive algorithms that exceed the industry norms for predictive accuracy. Traditionally, health plans have used actuarial models that determine risk and future costs at the population level. Our platform used leading-edge machine learning techniques to create a dynamic risk score for each member, which improves its precision and personalization as well as enables health plans to act on the insights to prevent health deterioration and future costs.
In Document Intelligence
Letter of Protection Management System
We created a web-based medical practice management system for linking patients to doctors and attorneys, scheduling medical procedures, and tracking the resulting claims. The Application is optimized for personal injury business processes. It provides a robust system for tracking claims throughout their lifetime which includes receiving new orders, requesting/receiving documents between attorneys and physicians, scheduling patients for their procedures, and generating reports and invoices. The application is designed in a multi-tenant architecture so that it can onboard different companies onto it’s system and process their requests concurrently. The system’s Document system is also designed to be HIPAA compliant for better security. The backend of the application is deployed in Google Cloud Platform using cloud Datastore and cloud storage as its main Database,while it is powered by a Flask server deployed in Google Compute Engine. The frontend of the Application is designed in Vuetify which is hosted in google firebase hosting.
In Smart City
Estimated Time of Arrival of Public Transport Vehicles to Stops
An application, sponsored by the World Bank Group, was developed to track all public transport vehicles in the city of Kolkata and disseminate their locations to citizens. As part of the implementation, a model was developed to accurately estimate the time of arrival of vehicles to stops, providing citizens to use this information about their choice of travel mode. The model used existing information collected from the buses during their travels and incorporated additional information related to the day of the week, time of day, weather conditions as well as special occasions. The network of routes was divided into route segments and duration was estimated for each segment. The base model was subsequently enhanced by incorporating the time of the most recent vehicles to traverse the segment weighted by recency. The model was incorporated into the application and citizens have enjoyed the benefits of the estimates since 2017.