One of client was in the process of consolidating data and building analytical platform for Revenue Cycle Management and we were approached to provide a solution to extract data from their Cerner system.

With the help of our healthcare expertise and knowledge on Cerner Millennium system we provided a solution to extract and load operational, financial data to data lake using CCL (Cerner command language) and this project was executed in a record time of 3 weeks to meet customer product roadmap.

Chest X -Ray Classification uses combination of Deep Learning and Visual Analytics/Image Processing to recognize patterns of Chest X-Ray Diagnosis which in turn can serve as a learning feed for them so that they can highlight the abnormalities in new incoming real-time Chest X-Ray Digital images

We used ~12k images to train our networked model to achieve a reliable validated accuracy of ~95 % in detection of accurate diagnosis from digital Chest X-Ray images. We also hierarchically classify our severity position of image in three categories. For the initial purposes, we have used 13 diagnosis abnormalities and “No-findings” for the training purpose.