Big Data and AI implementation in the Healthcare
Big Data and AI implementation in the Healthcare
Our contribution to cognitive Healthcare
Real-time Healthcare Big data Analytics
Healthcare payers and providers traditionally use multiple databases to create reports and gain insights which create a challenge to consolidate data on-demand. This can be addressed by building a data lake using Hadoop to integrate data across multiple sources and in multiple formats like DICOM image to support dynamic reporting.
Improve Underwriting Process
Underwriting group insurance policies is a tough task as multiple members have multiple factors for premium determination. This solution involves improving the pricing and customer service for its group insurance customers by predicting medical cost through a statistical model using input data like Claims data, Enrolment Data, Prescription Data and Member Data.
Report Insight
Manually reading scanned physician notes is a non-scalable and inefficient business model to determine risk conditions. The solution helps in reading the scanned prescription notes using Computer Vision and Deep Learning Models and categorizing the insights into disease, procedure, body organ, and drug-using a medical ontology.
ECG Data Analysis
Providers face huge challenges in retrieving and analyzing massive volumes of ECG image metadata from fragmented data storage. The solution by Abzooba migrated data to an integrated data lake based on Hadoop and generated relevant metadata for each ECG image using Machine Learning and image processing components.
Chargemaster Analytics
The chargemaster analytics solution quantifies and verifies whether payments to providers are within permissible limits as laid down by Contract Language and Charge Description Master. The solution uses advanced statistical and machine learning algorithms – both in the discrete and continuous domains – to detect billing amount increases with high confidence.
Actuarial Informatics
Medical screenings are conducted for early detection of potential health disorders and diseases to ensure risk mitigation. The solution, utilizing Machine Learning algorithms, pinpoints eligible individuals who have not availed medical screening facilities. Business rules, adhering to HEDIS measures and guidelines have been incorporated into the algorithms.
Sentiment Analytics
Analyzing patient sentiments from feedback received is imperative for providers to improve the overall patient experience. Machine learning techniques are being used to automatically categorize patient feedback as either positive, neutral or negative and relate them to separate business aspects.