Big Data and AI implementation in the Healthcare
Our contribution to cognitive Healthcarey
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
Improves patient experience by providing accurate assessment of patient opinions about different aspects of the hospitals
ReportInsight
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
42% reduction in time to analyze the notes
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
Income from Quality Performance Measure (QPM) incentives awarded on a PMPM (Per Member Per Month) basis
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
Improves cost prediction accuracy by 3% above the traditional underwriting model
Claim Adjudication Analytics
Claim Adjudication helps payers to address the issues of over-payment and pay as per contract guidelines. Our solution uses natural language processing to understand the context of the item description and classify each item description into a charge type (e.g. laboratory changes, monitoring charges etc.) using Machine Learning algorithms to accurately categorize diverse healthcare costs in itemized billing and automate the adjudication process
40% increase in assessment speed for claim adjudication
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
Detects an increase in billing amount