Accurate claim records can positively impact reserve requirements/loss ratio. This leads to premiums that are more reflective of your actual experience, than a conservative estimate. Knowledge of, and support for your claims history is powerful information when it comes to negotiating/pricing fair and competitive insurance premiums for your business.
Analytics gives organizations the knowledge and confidence to increase deductibles and retention limits.
How do you price a product without knowing production costs? The cost of the actual insurance product may not be known for years, until all the claims are paid. Insurance product pricing includes using tools such as predictive analytical models, impact analysis and what-if scenario simulation to increase rating accuracy and improve profits.
Digitization is driving an unprecedented shift toward lower cost structures and greater agility in the insurance industry.
Healthcare is a sector in which data is generated constantly with outstanding growth. There are several challenges in processing patient records which deals with variety of structured and unstructured format. Big Data Analytics in Healthcare deals with sensitive patient driven information mostly in unstructured format comprising of prescriptions, reports, data from imaging system, etc.
Big Data Analytics overcomes the challenges with enhanced efficiency in fetching and storing of data.
The hospital charge description master, or hospital chargemaster, is at the heart of the healthcare revenue cycle, serving as the hospital’s starting point for billing patients and payers.
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.
This Natural Language Understanding and Advanced Machine Learning solution helpes to accurately categorize diverse healthcare and hospital domain lingo in itemized billing and comes out with the claim adjudication automation process.
Analyze data at all stages in the claims cycle to help the users in making the right decision, at the right time, for the right party.
Using ML Algorithms, the solution allows our healthcare providers to identify eligible people who have not availed medical screening. The algorithms incorporate business rules, adhering to HEDIS measures and guidelines.
Increase revenue from medical screenings & generate income from quality performance measure incentives.
Machine learning techniques are being used to automatically categorize patient feedback as either positive, neutral or negative and relate them to separate business aspects.
The solution helps healthcare providers to evaluate and optimize new patient care strategies to reduce hospital readmission rates.
This solution utilizes image processing components that use Machine Learning are employed to generate relevant metadata for each ECG image. Big Data technology is used for quick analysis and retrieval of required data.
Significntly increase efficiency of processing huge volumes of image metadata at scale.
The solution uses Computer Vision technologies to read scanned prescription notes and categorizese the insights into diseases, procedures, and drugs, using Deep Learning Models and medical ontologies.
Significantly improve efficiency without increasing the number of employees.
