A NOUS Case-study on Claim Spending Analytics Solution

1. NOUS INFOSYSTEMS CMMi Level 5 SVC+SSD v1.3 ISO 9001:2008 ISO/IEC 27001:2013 L E V E R A G I N G I N T E L L E C T Claims Spending Analytics solution

2. NOUS INFOSYSTEMS SITUATION OVERVIEW • The client is a health informatics company focused on improving the quality and value of healthcare and other employee benefits • The target customers for the client are employers, insur ance bro kers and third party administrators (TPA). are key tools, technology choices and key features implemented: •Custom ETL tool developed using and .NET and SQL stored procedures to support multiple data sources. •Developed ASP.NET based web application to seamlessly access the CUBE through dynamic MDX queries. • Developed dynamic charting interface using ChartFX that can directly query SSAS cubes and provide end users with charts that can be manipulated interactively to specific need. CMMi Level 5 SVC+SSD v1.3 ISO 9001:2008 ISO/IEC 27001:2013 L E V E R A G I N G I N T E L L E C T The business challenge: •Required a sophisticated and flexible system with a Business Intelligence dash board and reporting solution having Data Visualization features that enables access to real-time actionable information on spends and performance. • Require a more reliable and robust system to locate outliers based on particular criteria with evidence based practical reports and other specific needs like identifying services with higher spending. •The existing Windows based application developed using FoxPro software, had limitations in handling large data nd issues with scalability to cater multiple clients requiring web based access. Batch Reporting: Built in Windows service to generate periodic re- ports in a batch for multiple employers and move to reporting area for review. Benefit Design Modeling: Developed intuitive user interface to define benefit plans, using a complex Excel based template model to create benefit design models. Chronic Disease Models:Developed windows services and user interface for generating chronic disease models for tracking / model- ing all major chronic diseases such asDiabetes, Depression, COPD, CHF, Asthma, Hypertension. THE NEED: The client required a SaaS enabled web based platform with the fol- lowing objectives: •Should contain generic data warehouse to hold eligibility and claims data together. •Should have a self-servicing capability with ability to generate and save ad- hoc analytics. •Support for multiple sources as well as multiple formats for eligibility and claims data. •Client specific skin for user interface. •Intuitive interface for benefit design and chronic • Predictive analytics. Predictive Modeling: Developed algorithms using SAS tool and im- plemented predictive risk modeling for all major diseases which included but not limited to Diabetes, Ischemic Heart Disease, CHF & Cardiomyopathy, Coronary Artery Disease, Osteoarthritis and Hyper- lipidemia. Domain - Health Care Platform – .NET, SQL Server Other key features delivered: • Supports around 10 million Claims. • Easy Drag and Drop of Dimensions/ Measures. • Supports around 150 Dimensions and more than 300 Measures for data analysis. • Reports in Charts, PDF and Excel Formats. Printable reports in PDF format. • Highly Interactive 2D/3D Charts with around 20 selectable chart types. • Client specific skin that dynamically changes based on the user logged in. • Broad analysis of integrated data that can be “sliced and diced” in an infinitely. Technologies – ASP.NET, MVC, C#, Ajax, JQuery, CSS Sprite, DynamicPDF, OWC, ChartFX, Windows/ Web Services, SQL Server, SSRS, SSAS, SSIS. NOUS SOLUTION Nous Infosystems developed a data warehouse solution, implement- ing the Ralf Kimball approach. The designed warehouse holds claims and eligibility data together in single Star Schema. Our solutions also provided the application framework including SaaS enabled web appli- cation, multiple Windows and web services. The objective of the web services is to provide greater flexibility to the user and enable design capabilities while permitting storage of ad-hoc analytics. The following Deployment – SaaS,Web-based

3. NOUS INFOSYSTEMS • Drill Down feature that helps the user to analyse the data at a different levels of summarization. The granularity of the data changes as drills down, essentially examining the data at different levels in the hierarchy. • Performance tracking feature to help identify controlling areas CMMi Level 5 SVC+SSD v1.3 ISO 9001:2008 ISO/IEC 27001:2013 Receives Raw data files ( Claims, Enrollment & Pharmacy ) L E V E R A G I N G I N T E L L E C T Populate Transformation Staging Import Raw files in to Raw tables through SSIS Packages Populate Dimension Tables Analyze Raw data Check Dimension & Claim ? Enrollment synchronization Application Layer Predictive Modelling Standard / Custom reports Batch Reporting Excel/PDF CDM / BDM Views Popluate Raw data into Historic table for backup Populate Rest of TS tables (Claims, Enrollment, Event, Episode etc) Internet Internet Populate formatted data into formatted_All tables Generate Pre Import Audit Report & Review the Report Application Layer Data warehouse Cubes Episode Event Building Populate all cross reference tables Production Data Warehouse Mining and Modeling Transformation Staging DB Bench- marking DB Popolate Unified ( Data warehouse ) DB Tagging Claims and Enrollment data Data is pushe into standard Data Format Eligibility, Claims, Labs and HRA source Data is cleaned, formatted and type exclustion rules and moved inti Raw integration and dimension population Eligibility Eligibility Eligibility Eligibility validatedand moved to formatted Eligibility Raw data is captured from Raw data is placed at FTP location QLY files into SQL tables in same (SDF) Stageby performing data Raw data is filtered based on structure as Raw files Run spModelCheck for data consistency Medical Medical Medical Medical ETL Process Layer Medical All stage <=5 >5 QLT Clamis Tagging % Pharmacy Pharmacy Pharmacy Pharmacy Pharmacy Process Cube Lab Lab Lab Lab Lab Analyse the data in the both Claims and Enrollment data Populate Claims and Enrollment data HRA HRA HRA HRA HRA Points Staging Application to the refreshed data High Level Data Refresh Flow: The ETL process was designed to handle Qly data refresh from multiple sources. The process includes among other things, episode, event building, provider population and employer population. QC the data to cinfirm on SDF Population CUSTOMER BENEFITS • Integration of claim data on an inpatient & outpatient basis • Broad analysis of integrated data that can be ‘sliced and diced’ infinitely. • Better timely and decision making on health insurance plans with reduced assumptions. • Periodic refreshing of the entire data base, which was not possible in the legacy application. • Better visibility on spends to optimize the procedure and reduce cost • Predictive models for performance scorecard and for risk analysis. Copyright© Nous Infosystems. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without written permission by Nous Infosystems. All other trademarks mentioned herein are the property of their respective owners. CONTACT US New Jersey, USA Tel: +1 732 985 9533 Brentford, UK Tel: +44 208 587 1411 Toronto, Canada Tel: +1 905 402 9943 Mainz, Germany Tel: +49 6131 28910 31 For more informtion, Please visit - www.nousinfosystems.com or mail us at info@nousinfo.com Sharjah, UAE Tel: +971 526264954 Bangalore, India Tel: +91 80 41939400 Coimbatore, India Tel: +91 422 3058800