Managing Multiple Data Sources for Effective Analytics

Transcription

1 Strategic Planning, J. Galimi Research Note 10 October 2003 Managing Multiple Data Sources for Effective Analytics Managing silos of information becomes critical as payers seek to share analysis across a heterogeneous application environment. Payers must develop short-term tactics to lay the foundation for long-term enterprisewide analytics. Core Topic Healthcare: Critical Healthcare Applications for Business Efficiency and Improvement Key Issue How will vendor applications enable healthcare organizations to respond to market demands for increased efficiency, customer service improvements and cost reductions? Strategic Planning Assumption By 2004, healthcare payer organizations that do not leverage data from operating systems will be unable to achieve the potential value required for greater customer satisfaction, efficiency and profitability (0.8 probability). Information management has been an ongoing "pain point" for healthcare payer organizations (payers). However, payers are now realizing that the number of silos of information is increasing with the ongoing implementation of tactical purchases (such as sales applications, customer service and support applications). Each department continues to "own" its data, and sharing information is not a common practice. Payers also are grappling with new reporting information requirements (for example, benefit choices for consumers or reminders to members for preventive screenings) and the need to perform various types of analysis, including: Financial Analysis: This enables payers to identify opportunities to improve the quality of care, while reducing costs. Financial data resides within the payers' core administrative systems. Typical types of financial analysis provide payers with an understanding of medical care budget analysis, profitability analysis, financial provider profiles, Health Plan Employer Data and Information Set (HEDIS) reporting, and fraud and abuse detection. Customer Analysis: This builds new insight into individual member/customer groups regarding issues such as loyalty, product or channel preferences. It creates deeper insights into the relationships that payers have with individual members/customers. It also reports on business operations, such as contact center activity, sales force performance and the results of marketing campaigns. Customer analysis data typically resides within the payer's customer relationship management (CRM) applications (such as sales, marketing and customer service). Patient Care Management (PCM) Analysis: This historically has been performed using claim and enrollment information and, as a result, has been financially oriented with little Gartner Reproduction of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice.

2 clinical value. Payers must leverage newly available information (such as risk assessment scores, laboratory and pharmacy information, vital signs, patient satisfaction) coming from disease management processes (see "Healthcare Payers Should Leverage Care Management Data"). As consumers become more engaged financially and clinically in healthcare, they will demand information that will help them make decisions. The healthcare payer market has limited understanding of consumer information requirements. Payers must begin the research to identify new data sources and to determine the needs of internal and external constituents. Perhaps the greatest analytic challenge is determining when to link care management information with other data sources, such as claims, enrollment and customer service. Each type of analysis requires payers to extract information from numerous data sources within various applications and perform analytics (see Figure 1). The importance of analytics as part of a payer's information management strategy has grown consistently as payers seek to reduce costs and make improvements in effectiveness to complement the efficiency gains they hope to get from operational systems. The information management challenge for payers is to effectively access the data, perform analysis and then deploy the results to extend their real-time environment for supporting more-timely business decisions. 10 October

3 Figure 1 A Typical Healthcare Payers' Data Sources Forecasting CRM (Customer Analysis) Product Preferences Customer Behavior on the Web Sales Marketing Personalization Source: Gartner Research (October 2003) Channel Preferences Event Triggers PCM (Historical Analysis) Provider Compliance With Treatment Guidelines Customer Profitability Customer Segmentation Treatment Patterns HealthRisk Assessments Patient Compliance to Disease Management Financial Profiles Core Administrative (Financial Analysis) Predictive Modeling Clinical Outcomes for Selected Populations HEDIS Fraud and Abuse Provider Profiling Payers should employ tactical and strategic approaches to perform effective enterprisewide analytics and meet the requirements of the information management model (see "Healthcare Payers' New Interest in Information Management"). In the Short Term There are various short-term tactical approaches: Establish programs to ensure data quality. Inaccuracies in data that is used in performing analysis can easily cause financial or operational performance analysis to present a less-than-complete or inaccurate picture of the enterprise. All too frequently, the data quality "radar" is limited to ensuring the accuracy of demographic data; however, a broader approach to identifying and addressing data quality issues is required. This involves a thorough analysis of all data sources. Undertaking a data quality "audit," with significant involvement of business and IT resources, is an effective way to identify inaccuracies and discrepancies. In addition, payers should assign a data "czar" who is accountable and has the authority to recommend changes to processes to improve data quality. Stabilize foundation applications (core administrative, CRM and PCM). Implementing effective foundation applications is 10 October

4 critical to set the stage for long-term information management and the ability to perform effective analytics. Payers must optimize the performance of current business processes and integrate foundation applications as a base for data sharing and integration. The value comes from the capability to leverage data sharing and integration to learn how the data can be used in conjunction with each application. Leverage business intelligence (BI) and data-mining tools for individual administrative, CRM or PCM applications. Choosing and implementing the right BI tools is only part of the formula for success. Most BI projects must integrate the requirements, data and priorities of the IS organization and multiple business units, which requires unique skills. Payers should establish BI competency centers. The BI competency center's role is to champion BI technologies and define standards, as well as the business alignment, project prioritization, management and skills issues associated with significant BI projects(see"bi Competency Center Is Core to BI Success"). Create a middleware architecture where data can be accessed and shared. Payers should consider application and data integration middleware technologies. Middleware is the software "glue" that helps independent programs and databases work together specifically for information integration (gathering data from many applications to support management functions). Integration brokers facilitate application-level interactions among programs in a distributed environment. Extraction/transformation/transport (ETT) tools can be used to select and move data from multiple source databases and transform it to a consistent format for storage in a target database. ETT tools enable business rules to be defined for syntax and semantic transformation. Payers can ease the burden of integration by insisting that all new applications have service-oriented architectures that enable other applications to assess their data and functionality through documented, request/reply application programming interfaces (APIs). Web services are the extension of these APIs to platform-independent interfaces, based on Extensible Markup Language and Internet transport protocols. Long-Term Strategic Analytic Options Payers must establish an enterprise logical data model that will be used to standardize data sharing and can be used to evaluate new system additions. The organization's long-term strategy has to start with an enterprise logical data model that ensures that the appropriate information is collected and effectively normalized to create the foundation for these analytic options: 10 October

5 Adopt the use of data marts. Individual data marts provide a better business value proposition because of quicker implementation time, lower cost and direct application support. Data marts can be used to pull data from the data warehouse, as well as incorporate additional data that is needed for a specific constituency, such as care management. Ensure that operating systems (for example, administrative, CRM and care management) have the ability to provide data to other applications and have flexible enough data models to populate data from other foreign applications. For example, payers can create new data repositories in which they can populate data from the PCM applications, as well as bring in claims and potentially CRM data on the patients that need to be managed. Implement an enterprisewide analytic suite. These suites offer the greatest leverage for the creation, deployment and use of enterprisewide analysis. However, enterprisewide analytic suites from vendors such as SAS Institute, Oracle and PeopleSoft are just entering the healthcare market. They are relatively immature from a healthcare market perspective and are a few years away from actually penetrating the healthcare industry. Payers should be aware that, historically, many vendors without healthcare experience have unsuccessfully tried to enter the healthcare industry. TriZetto Group (a healthcare-focused company) is in the process of developing an enterprisewide analytic package. This solution will not be available until later in Acronym Key API application programming interface BI business intelligence CRM customer relationship management ETT extraction/transformation/ transport HEDIS Health Plan Employer Data and Information Set PCM patient care management Bottom Line: To perform effective analytics, healthcare payer organizations must create short-term tactics, establish an enterprise logical data model strategy and analyze long-term analytic options. The key analytic component is the ability to combine information from the major processes claims transactions, customer service and care management. Data quality and data integration capabilities are also critical elements for long-term business analytic success. By 2004, healthcare payer organizations that do not leverage data from operating systems will be unable to achieve the potential value required for greater customer satisfaction, efficiency and profitability (0.8 probability). 10 October