Healthcare
White Paper
Extracting Value from Healthcare Data: An Analysis of Industry Leading Data Models
About the Authors
Pankaj Sinha Pratice Head - Information Management, Big Data & Analytics, Tata Consultancy Services Pankaj has 20+ years of experience in Information Technology with expertise in Strategy, Planning, Architecture and implementation of business systems across various industry verticals and technology platforms. Over the years he has led several strategic and large scale engagements in Information Management, CRM, Enterprise Application Development & Transformation areas. Currently, Pankaj heads up the Information Management, Big Data & Analytics Practice for TCS Insurance & Healthcare unit. Anupam Kumar Technology Excellence Group, Healthcare Anupam Kumar is part of the Technology Excellence Group of the Healthcare and Insurance Industry Solutions Unit at Tata Consultancy Services (TCS) where he focuses on analytics and data management. Anupam has a Ph.D in Statistics and over 18 years of industry experience, which spans technology, solution design, and consulting across the healthcare, insurance, and banking domains. He has worked with leading client organizations in the U.S. and Europe. Anupam also conceptualizes strategic solutions and platforms for healthcare and insurance customers including payers, integrated payer-providers, specialty providers, and pharmacy benefit management companies. Anantha Ramakrishnan Solution Architect, Healthcare As part of the Technology Excellence Group, Anantha Ramakrishnan is responsible for information management solutions and data architecture design for healthcare clients in North America. He has 20 years of experience in defining and implementing comprehensive, large-scale data architecture and management solutions. Rajaram Narasimhan Business Intelligence Solution Architect, Healthcare Rajaram Narasimhan works with the Healthcare Technology Practice, and is responsible for providing information management solutions to healthcare clients. He specializes in data architecture, business intelligence, and analytics. Narasimhan has 10 years of experience in the healthcare domain at TCS.
Abstract
The lack of data is not a problem today. The advent of Electronic Health Records (EHR) and regulations such as the Affordable Care Act (ACA) has resulted in billions of terabytes of data for payers and providers. Making effective use of this data however can be a huge challenge. The data deluge has opened up many challenges related to organizing, managing, and sharing healthcare data across the care continuum. Much of the critical information is fragmented and spread across different departments and systems in multiple formats. This makes it difficult to integrate clinical data with financial and operational data to gain a holistic picture. Transitioning to data-centric healthcare requires a strong focus on building the foundation for a robust information management infrastructure. Data models serve as blueprints to identify the structures necessary to design an operational data store, data ware house, or data marts. They also facilitate data services, integration, and exchange as well as development of analytics platforms that cater to the unique needs of the healthcare environment. They enable the extraction of actionable intelligence from data to improve stakeholder outcomes through a more cost efficient and higher quality healthcare delivery system. This white paper reviews three industry leading data models from IBM, Oracle, and Teradata, that can be used by payers, providers, and Pharmacy Benefit Management (PBM) companies in their data modeling initiatives. These models have been reviewed in the context of the Federal Health Architecture (FHA), the Federal Health Information Management (FHIM) model, and the Domain-Driven Design (DDD) concept. Choosing the right data model can help healthcare organizations obtain deeper strategic and operational insights to realize data driven healthcare improvements and further their cost optimization efforts.
Contents
Designing data models: A blueprint for healthcare intelligence
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Aligning data models with industry standards and best practices
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IBM Healthcare Provider Data Model
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Teradata Healthcare Logical Data Model
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Oracle Healthcare Data Model
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How the Healthcare Data Models Help Industry Players
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Data model decisions
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Turning the data onslaught into competitive advantage
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Designing data models: A blueprint for healthcare intelligence Today, there is a compelling need to use the available healthcare data in better ways to drive superior patient and business outcomes. However, the information flow in the healthcare ecosystem is turning increasingly complex, as shown in Figure 1. The growing flood of unstructured information further complicates the matter, making extraction and management of information a top priority. Is there a simple and cost effective way to enable seamless information flow between stakeholders to help healthcare organizations move to a patient centric and collaborative business model? Medical Research
Consumer (Patient / Member)
Explanation of Benefits
Prescription
Registration (Demographics)
Research
EMRs
Government Regulatory Body
Payer Benefits & Eligibility
Network Mgmt.
Pricing
Acc. Payable Receivable
Morbidity, Mortality Statistics
Point of Care
Pre-Authorization Claims
Treatment
Prevention
Claims
Recovery
Quality of Care Data
Diagnosis
Health Promotions
Disease Prevention
Education
Pharmacy
Provider
Standard Reporting Standard Reporting
Eligibility
&
TRR
Membership
Monitoring & Analysis
Surveillance
Payments
Sales
Genomics
TPA / Clearing House
Like HEDIS
Wholesaler Formulary Data
PBM
Rebate Info
Pharmaceutical
Figure 1: Information flow in the healthcare landscape
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Sophisticated data models offer industry players precisely this option. They simplify the information landscape by capturing the crucial data elements and structure needed for effective decision making. They can represent a single functional area or provide a big picture of the healthcare organization in the form of an Enterprise Data Model (EDM). An EDM aims to establish trust and confidence in the organizational data assets. It plays a crucial role in defining the data architecture as well as maintaining data quality, consistency, security, and accessibility. It also Pre-built industry data models provide faster time supports data governance, metadata management and master data to value, and enable management. superior decision making Significant skills and resources are required to build an EDM from scratch. and compliance. Pre-built industry data models enable understanding of the varied uses of data, their relationships and attributes, and provide faster time to value. In addition, they reduce operational expenditures by eliminating the need for skilled data modelers and integrators. Serving as the foundation of actionable intelligence, they help healthcare leaders manage budgets, prioritize technology investments, and ensure regulatory compliance.
Aligning data models with industry standards and best practices Electronic Health Record
Personal Health Record Quality Measures
Clinical Research
Public Health
Patient
Population
Practice Public Health Policy
Clinical Decision Support
National & International Health Analytics
Health Information Exchange
Public Clinical Guidelines
Source: HealthIT.gov
Integrated Care
Personalization of Care
Care Coordination
Outcomes
Patient Engagement
Patient Satisfaction
Cost Containment
Patient Safety
Personal Health Record
Wellness Initiatives
Genomics
Simplification of Business & IT Operations is essential to achieve the Industry Vision Figure 2: Driving efficient data exchange across healthcare stakeholders
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With increasing emphasis on providing integrated and personalized care, healthcare data models have become key to driving efficient data exchange and interoperability across the healthcare community and government healthcare programs (see figure 2). Several industry standards support the overarching vision of creating an interoperable ecosystem to improve the exchange of health data among stakeholders. The Federal Health Architecture (FHA) started in 2004 is one such initiative. It encompasses stakeholders such as the federal government, private sector healthcare providers, and others. The FHA is currently managed by the Office of the National Coordinator for Health IT (ONC) within the Department of Health and Human Services (HHS). FHA aims to improve access to and quality of care while reducing overall healthcare costs by focusing on the following1: n
Supporting federal efforts to deploy standardized health IT systems and measure health IT standard adoption.
n
Ensuring that federal agencies can seamlessly exchange health data among themselves as well as with the state, local, and tribal governments, and private-sector partners.
n
Providing guidance to federal agencies on how best to manage and maintain health IT investments.
The Federal Health Information Model: A standard for supporting healthcare interoperability The Federal Health Information Model (FHIM) is an information model of healthcare data developed for the FHA partner agencies. The FHIM seeks to support health interoperability by harmonizing information from federal partners and standards development organizations (SDOs) into a unified, logical, health information model. This logical model uses the HL7 Reference Information Model (RIM) as its reference point. It is designed to support multiple Office of Interoperability and Standards initiatives, including CONNECT and the Standards and Interoperability (S&I) Framework2.
The FHIM is an Information model and not a data model. Usually, data models are meant to be implemented, whereas information models are higher level specifications. An information model is like a building blueprint. It defines metadata types that are stored in a repository database and used by tools and applications.
The FHIM is designed to enable meaningful information exchange among the partner agencies as well as externally, with the broader health community. Its key features include: n
Integration with Model Driven Health Tools (MDHT) to support a Model Driven Architecture (MDA) approach for the development of health information exchange interoperability specifications.
n
Use of the Unified Modeling Language (UML) - that describes the health-related information needed by the FHA federal partner organizations - for model development
n
A semantic information base for information exchange, traceability, and alignment with industry information models and standards.
n
Suitability as a Logical Information Model to guide the enterprise architecture of the federal partner organizations.
[1] Healthit.gov, Federal Health Architecture, accessed, June 2015, https://www.healthit.gov/sites/default/files/pdf/fact-sheets/federal-health-architecture.pdf [2] Healthit.gov, Federal Health Architecture, accessed, June 2015, https://www.healthit.gov/sites/default/files/pdf/fact-sheets/federal-health-architecture.pdf
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Domain Driven Design: A best practice for emphasizing core domain concepts A domain is a sphere of knowledge, influence or activity. With respect to FHIM, it is a subject area in healthcare such as allergies, vitals, or orders. Domain-Driven Design (DDD) is a conceptual model of a domain of interest that describes the various entities, their attributes, roles and relationships. Data models encompass entities and their relationships, while domain models identify how these entities interact with each other to make these actions an integral part of entities’ behavioral specifications. It also describes the constraints that govern the integrity of the model elements comprising that problem domain. As an approach to developing software for complex needs, DDD places the project's primary focus on the core domain and domain logic. It supports collaboration between technical and domain experts to get closer to the conceptual heart of the business challenge – in this case - providing health information insights. In the following section, we look at three industry-leading data models that enable the seamless flow of information between stakeholders in the healthcare ecosystem to deliver patient-centric and accountable care.
What is unique about the IBM model? n
Integration across products to support all four major data warehouse use cases
n
Continuous investment in product innovation to meet emerging customer and market demands, including a data warehouse PaaS offering- ‘dashDB’ that offers in-memory columnar capabilities
n
Integration with the Cloudant NoSQL databases as well as PureData for indatabase analytics
n
Deployable with technologies such as BigInsights BigSQl, DB2 with BLU Acceleration and IBM PureData powered by Netezza (which supports high performance for complex analytic workloads)
IBM Healthcare Provider Data Model The IBM Healthcare Provider Data model is part of the IBM InfoSphere software portfolio. It integrates clinical, administrative, and financial data to support real time analytical needs. By leveraging this data model, healthcare delivery organizations can remain responsive to client and marketplace requirements, and the evolving healthcare regulatory environment. The data model builds a strong foundation for information management infrastructure, and helps drive evidence based, patient centric, accountable care. It supports Clinical Care and Research, Supply Chain, Service Line Analytics, Model Extensions and Business Glossary Enhancements. The key components³ and features⁴ of the IBM’s Healthcare Provider Data Model are given below:
[3] IBM Europe Sales Manual, November 2013, IBM Healthcare Provider Data Model V8.8, Accessed June 2015, http://www-01.ibm.com/common/ssi/printableversion.wss?docURL=/common/ssi/rep_sm/8/877/ENUS5725-I48/index.html [4] IBM Software, Accessed June 2015, http://www-03.ibm.com/software/products/en/healthcare-provider-data-model
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Components n
n
n
n
n
Business terms: Enterprise-wide vocabulary of business concepts that provide a view of itself and the industry. Business data model: Conceptual data model that specifies the third normal form (3NF) data structures required to represent concepts defined in business terms.
Features n
n
Atomic warehouse model: Design-level data model that represents an enterprise-wide repository of atomic data used for information processing.
n
Dimensional warehouse model: Enterprise-wide repository for analytical data. It contains star schema-style dimensional data structures organized around fact entities.
n
Business solution templates: Set of industry-relevant analytical reporting requirements organized around business focus areas.
Extensibility and scalability: Offers a robust set of business and technical data models that are extensible and scalable to address current as well as future needs. Cross functional enterprise views: Integrates business model, atomic warehouse model, and dimensional warehouse model to support cross-functional enterprise views, analytics, and applications. Comprehensive analytics: Addresses the analytical business requirements covering clinical research, financial, and operational data integrated across the enterprise. Content delivery: Supports quality analysis, shared savings programs, patient safety initiatives, and other industry-wide standards.
Benefits IBM’s Healthcare Provider Data Model helps correlate clinical, financial, operational, and payer data in a cohesive and flexible manner. This helps accelerate the understanding of population-level health, manage and quantify risk, and identify opportunities for transformation and innovation. It also offers the following benefits : n
Operational insight: Provides a comprehensive, analytical reporting framework
n
Risk and compliance reporting: Supports the reporting for a series of regulatory requirements
n
Enterprise architecture: Offers structure and content to support the business and application layers of an enterprise architecture
Teradata Healthcare Logical Data Model The Teradata Healthcare Logical Data Model (HC-LDM) offers cross-functional coverage and a single view of data across the enterprise. It provides a holistic view of healthcare insurers, providers, managed care organizations, healthcare data administrators, vendors, and consultants. In addition, the HC-LDM can be easily extended as the business grows by leveraging the Teradata iLDM unification. Key components and features of the Teradata Healthcare Logical Data Model are given below⁶: Components n n
n
n
Features
Conceptual, business high-level, subject area data model.
n
Business LDM Third Normal Form (3NF), fully attributed data model.
n
Preliminary Physical Database Design, populated with technical names and/or abbreviations.
n
A database, data warehouse construction, or implementation data model configured to maximize throughput. n
Includes structures which capture data elements and business rules that govern day-to-day operations. Built using the process of normalization and completely independent of both application and technology. Offers extensibility which allows healthcare organizations to add new structures and eliminate unnecessary existing structures. Consists of data elements in third normal form 3NF that support a number of industry standards.
[6] The Teradata Healthcare Industry Logical Data Model, Accessed June 2015, http://in.teradata.com/Resources/White-Papers/The-Teradata-Healthcare-Industry-Logical-Data-Model/?LangType=16393&LangSelect=true
9
Benefits The Oracle Healthcare Data Model integrates data from electronic medical records, clinical departmental systems, patient accounting, back office, research, and various other source systems. It supports diverse analytical requirements to unlock value from clinical and operational data quickly and cost-effectively and provides: n
Query and reporting for information: Supports the extraction of detailed and summary data
n
OLAP for data analysis: Provides summaries, trends, and forecasts
n
Data mining for insight and prediction: Uncovers hidden patterns and insights
How the Healthcare Data Models Help Industry Players The models discussed here are designed by industry experts and supported by best-in-class technologies. They help healthcare organizations avoid the pitfalls of complex integration requirements and reduce the total cost of ownership. These models also offer fast and predictable implementation, accelerating the return on investment while reducing deployment risks.
Healthcare data models: Key business outcomes n
Improved access to information across the healthcare ecosystem
n
Better insights from clinical and operational data through data mining
n
Fast and precise, clinical and non-clinical decision making through better analytics
n
Improved data governance and standardization
n
Predictable healthcare outcomes through accurate forecasting
n
Improved risk and compliance reporting
n
Identification and prioritization of key areas of improvements across service lines
The IBM Healthcare Data Model allows healthcare organizations looking for enhanced data governance and standardization to define a corporate set of n Better scalability to meet standard best practices related to their data. This enables IT to enforce future growth standards as well as use data profiling techniques for compliance requirements monitoring and exception alerting. In addition, the solution provides service line analytics through an enterprise framework to create visibility across the organization and gain insight into re-admission rates, quality indicators, operating margins and clinical outcomes. The Oracle Healthcare Data Model is a good fit for healthcare organizations interested in working with a single vendor solution to minimize compatibility issues, and accelerate deployment and training. The solution also enables data mining to identify inefficiencies and best practices, and supports forecasting to predict and manage healthcare outcomes. The Teradata Healthcare Model is a party (organization, individual) centric model which is derived from Teradata’s extensive experience in the payer industry. This model integrates the financial entities such as claims, payments etc. Moreover, since the party is defined as a common subject, the data model supports seamless integration and offers increased flexibility for analysis.
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Benefits The Teradata HC-LDM provides an integrated base of strategic business and clinical information. It supports the creation of an ideal framework for a wide range of knowledge applications as well as new payment models. It helps healthcare organizations launch new lines of business and meet evolving government mandates. In addition, the Teradata HC-LDM offers the following benefits⁷: Integrated base of information: Offers a single source of clinical and business information, more seamless care management, tighter customer and supplier relationships, and more accurate pricing
n
n
Operational insights: Provides additional insight to negotiate more favorable contracts, segment customers better, or identify greater cost saving opportunities
n
Scalable model: Establishes a base for adding more applications and capabilities to exploit data
n
Modular architecture: Allows users to design and implement a data warehouse strategy one functional area at a time
What is unique about the Teradata model? n
Demonstrates continuous technology enhancements to meet production demands
n
Has a broad user type support
n
Can be combined with the Teradata Healthcare Data Integration Roadmap (DIR) which is a visual reference model that helps align strategic organizational objectives with the supporting data in the integrated data warehouse.
Oracle Healthcare Data Model The Oracle Healthcare Data Model provides an integrated view of enterprise-wide clinical and operational data for better decision making. It includes both logical and physical data models that are designed to support Oracle data warehouses, including the Oracle Exadata Database Machine⁸. It supports common entities such as party and care site, core clinical activities such as observation, intervention and order, and financial and billing activities for accounting, equipment, HR, and payroll. The key components and features of the Oracle Healthcare Data Model are given below: Components n
n
n
Physical model: Physical manifestation of the logical data model into database tables and relationships. Partitions, indexes, parallel definitions, and Cube Views aid performance. Intra-ETL database packages: Pre-built ETL component which loads the information present in the foundation layer (3NF tables) into the Oracle Healthcare Data Model Analytical Layer. Oracle Interactive Dashboard: Sample reports and dashboards using Oracle Business Intelligence Suite Enterprise Edition.
Features n
n
n
n
Embedded analytics: Offers embedded advanced analytics, using pre-built data mining, Oracle Online Analytical Processing (Oracle OLAP), and dimensional models. Query and reporting: Enables extraction of detailed and summary data. 3NF data warehouse model: Provides a solid base for a healthcare data warehouse, while the derived layer provides the infrastructure for creating KPI's, cube views, and reports. Robust infrastructure: Supports the creation of a range of reports.
[7] Teradata Healthcare Data Model, Accessed June 2015, http://in.teradata.com/logical-data-models/healthcare/?LangType=16393&LangSelect=true [8] Oracle® Healthcare Data Model Reference, Accessed June 2015, https://www.db.bme.hu/files/Manuals/Oracle/Oracle11gR2/doc.112/e18026/intro_hdm.htm
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A detailed analysis is provided in figure 3. Features
IBM Healthcare Data Model
Oracle Healthcare Data Model
Teradata Healthcare Data Model
Caters complex and fluid analytical needs
[
[
[
Scalability Cross functional enterprise views
[ [
[ [
[ [
Handles Data governance and Standardization
[
Logical Data Model Support
[ [
[ [ [ [ [ [ [ [
[
Physical Data Model Support Single vendor solution package Provides metrics & insights Embedded Advanced Analytics OLAP
[ [
Data Mining & Forecasts BI Integrated Data Warehouse Integrated Strategy
[ [ [
[ [
[
[ [ [
[
[
[ [
[
Supports Ÿ
Clinical care and research
Ÿ
Service-line analytics
Ÿ
Supply chain
Ÿ
Platform
[ [ [ [
Figure 3: A comparative analysis of the healthcare data models
Data model decisions The move towards patient-centric and collaborative care delivery requires the seamless flow of information across the healthcare ecosystem. Several programs such as the Federal Health Architecture and Federal Health Information Model (FHIM) establish standards for creating an interoperable ecosystem to improve the exchange of health data among stakeholders. Integrating clinical, administrative, and financial data can help healthcare organizations answer complex strategic and tactical business questions faster and more accurately. This is made possible by data models that serve as blueprints for healthcare intelligence. They play a crucial role in defining the data architecture, and capturing data elements and structures. 12
As opposed to proprietary enterprise data models, pre-built industry models can help reduce the time and resources required to build a proprietary enterprise data model. The IBM Healthcare Data Model facilitates enhanced data governance and standardization to define a corporate set of standard best practices related to healthcare data. It also offers service line analytics. Healthcare organizations looking for a single vendor solution can leverage Oracle’s Healthcare Data Model. It enables data mining and supports forecasting to predict and manage healthcare outcomes. The Teradata Healthcare Model is a party centric model, which integrates the financial entities such as claims and payments. It supports seamless integration and provides increased flexibility for analysis.
All of these models offer predictable implementation, reduced deployment risks and faster time to value, while enabling a cost efficient and higher quality healthcare delivery system.
Turning the data onslaught into competitive advantage Delivering high quality healthcare is an information intensive effort, and organizations must evolve their data management approach to match the changing and complex needs of the industry. Moreover, with healthcare data growing in volume, velocity, and variety, the ability to leverage Big Data to derive insights has become an important competitive differentiator. The chosen data model must therefore be robust enough to support the current needs while being scalable to address future requirements. Healthcare organizations that identify the right data model for their unique needs will gain a truly comprehensive approach to healthcare intelligence, resulting in competitive advantage.
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About TCS' Healthcare Business Unit TCS partners with leading health payers, providers and PBMs globally to enable business model transformations to address healthcare reforms, improve quality of care, increase customer engagement and reduce overheads. By streamlining and modernizing business processes and systems, TCS helps healthcare organizations realize operational efficiencies and reduce operating costs. We work closely with healthcare players to empower them to meet their consumers' demands for higher levels of service, quality of care, and new ways of interacting and engaging. Our advanced data solutions, analytics, and cutting edge digital technologies deliver a higher degree of customer centricity. TCS' portfolio of services covers the entire payer value chain from Plan Definition, Eligibility and Enrollment, Policy Servicing, Billing, Claims Processing, Claims Adjudication, Benefit Management, Provider Management and Member Services. For providers, we deliver bespoke services for Provider Management, Claims Management, Patient Information and Financial Management, Clinical Data Management, Pharmacy Benefit Management and Revenue Cycle Management. Contact For more information about TCS’ Healthcare Business Unit, visit: http://www.tcs.com/healthcare Email:
[email protected] Subscribe to TCS White Papers TCS.com RSS: http://www.tcs.com/rss_feeds/Pages/feed.aspx?f=w Feedburner: http://feeds2.feedburner.com/tcswhitepapers About Tata Consultancy Services (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering and assurance services. This is delivered through its unique Global Network Delivery ModelTM, recognized as the benchmark of excellence in software development. A part of the Tata Group, India’s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India.
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