Extracting Value from Healthcare Data:
An Analysis of Industry Leading Data Models
White Paper
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.
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 5 Aligning data models with industry standards and best practices 6
IBM Healthcare Provider Data Model 8
Teradata Healthcare Logical Data Model 9
Oracle Healthcare Data Model 10
How the Healthcare Data Models Help Industry Players 11
Data model decisions 12
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?
5
Figure 1: Information flow in the healthcare landscape
Pharmaceutical PBM Wholesaler Pharmacy TPA / Clearing House Consumer (Patient / Member) Research Genomics Formulary Data Rebate Info Government Regulatory Body Provider Payer Standard Reporting Like HEDIS Standar d Repor ting
Surveillance Monitoring & Analysis
Disease Prevention Quality of Care Data Morbidity, Mortality Statistics Prevention Point of Care Treatment Diagnosis Education Recovery Medical Research Prescription EMRs Registration (Demographics) Explanation of Benefits
Benefits & Eligibility Pre-Authorization Claims Payments TRR Pricing Membership Acc. Payable Receivable Claims Sales Network Mgmt. Health Promotions & Eligibility
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 supports data governance, metadata management and master data management.
Significant skills and resources are required to build an EDM from scratch. 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
Pre-built industry data models provide faster time to value, and enable superior decision making and compliance.
6
Figure 2: Driving efficient data exchange across healthcare stakeholders
Practice
Quality
Measures Public Health
Clinical Research Clinical Decision Support Public Health Policy Clinical Guidelines Population Public Personal Health Record Electronic Health Record Health Information Exchange
National & International Health Analytics
Patient
Source: HealthIT.gov
Simplification of Business & IT Operations is essential to achieve the Industry Vision
Integrated Care Care Coordination
Personalization of Care Outcomes
Cost Containment
Patient Engagement SatisfactionPatient Personal Health
Record InitiativesWellness Patient Safety
7
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
1
following :
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
2
Interoperability (S&I) Framework .
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
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.
8
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.
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:
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 in-database 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) 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.
[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
9 n Business terms: Enterprise-wide vocabulary of business
concepts that provide a view of itself and the industry. n Business data model: Conceptual data model that specifies
the third normal form (3NF) data structures required to represent concepts defined in business terms.
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.
n 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.
n Cross functional enterprise views: Integrates business model, atomic warehouse model, and dimensional warehouse model to support cross-functional enterprise views, analytics, and applications.
n Comprehensive analytics: Addresses the analytical business requirements covering clinical research, financial, and operational data integrated across the enterprise.
n Content delivery: Supports quality analysis, shared savings programs, patient safety initiatives, and other industry-wide standards.
Components Features
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:
n 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.
n Built using the process of normalization and completely
independent of both application and technology.
n Offers extensibility which allows healthcare organizations to
add new structures and eliminate unnecessary existing structures.
n Consists of data elements in third normal form 3NF that
support a number of industry standards.
Components Features
[6] The Teradata Healthcare Industry Logical Data Model, Accessed June 2015,
11
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. The IBM Healthcare Data Model allows healthcare organizations looking for enhanced data governance and standardization to define a corporate set of standard best practices related to their data. This enables IT to enforce standards as well as use data profiling techniques for compliance
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.
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
n Better scalability to meet
future growth requirements
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:
n 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 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
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:
10 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.
[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
n Physical model: Physical manifestation of the logical data model into database tables and relationships. Partitions, indexes, parallel definitions, and Cube Views aid performance. n 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.
n Oracle Interactive Dashboard: Sample reports and dashboards using Oracle Business Intelligence Suite Enterprise Edition.
n Embedded analytics: Offers embedded advanced analytics, using pre-built data mining, Oracle Online Analytical Processing (Oracle OLAP), and dimensional models. n Query and reporting: Enables extraction of detailed and
summary data.
n 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. n Robust infrastructure: Supports the creation of a range of
reports.
12
A detailed analysis is provided in figure 3.
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.
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
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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.
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.
13 All of these models offer predictable implementation, reduced deployment risks and faster time to value,
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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
