RESEARCH & RESOURCES

Healthcare Transformation: The Expanding Roles of Business Intelligence, Analytics, and Data Warehousing

By David Stodder, Director, TDWI Research, Business Intelligence

Change is the only constant in many industries today—and none more so than healthcare. Turmoil in the healthcare industry is playing out in contentious public debates about policies and legislation. Everyone has a stake in the outcome, so the debates are heated; patient care and the relationships between patients, employers, insurers, and healthcare providers are in the balance. Flowing through most healthcare industry debates is what to do about data: how to realize its value for patient care and cost management, how to share it, and how to protect it.

For business intelligence (BI) and data warehousing (DW) professionals in the healthcare industry, these are exciting, if stressful, times. Traditionally, healthcare provider organizations have faced significant obstacles in implementing BI tools, analytics, and data warehousing. Data is diverse and usually distributed in hard-to-penetrate silos owned by independent partners. In hospitals and provider networks, individual departments run things their own way. IT budgets and experience are frequently insufficient, especially in small and midsize firms, and concerns about privacy regulations can block data integration and sharing. Yet the goals and deadlines embodied in the 2009 Patient Protection and Affordable Care Act and related legislation make it imperative for healthcare organizations to use and share data more effectively.

BI, analytics, and data warehousing are critical to the future of all players in the healthcare industry. With the right tools, systems, and methods, organizations can easily and quickly harvest data riches. Analytics can help organizations combat fraud, a problem that increases costs at every step. These capabilities will be essential for making the transition from familiar fee-for-service business models to fuzzier, less-defined performance goals based on “wellness” outcomes. Pay-for-performance (P4P) metrics, an integral part of federal funding and reimbursement, make this transition—and the data prowess needed to support it—a matter of fundamental importance.

Leading organizations are using BI, analytics, and data warehousing to reduce costs, manage resources (such as personnel), and improve patient care. These accomplishments enable organizations to be smarter and more agile when adjusting to changing conditions. What these leading organizations have done lays the groundwork for an undoubtedly challenging future in which organizations have to meet stringent cost and reimbursement standards, demands for greater integration between providers, and expectations for more intelligent and personalized patient relationships.

With the healthcare industry in flux, the competitive landscape is changing rapidly.

This article explores some of the key drivers and challenges affecting the use of BI, analytics, and data warehousing in the healthcare industry. The drivers fall into three main areas: improving information integration and sharing, managing costs, and refocusing patient care on outcomes. With most initiatives, addressing one driver impacts the others; for example, by using information to reduce readmissions, healthcare providers can demonstrate that they are lowering costs and improving patient outcomes. We will look at how leading organizations are using BI, analytics, and data warehousing tools and techniques to overcome business challenges and transform patient interaction.

Electronic Health Record: Touchstone for Change

In healthcare information systems, most of the attention has been on the development of electronic health records (EHRs) and electronic medical records (EMRs). Although these terms are often used interchangeably, the notion of an EMR came first, and in practice, many are limited to single providers and departments, or contain information for only a specific diagnosis or treatment. EMRs can become information silos. Thus, a key intention behind EHRs is to make them more comprehensive. EHRs should provide a single system of record for health-related information on a patient; it should draw from a variety of sources to provide a complete, “longitudinal” record of the patient’s entire care experience with multiple providers. In addition to care activity, the EHR can provide access to relevant evidence-based analysis and clinical, demographic, or other reports.

EHRs are the focus of federal stimulus funding; about $20 billion was provided in the Health Information Technology for Economic and Clinical Health (HITECH) Act that was part of the American Recovery and Reinvestment Act of 2009. Healthcare providers have been moving quickly to develop EHRs so they can take advantage of the stimulus funding and comply with Medicare payment incentives. However, given the difficulty of integrating the information and associated processes, it’s not surprising that most surveys show that adoption has been slow.

A larger percentage of organizations have “partial” EHRs that integrate a portion of the available information regarding inpatient and outpatient billing and clinical and operational processes. This means that EHRs (and EMRs) are not yet consistent across organizations, which can complicate information integration and analysis. To overcome the complexity, some providers are implementing master data management (MDM) or master patient indexes. MDM systems can help organizations establish and manage consistent definitions across sources and create information hubs that provide reference data or registries. An increasing number of EHRs and EMRs are being implemented with MDM tools from vendors such as IBM, Informatica, Kalido, Oracle, SAP, and Talend.

However, EHR implementations must do more than solve technical information integration challenges. To receive the stimulus dollars to develop EHRs, organizations must meet “meaningful use” requirements for EHRs. These requirements are still evolving through ongoing policy directives and definitions given by the U.S. Department of Health and Human Services (HHS). Generally, providers must show that their EHRs address health goals regarding quality, safety, and efficiency; patient engagement; care coordination; public health; and privacy and security protection. Demonstrating meaningful use can therefore be an important driver behind BI adoption. Organizations need tools for creating reports and dashboards to track meaningful use metrics and to support the information needs of applications and Web-based services for patient engagement.

Finally, EHRs must play a key role in information sharing. They are the information “containers” for Healthcare Information Exchanges (HIEs) and Affordable Care Organizations (ACOs). With funding primarily from the HITECH Act, HIEs are being set up regionally as well as nationally to standardize electronic information exchange. HIEs promise to reduce costs, errors, delays, and security vulnerabilities in handling data for patient care as well as billing and other operations. Although HIEs are somewhat dependent on the development of EHRs, there are other efforts under way to improve information exchange, such as the integration profiles and frameworks being developed by the Integrating the Healthcare Enterprise (IHE) industry consortium. ACOs, which HHS defines as groups of providers who share responsibility for the quality and cost of primarily Medicare beneficiaries, will require an EHR and information exchange infrastructure to function properly.

Data Warehousing and Integration: Seeking the Single View

EHRs may be the focus of government attention and funding, but organizations will also need to upgrade their access, reporting, analysis, and sharing of EHRs and make improvements to meet meaningful use and other policy objectives. Rather than provide BI tools with access to EHRs directly, many organizations create separate data marts and data warehouses that can consolidate information from a variety of health information systems and applications, including EHRs (as they mature). This is important because the range of data needed to satisfy users’ varied BI requirements can be broader than just one source. As mentioned earlier, some organizations implement MDM systems and related tools to coordinate the cleansing, merging, matching, and preparation of data. These organizations are using data governance to monitor compliance with Health Insurance Portability and Accountability Act (HIPAA) data privacy regulations.

With the healthcare industry in flux, the competitive landscape is changing rapidly. Provider organizations are consolidating, restructuring, and looking for ways to increase the value of their networks. Similarly, companies that offer health and related benefit plans are examining how they can use information more effectively to respond to market changes. The two co-winners of the 2010 TDWI Best Practices Award for Enterprise Data Warehousing offer good examples.

Blue Cross and Blue Shield of Kansas City (Blue KC) serves nearly one million customers in the greater Kansas City, Missouri, region. Working with Hewlett-Packard BI Solutions, the company has developed an enterprise data warehouse that effectively provides a single view of data from more than 45 sources. The integration effort has included the establishment of a center of excellence, which has made data governance, stewardship, and program execution a shared responsibility of business and IT staffs. Blue KC has been able to reduce costs and increase efficiency in data management, and support company objectives to increase revenue and margins through customer retention and reductions in medical costs.

The second co-winner, Arkansas Blue Cross and Blue Shield, participated in a collaborative effort with 19 other Blue Plans in the Blue Cross and Blue Shield Association to develop a “national perspective” on its business data. The initiative, called Blue Health Intelligence (BHI), aggregates data from medical and drug claims, membership, and provider information sources into a centralized data warehouse. BHI complements rather than replaces the members’ local data warehouses. BHI has enabled participating Blue Plans to improve healthcare benchmarking and national account reporting. Each Blue Plan is now better able to identify opportunities to apply best practices across the participating Blue Plans, which helps reduce costs and improve operations. BHI lets the Blue Plans govern patient privacy and confidentiality more effectively, and adhere to service-area restrictions. Arkansas and the other participating Blue Plans believe that BHI gives them a significant competitive advantage.

BI Dashboards: Performance and Standardization

BI dashboards are essential for gaining value from a single view of data. As an easy-to-use, graphical interface, a dashboard presents users with financial, operational, and patient care information. Dashboards also provide an interface for displaying performance management metrics and scorecards, which are becoming more prevalent as healthcare organizations find they must track strategic, operational, and policy objectives. Dashboards are critical to BI expansion because power users who can write queries are frequently scarce in healthcare organizations, and nontechnical operational users need intuitive means of interacting with actionable information.

Hospitals and clinics are implementing BI dashboards to share data with administrators and doctors about performance against metrics and industry benchmarks for factors that impact payment, reimbursement, and expense allocation. At one hospital, managers can see graphical comparisons of charges by payers, commercial insurance carriers, and Medicare and Medicaid. They can also view trends in number of visits per patient and the cost of treatments provided. Because managing costs is a paramount concern, the visibility into data and performance trends that BI dashboards provide is vital. Additionally, the visibility can help organizations spot errors, such as missed or incorrect charges, much sooner—that is, before they grow into a legal or reputational hazard. With fewer cycles wasted on incorrect billing, organizations can save operational costs.

A huge part of managing costs and providing quality care is allocating personnel resources—primarily doctors, nurses, and administrative personnel. With constant fluctuations in the number of patients and procedures scheduled, organizations often need a near-real-time view of information so they can adjust how personnel are allocated according to the number of admitted patients, beds available, and other factors. Actionable information in BI dashboards can provide this view, along with trending data, to help managers be proactive and forecast needs accurately. Labor productivity concerns are pushing healthcare organizations to deploy operational BI dashboards and adjust their data infrastructures to provide more timely updates for daily decision making.

Personalization: Making Data Meaningful for Patients

Leading organizations are building on the gains made with data warehouses and BI dashboards to engage more effectively with patients, not only during specific treatments but ultimately in a continuous fashion to help them achieve and maintain wellness. Indeed, an HHS “meaningful use” requirement that must be met to receive stimulus funding is “consumer engagement,” or the development of relationships that encourage and enable consumers to participate in the design, delivery, and evaluation of healthcare services.

Personal health record (PHR) applications and online services such as Google Health and Microsoft HealthVault are not yet widely used, but may become more significant as smartphones and tablets evolve. Patients could bring mobile devices to appointments and access their PHRs anywhere. Whereas EHRs are managed by providers, PHRs are managed by patients. Along with drug interaction and hereditary or other personal information, PHRs could contain clinical summaries of hospital stays, discharge instructions, and other artifacts of engagement. BI and data warehouse systems could manage this flow of information to PHRs.

Blue Cross Blue Shield of Massachusetts (BCBSMA), winner of the 2010 TDWI Best Practices Award for Customer Intelligence, replaced a passive and static system of customer engagement, based on rarely read newsletters, with a Web portal. Integrated with BCBSMA’s data warehouse, the portal delivers secure, nearreal- time messages and other personalized information. Patients can use the system to track and compare their own progress against data about other patients with similar chronic disease conditions. BCBSMA found that the portal was a hit with consumers, who are now better educated and can make more informed decisions about preventive care and their use of providers in the network. The portal system fits with BCBSMA objectives to reduce health costs and achieve better consumer engagement.

The Future in One Word: Analytics

Alongside BI and data warehousing implementation has been the rapid development of healthcare analytics. Statistical and data mining tools and techniques, data visualization, and predictive modeling hold great promise for organizations seeking to project financial results, optimize operational processes, and use data more effectively for patient care. Analytics will be important to the development of ACOs; risk analytics about patient populations and chronic care needs will play a big role in determining the expected financial performance of these shared accountability organizations. The push toward “evidence-based” medical treatment will lean heavily on the use of analytics to understand the risks and benefits of treatments for chronic conditions, with the objective of gaining a predictive understanding of potential outcomes.

Using analytics to combat fraud is also a high priority for healthcare organizations, as well as federal and state governmental functions responsible for detecting and preventing fraud. With fraud widely understood as a major contributor to high healthcare costs, these functions are becoming less reticent about opening up claims and billing records to data mining so that patterns of abuse can be uncovered. Analytics thrive with more data.

Fraud control organizations are employing predictive analytics tools and techniques to model and score fraud risks, uncover patterns, and anticipate damaging events. Many types of fraud can be highly complex and require correlating data from multiple sources. Medicare and Medicaid claims fraud is a good example; detecting this type of fraud is even more difficult now that crime syndicates are using the Internet and social networks to hide their affiliations. Social network analysis software is helping fraud control organizations gain a predictive understanding and see potentially criminal connections through advanced visualization.

Critical Success Factors

The healthcare industry is changing dramatically. To successfully develop and deploy BI, analytics, and data warehousing, organizations must beware the hype and make pragmatic decisions about how to transition successfully. In conclusion, keep in mind these three success factors:

  1. Know your culture. BI and analytics are not just technology. The ultimate goal is to improve—and change—the way people make decisions and use information to take action. Make sure users understand the role of BI and analytics in their decision making.
  2. Think big, act small. Most healthcare provider organizations do not have large IT budgets or departments. Aim initial projects at addressing well-defined needs. Look for quick wins by improving labor productivity and reimbursement processes. Choose BI tools and systems that do well without substantial IT involvement and offer self-service features.
  3. Don’t neglect data quality. The worst thing that can happen is for users to lose confidence in the data. Consistency and quality can be manageable problems in one data source, but once BI systems integrate data from multiple sources, the challenges increase. Map out the flow of data as it enters and is used in the organization. Evaluate data quality and MDM tools that can help ensure data quality and consistency.

Surviving and prospering in the dynamically changing, high-profile healthcare industry is not for the faint of heart. The rewards, however, go beyond business success: BI, analytics, and data warehousing are now vital to improving patient care and achieving better health outcomes.


David Stodder is director of TDWI Research for business intelligence. For more than two decades, he has provided thought leadership in BI, analytics, and information management as an analyst, editor, and writer. Previously, Stodder served as VP and research director with Ventana Research. He was the founding chief editor of Intelligent Enterprise and served as editorial director there for nine years. He can be reached at dstodder@tdwi.org.

This article originally appeared in the issue of .

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