Consumer-driven healthcare demands business agility

The healthcare industry is undergoing an upheaval of change that is yet to be fully appreciated by all the players in the industry. In past years, there have been a series of initiatives triggered by regulatory changes such as HIPAA compliance, medical innovations such as personalized medicine, and technology and efficiency initiatives such as Electronic Medical Records (EMR). However, all of these collectively will seem like small challenges compared to the big one; consumer-driven healthcare choice. Fostered by the Affordable Care Act, it is already happening in small measure today, but will likely rise to become a predominant force that reshapes healthcare. In the age of consumer-driven healthcare, everyone  – insurance companies, hospitals, physician groups, and others – will have to compete for consumer loyalties based on quality outcomes, cost/efficiency and going beyond the basics to delight the customer with long-term impact on holistic health.

In order to survive and thrive in this environment, healthcare companies have to do three things:

  1. Fundamentally reshape their culture and operations to put the consumer/patient in the center and foster close relationships with physicians and other care-givers.
  2. Build business agility as a core competency.
  3. Learn to leverage enormous amounts of data that originates from many sources, effectively for both analytics and operations.

When other industries follow this path of change, one of the major allies is modern data management and Data Virtualization. Healthcare is no different.

Consider the importance of data for all three initiatives above to ensure your success in consumer-driven healthcare. A focus on consumer and their long-term health means not only patient information in EMR, but also a holistic view of the person/family, their demographic and social data, their lifestyles, etc. In other words, a 360-view of the patient as they move through time. This view, however, can contain significant amounts of information. Business agility can only happen when the right information is in the right hands at the right time enabling them to make decisions and take action. As the healthcare industry continues to adopt “smart” devices that continually gather data in the clinical setting and outside, it creates a tremendous opportunity to share and utilize this information in treatment, but also raises security concerns.

Data Virtualization (DV) represents a straightforward way to deal with the complexity, heterogeneity and volume of patient and clinical information, while meeting the needs of the healthcare payers and providers for agility, near real-time information and analytics, and ensuring strong security compliance that surpass HIPAA needs. Providing integrated access to the data wherever it resides, DV speeds up the time it takes to make information actionable. This enables healthcare organizations to achieve better outcomes; all without having to worry about how to access, store, convert, integrate, copy, move, secure, and distribute data upfront and on an ongoing basis. DV enables disparate sources such as EMR, traditional billing/claims/CRM systems, “smart” device data, or social media information from anywhere it lives without necessarily moving it to a central location like a data warehouse, and allows it be consumed securely as virtual data services. As DV reduces data replication it promotes unified data governance and security best practices.

Trends such as pay for outcomes, lowering lifetime health costs, predictive models for healthcare costs, and the ongoing conversion from paper to (EMR) provide significant challenges to the healthcare industry. Data Virtualization addresses these issues by providing:

  • The ability to quickly generate new investigative data sand-boxes for analyzing risk scenarios, data exploration, what if analysis, etc.
  • Agile reporting that will evolve quickly from old ways of understanding patient, clinical and claims data to new ways. This may also be required by payers/insured and regulatory bodies.
  • A single view of the patient across demographic, medical, lifestyle, social and other data sets across multiple entities, virtually integrated with layered security. This helps deliver better and more tailored solutions for different risk classes and personalize care plan for patients.
  • Connections between historical (data warehouse-based data) and operational data (clinical systems) to rapidly identify operational or clinical quality issues and deliver “actionable” recommendations.
  • An abstraction layer between sources and users, which enables orderly migration and management of data and applications, particularly in conversion to EMRs. Also, DV reduces replication and provides additional security layer therefore increases security/privacy concerns of EMR.

Several leading organizations have adopted DV to help reshape them into agile, patient-focused, healthcare providers or payers. Below are some real-world use cases that have provided rapid short-term ROI while providing transformative capabilities for the organization:

  1. Single View of Patient – A major health insurance provider in the US is transforming its business culture and business model from the inside out to become a true patient advocate. It created a separate division from the mother-ship insurance company to incubate, acquire and build innovative business units that focused on patient choice, social-media based interactions with care providers, health quality reporting, and technologies for real-time eligibility. At the same time they use DV to link relevant Patient360 information across these units and the insurance company. Virtual data services then allows applications and users to access facets of this unified Patient360 in both operational and analytical contexts.
  2. Single View of Provider – A leading 70-year old company in the eye-care insurance industry has also developed many lines of businesses (frame manufacturing, lens labs, optometric practice software, etc.) to serve eye-care providers from individual optometrist stores to online and mall chains. DV was used to develop a hybrid virtual Master Data Management (MDM) solution combining core provider data in a centralized hub, with business-centric provider data federated from the spoke business units. This could also be combined with transactional (non-master) data from operational sources.
  3. Real-time claims adjudication – DV is not just for informational or analytic use. It is making real-time integration across complex and often-changing operational systems a reality today. One such use by an insurance provider is to enable real-time claims adjudication, so that a provider can collect co-payments from patients at the point of service rather than waiting. This benefits the provider and increases their revenue and patient acceptance, but it is also good for the patients since it reduces overall friction and inefficiency in the system, which decreases overall healthcare costs.
  4. Unified SaaS Healthcare Application – Social Interest Solutions, a non-profit provider of healthcare technology solutions has created a Software-as-a-Service (SaaS) offering in the cloud called One-e-App offered through various social services agencies at the state and local levels. This application uses DV in the background to unify eligibility and enrollment across disparate and tiered health, childcare, job training, and other social programs offered by local, state and federal agencies. This not only is a great benefit to the economically disadvantaged segments of society, but it also frees up time for social service workers to provide value-added advice to their clients.
  5. Clinical Data Quality and Benchmarking – A major medical center in the Northeast wanted to get serious about improving its clinical quality and outcomes by providing near real-time feedback and information to its care-givers and hospital staff. DV collects and organizes departmental data into data services that can be combined in the aggregate to report on quality outcomes such as relapses, contamination, infection, etc. This information is further enhanced by combining with public benchmark data from other hospitals in the region through programs such as Massachusetts Health Quality Partners (
  6. Reducing the Risk of Medical Malpractice using Big Data – Traditional insurance primarily relies on historical claims data for actuarial risk analysis. However in medical malpractice a substantial portion of claims arise from new procedures and treatments invented in recent years that have little formal claims history. To solve this problem a leading provider of malpractice insurance and consulting to over 10000 physicians has relied on big data from many public, private research institutions, clinical studies and emerging consumer-reported sources, combined and filtered that data using DV and creates data services around specific medical disciplines to provide key insights into risk pricing and risk mitigation that benefits both doctors and patients.

DV is an essential capability to provide agile information access. As the above examples show, it supports many use cases in healthcare insurance, hospitals and clinics, research institutions, pharmaceutical and life sciences companies It delivers near-real time integration at lower cost across disparate structured, unstructured, internal and external sources, including EMR and HIPAA compliant sources, and delivers secure data services that are consumed by analytical and reporting tools, operational applications and self-service access to unified information for patients, doctors, care-givers and healthcare administrators. Thus DV increases consumer-centered focus in healthcare, the ability to leverage any and all information that is of value with lower cost, and increased agility that contribute to better patient outcomes, lower costs, and more universal health coverage.

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