Health insurance and healthcare providers operate in an environment with high regulation and extreme competition due to the onset of new legislation with mandates to provide universal coverage and control healthcare costs. Where previously they sold mainly to insurance brokers and large companies or groups that had captive patients, the new laws mandating universal coverage and insurance exchanges now requires both providers and payers to compete directly for healthcare consumers on the basis of cost, quality and customer experience. At the same time there is intense pressure to reduce cost from the major government programs and major employers choosing to self-insure their employees. Disruptive new health insurance startups and providers / health facility partnerships are emerging to challenge the dominance of large health insurers and hospital groups. And all this is happening in the midst of major technological changes and innovations.
CEOs of healthcare companies are among the most active in refreshing their business strategies to become more agile and consumer-friendly, increase efficiency and adapt to these changes. A critical part of their strategy is having a significantly more agile information architecture to deliver new business metrics and processes such as pay-for-outcomes, lowering lifetime health costs, predictive models for health care costs, and the ongoing conversion from paper to electronic medical records (EMR). Enter data virtualization.
As healthcare organizations adopt new processes and share information, they must have the ability to access any kind of data from anywhere it lives without necessarily moving it to a central location. Data virtualization for health insurance and healthcare providers makes easy to deal with the complexity, heterogeneity and volume of patient and clinical information, while providing agility, near real-time information and analytics that can be securely shared internally or externally as data services. In short, data virtualization increases access, reduces integration costs, and distributes high quality and timely information to achieve better outcomes.
One of the biggest initiatives in this industry today is converting paper-based systems to EMR which is a collection of all health information about a patient in digital format. While specialized EMR systems manage the records, several hospitals are using data virtualization to provide the integration of data located in multiple doctor offices, clinics, labs, hospitals and insurance companies to feed EMR systems and also to expose parts of the EMR data accurately and securely as data services. Data virtualization also provides an essential abstraction layer between sources and users, which enables orderly migration from physical systems to EMRs while reducing the amount of data replication which therefore increases security / privacy concerns of EMR.
This goes beyond the EMR to look at the patient as customer / consumer including their complete health and lifestyle profile. Organizations using data virtualization to achieve this create a macro layer abstraction of the patient organized into sub-views such as medical history, transaction history, health topics of interest, lifestyle profile, social feedback, etc. Thus a single view of a patient based on data virtualization is typically defined as modular canonical schema most useful for different functions like nurse practitioner, doctor, billing, rehabilitation or home care services etc. from multiple physical schemas spread across different locations. Working from this single view, the users can always drill down into individual systems of record to retrieve the details, based on security.
Research-based health care providers manage not only the current pipeline of patient needs but have several initiatives to improve the state-of-art medical practices to increase efficacy, safety while reducing risk and cost. As with other types of analytics, data virtualization plays an important role in these initiatives to access large and disparate data sets easily, and to deliver those insights in operational contexts for decision making. Some examples of customers using data virtualization include comparative effectiveness analytics to decide between different treatment options, lifetime insurance costs of different patient populations to determine risk and profitability, identifying patterns in clinical risk and quality versus external benchmarks, etc.
Data virtualization is a well proven solution for agile BI and reporting - internal needs such as financial, billing, human resources; benchmarking of clinical quality and relapse metrics, patient satisfaction, and six sigma type initiatives; External reporting to regulators -- all require agile integration of disparate data.