The global healthcare industry is witnessing a fundamental transformation from being a volume-based business to an outcome-based business. Healthcare payers and providers are struggling to reduce costs, and become more patient-centric. The cost dynamics of the industry are also changing with improved longevity of people, higher number of patients with chronic illness, and increase in geriatric population. At the same time, the industry has been lagging behind the others in adopting technology-enabled process improvements. This gets compounded by the fragmentation and dispersion of data amongst various stakeholders of the healthcare ecosystem. This data is difficult to integrate considering privacy and security issues, differences in schema sets, and the underlying standards holding these schemas within each silo.
Healthcare organizations are increasingly using analytics to ingest, evaluate, and derive insights from new and traditional datasets.
According to Gartner, healthcare analytics is a rapidly emerging phenomenon with huge future potential. Healthcare is starting to catch up with other industries in its demand for more performance analytics and advanced data mining techniques. Organizations across the world are now using data-driven insights for clinical, financial, and operational excellence. Analytics is now at the forefront for HIMSS (Healthcare Information and Management Systems Society), which describes healthcare analytics as the “systematic use of data and related clinical and business insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums.
The key to analytics is managing and utilizing data that has become an important part of production comparable or at par with any other organization asset. The healthcare industry is gathering information along with the rise of medical imaging technology (images), multimedia (video, audio), and the real-time device data to monitor patient vitals, etc. This will continue to fuel exponential growth in data in the foreseeable future. Social media is enabling communication between patients, providers, and communities (e.g. patients with similar medical conditions and providers with similar specialties). This is potentially becoming an important source for Big Data.
Big data in healthcare can be categorized on the following dimensions of scale:
- Volume (large databases across healthcare ecosystem stakeholders)
- Variety of information assets (structured, semi-structured, and unstructured)
- Velocity of data creation and ingestion
- Veracity complexity of data types, composition, formats, and rules
Some of the key datasets that exist within the healthcare ecosystem are:
- Drug/ device research and development data (pharmaceutical/ device companies, contract research organizations, drug research organizations, etc.)
- Healthcare provider clinical data (hospitals, physician practices, laboratories, etc.)
- Healthcare payer claims data (government payers, private health plans, TPA, PBM, etc.)
- Patient behavior and sentiment data (retail purchases, social networking sites, etc.)
Some of the healthcare big data business cases that have seen tangible results are:
- The move from ‘Cookbook Medicine’ to event-based medicine. It refers to the practice of applying the same sequence of tests to all patients who come into the emergency department with similar symptoms. This is efficient, but it is rarely effective. Detailed analysis of patient data helps caregivers take an evidence-based approach to medicine.
- Understand patient hereditary genotype for effective disease management. By analyzing detailed imaging tests, and case histories, physicians were able to extrapolate the likely course of the disease’s progression.
- Study the use of medications in very large populations to determine which drugs are most likely to effectively treat the patients’ medical conditions, and which are more likely to cause adverse events
- Build applications that make EHR SMART
- Fraud analytics with the power of using predictive modeling and business rules to score claims based on a number of known risk factors. This helps reduce the claims settlement time.
Big Data in healthcare is poised to change the ecosystem. While it is still early in the game, there are many ways by which Big Data is currently being leveraged to create value across healthcare. Huge amounts of diverse medical data has become accessible in various healthcare organizations (payers, providers, pharmaceuticals, and regulatory). In future, with newer models of analytics and more strategic data collaboration between healthcare organizations, patients could see reduced costs for better care and visibility to a variety of health care information, making them much more informed consumers.