Big data analytics in healthcare
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Big data in healthcare refers to large, diverse, and complex datasets that are difficult to analyze using traditional methods. The healthcare industry generates huge amounts of data from sources like electronic health records, medical imaging, and fitness trackers. Analyzing this big data can help improve patient outcomes, reduce costs, and advance personalized medicine. However, healthcare also faces challenges like data silos, privacy concerns, and resistance to change. Opportunities include disease prediction and prevention, reducing readmissions and fraud, and optimizing care through remote monitoring. Some organizations are starting to see benefits from big data initiatives focused on areas like evidence-based treatment and integrated health records.
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Big data analytics in healthcare
- 1. Big Data Analytics in Healthcare Joseph Thottungal Healthcare Consultant
- 2. What is Big data? Big data means different things for different industries. The definition also differs within an organization, across departments and management layers within IT and business. At The Big Data Institute (TBDI), big data is a “term applied to voluminous data objects that are variety in nature – structured, unstructured or a semi-structured, including sources internal or external to an organization, and generated at a high degree of velocity with an uncertainty pattern, that does not fit neatly into traditional, structured, relational data stores and requires strong sophisticated information ecosystem with high performance computing platform and analytical capabilities to capture, process, transform, discover and derive business insights and value within a reasonable elapsed time.”
- 3. Why Big data analytics in Healthcare? Healthcare Industry generates a huge amount of data such as ◦ Clinical data from CPOE ◦ Clinical decision support systems such as physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance ◦ Patient data in electronic health records (EHRs) ◦ Claims data ◦ Machine generated/sensor data, such as from monitoring vital signs ◦ Social media posts, including Twitter feeds, status updates on Facebook and other platforms ◦ Data maintained for regulatory compliance such as Affordable Care Act, HIE, ACO etc.
- 4. Why Big data analytics in Healthcare? Reports say data from the U.S. healthcare system alone reached, in 2011, 150 Exabytes At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes) scale and, not long after, the yottabyte (1024 gigabytes) Industry has faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of the big data
- 5. Challenges Healthcare Industry is facing several challenges in order to leverage potential benefits of Data analytics ◦ Underinvested due to uncertain ROI ◦ Many players – data sharing is cumbersome. Accurate analytics are driven by integrating disparate sets of information, such as clinical, financial and operational data ◦ Data in silos due to lack of procedures to integration ◦ Resistance to change - Providers are used to making treatment decisions based on their clinical judgment instead of relying on the protocols based on big data analytics ◦ Patient privacy and security
- 6. Opportunities Big data analytics has potential for benefit for everyone in the value chain Provider, Payer and the Patient ◦ Optimizing Care by Device/remote monitoring ◦ Clinical efficiency, quality, and outcomes by Patient profile analytics ◦ Disease Identification and Risk Stratification ◦ Supporting participatory healthcare Public health analytics ◦ Reducing the Cost of Care by Genomic analytics ◦ Reducing Hospital Readmissions by Evidence-based medicine ◦ Reducing Fraud by Pre-adjudication fraud analysis
- 7. Trends Few healthcare players are already started taking advantage of the potential of big data analytics ◦ Kaiser Permanente has fully implemented a new computer system, HealthConnect, to ensure data exchange across all medical facilities and promote the use of electronic health records. The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests. ◦ Blue Shield of California, in partnership with NantHealth, is improving health-care delivery and patient outcomes by developing an integrated technology system that will allow doctors, hospitals, and health plans to deliver evidence-based care that is more coordinated and personalized. This will help improve performance in a number of areas, including prevention and care coordination.
- 8. Trends ◦ AstraZeneca established a four-year partnership with WellPoint’s data and analytics subsidiary, HealthCore, to conduct real-world studies to determine the most effective and economical treatments for some chronic illnesses and common diseases. AstraZeneca will use HealthCore data, together with its own clinical-trial data, to guide R&D investment decisions. The company is also in talks with payers about providing coverage for drugs already on the market, again using HealthCore data as evidence
- 9. What’s next? Every healthcare player want to use the big data analytics to gain insights of the data from various sources to contribute to the following immediate goals of the industry ◦ Increasing provider and payer efficiencies, reducing errors and costs ◦ Enabling comparative effectiveness research for current treatments and to inform R&D ◦ Moving toward patient-centered, outcome-oriented medicine ◦ Empowering consumers - “Health 2.0,” participatory healthcare ◦ Making personalized medicine possible for everyone