The status of healthcare industry historically has created huge amounts of data, fueled by record keeping, compliance & regulatory requirements, and patient care. Driven by inevitable requirements and the potential to enhance the quality of healthcare delivery all the while reducing the costs, these immense quantities of data or big data has the promise of supporting a plethora of medical and healthcare functions, which also includes but are not limited to clinical decision support, disease surveillance, and population health management.
According to reports, the 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 or soon maybe the yottabyte scale (1024 gigabytes).
Big data in healthcare refers to electronic health data sets potentially too large and complex to manage with traditional software and/ or hardware; nor can they be easily managed with traditional or common data management tools and methods. There is an overwhelming amount of big data in healthcare because of its volume and the diversity of data types and the speed at which it must be managed.
The data gathered from healthcare industry includes clinical data from CPOE and clinical decision support systems --physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data; patient data in electronic patient records (EPRs); machine generated/sensor data, such as from monitoring vital signs; social media posts, including Twitter feeds (so-called tweets), blogs, status updates on Facebook and other platforms, and web pages; and less patient-specific information, including emergency care data, news feeds, and articles in medical journals.
Big Data in Healthcare Industry Advantages
When one digitizes, combines and effectively uses big data, healthcare organizations ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations stand to realize significant benefits.
- Detecting diseases at earlier stages when they can be treated more easily and effectively
- Managing specific individual and population health and detecting health care fraud more quickly and efficiently.
- Numerous questions can be addressed with big data analytics.
- Certain developments or outcomes may be predicted and/or estimated based on vast amounts of historical data, such as length of stay
- Patients who will choose elective surgery
- Patients who are likely to not benefit from surgery
- Uncertain complications
- Patients at risk for medical complications and patients at risk for sepsis, MRSA, C. difficile, or other hospital-acquired illness
- Illness progression in patients
- Patients at risk for advancement in disease states
- Causal factors of illness progression
Clinical operations: It provides comparative effectiveness research in order to determine more clinically relevant and cost-effective paradigms to diagnose and treat patients.
Research & development: 1) The first sub-category in R&D is predictive modeling where it helps to lower attrition and produce a leaner, faster, more targeted R&D pipeline in drugs and devices; 2) the second method includes the use of statistical tools and algorithms to improve clinical trial design and patient recruitment to better match treatments to individual patients. This helps reduce trial failures and speeds up new treatments to market; and 3) lastly it includes analyzing clinical trials and patient records which helps identify follow-on indications and discover adverse effects before products reach the market.
Evidence-based medicine: Here data analysts can combine and analyze a variety of structured and unstructured data-EMRs, financial and operational data, clinical data, and genomic data to match treatments with outcomes, predict patients at risk for disease or readmission and provide more efficient care.
Patient profile analytics: Medical practitioners apply advanced analytics to patient profiles, like segmentation and predictive modeling and can identify individuals who would benefit from proactive care or lifestyle changes. As an example, the patients at risk of developing a specific disease can benefit from preventive care.
Big Data in Healthcare Industry Case Studies
Premier, the U.S. healthcare alliance network, has more than 2,700 members, hospitals and health systems, 90,000 non-acute facilities and 400,000 physicians and is reported to have data on approximately one in four patients discharged from hospitals. Naturally, the network has assembled a large database of clinical, financial, patient, and supply chain data, with which the network has generated comprehensive and comparable clinical outcome measures, resource utilization reports and transaction level cost data. These outputs have informed decision-making and improved healthcare processes at approximately 330 hospitals, saving an estimated 29,000 lives and reducing healthcare spending by nearly $7 billion.
North York General Hospital, a 450-bed community teaching hospital in Toronto, Canada, reports using real-time analytics to improve patient outcomes and gain greater insight into the operations of healthcare delivery. North York is reported to have implemented a scalable real-time analytics application to provide multiple perspectives, including clinical, administrative, and financial.
Another example of big data analytics in healthcare is Columbia University Medical Center’s analysis of “complex correlations” of streams of physiological data related to patients with brain injuries. The goal is to provide medical professionals with critical and timely information to aggressively treat complications. The advanced analytics is reported to diagnose serious complications as much as 48 hours sooner than previously in patients who have suffered a bleeding stroke from a ruptured brain aneurysm.
A recent New Yorker magazine article by Atul Gawande, MD described how orthopedic surgeons at Brigham and Women’s Hospital in Boston relied on personal experience along with insight extracted from research on data based on a host of factors critical to the success of jointreplacement surgery to systematically standardize knee joint-replacement surgery. The result: improved outcomes at lower costs.
Case Study Source:- IBM
Big data analytics contains the potential to largely transform the way healthcare providers use sophisticated technologies and gain insight from their clinical and other data repositories and make informed decisions.
As big data analytics solutions become more mainstream, issues such as guaranteeing privacy, safeguarding security, establishing standards and governance, and continually improving the tools and technologies will garner attention. Big data analytics and applications in healthcare are at a nascent stage of development, but rapid advances in platforms and tools can accelerate their maturing process.3