Technological advances are enabling scientists to sequence the genomes of cancer tumors, revealing a detailed portrait of genetic mutations that drive these diseases. But genomic studies are only one piece of the puzzle that is precision medicine. In order to realize the promise of this field, there needs to be an increased focus on creating robust clinical databases that include medical histories from patients around the country, which physicians can then use along with genomic data to tailor individual treatments.
HOW THE MEDICAL DATABASE IS ACCUMULATED FOR ANALYSIS
Big data analytics in healthcare
Health data volume is expected to grow dramatically in the years ahead. In addition, healthcare reimbursement models are changing; meaningful use and pay for performance are emerging as critical new factors in today’s healthcare environment. Although profit is not and should not be a primary motivator, it is vitally important for healthcare organizations to acquire the available tools, infrastructure, and techniques to leverage big data effectively or else risk losing potentially millions of dollars in revenue and profits.
What exactly is big data? A report delivered to the U.S. Congress in August 2012 defines big data as “large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management and analysis of the information”. Big data encompasses such characteristics as variety, velocity and, with respect specifically to healthcare, veracity. Existing analytical techniques can be applied to the vast amount of existing (but currently unanalyzed) patient-related health and medical data to reach a deeper understanding of outcomes, which then can be applied at the point of care. Ideally, individual and population data would inform each physician and her patient during the decision-making process and help determine the most appropriate treatment option for that particular patient.
Advantages to healthcare
By digitizing, combining and effectively using 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. Potential benefits include 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 (LOS); patients who will choose elective surgery; patients who likely will not benefit from surgery; complications; patients at risk for medical complications; patients at risk for sepsis, MRSA, C. difficile, or other hospital-acquired illness; illness/disease progression; patients at risk for advancement in disease states; causal factors of illness/disease progression; and possible co-morbid conditions (EMC Consulting). McKinsey estimates that big data analytics can enable more than $300 billion in savings per year in U.S. healthcare, two thirds of that through reductions of approximately 8% in national healthcare expenditures. Clinical operations and R & D are two of the largest areas for potential savings with $165 billion and $108 billion in waste respectively. McKinsey believes big data could help reduce waste and inefficiency in the following three areas:
Clinical operations: Comparative effectiveness research to determine more clinically relevant and cost-effective ways to diagnose and treat patients.
Research & development: 1) predictive modeling to lower attrition and produce a leaner, faster, more targeted R & D pipeline in drugs and devices; 2) statistical tools and algorithms to improve clinical trial design and patient recruitment to better match treatments to individual patients, thus reducing trial failures and speeding new treatments to market; and 3) analyzing clinical trials and patient records to identify follow-on indications and discover adverse effects before products reach the market.
Public health: 1) analyzing disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response; 2) faster development of more accurately targeted vaccines, e.g., choosing the annual influenza strains; and, 3) turning large amounts of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises, especially for the benefit of populations.
In addition, suggests big data analytics in healthcare can contribute to
Evidence-based medicine: 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;
Genomic analytics: Execute gene sequencing more efficiently and cost effectively and make genomic analysis a part of the regular medical care decision process and the growing patient medical record;
Pre-adjudication fraud analysis: Rapidly analyze large numbers of claim requests to reduce fraud, waste and abuse;
Device/remote monitoring: Capture and analyze in real-time large volumes of fast-moving data from in-hospital and in-home devices, for safety monitoring and adverse event prediction;
Patient profile analytics: Apply advanced analytics to patient profiles (e.g., segmentation and predictive modeling) to identify individuals who would benefit from proactive care or lifestyle changes, for example, those patients at risk of developing a specific disease (e.g., diabetes) who would benefit from preventive care.
According to, areas in which enhanced data and analytics yield the greatest results include: pinpointing patients who are the greatest consumers of health resources or at the greatest risk for adverse outcomes; providing individuals with the information they need to make informed decisions and more effectively manage their own health as well as more easily adopt and track healthier behaviors; identifying treatments, programs and processes that do not deliver demonstrable benefits or cost too much; reducing readmissions by identifying environmental or lifestyle factors that increase risk or trigger adverse events and adjusting treatment plans accordingly; improving outcomes by examining vitals from at-home health monitors; managing population health by detecting vulnerabilities within patient populations during disease outbreaks or disasters; and bringing clinical, financial and operational data together to analyze resource utilization productively and in real time.