2- Research a minimum of six articles on big data, its usefulness in healthcare, and achieving the goal of improving patient outcomes.
3- Do a SWOT (strengths, weaknesses, opportunities, and threats) analysis on the use of health data and the potential to improve patient outcomes based on these articles.
Your paper should include the following:
· 4-5 pages in length, not including the title and reference pages.
· 6 references cited in the assignment. Remember, you must support your thinking/opinions and prior knowledge with references; all facts must be supported; and in-text references used throughout the assignment must be included in an APA-formatted reference list.
Glaser, J. (2014, Dec. 9). Solving big problems with big data. Retrieved October 11, 2016, from http://www.hhnmag.com/ articles/3809-solving-big-problems-with-big-data
Macadamian. (n.d.). Big data vs. small data: Turning big data into actionable insights. Retrieved August 3, 2016, from http://www.macadamian.com/ guide-to-healthcare-software-development/big-data-vs-small-data/
Kayyil, B., Knott, D., & Van Kuiken, S. (2013). The “big data” revolution in health-care. New York, NY: McKinsey and Co. Retrieved July 8, 2016, from http:// healthcare.mckinsey.com/sites/default/fi les/The_big-data_revolution_in_US_ health_care_Accelerating_value_and_innovation%5B1%5D.pdf
About Big Data
Big data is not a data store (warehouse or database), nor is it a specific analytical tool, but rather it refers to a combination of the two. Experts describe big data as characterized by three Vs (the fourth V—veracity, or accuracy—is sometimes added). These characteristics are present in big but not small data:
· Very large volume of data
· A variety (e.g., images, text, discrete) of types and sources (EHR, wearable fi tness technology, social media, etc.) of data
· The velocity at which the data is accumulated and processed (Glaser, 2014; Macadamian, n.d.)
The following is helpful information copied from
Wager, K., Lee, F., & Glaser, J. (2017). Health care information systems (4th ed., pp. 43-47). San Francisco: John Wiley & Sons.
In-text citation (Wager, Lee & Glaser, 2017).
Big Data Examples
Health care organizations today contend with data from EHRs, internal databases, data warehouses, as well as the availability of data from the growing volume of other health-related sources, such as diagnostic imaging equipment, aggregated pharmaceutical research, social media, and personal devices such as Fitbits and other wearable technologies. No longer is the data needed to support health care decisions located within the organization or any single data source. As we begin to manage populations and care continuums we have to bring together data from hospitals, physician practices, long-term care facilities, the patient, and so on. These data needs are bigger than the data needs we had (and still have) when we focused primarily on inpatient care. Big data is a practice that is applied to a wide range of uses across a wide range of industries and efforts, including health care. There is no single big data product, application, or technology, but big data is broadening the range of data that may be important in caring for patients. For instance, in the case of Alzheimer’s and other chronic diseases such as diabetes and cancer, online social sites not only provide a support community for like-minded patients but also contain knowledge that can be mined for public health research, medication use monitoring, and other health-related activities. Moreover, popular social networks can be used to engage the public and monitor public perception and response during flu epidemics and other public health threats (Glaser, 2014).
As important and perhaps more important than the data themselves are the novel analytics that are being developed to analyze these data. In health care we see an impressive range of analytics:
· Post-market surveillance of medication and device safety • Comparative effectiveness research (CER)
· Assignment of risk, for example, readmissions
· Novel diagnostic and therapeutic algorithms in areas such as oncology
· Real-time status and process surveillance to determine, for example, abnormal test follow-up performance and patient compliance with treatment regimes
· Determination of structure including intent, for example, identifying treatment patterns using a range of structured and unstructured and EHR and non-EHR data
· Machine correction of data-quality problems
The potential impact of applying data analytics to big data is huge. McKinsey & Company (Kayyil, Knott, & Van Kuiken, 2013) estimates that big data initiatives could account for $300 to $450 billion in reduced health care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health care costs. There are several early examples of possibly profound
impact. For example, an analysis of the cumulative sum of monthly hospitalizations because of myocardial infarction, among other clinical and cost data, led to the discovery of arthritis drug Vioxx’s adverse effects and its subsequent withdrawal from the market in 2004.
A Deloitte (2011) analysis identified five areas of analysis that will be crucial in the emerging era of providers being held more accountable for the care delivered to a patient and a population:
· Population management analytics. Producing a variety of clinical indicator and quality measure dashboards and reports to help improve the health of a whole community, as well as help identify and manage at-risk populations
· Provider profiling/physician performance analytics. Normalizing (severity and case mix–adjusted profiling), evaluating, and reporting the performance of individual providers (PCPs and specialists) compared to established measures and goals
· Point of care (POC) health gap analytics. Identifying patient specific health care gaps and issuing a specific set of actionable recommendations and notifications either to physicians at the point of care or to patients via a patient portal or PHR
· Disease management. Defining best practice care protocols over multiple care settings, enhancing the coordination of care, and monitoring and improving adherence to best practice care protocols
· Cost modeling/performance risk management/comparative effectiveness. Managing aggregated costs and performance risk and integrating clinical information and clinical quality measures