The changing landscape of healthcare is creating a huge demand for health data analytics. According to a recent Research and Markets report, health data analytics is poised to grow into a $34.27 billion industry by the end of 2022.
Data and analytics are already revolutionizing healthcare, both by improving the delivery of care and increasing efficiency in the operations of healthcare organizations.
Health data analytics, also known as clinical data analytics, involves the extrapolation of actionable insights from sets of patient data, typically collected from electronic health records (EHRs).
Healthcare analytics have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Traditional claims-based analytics alone can no longer address the problems that arise from too few resources and too many patients, and with the sheer amount of clinical data available in EHRs, it is understandable why this methodology is so hotly desired.
Understanding the tools analysts need to transform data requires some background knowledge. Any type of data, including healthcare data, goes through three stages before an analyst can use it to achieve sustainable, meaningful analytics:
Coming March 31st 2018, at TiECon Florida, We will hear from one of the prominent healthcare organizations in the Tampa Bay area as Balaji Apparsamy discusses the use of data analytics at BayCare. He will also shed light on the continuing evolution of AI at BayCare to improve outcomes and reduce costs across all of its care centers.
Predictive analytics can tell healthcare leaders a lot about trends in their facilities, but turning that information into action is a bigger challenge. Just over 30% of hospitals have used some type of predictive data analytics for a year or longer, according to an article from Hospitals & Health Networks, and the vast majority of healthcare executives (80%) say they think the technology can improve patient care. Predictive analytics (PA) uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. That information can include data from past treatment outcomes as well as the latest medical research published in peer-reviewed journals and databases. Not only can PA help with predictions, but it can also reveal surprising associations in data that our human brains would never suspect. In medicine, predictions can range from responses to medications to hospital readmission rates. Examples are predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis, and even predicting future wellness. The statistical methods are called learning models because they can grow in precision with additional cases. There are two major ways in which PA differs from traditional statistics (and from evidence-based medicine):
Physicians can use predictive algorithms to help them make more accurate diagnoses. For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.
With early intervention, many diseases can be prevented or ameliorated. Predictive analytics, particularly within the realm of genomics, will allow primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can make lifestyle changes to avoid risks. As lifestyles change, population disease patterns may dramatically change with resulting savings in medical costs. These changes that can literally revolutionize the way medicine is practiced for better health and disease reduction.
Employers providing healthcare benefits for employees can input characteristics of their workforce into a predictive analytic algorithm to obtain predictions of future medical costs. Predictions can be based upon the company’s own data or the company may work with insurance providers who also have their own databases in order to generate the prediction algorithms.
Patients will become aware of possible personal health risks sooner due to alerts from their genome analysis, from predictive models relayed by their physicians, from the increasing use of apps and medical devices (i.e., wearable devices and monitoring systems), and due to better accuracy of what information is needed for accurate predictions.
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