Cutting-edge data analytics, if used properly, improves patient care in the health care system. With the change in health care toward outcome and value-based payment initiatives, analyzing available data to discover which practices are most effective helps cut costs and improves the health of the populations served by health care institutions.
“Data analytics” refers to the practice of taking masses of aggregated data and analyzing them in order to draw important insights and information contained therein. This process is increasingly aided by new software and technology that helps examine large volumes of data for hidden information.
In the context of the health care system, which is increasingly data-reliant, data analytics can help derive insights on systemic wastes of resources, can track individual practitioner performance, and can even track the health of populations and identify people at risk for chronic diseases. With this information, the health system can more efficiently allocate resources in order to maximize revenue, population health and — very importantly — patient care.
Evaluating Practitioner Performance
Along with the seismic shift away from volume care to value-based care, the implementation of health care analytics provides new methods to evaluate the performance and effectiveness of health care practitioners at the point of delivery. With ongoing performance evaluations, along with health data related to patient wellness, data analytics can be utilized to provide ongoing feedback on health care practitioners.
As health care analytics continues to be better understood and implemented, this promises positive shifts in the patient experience and quality of care. The McKesson Ongoing Professional Practice Evaluation, for example, continually evaluates the performance of health care practitioners by aggregating data from direct observation, complaints, practice patterns, patient outcomes and resource use. The data are compared alongside various performance measurements such as professionalism, patient care and interpersonal communication skills.
At the point of delivery, data analytics can continually evaluate physicians in real time, in order to track and improve the effective practices of practitioners and improve patient care.
Outcome- and value-based payment initiatives incentivize performance improvement in health care. Accounting for costs is therefore tied to measuring performance and valuing best practices.
This means that, instead of focusing on reimbursement on a case-by-case basis, overall outcomes determine payment. Ongoing health care analytics can help identify large patterns that lead to a greater understanding of population health. A system of interconnected electronic health records available to physicians helps provide detailed information that can help cut costs by reducing unnecessary care. Additionally, by identifying trends in population outcomes, prescriptive analytics can estimate individual patient costs; by doing so, the health care system can better allocate personnel and resources in order to reduce waste and maximize efficiency.
Understanding patient costs, as well as total program costs, also involves accounting for what happens to patients outside, as well as inside, of care. Through data analysis we can understand the cost of type-II diabetes to the health care industry. Because diabetes is preventable through programs of diet and exercise, paying for the health counseling of high-risk individuals in the population can greatly cut overall costs to the industry.
One of the largest costs to the health care industry involves the treatment of chronic diseases. On a population-wide level, predictive analytics can help greatly cut costs by predicting which patients are at higher risk for disease and arrange early intervention, before problems develop. This involves aggregating data that are related to a variety of factors. These include medical history, demographic or socio-economic profile, and comorbidities.
Medical history usually includes age, blood pressure, blood glucose, family history of chronic conditions, and cholesterol levels.
A large percentage of what affects health outcomes is associated with factors outside the purview of traditional health care. These factors include patient health habits and behaviors, socio-economic factors like employment and education, and physical environment. In order to improve outcomes, the public health system must expand its boundaries to account for these ‘outside’ factors. In data analytics, these metrics can be modeled to predict risk of chronic disease.
Finally, analytics must model risk by accounting for the multiple medical conditions that a patient might have. In aggregating and analyzing all these forms of data, the health care industry can more effectively allocate resources, enabling it to aggressively intervene in high-risk populations early on and prevent long-term systemic costs.
Source : (https://goo.gl/PZ4ex8)