3 EHR Analytics Functions You Need to Be Aware of This Year
The recent evolution of EHR analytics technology is best described as a work in progress. The ability to harness big data and draw useful inferences from this data is a long established practice in non-healthcare applications.
For some time, the retail industry has used Big Data to model consumer behavior, reaching reliable inferences regarding where to market products. For health care, the promise of analyzing large quantities of data from EHRs at the level of sophistication of the commercial sector remains largely unrealized. One might consider this an "apples and oranges" comparison. However being able to model and predict future trends through data is just as an important to healthcare as it is to the commercial sector. A hospital certainly does not worry about what websites their patients click through to online shop, but they certainly should take notice if data contained in their EHR could predict and model disease and health within their patient populations.
However being able to model and predict future trends through data is just as an important to healthcare as it is to the commercial sector
As such, in the next year, more providers will adopt EHR analytic functionality. With this trend, it is likely that EHR providers will be to place more emphasis on making this analytic functionality affordable and make the case for analytics adding value to the business of providing health care. In the next year, these three EHR analytics functionalities will gain traction and their impact on healthcare.
1) Predictive Analytics
Simulation and modeling capabilities using EHR data to identify trends among patient populations will likely become more prominent. This technology uses Big Data as a source of modeling predictions concerning future patient health and risk. For example, predictive analytics has been applied to modelling the prospective economic cost of illness such as in the case of advanced financial analytics.
Predictive analytics can identify individuals most likely to be hospitalized or determine whose current lifestyle will probably turn into a chronic condition, and provide specific actions to avoid adverse outcomes. According to Healthcare Payer News, “This information can pinpoint at-risk patients for specific interventions that could potentially reduce the need for treatment in the future.” In this sense, EHR can move from a reporting based platform to a more forward thinking and intuitive technology.
2) Real-Time Analytics
In the healthcare industry, real-time analytics offers significant promise. When combined with predictive analytics, real-time inferences regarding patient care can be combined with predictive models to improve clinical decision-making. For example in an ICU setting where diagnoses rely on near-instantaneous analysis of patient data from multiple monitors and devices, real time data can be combined with predictive data about a patient to determine the most effective course treatment.
3) Advanced Economic Analytics
Advanced economic analytics have been proposed to model the prospective economic cost of illness. This serves two functions the first is to head off potential costly and unnecessary treatment by using predictive analytics. Secondly, cost and by association risk can be measured through advanced economic analytics thus providing a way to make care delivery more efficient by being able to balance the cost of a treatment with its efficacy.
An EHR utilising analytics with at least one of these functionalities would be a very valuable asset no matter how big your practice may be. If you are in the market for a new EHR make sure you address it’s analytics functionality.
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