Predictive analytics is rapidly becoming one of the most-discussed, perhaps most-hyped topic in healthcare analytics.. Healthcare industry can learn valuable lessons from this previous success to jumpstart the utility of predictive analytics for improving patient care, chronic disease management, hospital administration and supply chain efficiencies.
Predictive models use known results to develop a model that can be used to predict values for different or new data.
The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables. This is different from descriptive models that help you understand what happened or diagnostic models that help you understand key relationships and determine why something happened.
More and more organizations are turning to predictive analytics to
increase their bottom line and competitive advantage using predictive analytics. From effective population health management, quality reporting, revenue accuracy, and chart retrieval to tackling fraud, waste, and abuse, ezURs is committed to answering the unique challenges of the industry.
predictive analytics for improving patient care and more
Preventable Hospital Admissions
Hospital admissions are very expensive. We are very focused on improving outcomes and reducing high-cost care that is not good for patients. Using big data analytics to survey patient activities across the healthcare continuum can do much more than just save dollars for the individual emergency departments or hospitals at hand. By improving population health management, chronic disease care, and expanding access to preventative primary care for vulnerable patient groups, the healthcare system as a whole can achieve savings that radiate into
unexpected places.
Chronic Care Management
Chronic disease management is the most expensive, fastest growing, and most intractable problem facing healthcare providers in every nation on Earth. More than 95 percent of the world’s population suffers from one or more chronic health problem. Improved healthcare analytics leads to improved programs and the ability to create new ones. The potential to improve outcomes and contain costs from the analyzing big data in healthcare are, well, big. It has been reported that preventive actions – such as early cholesterol screening for patients with associated histories, hypertension screening for adults or smoking cessation – could reduce the total cost of care by over $38 billion.
Predictive Illness / Disease Progression
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.
Identification of High Cost Cases
Opportunities exist to reduce costs through use of big data: high-cost patients, re-admissions, triage, decompensation, adverse events, and treatment optimization for diseases affecting multiple organ systems. Pay special attention to high cost patients. Predict which patients are likely to be high cost, what measurements can be incorporated to improve this prediction, particularly those focused on mental health, and how to make these predictions actionable. But making the most of this data depends on making predictions easily available to clinicians in a manner that doesn’t disrupt current workflow.
