Using Predictive Analytics to Improve Healthcare

Healthcare 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. Healthcare prediction is most useful when that knowledge can be transferred into action. For predictive analytics to be successful in healthcare, it must have three characteristics that is timely, role-specific, and actionable.

Electronic Health Records (EHR) in conjunction with Electronic Medical Records (EMR) have been steadily increasing in use over the last 15 years. In the time from 2001 to the end of 2014 EMR usage in physician offices rose from 20% to over 82%. With the introduction of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, Meaningful Use incentives for higher billing and reimbursement rates from the federal government continue to drive adoption rates.

Download PDF Brochure of study @ https://bit.ly/2J3kfKh

With the adoption of EMRs, the increase in EHRs has grown exponentially. EHRs are a broader view of a patient’s collective individual EMR experience and contain a historical 360-degree view of a patient’s medical history. While the exchange and sharing of this EHR data has been a primary focus in recent years (through Continuity of Care Documents (CCD) and Consolidated Clinical Document Architecture (C-CDA)), the massive collection of clinical data by large health systems and treatment centers (public, private, and academic) has moved into the realm of big data.

Health systems continually face the dichotomy of medicine: improving patient outcomes and maintaining effective operating costs. It is difficult to maintain both and to give the highest level of satisfaction to a patient at the same time. By having to meander through treatment plans, trying one treatment after another, a physician continues to prolong the condition or symptoms of a condition leading to patient dissatisfaction.

Take the example presented here:

A patient of a certain age bracket, with certain race characteristics, a given gender, specific medical history, problem lists, allergies, etc. presents at a clinic with a newly diagnosed condition.

Based on this condition, taking into account what is known about a patient’s existing conditions, medications, personal history, etc., search is used to find other patients within a population cohort that are similar.

Based on those specific facets (now aggregations) of data, the treatment plans given to these patients could be analyzed by the physician to determine the most appropriate option for a patient.

This could then be used as an additional tool for the attending physician to make determinations about what their treatment plan should be, either one that they would have suggested reinforced, or another path that may not have been seen, with a level of certainty that this initial treatment plan would be best for this particular patient based on the population cohort.

 Source: accenture