How Healthcare Industry is Leveraging Predictive Analytics to Improve Patient Outcome
The backbone of a successful healthcare service is positive patient outcomes. Today, several healthcare reimbursement models look at patient outcomes to determine the level of payments. This has resulted in increased focus from healthcare providers in improving their level of services to deliver results. So the question arises, how can they go about improving patient outcomes? The answer lies in predictive analytics. With massive amounts of data collected from Electronic Health Records (EHR) and Electronics Medical Reports (EMR), predictive models can predict individual outcomes like post-operative complications and diabetes risk.
If a patient visits a physician with chest pain, it would be complicated to ascertain if the he/she should be hospitalized. However, if the patient’s medical history can be fed into a predictive algorithm, it would assist the physician in deciding between keeping him/her under observation or sending them home. Additionally, predictive modeling can perform complicated calculations to increase diagnoses accuracy by factoring in genomes and gene markers to discover hidden conditions.
Treatment and Medications
Medicines are not as simple as they look; a medicine that works for the majority of the people may not work for few. Additionally, some people may exhibit adverse effects to certain medications. Using predictive modeling and patient-reported outcomes, individuals can receive treatments that will work for them. The role of the patient will augment to being a more informed consumer working closely with their physicians to achieve better outcomes. By factoring in personal health issues, genome analysis, and data from wearable devices, prescriptive analytics can suggest the best treatment plan and medications to improve patient outcomes. It can also suggest necessary lifestyle changes for their wellbeing.
Focusing on Low-value Decision Points
Physicians and healthcare providers are humans after all. Uncertainty over a clinical decision can result in physicians overtreating or undertreating patients. Predictive analytics can allow physicians to administer essential treatment plans to those patients who absolutely need them. For instance, when treating a newborn with antibiotics, a large percentage of infants are treated with antibiotics, whereas only a minority among them actually have infections confirmed by blood culture. Researchers can develop sophisticated algorithms to predict the cases of infection accurately, so antibiotics can be administered appropriately; thus, reducing its misuse. This would significantly reduce the medical costs and adverse side-effects.
To know more about how the healthcare industry can use predictive analytics to improve patient outcomes along with predictive modeling, patient-reported outcome measures, and outcome measures: