Predictive Analytics Assistance in Patient Care
Published on : Tuesday 02-06-2020
Predictive analytics is primarily assisting doctors in making predictions about patient health to assist in administered care based on palliative care to medical imaging.
Considering the current industry scenario, healthcare associations and organizations are mainly facing issues in addressing the care coordination and care outcomes of patients. This is widely increasing in the use of predictive analytics by the healthcare service providers to effectively identify pain points through the phases of intake and care to enhance both healthcare delivery and patient care experience. Furthermore, predictive analytics is a statistical model based on machine learning algorithms to effectively predict the likelihood of future patient outcomes based on past data results.
“The blend of human and analytics car-focused to provide and address the inadequacies along with the patient treatment and further meet the need of the wide population of patients. This will meet the shortage and inefficiencies of doctor or physician” – Booz Allen Hamilton
How Predictive Analytics Effectively in Medical Imaging
Predictive analytics are massively impacting its application in medical imaging to identify symptoms with speed and accuracy such as cancer and kidney disease. Moreover, it also uses to enhance the identification of specific things on images to easily pre-identify to initiate patient care with effectiveness.
At Stanford University, there already studying and implementing predictive analytics to screen chest X-rays to spot over 14 different pathologies with an accuracy equaling that of radiologists in few seconds. The university using CheXNeXt, an artificial intelligence algorithm, to assist the urgent patient care, who primarily indulges with severe cough and others.
In other words, the healthcare sector is incorporating predictive modeling in daily practice to assist patient care. For instance, predictive analytics integrally helps oncologists to make the best statistical-based decisions regarding patient care. Also, it operates on AI algorithms, which reducing the use of tissue-destructive testing or dependency on genomics and identification of disease to implement most aggressively treatment through harness information from patient disease images. This concept of prediction in healthcare can widely impact the ground level physicians to identify less aggressive cancer and simultaneously it can evade the side effects of chemotherapy.
Predictive analytics is in the developing stage of manufacturing and the incorporation of the prediction model is primarily across the testing zones of the health universities and leading hospitals to make it commercialize in the ground-level radiologists and oncologists to better implement treatments for patient care.