Predictive Analytics in Health Care

Research on Predictive Analytics in Health Care
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Prediction analytics is fascinating, and I have learned much about its healthcare applications. This concept is implemented when using electronic health records (EHRs) to speculate about a patient's prognosis. Excellent work in this area has been done by the UCSF Medical Centre, which uses predictive analytics to determine when a patient's condition is likely to deteriorate (Shahid et al., 2019). Data from many sources, including EHRs, wearables, and laboratory results, are combined to form prediction models. The models then determine the prognosis for the patient, including the likelihood that they will develop sepsis, have a heart attack, or worsen. Patients can recover quickly and spend less time in the hospital if doctors and nurses can anticipate and prevent complications.
There are several advantages to employing this strategy. Applying analytics for prediction can potentially improve efficiency, reduce costs, and ultimately reduce mortality rates in the healthcare system. Doctors can also meet the specific requirements of each patient by using this technology. Because of this, health care for all people improves. However, it would help if you considered the risks (Shrestha et al., 2019). The uncertainty of the forecasts causes significant concern. False positives and negatives can lead to unnecessary interventions or overlooking crucial events. Furthermore, there may be challenges in incorporating predictive analytics into the healthcare process, which could lead to resistance from medical staff. It takes a well-rounded perspective to weigh the positives against the risks and negatives in any situation.
The potential benefits to patient care and resource optimization are enormous; however, it is essential to rigorously assess the accuracy of forecasts to reduce the number of false alarms. During deployment, healthcare professionals should receive extensive clinical decision-making training on using predictive analytics. Using predictive analytics that incorporates genomics data raises issues from an ethical standpoint. It can perpetuate prejudice, sexism, homophobia, and discrimination if not appropriately managed. For instance, if genomics data is used in prediction models without considering the inclusion of different populations, racially biased recommendations may result (WHO, 2021). Therefore, using comprehensive ...