Predictive Analytics
Big data is a phrase used to refer to extremely large datasets that are scrutinized using computerized methods to reveal patterns, associations, and trends. In the dynamic healthcare setting, data is quite important (Nelson, & Staggers, 2018). In the modern healthcare environment, predictive analytics is gradually becoming the most discussed. Predictive analytics refers to a branch of advanced analytics that is used in making predictions about the uncertain future events that lead to decisions (Nelson, & Staggers, 2018). The technology uses several techniques such as data mining, modeling, artificial intelligence, and statistics. Essentially, the predictions provide a special opportunity to view the future and determine the future trends in the care of patients in both at a cohort scale and individual level.
Predictive analytics enables the enhancement of operational efficiency. In other words, predictive analytics plays an important role in healthcare business intelligence. Arguably, real-time reporting is a new concept and can offer crucial insight into data. It can also be used to adjust the predictive algorithms in line with new insights and discoveries. Additionally, predictive analytics can be used in the recruitment and evaluation of new employee competencies. With the increased need for aged-care services, there would be pressure to increase aged-care organizations to ensure that employees are fully trained to meet the needs of the population. The other benefit of predictive analytic is that it improved the accuracy of diagnoses. For instance, when patients come to the emergency room with chest pain it is quite hard to know whether the patient needs to be hospitalized (Nelson, & Staggers, 2018). If the physicians can find answers about the patient and his status into a system with an accurate predictive algorithm that would evaluate the probability that the individual would be sent home safely, their clinical judgment would be assisted. Fundamentally, the predictions do not replace the physicians’ judgment, but rather help in decision-making.
References
Nelson, R., & Staggers, N. (2018). Health informatics: An interprofessional approach (2nd ed.). St. Louis, MO: Elsevier. Retrieved from https://www.gcumedia.com/digital-resources/elsevier/2017/health-informatics-an-interprofessional-approach_2e.php