Acute care is a healthcare organization that provides active treatment for emergency patients requiring immediate medical intervention. Big data analytics can enhance the functioning of such organizations; however, as data science predictions are not entirely accurate by now, it is the combined effort of big data and clinicians that can transform the healthcare system. Specifically, big data analytics can assist managers in reaching cost-efficient outcomes by forecasting health risks for a big pool of patients, diagnosing illnesses, formulating the most effective treatment recommendations, and optimizing staff management.
Big data analytics obtain information by identifying patterns in an enormous set of previously filled and continually added information on a particular issue. Thereby data science can predict many crucial outcomes, including patient mortality. This information can help healthcare providers better evaluate the severity of the condition, diagnose it, and choose appropriate medical interventions before it becomes fatal (Sanchez-Pinto et al., 2018). Moreover, this prognosis results in more informed managerial decisions regarding the distribution of resources. However, it is vital to remember that predictive models do not incorporate causal effects, meaning that they do not show a change in mortality rate after implementing specific medical care (Maley et al., 2020). Therefore, even though mortality prediction can assist in better-informed decisions, managers should consider the challenges data science has yet to overcome.
Furthermore, big data analytics helps monitor the satisfaction, efficiency, error rates, and other key performance indicators of healthcare personnel. This information can enhance the functioning of the HR department and make informed decisions regarding employee analysis, HR management, and talent acquisition. Moreover, big data can improve staff training by identifying the best learning models (Sousa et al., 2019). Such data is crucially important in acute care hospitals, as they require extra stress resistance compared to other hospitals and, therefore, should look for specific healthcare providers. Predictive analytics and HR analytics can enhance the functioning of healthcare organizations by timely informing about high-risk patients and their health outcomes and providing insights about HR strategies. Information provided by big data analytics can help managers of acute care hospitals optimize their patient care and administrative strategies by eliminating risks and maximizing profits.
References
Maley, J., Wanis, K., Young, J., & Celi, L. (2020). Mortality prediction models, causal effects, and end-of-life decision making in the intensive care unit.BMJ Health &Amp; Care Informatics, 27(3), e100220. Web.
Sanchez-Pinto, L., Luo, Y., & Churpek, M. (2018). Big Data and Data Science in Critical Care.Chest, 154(5), 1239-1248. Web.
Sousa, M., Pesqueira, A., Lemos, C., Sousa, M., & Rocha, Á. (2019). Decision-Making based on Big Data Analytics for People Management in Healthcare Organizations.Journal Of Medical Systems, 43(9). Web.