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HCI 660 Describe how predictive analytics is used operationally in clinical and business processes in health care

HCI 660 Describe how predictive analytics is used operationally in clinical and business processes in health care

Today, health care organizations encounter more pressure to realize effective care coordination and enhance patient outcomes. To attain these outcomes, health care organizations are resorting to predictive analytics. Predictive analytics characterizes the utilization of forecasting and modeling techniques to establish possible future events (Kamble et al., 2018). Through statistical and data analysis, predictive analysis answers the question, “what might occur?” In turn, health care stakeholders can utilize the predictions to inform their actions and provide best health care for patients.

HCI 660 Describe how predictive analytics is used operationally in clinical and business processes in health care

Predictive analytics is critical in the clinical and business processes of health care because it informs every step patients’ health care journey ranging from diagnosis to prognosis, and treatment (Muniasamy et al., 2019). Regarding diagnosis, predictive analysis is used to anticipate health issues in a patient through timely diagnosis. Patients diagnosed early can initiate treatment instantly, which enhances their survival rates. Concerning prognosis, health care providers and researchers can use predictive analytics on patients’ data to predictive which patients are at greater risk. In turn, this information could be used by the health care providers to implement timely interventions to prevent the predicted adverse events (Kamble et al., 2018). In treatment, machine-based predictive analytic models are used by the health care providers to establish the most effective treatment course for a given illness in patients.

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Predictive analytics can also be used to inform distant patient monitoring and minimize negative events. Moreover, predictive analytics can be used to provide real-time support for clinical decisions at the point of care, which is essential in ensuring personalized healthcare are made efficient. Predictive analytics can reduce costs and enhance quality of care, enhance patient outcomes, and help in detection of warning signs of adverse medical events and effectively prevent their occurrences (Nikolova-Simons et al., 2021). Ultimately, predictive analytics supports holistic health care.

Also Read: HCI 660 What is the purpose of data analytics in health care?

References

Kamble, S. S., Gunasekaran, A., Goswami, M., & Manda, J. (2018). A systematic perspective on the applications of big data analytics in healthcare management. International Journal of Healthcare Management. https://doi.org/10.1080/20479700.2018.1531606

Muniasamy, A., Tabassam, S., Hussain, M. A., Sultana, H., Muniasamy, V., & Bhatnagar, R. (2019, March). Deep learning for predictive analytics in healthcare. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 32-42). Springer, Cham. doi: 10.1007/978-3-030-14118-9_4

Nikolova-Simons, M., Golas, S. B., op den Buijs, J., Palacholla, R. S., Garberg, G., Orenstein, A., & Kvedar, J. (2021). A randomized trial examining the effect of predictive analytics and tailored interventions on the cost of care. NPJ digital medicine, 4(1), 1-10. https://doi.org/10.1038/s41746-021-00449-w

 

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