Posted: September 22nd, 2022

Healthcare Data and Analytics

Healthcare Data and Analytics
a. How do the analytics needs of health care enterprises vary based on organization size? An acute care hospital is facing challenges related to an increased number of preventable readmission. Describe the role of analytics in reducing the number of preventable readmissions. Which analytics would be important? How do analytics help to identify patients who are at risk for preventable readmissions?

The growing need to have accurate and reliable information is transforming the modern healthcare industry, especially through healthcare data analytics. This technique aims to develop and implement sustainable analytics with the ability to handle a growing data volume to enhance both a healthcare organization’s business performance and patient care (Qureshi, 2014). Healthcare enterprise’s analytics needs vary based on the overall size of an organization in many ways. For instance, Mukherjee (2019) notes that large healthcare organizations have numerous departments, a large workforce, and tend to admit a high number of patients. As such, they require more sophisticated data analytics systems compared to smaller healthcare organizations.
Readmissions are becoming a serious issue affecting modern health systems, and modern healthcare organizations can leverage data analytics to improve their healthcare standards and avoid Medicare penalties that are imposed on the hospitals that have excessive re-hospitalizations cases. By applying techniques such as predictive analytics, Wickramasinghe (2013) adds that healthcare organizations can easily identify patients at risk of hospital readmission. However, Chin, Liu, and Roy (2016) argue that patient’s risks of admission can only be effectively predicted if modern healthcare processes are redesigned in such a way to accommodate these needs.
Predictive analytics helps to identify the patients at risk of preventable readmission through undertaken a series of coordinated measures. First, Thenmozhi and Ilavarasi (2017) argue that a healthcare organization representative or care team organizes a meeting with the patient to collect information about their pre-hospital care or services. This meeting can take place in person or through a phone conversation with the information obtained about the patient’s medical history or current healthcare state being used to help to identify whether the patients at risk of preventable readmission. Adopting these measures is important in helping modern healthcare organizations overcome preventable readmissions.

References
Chin, S.-C., Liu, R., & Roy, S. B. (2016). Predictive Analytics in 30-Day Hospital Readmissions for Heart Failure Patients. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, 477–499. DOI: 10.1002/9781118919408.ch16
Mukherjee, S. (2019). Predictive Analytics and Predictive Modeling in Healthcare. SSRN Electronic Journal. DOI: 10.2139/ssrn.3403900
Qureshi, B. (2014). Towards a Digital Ecosystem for Predictive Healthcare Analytics. Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems – MEDES 14. DOI: 10.1145/2668260.2668286
Thenmozhi, K., & Ilavarasi, A. (2017). Quality of Service Measurement and Effectiveness for Healthcare Data Using Predictive Analytics. SSRN Electronic Journal. DOI: 10.2139/ssrn.3125965
Wickramasinghe, N. (2013). Predictive Analytics and Intelligent Risk Detection in Healthcare Contexts. Encyclopedia of Information Science and Technology, Fourth Edition, 6806–6812. DOI: 10.4018/978-1-5225-2255-3.ch589

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