Healthcare has realized tremendous improvements in patient care practices and efforts focused on improving patient safety and work efficiency, largely thanks to large volumes of data that can be collected, analyzed, and interpreted. One part of data analytics is predictive analytics, which entails the use of advanced data analysis techniques in forecasting patient outcomes, health risk identification, and optimization of healthcare delivery (Guo & Chen, 2023). Predictive analytics can be applied in my area of nursing to help identify potential adverse events such as medication errors. For example, predictive analytics can apply the data obtained from electronic health records to identify patterns associated with medication errors, such as prescribing errors, drug interactions, and wrong dosages, among others (Razzak et al.,2020). In this aspect, predictive models based on predictive analytics can flag potential inconsistencies or discrepancies that may indicate the risk of medication errors. Predictive analytics can also be integrated into clinical decision support systems to offer real-time recommendations and alerts to healthcare providers to enhance safe prescribing
Predictive analytics has grown from strength to strength. Therefore, it is expected to have both opportunities and challenges in healthcare in the future. One of the opportunities increased early disease detection and prevention. Predictive analytics can support early disease detections and particular health risks through anomaly, trend, and pattern identification in patient data (Lee & Yoon, 2021). This strategy can help predict future health risks and outcomes. Hence, healthcare professionals can proactively intervene and mitigate potential adverse events and risks to ensure better patient outcomes. On the other hand, there are also expected challenges. For example, there are likely to be more questions on transparency and interpretability (Naeem et al.,2022). The predictive analytic models can use complex machine learning strategies and algorithms with insufficient transparency. The implication is that it can be difficult to gain insight into how the predictions are formulated and to gain an accurate interpretation of the outputs.
Guo, C., & Chen, J. (2023). Big data analytics in healthcare. In Knowledge technology and systems: Toward establishing knowledge systems science (pp. 27–70). Singapore: Springer Nature Singapore. Doi: 10.1007/978-981-99-1075-5_2
Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., … & De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. In Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania (pp. 309-325). Springer Singapore. Doi: 10.1007/978-981-16-5036-9_30
Razzak, M. I., Imran, M., & Xu, G. (2020). Big data analytics for