AI-Driven Predictive Analytics for Clinical Decision Support and Improved Patient Outcomes
Abstract
AI's predictive analytics is becoming a valuable tool to enhance clinical decision support and patient outcomes. With all that information in the EHR, laboratory reports, medical histories, medical records and real-time tracking of patient monitoring data, doctors can use the wealth of information to predict health risks and possible clinical deterioration, and make educated decisions on treatment. The study highlights the use of AI-driven predictive clinical analytics for clinical decision-making, such as early disease detection, risk stratification, personalised treatment planning, and avoiding unnecessary complications. It also touches on the possibilities of predictive models to facilitate timely patient interventions, the efficient utilization of health care resources and the provision of efficient, patient-centred care. However, the benefits of predictive analytics are limited in the case of poor data quality, inaccurate models, lack of data platform integration, patient privacy concerns, algorithmic bias, and the interpretability of AI predictions. This can also lead to clinical risks, if the models were not validated and the automated recommendations are over-relied upon, especially when the population or the health care context in which the recommendations are used is different from the one used to develop the models. Some of the key suggestions given in the paper are the need for human oversight, continuous monitoring of algorithm performance, Clinical validation and transparency of algorithms. It says that although AI predictions are useful they can't be relied upon instead of medical expertise. While these technologies can be used as tools for evidence informed decision making, to support earlier interventions and ultimately to improve patient safety and quality of care, they must be used responsibly within the clinical workflow. Beyond predicting outcomes, the impact of AI clinical decision support must be assessed by the actual outcomes on the patient, clinical effectiveness, and equity.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 4 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
12447-12457 |
Published |
July 11, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Ganesh Sai Kopparthi (%2024). AI-Driven Predictive Analytics for Clinical Decision Support and Improved Patient Outcomes. International Journal of Science, Research and Technology , Vol. 7 No. 4 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12447-12457. https://doi.org/10.15662/IJSRAT.2024.0704004 |
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