Validating Predictive Intelligence: Data Science Frameworks for Reliable Analytics in Large-Scale Healthcare Systems
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-119Keywords:
Predictive Analytics, Healthcare Data Science, Model Validation, Reliable AI, Clinical Decision Support, Data Governance, Explainable AIAbstract
Predictive analytics is becoming more and more crucial for how healthcare operates today. If used properly, technology may help clinicians make rapid, smart decisions, better care for large groups of patients, and even guess which patients are most likely to relapse or be readmitted. Hospitals, insurers, and public health groups now have a lot of health data, and computers and machine learning technologies are growing better. This means that they can make and apply prediction models. These technologies may help individuals make better choices, improve patient outcomes, and ease the burden on limited healthcare resources when used appropriately. But there are a lot of worries about the introduction of predictive analytics. An incorrect, biased, or insufficiently verified model may provide misleading results, leading to erroneous clinical decisions, inequitable treatment of certain patient groups, and a decline in trust in the healthcare system and professionals. In healthcare, these shortcomings are not only technical; they may elicit substantial ethical, safety, and legal challenges. Some of the most prevalent concerns include poor or not enough data, training datasets that don't cover a broad variety of individuals, models that don't operate in all hospitals or places, and not enough follow-up after deployment. Sadly, a lot of significant healthcare analytics projects just focus on creating the initial model. They don't have a regular and organized mechanism to maintain checking and verifying models over time. This study supports a thorough, organized methodology for evaluating healthcare predictive analytics. It is also expected to follow the regulations that are already in place for running a company and providing health care. The findings suggest that a rigorous evaluation process makes predictive models more accurate, less biased, and more trustworthy for firms that use analytics to make decisions. This study provides data scientists, physicians, and healthcare IT experts with valuable insights and a structured methodology for assessing predictive analytics in healthcare environments to enhance their accuracy and utility.
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