Data Quality, Bias, and Strategic Challenges in Reinforcement Learning for Healthcare: A Survey

Data quality is a critical aspect of data analytics since it directly influences the accuracy and effectiveness of insights and predictions generated from data. Artificial Intelligence (AI) schemes have grown in the existing era of technological advancement, which provides innovative exposure to healthcare applications. Reinforcement Learning (RL) is a subfield and an influential Machine Learning (ML) model aimed at optimizing decision-making by association with dynamic environments. In healthcare applications, RL can modify conduct strategies, enhance source application, and improve patient investigation history by using various data modalities. The worth of the data quality regulates how effective RL is in healthcare applications. In healthcare, the model predictions have a direct impact on patient's lives, and poor data quality often leads to wrong evaluations that expose patient safety and treatment quality. Biases in data quality have also presented a challenging influence on the RL model's effectiveness and accuracy. RL models have enormous potential in healthcare; however, various strategic limitations prevent their widespread acceptance and deployment. The implementation of RL in healthcare faces serious issues, mostly around data quality, bias, and tactical difficulties. This study delivers a broad survey of these challenges, emphasizing how imbalanced, imperfect, and biased data can affect the generalizability and performance of RL models. We critically assessed the sources of data bias, comprising demographic imbalances and irregularities in electronic health records (EHRs), and their impact on RL algorithms. This survey aims to present a detailed study of the complex circumstances relating to data quality, data biases, and strategic barriers in RL models deploying in healthcare applications. However, the main contribution of the proposed study is that it provides a systematic review of these challenges and delivers a roadmap for future work intended to refine the consistency, fairness, and scalability of RL in healthcare sectors.