## 1 Introduction  
Large language models (LLMs) have emerged as powerful tools in natural language processing, driving innovation across various industries, including healthcare [1, 2]. The rapid expansion of both open and closed source LLMs marks a transformative shift in how data are processed, interpreted, and utilized, ushering in a new era of computational intelligence [3, 4]. LLMs have demonstrated significant potential in extracting patient information from free-text clinical records [5]. Clinical notes, discharge summaries, and medical reports--key components of medical documentation--contain free-text descriptions of patient characteristics that provide invaluable insights for clinical decision-making, research, and education. Accurate extraction of this information is critical for enhancing patient care, supporting clinical decisions, advancing research, and informing policy-making [6]. However, a fundamental question remains: can we rely on LLMs to extract meaningful insights from free-text medical reports? This study is designed to bridge critical gaps in evaluating LLMs for healthcare applications, addressing two complementary objectives: first, to propose a novel evaluation framework specifically tailored for the healthcare domain, leveraging advanced multi-criteria decision-making techniques to provide rigorous and context-specific benchmarks; second, to systematically assess the performance of LLMs in extracting diverse patient attributes, such as age, gender, BMI, disease presence, and hematological parameters, from unstructured clinical records, thus shedding light on their practical utility and limitations in this sensitive field [7]. Multi-Criteria Decision Analysis (MCDA) is a structured approach used to evaluate and prioritize alternatives based on multiple conflicting criteria [8]. In healthcare, this is particularly relevant as decisions often involve trade-offs between accuracy, efficiency, and ethical considerations. MCDA provides a systematic framework to weigh these criteria, ensuring a balanced evaluation that aligns with the complex demands of healthcare applications [9]. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, a key component of MCDA, ranks alternatives by comparing their distance to an ideal solution [10]. This technique identifies the best option as the one closest to the positive ideal (most desirable) and furthest from the negative ideal (least desirable). TOPSIS is particularly effective in contexts where multiple quantitative metrics must be balanced, making it well suited for evaluating LLMs based on diverse performance criteria such as precision, recall, semantic coherence, and completeness [9]. Although extensive research has focused on benchmarking LLMs for general tasks such as named entity recognition [11], relation extraction [12], and question answering [12], these studies often lack a focus on real-world clinical applications. In particular, there remains a critical gap in frameworks specifically designed to evaluate the nuanced task of extracting patient-specific characteristics from free-text clinical records, a process that requires balancing precision, relevance, and context-awareness. This study aims to address this gap, introducing a novel framework for evaluating LLMs against diverse criteria reflective of healthcare-specific needs [13]. To ensure a robust and nuanced evaluation, this study employs a hybrid methodology that integrates automated processes with human oversight. Automated scripts, utilizing metrics such as precision, recall, and F1-score, provide an initial layer of evaluation, particularly suited for large-scale data processing [14]. However, recognizing the limitations of automated systems in capturing the full context of clinical text, human evaluators are incorporated to validate outputs where scripts alone prove insufficient. By combining the methodological rigor of MCDA with the ranking capabilities of TOPSIS, this study introduces a robust framework for systematically evaluating LLMs.