EMR s2 CSP : Mining Clinical Status Pathway from Electronic Medical Records (2025)

Abstract: Many current studies focus on extracting tests or treatments when constructing clinical pathways, often neglecting the patient’s symptoms and diagnosis, leading to incomplete diagnostic and therapeutic logic. Therefore, this paper aims to extract clinical pathways from electronic medical records that encompass complete diagnostic and therapeutic logic, including temporal information, patient symptoms, diagnosis, and tests or treatments. To achieve this objective, we propose a novel clinical pathway representation: the clinical status pathway. We also design a LLM-based pipeline framework for extracting clinical status pathway from electronic medical records, with the core concept being to improve extraction accuracy by modeling the diagnostic and treatment processes. In our experiments, we apply this framework to construct a comprehensive breast cancer-specific clinical status pathway and evaluate its performance on medical question-answering and decision-support tasks, demonstrating significant improvements over traditional clinical pathways. The code is publicly available at https://github.com/finnchen11/EMRs2CSP.

## introduction the clinical pathways (cps) is a set of standardized diagnostic, therapeutic, and care protocols established for specific diseases based on evidencebased medicine and diagnostic and treatment guidelines. it encompasses the entire diagnostic and therapeutic process, from patient admission to discharge, and outlines the specific medical activities at each stage, along with their chronological sequence @xcite . clinical pathways play a significant role in standardizing medical behavior and enhancing the quality and safety of medical care @xcite . * corresponding author. however, current clinical pathway research primarily focuses on medical activities such as examinations or treatments @xcite @xcite ) and the analysis of temporal sequences @xcite , often neglecting the patient's symptoms and diagnostic results during the diagnostic and treatment process. in clinical diagnostic and therapeutic logic, determining the most appropriate treatment path requires identifying the examinations or treatments the patient needs based on their symptoms and diagnosis. therefore, considering only medical activities and chronological order results in incomplete diagnostic and therapeutic logic in cps, leading to high variation and incompleteness in their practical application @xcite . as illustrated in figure 1 , the physician will make an appropriate diagnosis based on the presence of reddened skin or nipple discharge, followed by determining whether a total or partial mastectomy is necessary. therefore, an effective clinical pathway should include chronological information, the patient's symptoms, diagnosis, and the required examinations and treatments. to address the aforementioned issues, we propose the clinical status pathway (csp), a new method for representing clinical pathways. unlike traditional clinical pathways, which construct a series of tests and treatments based on the length of hospital stay, our representation employs three states-feature, diagnosis, and operation-to represent the patient's treatment process, integrating decision conditions and diagnostic and therapeutic information. to derive the csp, we design an llm-based pipeline framework for extracting it from electronic medical records (emrs). this framework first filters the data, then extracts patient state information from emrs, constructs the clinical status pathway from individual records by integrating the length of hospital stay and the sequence of states, and finally consolidates results from multiple records to form the csp for a specific disease. to evaluate the effectiveness of our representations, we use various representations as knowledge bases in llms for medical quizzing and decision support tasks. we found that using csp as a knowledge base outperforms other data types in both medical quizzing and decision support effectiveness. furthermore, to assess the effectiveness of our extraction framework, we perform the extraction of clinical status pathway using multiple methods. the results demonstrate that the clinical status pathway obtained using our extraction method outperform alternative methods in terms of recall and accuracy. these findings validate the rationality of our representation and the efficacy of our extraction framework. the main contributions of this paper are as follows: ## method the goal of our approach is to extract csp from emrs. in this section, we first introduce a new representation for clinical state pathways, then outline the process of filtering high-quality information from emr, followed by an explanation of how to extract clinical state pathways from the filtered emrs, and finally, we discuss how to integrate the extraction results from multiple emrs into a comprehensive clinical state pathway.figure 2 illustrates the overall framework of our approach. ## conclusion in this paper, we propose representation method called the clinical status pathway which describes the diagnosis and treatment process through the patient's state, enriching the pathway with more comprehensive diagnosis and treatment logic, thereby improving its effectiveness in application. meanwhile, to mine clinical pathways from emrs, we design a llm-based pipeline method that automates the construction of clinical pathways based on the abstract admission process we define, thus reducing the time required for manual construction by doctors. experimental results demonstrate that the csp extracted using our method yield superior performance and are more effective on downstream tasks, thereby validating the effectiveness of our approach. we believe this work will offer new insights into clinical pathway construction and promote the development of llm-based clinical pathway execution. our goal is to establish an intelligent healthcare system based on llms and csp to address the challenges and limitations of current clinical pathway systems.