Approximately 51 million Americans (more than 1 out of 5 Americans) suffer from chronic pain. Chronic pain patients often have symptoms that lack a clear cause of identifiable physical pathology, and traditional pain assessment methods, which focus on clinical history and physical examinations, tend to overlook the unique lived experiences of these patients. These experiences encompass the daily challenges, routines, personal needs, and aspirations disrupted by chronic pain. Current documentation practices, including clinician notes and patient-completed medical intake forms, offer a limited perspective and fail to capture the patient’s voice fully. Similarly, patient-reported outcomes may not genuinely reflect the patient’s experience, potentially omitting vital information relevant to their clinical status and overall well-being. Although clinicians desire to capture this patient information, it has remained impractical to collect, analyze, and summarize all the salient information of a patient’s lived experiences.  However, emerging machine learning technologies, such as Natural Language Processing (NLP), show promise for examining the language and expression used to describe their pain and allow for efficient analysis over large datasets. This method could provide insights often overlooked by traditional documentation, promoting a holistic and patient-centered approach. The objective of this ongoing study is to collect and analyze first-hand narratives of patients’ lived experiences with chronic pain using NLP. This research aspires to foster better communication between patients and healthcare providers, ultimately leading to more personalized and effective pain management strategies.