AI-Driven Patient Recruitment for Clinical Trials Using NLP on E-lääkärinlausunto in Finland’s Healthcare System

Abstract

Traditional drug testing through clinical trials is time-consuming, expensive, and often limited in scope, prompting the need for more efficient methods. This study explores an AI-driven framework leveraging Natural Language Processing (NLP) on Finland’s medical certificates—commonly referred to as E-lääkärinlausunto—to streamline patient recruitment. These documents, include A, B, C, and E certificates in Finnish Health care system, but our focus will mainly be on E-certificates as this contain critical healthcare data such as diagnoses, treatments, and patient histories. By employing Optical Character Recognition (OCR) for data extraction and NLP techniques—Named Entity Recognition, ICD mapping, temporal analysis, and keyword matching—unstructured medical text is transformed into structured formats suitable for automated eligibility checks. The resulting patient profiles are compared against predefined clinical trial criteria, enabling a high-precision matching process that reduces recruitment time and cost. A feedback loop further refines accuracy, as clinicians validate or reject suggested matches. and can scale securely on platforms complying with GDPR and HIPAA regulations. This approach not only accelerates clinical trial timelines but also enhances inclusivity by identifying underrepresented groups more effectively. Ultimately, AI-driven patient recruitment holds the potential to revolutionize drug testing, enabling faster, safer, and more diverse trials that benefit healthcare providers, pharmaceutical companies, and patients alike. Building on these findings, this study will further investigate how advanced NLP methods applied to Finland’s diverse medical certificates can reshape the clinical trial process. By systematically harnessing the granular patient data contained in E-lääkärinlausunto, it aims to demonstrate a scalable approach for identifying trial candidates more quickly and accurately. This direction not only has the potential to reduce the overall costs and timelines associated with traditional drug testing but also fosters inclusivity by capturing underrepresented patient demographics. Ultimately, the research aspires to illustrate how AI-driven patient recruitment can accelerate the discovery of safer, more effective treatments and usher in a future of truly personalized medicine.