Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the processing and analysis of natural language. NLP is often not a standalone product but is embedded within existing systems, technologies, and applications. A key advantage is its ability to make use of unstructured data. While conventional analytics tools handle text only to a limited extent, NLP can identify relevant data points in text-based documents efficiently. These data points can then be analyzed and processed in different ways.

Healthcare organizations such as hospitals and clinics manage and use very large volumes of unstructured medical documents. It is commonly assumed that around 80 percent of patient-related information is unstructured, for example in physician letters, findings, or clinical reports. NLP helps make this data usable. This supports medical professionals who rely on access to clinical information within an organization in their daily work. NLP can also support data-driven processes in various key areas of healthcare.

Automated structuring of medical documents

Manual documentation remains part of everyday work in hospitals and clinics. With NLP, medical documents and free-text content (such as physician letters or discharge summaries) can be structured automatically. This allows health data to be processed, analyzed, and transferred to clinical applications more efficiently. As a result, medical professionals can reduce administrative workload, while organizations can achieve efficiency gains.
 

Improving clinical documentation with NLP

A patient’s medical history consists of numerous findings, reports, laboratory results, and related documents. Medical professionals often need to review and assess this information manually, which requires significant time. NLP is well suited to support this process. The technology can aggregate documents available for a patient and provide a consolidated overview, allowing medical professionals to capture patient histories more efficiently.
 

Supporting clinical decision-making processes with NLP

NLP can support processes such as extracting relevant information from physician letters or pathology reports to compile structured patient profiles. These profiles can serve as a structured information basis within clinical workflows. This can support the consistency and completeness of clinical documentation and facilitate ongoing review of patient information.
 

Supporting medication-related processes

Documenting medication use and related information is essential within clinical workflows. This requires clarity regarding prescribed medications as well as consideration of individual patient-related factors. Relevant information, including references to potential side effects or genetic markers, is often contained in unstructured clinical documents and may not be readily accessible. NLP can help identify and structure this information so it can be made available for documentation, analysis, and review processes.

How does medical NLP work in practice? The AI platform Health Discovery is designed to integrate NLP seamlessly into existing systems and applications. It structures health data in real time and can process a wide range of unstructured data formats, including PDFs, Word documents, free-text database entries, and speech-based input. The software extracts more than 50 medical entities, such as diagnoses, medications, laboratory results, and vital parameters, and translates them into established standards including FHIR, SNOMED CT, ICD-10, and LOINC. The platform provides 150 pre-trained AI models and supports more than 15 terminologies.