Diagnosis decoded: a taxonomy and natural language processing analysis of the diagnosis section in German hospital discharge summaries

The diagnosis section in hospital discharge summaries plays a critical role in ensuring continuity of care by providing essential diagnostic information and a succinct summary of a patient’s condition to subsequent caregivers. However, the lack of standardized structure and content can lead to incomplete, ambiguous, or inaccurate documentation, potentially compromising patient safety. This study takes a foundational step toward standardizing the diagnosis section in German, and potentially international, discharge summaries by developing a taxonomy of structural and content elements and examining the use of standardized terminologies and abbreviations. We conducted a retrospective analysis of 436 de-identified discharge summaries from 112 hospitals across 12 German states. A structured taxonomy development process was applied, supported by natural language processing, to examine structural and content elements as well as the use of standardized terminologies (SNOMED-CT, ICD-10 codes) and abbreviations. The resulting taxonomy for diagnosis sections comprises 87 distinct characteristics across three meta-dimensions: structure, content, and levels of detail. The analysis revealed limited adoption of standardized terminologies; only 8.1% of terms conformed to SNOMED-CT, and only 14.2% of diagnosis sections included ICD-10 codes. Abbreviations appeared in 92% of diagnosis sections, constituting 14.5% of all words, many of which were obscure or infrequently used. These findings underscore the urgent need for a standardized, interoperable, and clinically meaningful diagnosis section to support continuity of care and data-driven healthcare. The proposed taxonomy offers a foundational framework for future standardization efforts by providing structural and content "design options."