The benefit of standards such as SNOMED and LOINC in everyday clinical practice and for digitization

The challenge of structuring data in the healthcare system

Structured data is part of a schema and is grouped with a specific purpose. This makes extraction and use easier. Unstructured data, on the other hand, can only be organized and categorized with great effort. Examples of this are notes from doctors, diagnostic reports or voice notes [1].

Only 25% of the data in the health system is available in a digitally structured form [2]. In addition, they are often isolated due to special data protection measures. This stands in the way of an individual networking of the data, which is common in other industries and makes companies such as Facebook or Google successful [3]. The implementation of an electronic health record alone cannot solve this problem [2]. On the one hand, because these are often inconsistent too and, on the other hand, because they again represent a closed system, while health data can be recorded and used much more extensively in the 21st century [3].

Standards in data processing are crucial for interaction in the healthcare system, for example in the practical implementation of research results. Accordingly, in its position paper of 2021, the german Federal Association of Medical Technology called for the implementation of standards in data acquisition as an essential building block for high-performance digital health care in the future [4].

These are required in the health system for a large number of applications, such as AI systems that live from a large set of uniform data.

Solutions

The good news: The standards already exist. SNOMED (Systematized Nomenclature of Medicine) constitutes a globally standardized directory of medical terms such as symptoms, findings, diagnoses and procedures. In addition to laboratory tests, the use of LOINC (Logical Observation Identifiers Names and Codes) ensures systematic documentation. The nomenclature is updated annually and can be downloaded free of charge from the website. Therefore, there is more a lack of consistent data collection based on these terminologies.

NLP (natural language processing), i.e. the acquisition and machine processing of natural language, offers the advantage of easy data acquisition at the point of origin. In order to record spoken and written language, however, the complexity and ambiguity of entire textual contexts must be captured. This demanding processing is therefore associated with a high potential for errors, so that information and the context can potentially be lost. This can have fatal consequences, especially in the case of critical data in the healthcare system, at a time when medical errors are already one of the leading causes of death [5].

A far better option is data acquisition directly at the source. The closer the coding according to standards such as SNOMED and LOINC takes place to the data generation, the better the quality and timeliness of the data. For a medical scenario, this means that the coding must be done directly by the doctor. Additional effort for the doctor typically has little lobby in the health system and this concept has therefore often caused annoyance in the past. This is not just specific to the healthcare industry. I have had similar experiences in the field of customer relationship management (CRM) and visitor documentation, although there were usually fewer external standards such as SNOMED. So how can doctors be motivated to document correctly?