Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study.

With cancer being a leading cause of death globally, epidemiological and clinical cancer registration is paramount for enhancing oncological care and facilitating scientific research. However, the heterogeneous landscape of medical data presents significant challenges to the current manual process of tumor documentation. This paper explores the potential of Large Language Models (LLMs) for transforming unstructured medical reports into the structured format mandated by the German Basic Oncology Dataset. Our findings indicate that integrating LLMs into existing hospital data management systems or cancer registries can significantly enhance the quality and completeness of cancer data collection - a vital component for diagnosing and treating cancer and improving the effectiveness and benefits of therapies. This work contributes to the broader discussion on the potential of artificial intelligence or LLMs to revolutionize medical data processing and reporting in general and cancer care in particular.

Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments.

BACKGROUND: Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. OBJECTIVES: The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. METHODS: We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. RESULTS: Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. CONCLUSION: We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.

A Privacy-Preserving Distributed Analytics Platform for Health Care Data.

BACKGROUND: In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest. OBJECTIVE: We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location. METHODS: In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers. RESULTS: We show that our infrastructure enables the training of data models based on distributed data sources. CONCLUSION: Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

Distributed Skin Lesion Analysis Across Decentralised Data Sources.

Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location and execute the analysis tasks on central servers. However, centralisation of medical data does not often comply with local data protection regulations due to its sensitive nature and the loss of sovereignty if data providers allow unlimited access to the data. The Personal Health Train (PHT) is a Distributed Analytics (DA) infrastructure bringing the algorithms to the data instead of vice versa. By following this paradigm shift, it proposes a solution for persistent privacy- related challenges. In this work, we present a feasibility study, which demonstrates the capability of the PHT to perform statistical analyses and Machine Learning on skin lesion data distributed among three Germany-wide data providers.