The University Medicine Greifswald’s Trusted Third Party Dispatcher: State-of-the-Art Perspective Into Comprehensive Architectures and Complex Research Workflows
Bibliographic record
Abstract
With great interest we read the article entitled “Development of a Trusted Third Party at a Large University Hospital: Design and Implementation Study” by Wündisch et al. (1). The objective of the article was to introduce a “comprehensive architecture for a Trusted Third Party (TTP) that aims to support a wide range of different research projects” incorporating “a fine-grained authentication and authorization model [and] a modern REST-API” in order to “support cross-service workflows”. Their work is based on well-established software components of the University Medicine Greifswald for record linkage (E PIX®), pseudonymisation (gPAS®) and consent management (gICS®) (2). With this letter, we aim to place the authors’ statement that “the literature lacks insights into the design of more comprehensive architectures that support complex research workflows that are actually in production use” into a state-of-the-art perspective to prevent any misleading impressions. While the authors concede that “research exists on the components mentioned above”, their article contains several inaccuracies that we would like to highlight in the following. The functional scope of the existing solutions (E-PIX, gPAS, gICS) is presented in Table 1. However, the existing workflow management solution of the University Medicine Greifswald (TTP Dispatcher) was not displayed (2). The authors only reference this highly relevant component later in text of their article. Furthermore, the content and designation of Table 2 “additional functional requirements” misleadingly suggests that the listed requirements are not covered by the solutions mentioned in Table 1. In published work (2) (3) and available materials (4), many of the checkmarks listed in Table 2 have been successfully validated, and moreover, the compliance of the tools with the pertinent TMF guidelines (3) has been demonstrated. Unlike the authors’ indication, the TTP dispatcher solution from the University Medicine Greifswald provides a common REST-API across all TTP services (based on E-PIX, gPAS and gICS) and enables cross-service workflows (2). Contrary to the description by Wündisch et. al., the dispatcher architecture allows the implementation of complex research workflows. We published a list of available workflows together with a corresponding example (“automatic creation of pseudonyms linked to the primary identifier when registering a patient or study participant”)(2). Since 2018, the existing TTP dispatcher solution has been made available in various project collaborations (3). In 2024, the TTP dispatcher is used in projects throughout Germany and the comprehensive documentation for the latest software version is publicly available (4). With regard to the relevance of the secure authentication mechanisms, we fully agree with the authors that OAuth 2.0 support based on OIDC and a fine-grained authorisation model are essential for securing TTP-Services. Therefore, Keycloak-support for E-PIX, gPAS and gICS is operational since 2022 (5). We can also only encourage the interoperability endeavours of the authors with regard to HL7 FHIR. For this reason, the University Medicine Greifswald has actively contributed to the HL7 FHIR standard and has fully implemented it (5). We hope that our additions have clarified any remaining uncertainties and welcome further opportunities to exchange and share our practical experience with the authors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".