Enhancing Data Protection via Auditable Informational Separation of Powers Between Workflow Engine Based Agents: Conceptualization, Implementation, and First Cross-Institutional Experiences.

German best practice standards for secondary use of patient data require pseudonymization and informational separation of powers assuring that identifying data (IDAT), pseudonyms (PSN), and medical data (MDAT) are never simultaneously knowable by any party involved in data provisioning and use. We describe a solution meeting these requirements based on the dynamic interaction of three software agents: the clinical domain agent (CDA), which processes IDAT and MDAT, the trusted third party agent (TTA), which processes IDAT and PSN, and the research domain agent (RDA), which processes PSN and MDAT and delivers pseudonymized datasets. CDA and RDA implement a distributed workflow by employing an off-the-shelf workflow engine. TTA wraps the gPAS framework for pseudonym generation and persistence. All agent interactions are implemented via secured REST-APIs. Rollout to three university hospitals was seamless. The workflow engine allowed meeting various overarching requirements, including auditability of data transfer and pseudonymization, with minimal additional implementation effort. Using a distributed agent architecture based on workflow engine technology thus proved to be an efficient way to meet technical and organizational requirements for provisioning patient data for research purposes in a data protection compliant way.

Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial.

INTRODUCTION: Staphylococci are the most commonly identified pathogens in bloodstream infections. Identification of Staphylococcus aureus in blood culture (SAB) requires a prompt and adequate clinical management. The detection of coagulase-negative staphylococci (CoNS), however, corresponds to contamination in about 75% of the cases. Nevertheless, antibiotic therapy is often initiated, which contributes to the risk of drug-related side effects. We developed a computerised clinical decision support system (HELP-CDSS) that assists physicians with an appropriate management of patients with Staphylococcus bacteraemia. The CDSS is evaluated using data of the Data Integration Centers (DIC) established at each clinic. DICs transform heterogeneous primary clinical data into an interoperable format, and the HELP-CDSS displays information according to current best evidence in bacteraemia treatment. The overall aim of the HELP-CDSS is a safe but more efficient allocation of infectious diseases specialists and an improved adherence to established guidelines in the treatment of SAB. METHODS AND ANALYSIS: The study is conducted at five German university hospitals and is designed as a stepped-wedge cluster randomised trial. Over the duration of 18 months, 135 wards will change from a control period to the intervention period in a randomised stepwise sequence. The coprimary outcomes are hospital mortality for all patients to establish safety, the 90-day disease reoccurrence-free survival for patients with SAB and the cumulative vancomycin use for patients with CoNS bacteraemia. We will use a closed, hierarchical testing procedure and generalised linear mixed modelling to test for non-inferiority of the CDSS regarding hospital mortality and 90-day disease reoccurrence-free survival and for superiority of the HELP-CDSS regarding cumulative vancomycin use. ETHICS AND DISSEMINATION: The study is approved by the ethics committee of Jena University Hospital and will start at each centre after local approval. Results will be published in a peer-reviewed journal and presented at scientific conferences. TRIAL REGISTRATION NUMBER: DRKS00014320.

Approaching Clinical Data Transformation from Disparate Healthcare IT Systems Through a Modular Framework.

Many healthcare IT systems in Germany are unable to interoperate with other systems through standardised data formats. Therefore it is difficult to store and retrieve data and to establish a systematic collection of data with provenance across systems and even healthcare institutions. We outline the concept for a Transformation Pipeline that can act as a processor for proprietary medical data formats from multiple sources. Through a modular construction, the pipeline relies on different data extraction and data enrichment modules as well as on interfaces to external definitions for interoperability standards. The developed solution is extendable and reusable, enabling data transformation independent from current format definitions and entailing the opportunity of collaboration with other research groups.