RD-MON - Building a Rare Disease Monitor to Enhance Awareness for Patients with Rare Diseases in Intensive Care.

Rare diseases are commonly defined by an incidence of less than 5/10000 inhabitants. There are some 8000 different rare diseases known. So even if a single rare disease is seldom, together they pose a relevant problem for diagnosis and treatment. This is especially true if a patient is treated for another common disease. University hospital of Gießen is part of the CORD-MI Project on rare diseases within the German Medical Informatics Initiative (MII) and a member of the MIRACUM consortium within the MII. As part of the ongoing Development for a clinical research study monitor within the use case 1 of MIRACUM, the study monitor has been configured to detect patients with rare diseases during their routine clinical encounters. The goal was to send a documentation request to the corresponding patient chart within the patient data management system for extended disease documentation to enhance clinical awareness for the patients' potential problems. The project was started in late 2022 and has so far been successfully tuned to detect patients with Mucoviscidosis and place notifications within the patient chart of the patient data management system (PDMS) on intensive care units.

Building a Comprehensive Clinical Data Repository Using FHIR, LOINC and SNOMED.

In 2018 the University Hospital of Giessen (UHG) moved its hospital information system from an in-house solution to commercial software. The introduction of MEONA and Synedra-AIM allowed for the successful migration of clinical documents. The large pool of structured clinical data has been addressed in a second step and is now consolidated in a HAPI-FHIR server and mapped to LOINC and SNOMED for semantic interoperability in multicenter research projects, especially the German Medical Informatics Initiative (MII) and the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium.

Pulmonary Vascular Research Institute GoDeep: A meta-registry merging deep phenotyping datafrom international PH reference centers.

The Pulmonary Vascular Research Institute GoDeep meta-registry is a collaboration of pulmonary hypertension (PH) reference centers across the globe. Merging worldwide PH data in a central meta-registry to allow advanced analysis of the heterogeneity of PH and its groups/subgroups on a worldwide geographical, ethnical, and etiological landscape (ClinTrial. gov NCT05329714). Retrospective and prospective PH patient data (diagnosis based on catheterization; individuals with exclusion of PH are included as a comparator group) are mapped to a common clinical parameter set of more than 350 items, anonymized and electronically exported to a central server. Use and access is decided by the GoDeep steering board, where each center has one vote. As of April 2022, GoDeep comprised 15,742 individuals with 1.9 million data points from eight PH centers. Geographic distribution comprises 3990 enrollees (25%) from America and 11,752 (75%) from Europe. Eighty-nine perecent were diagnosed with PH and 11% were classified as not PH and provided a comparator group. The retrospective observation period is an average of 3.5 years (standard error of the mean 0.04), with 1159 PH patients followed for over 10 years. Pulmonary arterial hypertension represents the largest PH group (42.6%), followed by Group 2 (21.7%), Group 3 (17.3%), Group 4 (15.2%), and Group 5 (3.3%). The age distribution spans several decades, with patients 60 years or older comprising 60%. The majority of patients met an intermediate risk profile upon diagnosis. Data entry from a further six centers is ongoing, and negotiations with >10 centers worldwide have commenced. Using electronic interface-based automated retrospective and prospective data transfer, GoDeep aims to provide in-depth epidemiological and etiological understanding of PH and its various groups/subgroups on a global scale, offering insights for improved management.

Data Integration into OMOP CDM for Heterogeneous Clinical Data Collections via HL7 FHIR Bundles and XSLT.

Data integration is an important task in medical informatics and highly impacts the gain out of existing health information data. These tasks are using implemented as extract transform and load processes. By introducing HL7 FHIR as an intermediate format, our aim was to integrate heterogeneous data from a German pulmonary hypertension registry into an OMOP Common Data Model. First, domain knowledge experts defined a common parameter set, which was subsequently mapped to standardized terminologies like LOINC or SNOMED-CT. Data was extracted as HL7 FHIR Bundle to be transformed to OMOP CDM by using XSLT. We successfully transformed the majority of data elements to the OMOP CDM in a feasible time.

Generating Surveillance Data for Nosocomial Infections from Routine Charting in Intensive Care Units.

The University Hospital of Giessen and Marburg (UKGM), Giessen, Germany participates in a country wide nosocomial infections surveillance project on intensive care units (ICU) called KISS. KISS data must be gathered daily and is comprised of detailed clinical data like patient movements, ward utilization, presence of central venous and urinary catheters and types of artificial respiration. This happens for all of the 10 ICU's at UKGM Giessen and proved to be very tedious and time consuming for the institute for hospital hygiene. The goal of this project was the automated generation of the KISS reports from routinely collected data in the patient data management system (PDMS). The results show that this is largely feasible without changing the documentation habits of physicians and nurses.

StudyAlert: From eCharts to Modern Messengers.

We developed a tool to detect patients possibly elegible for clinical studies by analysing the HL7-message-stream of the patient management system and notifying study investigators by email or a common messenger service via secured communication channels.

Challenges and Opportunities in Changing Data Structures of Clinical Document Archives from HL7-V2 to FHIR-Based Archive Solutions.

In 2018, a major replacement of clinical applications took place at the University Hospital of Giessen. One key part was the clinical document archive containing a vast collection of clinical data from the last 30 years. The aim of this sub-project was to move all data to a new system without any loss, while maintaining all functionality and all communication interfaces. This project successively resulted in a complete paradigm change in document storage. While the legacy clinical data repository (LCDR) was designed according to HL7-V2 principles, the replacement resulted in an HL7-FHIR implementation. The aim of this work is to discuss the differences between both approaches, the obstacles that appeared during migration, but also the opportunities resulting from the new philosophy, especially as far as the impact on the use of scientific data is concerned.

Online guideline assist in intensive care medicine--is the login-authentication a sufficient trigger for reminders?

INTRODUCTION: Rising cost pressure due to the implementation of the DRG-System and quality assurance lead to an increased use of therapy standards and standard operating procedures (SOPs) in intensive care medicine. The intention of the German Scientific Society supported project "OLGA" (Online Guideline Assist) is to develop a prototype of a knowledge based system supporting physicians of an intensive care unit in recognizing the indication for and selecting a specific guideline or SOP. While the response of the prototype on user entries can be displayed as a signal on the used workstation itself, the location and time for a reminder of scheduled or missed procedures or reactions to imported information is a difficult issue. One possible approach to this task is the display of non acknowledged reminders or recommendations while logging on to a system. The objective of this study is to analyse user behaviour of the physicians working on the surgical intensive care unit to decide whether the login authentication is a sufficient trigger for clinical reminding. METHODS: The surgical intensive care unit examined in this study comprises 14 beds. Medical care is provided by physicians working in shifts 24 hours a day, 7 days a week, with two anaesthetists at a time and an additional senior consultant during daytime. The entire documentation (examinations, medication, orders, care) is performed using the patient data management system ICUData. The authentication process of the physicians was logged and analysed. RESULTS: Throughout the observation period from December 13th 2005 to January 11th 2006 3563 physician logins were counted in total. The mean span between logins was in 11.3 minutes (SD 14.4), the median 7 minutes. The 75% centile was 14 minutes, the 95% centile 38 min. Intervals greater than 60 minutes occurred in 75%, and greater than 90 minutes in 25% of the days. DISCUSSION: It seems reasonable that reminders sent during authentication are able to enforce workflow compliance. It is possible to send notifications caused by external events to the physician depending on the importance of the event. Serious events with high urgency should be reliably passed using wireless pager or handheld technology. It seems that after the implementation of the prototype guideline assist further investigation is needed to monitor changes in authentication behaviour and reactions to the guideline advisory. This is also required to investigate the influence of unit's size, medical specialty and actual ward workload.

Realizing a Realtime Shared Patient Chart using a Universal Message Forwarding Architecture.

The goal of this paper is to describe the clinical needs and the informational methodology which led to the realization of a realtime shared patient chart. It is an integral part of the communications infrastructure of the Patient Data Management System (PDMS) ICUData which is in routine use at the intensive care unit (ICU) of the Department for Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. ICUData utilizes a four tier system architecture consisting of modular clients, message forwarders, application servers and a relational database management system. All layers communicate with health level seven messages. The innovative aspect of this architecture consists of the interposition of a message forwarder layer which allows for instant exchange of patient data between the clients without delays caused by database access. This works even in situations with high workload as in patient monitoring. Therefore a system with many workstations acts a blackboard for patient data allowing shared access under realtime conditions. Realized first as an experimental feature, it has been embraced by the clinical users and served well during the documentation of more than 18000 patient stays.