Research & Development of Digital Healthcare Solutions.

Creating the Smart Hospital of tomorrow using Data Integration, FHIR and AI at the University Medicine Essen

SHIP ahoy!

AHOY!

Smart Hospital Information Platform

We at SHIP strive for the fusion of research and clinical applicability to bring forward a valuable clinical impact. In that context, the SHIP backend allows for tackling state-of-the-art research questions and simultaneously focusing on clinical relevance

This allows us to combine state of the art AI and web development to solve clinical relevant problems and research question

WHO WE ARE

A Ship in a Port is safe but that’s not what Ships are made for

Drawing inspiration from the pioneering spirit of exploration, our unique approach blends cutting-edge AI and web development to navigate the vast seas of clinical research and application. Just as a ship ventures beyond the safety of the harbor, we embarks on a journey to redefine the boundaries of healthcare innovation, ensuring that its voyage yields practical solutions for the most pressing clinical challenges.

As part of the University Hospital Essen and the Institute for Artificial Intelligence in Medicine (IKIM) we are closely connected to the real world problems and infrastructure of one of the biggest hospitals in western germany.

Billion

FHIR Resources

ETL Importers

AI & Clinical Papers

SHIP Apps

INTEROPERABILITY & AI

At the forefront of of interoperability & AI development using FHIR

We operate one of the largest FHIR servers in Europe, driving both AI-powered and non-AI applications for research and clinical care. Our cutting-edge infrastructure bridges data interoperability and actionable insights, enabling seamless workflows like the Patient Dashboard, which provides clinicians with a clear, comprehensive view of patient data.

In research, we leverage this ecosystem to develop advanced AI models, including image analysis, risk prediction, and survival analysis tools. Beyond AI, we optimize workflows, enhance decision support systems, and foster interoperability across healthcare systems.

By combining FHIR expertise with clinical impact, we are transforming healthcare and shaping its future at the intersection of technology and medicine.

WHAT WE DO

Our Three Areas

Data Integration

Integrating clinical subsystems into an interoperable environment using high-performance ETL streams and the Fast Healthcare Interoperability Resources (FHIR) standard as a data schema.

Data Science & AI

Access real-world clinical data and use it with SOTA data science and AI approaches for computer vision, time-series forecasting or Large Language Models (LLMs) backed by an high-performance GPU cluster.

Web & Mobile Development

Improving workflows for clinicians by creating easy to use applications based on real-world clinical problems allows us to bridge the gap between data to the point of care.

Our Tech Stack

Using modern and scalable technologies to shape the future of healthcare

Our tech stack leverages cutting-edge packages, open-source technologies, and high-performance programming languages tailored for rapid AI development and efficient ETL processes. With an always up-to-date and dynamic tech stack, we strive to stay at the pulse of healthcare technologies, driving innovation and excellence in the field.

Pytorch

Firemetrics

Monai

Nvidia

Scala

React

Python

Postgres

Swift

Our latest Research

Papers

Comput Struct Biotechnol J (2024)

Schäfer H et al.

BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature

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J Clin Med (2024)

Welsner M et al.

Opportunistic Screening for Low Bone Mineral Density in Adults with Cystic Fibrosis Using Low-Dose Computed Tomography of the Chest with Artificial Intelligence

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Eur Radiol Exp (2024)

Salhöfer L et al.

CT-based body composition analysis and pulmonary fat attenuation volume as biomarkers to predict overall survival in patients with non-specific interstitial pneumonia

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J Neurointerv Surg (2024)

Styczen H et al.

Impact of imaging biomarkers from body composition analysis on outcome of endovascularly treated acute ischemic stroke patients

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J Med Internet Res (2024)

Brehmer A et al.

Establishing Medical Intelligence-Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study

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Eur J Radiol (2024)

Nensa F et al.

Beyond accuracy: Reproducibility must lead AI advances in radiology

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BMC Med Inform Decis Mak (2024)

Klug K et al.

From admission to discharge: a systematic review of clinical natural language processing along the patient journey

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J Thorac Imaging (2024)

Salhöfer L et al.

Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis

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JHEP Rep (2024)

Müller L et al.

AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study

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Crit Care (2024)

Kohnke J et al.

A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study

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Sci Data (2024)

Rückert J et al.

ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset

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Sci Data (2024)

Koitka S et al.

SAROS: A dataset for whole-body region and organ segmentation in CT imaging

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Sci Rep (2024)

Westhölter D et al.

Elexacaftor/tezacaftor/ivacaftor influences body composition in adults with cystic fibrosis: a fully automated CT-based analysis

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Sci Rep (2024)

Wienker J et al.

Body composition impacts outcome of bronchoscopic lung volume reduction in patients with severe emphysema: a fully automated CT-based analysis

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J Nucl Med (2024)

Schuler M et al.

Early Metabolic Response by PET Predicts Sensitivity to Next-Line Targeted Therapy in EGFR-Mutated Lung Cancer with Unknown Mechanism of Acquired Resistance

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