Evidence map›Paper›PMID 38510279›Full record

ArticleFrontiers in digital health2024

Smart hospital: achieving interoperability and raw data collection from medical devices in clinical routine.

Eimo Martens, Hans-Ulrich Haase, Giulio Mastella, Andreas Henkel, Christoph Spinner, Franziska Hahn, Congyu Zou, Augusto Fava Sanches, Julia Allescher, Daniel Heid and 13 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 5 institutions in 4 countries.

Eimo MartensTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Hans-Ulrich HaaseTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Giulio MastellaTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Andreas HenkelTUM School of Medicine and Health, Department of Clinical Medicine-Department of Information Technology, University Medical Center, Technical University of Munich, Munich, Germany.
Christoph SpinnerTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine II, University Medical Center, Technical University of Munich, Munich, Germany.
Franziska HahnTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Congyu ZouTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Augusto Fava SanchesTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Julia AllescherTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Daniel HeidTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Elena StraussTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Melanie-Maria MaierTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Mark LachmannTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Georg SchmidtTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Dominik WestphalTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Tobias HaufeTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
David FederleTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Daniel RueckertTUM School of Medicine and Health, Center for Digital Health & Technology-Institute for Artificial Intelligence and Informatics in Medicine, University Medical Center, Technical University of Munich, Munich, Germany.
Martin BoekerTUM School of Medicine and Health, Center for Digital Health & Technology-Institute for Artificial Intelligence and Informatics in Medicine, University Medical Center, Technical University of Munich, Munich, Germany.
Matthias BeckerDevelopment Department, Fleischhacker GmbH & Co, Schwerte, Germany.
Karl-Ludwig LaugwitzTUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Alexander Steger *TUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Alexander Müller *TUM School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Internal Medicine I, University Medical Center, Technical University of Munich, Munich, Germany.
Technical University of Munich · DEArtificial Intelligence in Medicine (Canada) · CAAcino International (Germany) · DEERN GUARD-Heart · NLGerman Centre for Cardiovascular Research · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Today, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures-Especially in Germany-is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine. Methods: The CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces. Results: In the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations. Discussion: Fully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR.

Indexed as

biosignalsclinical information systemdata integrationFHIRinteroperabilitymedical device data

Identifiers

PMID38510279
PMCPMC10951085
OpenAlexW4392516007

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.