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Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin

Standard

Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin. / Eckelt, Felix; Remmler, Johannes; Kister, Thea; Wernsdorfer, Mark; Richter, Heike; Federbusch, Martin; Adler, Markus; Kehrer, Alexander; Voigt, Markus; Cundius, Carina; Telle, Jörg; Thiery, Joachim; Kaiser, Thorsten.

in: INNERE MED, Jahrgang 61, 27.03.2020, S. 452-459.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Review › Forschung

Harvard

Eckelt, F, Remmler, J, Kister, T, Wernsdorfer, M, Richter, H, Federbusch, M, Adler, M, Kehrer, A, Voigt, M, Cundius, C, Telle, J, Thiery, J & Kaiser, T 2020, 'Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin', INNERE MED, Jg. 61, S. 452-459. https://doi.org/10.1007/s00108-020-00775-3

APA

Eckelt, F., Remmler, J., Kister, T., Wernsdorfer, M., Richter, H., Federbusch, M., Adler, M., Kehrer, A., Voigt, M., Cundius, C., Telle, J., Thiery, J., & Kaiser, T. (2020). Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin. INNERE MED, 61, 452-459. https://doi.org/10.1007/s00108-020-00775-3

Vancouver

Eckelt F, Remmler J, Kister T, Wernsdorfer M, Richter H, Federbusch M et al. Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin. INNERE MED. 2020 Mär 27;61:452-459. https://doi.org/10.1007/s00108-020-00775-3

Bibtex

@article{153d74e045134e92a3dced69a7d64f94,
title = "Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin",
abstract = "Background: Laboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response.Objectives: The research project on digital laboratory medicine (AMPEL) aims at developing a CDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences.Materials and methods: A literature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of a framework and reporting system is illustrated through the clinical example of severe hypokalemia.Results and conclusion: Through interdisciplinary development and constant optimization, a specific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show a positive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers a promising perspective for future developments.BackgroundLaboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response.ObjectivesThe research project on digital laboratory medicine (AMPEL) aims at developing a CDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences.Materials and methodsA literature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of a framework and reporting system is illustrated through the clinical example of severe hypokalemia.Results and conclusionThrough interdisciplinary development and constant optimization, a specific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show a positive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers a promising perspective for future developments.",
author = "Felix Eckelt and Johannes Remmler and Thea Kister and Mark Wernsdorfer and Heike Richter and Martin Federbusch and Markus Adler and Alexander Kehrer and Markus Voigt and Carina Cundius and J{\"o}rg Telle and Joachim Thiery and Thorsten Kaiser",
year = "2020",
month = mar,
day = "27",
doi = "10.1007/s00108-020-00775-3",
language = "Deutsch",
volume = "61",
pages = "452--459",
journal = "INNERE MED",
issn = "2731-7080",
publisher = "Springer Medizin",

}

RIS

TY - JOUR

T1 - Verbesserte Patientensicherheit durch „clinical decision support systems“ in der Labormedizin

AU - Eckelt, Felix

AU - Remmler, Johannes

AU - Kister, Thea

AU - Wernsdorfer, Mark

AU - Richter, Heike

AU - Federbusch, Martin

AU - Adler, Markus

AU - Kehrer, Alexander

AU - Voigt, Markus

AU - Cundius, Carina

AU - Telle, Jörg

AU - Thiery, Joachim

AU - Kaiser, Thorsten

PY - 2020/3/27

Y1 - 2020/3/27

N2 - Background: Laboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response.Objectives: The research project on digital laboratory medicine (AMPEL) aims at developing a CDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences.Materials and methods: A literature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of a framework and reporting system is illustrated through the clinical example of severe hypokalemia.Results and conclusion: Through interdisciplinary development and constant optimization, a specific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show a positive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers a promising perspective for future developments.BackgroundLaboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response.ObjectivesThe research project on digital laboratory medicine (AMPEL) aims at developing a CDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences.Materials and methodsA literature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of a framework and reporting system is illustrated through the clinical example of severe hypokalemia.Results and conclusionThrough interdisciplinary development and constant optimization, a specific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show a positive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers a promising perspective for future developments.

AB - Background: Laboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response.Objectives: The research project on digital laboratory medicine (AMPEL) aims at developing a CDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences.Materials and methods: A literature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of a framework and reporting system is illustrated through the clinical example of severe hypokalemia.Results and conclusion: Through interdisciplinary development and constant optimization, a specific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show a positive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers a promising perspective for future developments.BackgroundLaboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response.ObjectivesThe research project on digital laboratory medicine (AMPEL) aims at developing a CDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences.Materials and methodsA literature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of a framework and reporting system is illustrated through the clinical example of severe hypokalemia.Results and conclusionThrough interdisciplinary development and constant optimization, a specific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show a positive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers a promising perspective for future developments.

U2 - 10.1007/s00108-020-00775-3

DO - 10.1007/s00108-020-00775-3

M3 - SCORING: Review

VL - 61

SP - 452

EP - 459

JO - INNERE MED

JF - INNERE MED

SN - 2731-7080

ER -