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  1. Article ; Online: ARTEMIS: An alarm threshold and policy mining system for the intensive care unit.

    Chromik, Jonas / Flint, Anne Rike / Arnrich, Bert

    International journal of medical informatics

    2024  Volume 184, Page(s) 105349

    Abstract: Background: Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason ... ...

    Abstract Background: Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety.
    Objective: We present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems.
    Methods: Policy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models - for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads.
    Results: ARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric.
    Conclusion: ARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike - with organisational, economic, and clinical benefits for the implementing hospital.
    MeSH term(s) Humans ; Alert Fatigue, Health Personnel ; Clinical Alarms ; Intensive Care Units ; Monitoring, Physiologic/methods ; Policy
    Language English
    Publishing date 2024-01-29
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2024.105349
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Conference proceedings: How data science can inform alarm management in intensive care units: a ventilation therapy case study

    Chaoui, Amin / Flint, Anne Rike / Balzer, Felix / Poncette, Akira-Sebastian

    2023  , Page(s) Abstr. 285

    Event/congress 68. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS); Heilbronn; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2023
    Keywords Medizin, Gesundheit ; intensive care medicine ; alarm management ; alarm fatigue ; actionable alarms
    Publishing date 2023-09-15
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/23gmds127
    Database German Medical Science

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  3. Article: Digitalisierung. Grundvoraussetzung für eine erfolgreiche digitale Transformation. Ganzheitliche Strategie für das KIS. Die digitale Transformation ist in vollem Gange. Eine Grundvoraussetzung dafür sind moderne Krankenhausinformationssysteme (KIS) - das zentrale Nervensystem der Krankenhäuser. Nicht nur das beachtliche Investment, sondern auch die große Zahl an vielschichtigen Herausforderungen spielt für den Erfolg einer Implementierung eine wesentliche Rolle. Es erfordert eine klare und konsequente Governance, die Innovationen und Veränderungen fördert und Anforderungen von Mitarbeitenden sowie Patientinnen und Patienten auf allen Ebenen des Krankenhauses berücksichtigt.

    Poncette, Akira-Sebastian / Flint, Anne Rike / Peuker, Martin / Moertel, Andreas / Balzer, Felix

    KU-Gesundheitsmanagement

    2023  Volume 92, Issue 4, Page(s) 17

    Language German
    Document type Article
    ZDB-ID 2420760-3
    ISSN 1867-9269
    Database Current Contents Medicine

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  4. Article: Digitalisierung. Grundvoraussetzung für eine erfolgreiche digitale Transformation. Ganzheitliche Strategie für das KIS. Die digitale Transformation ist in vollem Gange. Eine Grundvoraussetzung dafür sind moderne Krankenhausinformationssysteme (KIS) - das zentrale Nervensystem der Krankenhäuser. Nicht nur das beachtliche Investment, sondern auch die große Zahl an vielschichtigen Herausforderungen spielt für den Erfolg einer Implementierung eine wesentliche Rolle. Es erfordert eine klare und konsequente Governance, die Innovationen und Veränderungen fördert und Anforderungen von Mitarbeitenden sowie Patientinnen und Patienten auf allen Ebenen des Krankenhauses berücksichtigt.

    Poncette, Akira-Sebastian / Flint, Anne Rike / Peuker, Martin / Moertel, Andreas / Balzer, Felix

    KU-Gesundheitsmanagement

    2023  Volume 92, Issue 4, Page(s) 17

    Language German
    Document type Article
    ZDB-ID 2420760-3
    ISSN 1867-9269
    Database Current Contents Medicine

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  5. Conference proceedings: Application of the ISO standard for alarm floods in the intensive care setting – a data-driven exploration

    Prendke, Mona / Flint, Anne Rike / Heeren, Patrick / Balzer, Felix / Poncette, Akira-Sebastian

    2023  , Page(s) Abstr. 309

    Event/congress 68. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS); Heilbronn; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2023
    Keywords Medizin, Gesundheit ; alarm management ; international standards ; intensive care medicine
    Publishing date 2023-09-15
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/23gmds154
    Database German Medical Science

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  6. Conference proceedings: Towards user-centered information display: a concept for intensive care alarm data

    Flint, Anne Rike / Chaoui, Amin / Agha-Mir-Salim, Louis / Balzer, Felix / Poncette, Akira-Sebastian

    2023  , Page(s) Abstr. 259

    Event/congress 68. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS); Heilbronn; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2023
    Keywords Medizin, Gesundheit ; alarm metrics ; intensive care medicine ; alarm management
    Publishing date 2023-09-15
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/23gmds031
    Database German Medical Science

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  7. Article: A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study.

    Williams, Elena / Kienast, Manuel / Medawar, Evelyn / Reinelt, Janis / Merola, Alberto / Klopfenstein, Sophie Anne Ines / Flint, Anne Rike / Heeren, Patrick / Poncette, Akira-Sebastian / Balzer, Felix / Beimes, Julian / von Bünau, Paul / Chromik, Jonas / Arnrich, Bert / Scherf, Nico / Niehaus, Sebastian

    JMIR medical informatics

    2023  Volume 11, Page(s) e43847

    Abstract: Background: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data ...

    Abstract Background: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.
    Objective: In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard.
    Methods: We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database.
    Results: We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.
    Conclusions: Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.
    Language English
    Publishing date 2023-03-21
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2798261-0
    ISSN 2291-9694
    ISSN 2291-9694
    DOI 10.2196/43847
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study.

    Sinno, Zeena-Carola / Shay, Denys / Kruppa, Jochen / Klopfenstein, Sophie A I / Giesa, Niklas / Flint, Anne Rike / Herren, Patrick / Scheibe, Franziska / Spies, Claudia / Hinrichs, Carl / Winter, Axel / Balzer, Felix / Poncette, Akira-Sebastian

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 21801

    Abstract: Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to ... ...

    Abstract Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15-5.29, p < 0.001), invasive mechanical ventilation (aOR 1.24, 95%CI 1.16-1.32, p < 0.001), heart failure (aOR 1.26, 95%CI 1.19-1.35, p < 0.001), chronic renal failure (aOR 1.18, 95%CI 1.10-1.27, p < 0.001), hypertension (aOR 1.19, 95%CI 1.13-1.26, p < 0.001), high RASS (aOR 1.22, 95%CI 1.18-1.25, p < 0.001) and scheduled surgical admission (aOR 1.22, 95%CI 1.13-1.32, p < 0.001) were significantly associated with a high alarm rate. Our study suggests that patient-specific alarm management should be integrated in the clinical routine of ICUs. To reduce the overall alarm load, particular attention regarding alarm management should be paid to patients with invasive blood pressure monitoring, invasive mechanical ventilation, heart failure, chronic renal failure, hypertension, high RASS or scheduled surgical admission since they are more likely to have a high contribution to noise pollution, alarm fatigue and hence compromised patient safety in ICUs.
    MeSH term(s) Adult ; Humans ; Clinical Alarms ; Retrospective Studies ; Intensive Care Units ; Monitoring, Physiologic ; Heart Failure ; Hypertension ; Kidney Failure, Chronic
    Language English
    Publishing date 2022-12-16
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-26261-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Utilizing Intensive Care Alarms for Machine Learning.

    Flint, Anne Rike / Klopfenstein, Sophie A I / Heeren, Patrick / Balzer, Felix / Poncette, Akira-Sebastian

    Studies in health technology and informatics

    2017  Volume 294, Page(s) 273–274

    Abstract: Alarms help to detect medical conditions in intensive care units and improve patient safety. However, up to 99% of alarms are non-actionable, i.e. alarm that did not trigger a medical intervention in a defined time frame. Reducing their amount through ... ...

    Abstract Alarms help to detect medical conditions in intensive care units and improve patient safety. However, up to 99% of alarms are non-actionable, i.e. alarm that did not trigger a medical intervention in a defined time frame. Reducing their amount through machine learning (ML) is hypothesized to be a promising approach to improve patient monitoring and alarm management. This retrospective study presents the technical and medical pre-processing steps to annotate alarms into actionable and non-actionable, creating a basis for ML applications.
    MeSH term(s) Clinical Alarms ; Critical Care ; Humans ; Intensive Care Units ; Machine Learning ; Monitoring, Physiologic ; Retrospective Studies
    Language English
    Publishing date 2017-10-05
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI220453
    Database MEDical Literature Analysis and Retrieval System OnLINE

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