On this page, we list the scientific papers published to date, as well as posters from events and conferences.
Posters
DGINA Annual Conference 2023
Emergency Medical Service Dispatches to Emergency Departments in Hesse Based on Hospital Size—An Analysis of IVENA Data
Authors: Jens Christoph STELTNER, Patrick MUELLER-NOLTE, Klaus WEBER, Daniel KERSTEN, Rudolf ALEXI, Andreas JERRENTRUP
Introduction: Systematic analyses of emergency medical service referrals based on the size of the receiving hospital are not yet available. Only anecdotal assumptions and isolated evaluations of triage data exist. Since 2017, the IVENA system has been used throughout Hesse for the referral and registration of emergency medical service patients to emergency departments. Data from 21 hospitals in Hesse form the basis of this initial analysis.
Methods: These analyses are based on referral data from the 21 hospitals exported from the IVENA system. For the collaborative analysis of this data, a web-based database platform was created, which currently contains approximately 1.1 million referrals since 2017. From this database, referrals with the following characteristics were identified and classified into groups based on the number of beds: “small” (< 250 beds), “medium” (250–750 beds), and “large” (> 750 beds). The study examined distributions by age, time of day, and day of the week, as well as the relationship to treatment urgency (triage categories 1 through 3). In addition, the proportions of referrals accompanied by an emergency physician and secondary transports were analyzed.
Results: The specific results will be presented during the session. Numerous size-related differences are evident: While referrals to small hospitals remain consistent across all age groups, those to large hospitals decrease significantly with age. Only 24% of patients over 90 years of age are referred to large hospitals, compared to 41% of those aged 18–29. No significant differences were found regarding referrals by time of day or day of the week. SK1 referrals (requiring immediate treatment, predominantly requiring a trauma room) account for 16% of referrals to large hospitals, compared to 8% and 5% for small and medium-sized hospitals, respectively—representing a proportion that is twice as high and more than three times as high, respectively. This also applies to transfers accompanied by an emergency physician (18% at large hospitals). Overall, nearly half of all recorded transfers accompanied by an emergency physician occurred at large hospitals. Secondary transfers account for only a very small proportion (3%) at small and medium-sized hospitals, whereas they make up nearly 6% of all transfers at large hospitals.
Discussion: In our view, these data can serve as a valuable tool for resource planning in emergency departments; therefore, additional metrics and categorizations should be evaluated in the future to obtain more precise results.
Related files: View Poster as PDF
Authors: Jens Christoph STELTNER, Patrick MUELLER-NOLTE, Klaus WEBER, Daniel KERSTEN, Rudolf ALEXI, Andreas JERRENTRUP
Introduction: Systematic analyses of emergency medical service referrals based on the size of the receiving hospital are not yet available. Only anecdotal assumptions and isolated evaluations of triage data exist. Since 2017, the IVENA system has been used throughout Hesse for the referral and registration of emergency medical service patients to emergency departments. Data from 21 hospitals in Hesse form the basis of this initial analysis.
Methods: These analyses are based on referral data from the 21 hospitals exported from the IVENA system. For the collaborative analysis of this data, a web-based database platform was created, which currently contains approximately 1.1 million referrals since 2017. From this database, referrals with the following characteristics were identified and classified into groups based on the number of beds: “small” (< 250 beds), “medium” (250–750 beds), and “large” (> 750 beds). The study examined distributions by age, time of day, and day of the week, as well as the relationship to treatment urgency (triage categories 1 through 3). In addition, the proportions of referrals accompanied by an emergency physician and secondary transports were analyzed.
Results: The specific results will be presented during the session. Numerous size-related differences are evident: While referrals to small hospitals remain consistent across all age groups, those to large hospitals decrease significantly with age. Only 24% of patients over 90 years of age are referred to large hospitals, compared to 41% of those aged 18–29. No significant differences were found regarding referrals by time of day or day of the week. SK1 referrals (requiring immediate treatment, predominantly requiring a trauma room) account for 16% of referrals to large hospitals, compared to 8% and 5% for small and medium-sized hospitals, respectively—representing a proportion that is twice as high and more than three times as high, respectively. This also applies to transfers accompanied by an emergency physician (18% at large hospitals). Overall, nearly half of all recorded transfers accompanied by an emergency physician occurred at large hospitals. Secondary transfers account for only a very small proportion (3%) at small and medium-sized hospitals, whereas they make up nearly 6% of all transfers at large hospitals.
Discussion: In our view, these data can serve as a valuable tool for resource planning in emergency departments; therefore, additional metrics and categorizations should be evaluated in the future to obtain more precise results.
Related files: View Poster as PDF
Quantitative Trends in Emergency Medical Service Tracer Diagnoses Before and During the COVID-19 Pandemic in Hesse Based on IVENA Data (2019–2022)
Authors: Patrick MUELLER-NOLTE, Klaus WEBER, Jens Christoph STELTNER, Andreas JERRENTRUP, Rudolf ALEXI, Daniel KERSTEN
Introduction: During the pandemic years 2020–2022, changes in emergency medical service referral patterns were observed. Analyses of emergency department data show a reduction in respiratory diseases at the start of the pandemic. Diagnoses such as stroke or ACS remained unchanged. The aim of this study is to analyze the referral patterns of emergency medical services in Hesse using the tracer diagnoses ACS/STEMI, stroke, COPD/bronchitis/pneumonia, pulmonary embolism, and CPR.
Methods: The analysis is based on data exported from hospitals via the IVENA system. Upon registration, a data record is generated and stored for each call, containing all relevant information in anonymized form. For the collaborative analysis of this data, a web-based database platform was created, which currently contains approximately 1.1 million referrals from 21 hospitals since 2017. Using SQL queries, the number of referrals with the following characteristics was determined from this database and evaluated in groups according to the following categories: Total number of emergency medical service referrals, as well as the tracer diagnoses STEMI/ACS, COPD/bronchitis/pneumonia, stroke, pulmonary embolism, and CPR per quarter from 2019 to 2022.
Results: COPD/bronchitis/pneumonia: Starting from the peak in Q1 2019 (6.41%), a significant decline in referrals can be observed beginning in Q2 2020, which continued through 2020, 2021, and most of 2022 compared to the pre-pandemic year of 2019. It was not until IV/22 that a sharp rise back to pre-pandemic levels (5.98%) was observed. STEMI/ACS and strokes did not show such a downward trend and remained stable. Pulmonary embolism and CPR also showed sharp declines in the early phase of the pandemic (II/20–IV/20).
Discussion: In addition to a reduction in the number of emergency department visits during the first months of the pandemic compared to the same months of the previous year (–8% in our data), there was a noticeable decline in admissions with diagnoses of COPD, bronchitis, and pneumonia. The causes of this require further research but are likely related to general infection control measures. The sharp resurgence in IV/22 aligns with feedback from emergency departments and epidemiological forecasts. For STEMI/ACS, no decline was observed during the pandemic months; further evaluation is needed regarding prehospital care and actual hospital diagnoses. The available data show the utilization of emergency medical services and emergency departments.
Related files: View Poster as PDF
Authors: Patrick MUELLER-NOLTE, Klaus WEBER, Jens Christoph STELTNER, Andreas JERRENTRUP, Rudolf ALEXI, Daniel KERSTEN
Introduction: During the pandemic years 2020–2022, changes in emergency medical service referral patterns were observed. Analyses of emergency department data show a reduction in respiratory diseases at the start of the pandemic. Diagnoses such as stroke or ACS remained unchanged. The aim of this study is to analyze the referral patterns of emergency medical services in Hesse using the tracer diagnoses ACS/STEMI, stroke, COPD/bronchitis/pneumonia, pulmonary embolism, and CPR.
Methods: The analysis is based on data exported from hospitals via the IVENA system. Upon registration, a data record is generated and stored for each call, containing all relevant information in anonymized form. For the collaborative analysis of this data, a web-based database platform was created, which currently contains approximately 1.1 million referrals from 21 hospitals since 2017. Using SQL queries, the number of referrals with the following characteristics was determined from this database and evaluated in groups according to the following categories: Total number of emergency medical service referrals, as well as the tracer diagnoses STEMI/ACS, COPD/bronchitis/pneumonia, stroke, pulmonary embolism, and CPR per quarter from 2019 to 2022.
Results: COPD/bronchitis/pneumonia: Starting from the peak in Q1 2019 (6.41%), a significant decline in referrals can be observed beginning in Q2 2020, which continued through 2020, 2021, and most of 2022 compared to the pre-pandemic year of 2019. It was not until IV/22 that a sharp rise back to pre-pandemic levels (5.98%) was observed. STEMI/ACS and strokes did not show such a downward trend and remained stable. Pulmonary embolism and CPR also showed sharp declines in the early phase of the pandemic (II/20–IV/20).
Discussion: In addition to a reduction in the number of emergency department visits during the first months of the pandemic compared to the same months of the previous year (–8% in our data), there was a noticeable decline in admissions with diagnoses of COPD, bronchitis, and pneumonia. The causes of this require further research but are likely related to general infection control measures. The sharp resurgence in IV/22 aligns with feedback from emergency departments and epidemiological forecasts. For STEMI/ACS, no decline was observed during the pandemic months; further evaluation is needed regarding prehospital care and actual hospital diagnoses. The available data show the utilization of emergency medical services and emergency departments.
Related files: View Poster as PDF
What Time-of-Day and Specialty-Specific Resources Do Emergency Medical Service Patients Need in Emergency Departments? A Big Data Analysis.
Authors: Andreas JERRENTRUP, Klaus WEBER, Patrick MUELLER-NOLTE, Rudolf ALEXI, Daniel KERSTEN, Jens Christoph STELTNER
Introduction: Due to the COVID-19 pandemic, as well as fundamental structural issues, hospital capacities are limited nationwide. Emergency department patients compete with elective patients for hospital beds, diagnostic, and therapeutic resources. Given this situation, precise and forward-looking planning that accounts for the likely volume of emergency patients by medical specialty, time of day, and specific resource needs—such as cardiac catheterization and trauma rooms—within a region is essential. This study analyzes the general temporal patterns and volume of emergency medical service patients using a very large database.
Methods: All emergency medical service referrals in Hesse are registered at hospitals via the IVENA electronic bed allocation system. As part of the registration process, a data record is generated and stored for each call, containing all relevant information in anonymized form. To analyze this data, a web-based database platform was created, which currently contains nearly 1.1 million referrals to 21 hospitals since 2017. Using SQL queries, the number of referrals per hour of the day was analyzed from this database, broken down by specialty (internal medicine, trauma surgery, neurology, pediatrics, and urology) and specific resource requirements (trauma room, cardiac catheterization).
Results: The peak in emergency medical service referrals for patients with conservative clinical presentations (internal medicine, neurology) is reached as early as 10 a.m.; after a brief plateau lasting 2–3 hours, there is a significant decline in referrals. Emergency medical service referrals for trauma surgery and pediatric conditions reach their peak around 11 a.m. and show a second peak in the late afternoon or early evening. Requests for urgent cardiac catheterization peak at 10 a.m., while most trauma surgery emergency rooms are referred by the emergency medical service around 5 p.m.
Discussion: The figures derived from a very large database reveal very clear and striking diurnal variations in the demand for acute cardiac catheterization, trauma rooms, and specialty-specific diagnostic and treatment resources. This generally allows for advance planning of time-of-day and specialty-specific resource allocation in emergency departments, especially when the planning involves multiple emergency departments.
Related Files: View Poster as PDF
Authors: Andreas JERRENTRUP, Klaus WEBER, Patrick MUELLER-NOLTE, Rudolf ALEXI, Daniel KERSTEN, Jens Christoph STELTNER
Introduction: Due to the COVID-19 pandemic, as well as fundamental structural issues, hospital capacities are limited nationwide. Emergency department patients compete with elective patients for hospital beds, diagnostic, and therapeutic resources. Given this situation, precise and forward-looking planning that accounts for the likely volume of emergency patients by medical specialty, time of day, and specific resource needs—such as cardiac catheterization and trauma rooms—within a region is essential. This study analyzes the general temporal patterns and volume of emergency medical service patients using a very large database.
Methods: All emergency medical service referrals in Hesse are registered at hospitals via the IVENA electronic bed allocation system. As part of the registration process, a data record is generated and stored for each call, containing all relevant information in anonymized form. To analyze this data, a web-based database platform was created, which currently contains nearly 1.1 million referrals to 21 hospitals since 2017. Using SQL queries, the number of referrals per hour of the day was analyzed from this database, broken down by specialty (internal medicine, trauma surgery, neurology, pediatrics, and urology) and specific resource requirements (trauma room, cardiac catheterization).
Results: The peak in emergency medical service referrals for patients with conservative clinical presentations (internal medicine, neurology) is reached as early as 10 a.m.; after a brief plateau lasting 2–3 hours, there is a significant decline in referrals. Emergency medical service referrals for trauma surgery and pediatric conditions reach their peak around 11 a.m. and show a second peak in the late afternoon or early evening. Requests for urgent cardiac catheterization peak at 10 a.m., while most trauma surgery emergency rooms are referred by the emergency medical service around 5 p.m.
Discussion: The figures derived from a very large database reveal very clear and striking diurnal variations in the demand for acute cardiac catheterization, trauma rooms, and specialty-specific diagnostic and treatment resources. This generally allows for advance planning of time-of-day and specialty-specific resource allocation in emergency departments, especially when the planning involves multiple emergency departments.
Related Files: View Poster as PDF
EUSEM22 / DGINA Jahrestagung 2022
Kollaborative Statistiken zur Versorgungsforschung aus dem hessischen IVENA – eine Erstvorstellung
Autoren: Jens Christoph STELTNER, Andreas JERRENTRUP, Klaus WEBER, Patrick MUELLER-NOLTE, Rudolf ALEXI, Daniel KERSTEN
Abstract: In Deutschland wird seit 2010 das IVENA eHealth System genutzt, um Patienten entsprechend der aktuellen Behandlungs- und Versorgungsmöglichkeiten gezielt in Kliniken disponieren zu können. Die webbasierte Anwendung ermöglicht darüber hinaus auch überregionale Ressourcenübersichten und den schnellen Austausch zwischen den Krankenhäusern, den Zentralen Leitstellen, den Gesundheitsbehörden und anderen medizinischen Diensten.
Aus diesem System können sich die teilnehmenden Kliniken umfassende Datensätze exportieren, bislang gab es allerdings keine Möglichkeit zur zielorientierten Weiterverarbeitung dieser Daten. Aus der Landesgruppe der DGINA Hessen entstand 2021 die AG Versorgungsforschung, welche sich den Aufbau einer webbasierten Plattform zur kollaborativen Auswertung der Zuweisungsdaten als Ziel gesetzt hat.
Im Rahmen der Entwicklung dieser Plattform beteiligen sich in der derzeitigen Testphase bereits 16 hessische Kliniken, mit aktuell über 625.000 Einsätzen die bis ins Jahr 2017 zurückdatieren. Dies stellt bereits heute eine völlig neuartige Datenbasis mit bislang unerreichter Menge an individuellen Datensätzen dar. So können Informationen wie Zuweisungscodes, Transportdaten, oder auch die Anfrage bestimmter Ressourcen wie z.B. des Schockraumes, oder es Herzkatheters betrachtet werden. Ebenfalls enthalten sind Merkmale des Einsatzes wie Notarztbegleitung, Beatmung, oder Infektiosität. Dadurch ist es u.a. erstmals möglich ein genaues Bild des zeitlichen Ablaufs von bestimmten Zuweisungen abhängig von Uhrzeiten, Wochentagen oder nach anderen Kriterien zu erhalten.
Wir präsentieren eine Reihe von informativen Auswertungen auf Basis der aktuellen Datenbasis, um statistische Aussagen über die Zuweisungen entsprechend verschiedener Kriterien zu treffen und dadurch bislang unbekannte Einblicke in die Realität der rettungsdienstlichen Zuweisungen zu liefern.
Aus diesem System können sich die teilnehmenden Kliniken umfassende Datensätze exportieren, bislang gab es allerdings keine Möglichkeit zur zielorientierten Weiterverarbeitung dieser Daten. Aus der Landesgruppe der DGINA Hessen entstand 2021 die AG Versorgungsforschung, welche sich den Aufbau einer webbasierten Plattform zur kollaborativen Auswertung der Zuweisungsdaten als Ziel gesetzt hat.
Im Rahmen der Entwicklung dieser Plattform beteiligen sich in der derzeitigen Testphase bereits 16 hessische Kliniken, mit aktuell über 625.000 Einsätzen die bis ins Jahr 2017 zurückdatieren. Dies stellt bereits heute eine völlig neuartige Datenbasis mit bislang unerreichter Menge an individuellen Datensätzen dar. So können Informationen wie Zuweisungscodes, Transportdaten, oder auch die Anfrage bestimmter Ressourcen wie z.B. des Schockraumes, oder es Herzkatheters betrachtet werden. Ebenfalls enthalten sind Merkmale des Einsatzes wie Notarztbegleitung, Beatmung, oder Infektiosität. Dadurch ist es u.a. erstmals möglich ein genaues Bild des zeitlichen Ablaufs von bestimmten Zuweisungen abhängig von Uhrzeiten, Wochentagen oder nach anderen Kriterien zu erhalten.
Wir präsentieren eine Reihe von informativen Auswertungen auf Basis der aktuellen Datenbasis, um statistische Aussagen über die Zuweisungen entsprechend verschiedener Kriterien zu treffen und dadurch bislang unbekannte Einblicke in die Realität der rettungsdienstlichen Zuweisungen zu liefern.
Preise: 2. Posterpreis der DGINA e.V. 2022
Zugehörige Dateien: Poster als PDF ansehen