Publikationen
Patientendatenmangementsysteme (PDMS) sind computerbasierte Systeme zur Verarbeitung von medizinischen Daten, die auf Intensivstationen sowohl positive als auch negative Einflüsse auf die Patientenversorgung haben können. Die Faktoren, die darüber entscheiden, ob ein PDMS eher Vor- oder Nachteile bringt und wie deutlich diese ausfallen, werden unter dem Begriff „Gebrauchstauglichkeit“ zusammengefasst. Diese Arbeit fasst die Ergebnisse aus 3 bereits publizierten Arbeiten zu diesem Thema zusammen.
We live in an age characterized by computerized information, but ubiquitous information technology has profoundly changed our healthcare systems and, if not adequately trained to deal with it, healthcare professionals can all too easily be overwhelmed by the complexity and magnitude of the data. This demands new skills from physicians as well as novel ways to provide medical knowledge. Selecting and assessing relevant information presents a challenge which can only be met by bridging the various disciplines in healthcare and the data sciences. This book presents the proceedings of the 62nd annual meeting of the German Association of Medical Informatics, Biometry and Epidemiology (German Medical Data Sciences – GMDS 2017): Visions and Bridges, held in Oldenburg, Germany, in September 2017. The 242 submissions to the conference included 77 full papers, of which 42 were accepted for publication here after rigorous review. These are divided into 7 sections: teaching and training; epidemiological surveillance, screening and registration; research methods; IT infrastructure for biomedical research/data integration centers; healthcare information systems; interoperability – standards, terminologies, classification; and biomedical informatics, innovative algorithms and signal processing. The book provides a vision for healthcare in the information age, and will be of interest to all those concerned with improving clinical decision making and the effectiveness and efficiency of health systems using data methods and technology.
Reusing data from electronic health records for clinical and translational research and especially for patient recruitment has been tackled in a broader manner since about a decade. Most projects found in the literature however focus on standalone systems and proprietary implementations at one particular institution often for only one singular trial and no generic evaluation of EHR systems for their applicability to support the patient recruitment process does yet exist. Thus we sought to assess whether the current generation of EHR systems in Germany provides modules/tools, which can readily be applied for IT-supported patient recruitment scenarios.
Emergency rooms of hospitals provide care to a lot of patients and have great impact on their outcome, so researching the quality of care seems important. Research using registries has yielded impressive results in other areas of medicine. However centralized data-storage has its pitfalls, especially regarding data privacy. We therefore drafted an IT infrastructure that uses decentralized storage to ensure data privacy, but still enables data transfer between participating hospitals. It implements an independent information broker to ensure anonymity of patients. Still it provides a way for researchers to request data and hospitals to contribute data on an opt-in basis. Although not an entirely new approach, the emphasis on data privacy throughout the design is a novel aspect providing a better balance between the need for big sample sizes and patient privacy.
Clinical decision support systems are an important aspect of medical informatics. The increasing amount of available patient data requires physicians to rely on information technology for research and during their day by day work. In intensive care medicine, fast actions are especially important. One major step towards enabling direct interaction of medical staff with patient data was the development of clinical data repositories with easy query frontends. While clinical data repositories can be extended for the use of real-time data, the corresponding query frontends do not support the time concepts necessary for real-time queries and decision support. Aim of this project is the development of a user interface to give physicians visual understanding of propositional logic combined with time concepts. Thus, physicians should be able formulate simple time based queries on their own–and validate and quality check complex queries created by medical informatics experts.
More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: “patient-centred recording and use of medical data for cooperative care”; “process-integrated decision support through current medical knowledge” and “comprehensive use of patient data for research and health care reporting” have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.
Das Wort „Sekundäralarm“ oder manchmal auch „Komfortalarm“ bedeutet, dass ein System auf einen kritischen Zustand (Alarmbedingung) hinweist, dies aber nicht mit der Sicherheit eines Alarmsystems geschieht. Im Kleingedruckten findet man dann auch den Hinweis, dass der Sekundäralarm eigentlich gar kein Alarm, sondern nur eine zusätzliche Information sei. Welche Konsequenzen dies für den Anwender hat und was bei der Anschaffung eines Alarmsystems beachtet werden sollte, fasst der vorliegende Beitrag in kompakter Form zusammen.
To define features and data items of a Patient Recruitment System (PRS); (2) to design a generic software architecture of such a system covering the requirements; (3) to identify implementation options available within different Hospital Information System (HIS) environments; (4) to implement five PRS following the architecture and utilizing the implementation options as proof of concept.
Ziel der Sektion Notaufnahmeprotokoll der Deutschen Interdisziplinären Vereinigung für Intensivmedizin und Notfallmedizin (DIVI) war die Entwicklung eines Datensatzes zur Dokumentation von Notfallpatienten in zentralen Notaufnahmen (ZNA).
Treatment of patients picked up by emergency services can be improved by data transfer ahead of arrival. Care given to emergency patients can be assessed and improved through data analysis. Both goals require electronic data transfer from the emergency medical services (EMS) to the hospital information system. Therefore a generic semantic standard is needed.
In recent years, prehospital emergency care adapted to the technology shift towards tablet computers and mobile computing. In particular, electronic patient care report (e-PCR) systems gained considerable attention and adoption in prehospital emergency medicine [1]. On the other hand, hospital information systems are already widely adopted. Yet, there is no universal solution for integrating prehospital emergency reports into electronic medical records of hospital information systems. Previous projects either relied on proprietary viewing workstations or examined and transferred only data for specific diseases (e.g. stroke patients[2]). Using requirements engineering and a three step software engineering approach, this project presents a generic architecture for integrating prehospital emergency care reports into hospital information systems. Aim of this project is to describe a generic architecture which can be used to implement data transfer and integration of pre hospital emergency care reports to hospital information systems. In summary, the prototype was able to integrate data in a standardized manner. The devised methods can be used design generic software for prehospital to hospital data integration.
Clinical data warehouses are used to consolidate all available clinical data from one or multiple organizations. They represent an important source for clinical research, quality management and controlling. Since its introduction, the data warehouse i2b2 gathered a large user base in the research community. Yet, little work has been done on the process of importing clinical data into data warehouses using existing standards. In this article, we present a novel approach of utilizing the clinical integration server as data source, commonly available in most hospitals. As information is transmitted through the integration server, the standardized HL7 message is immediately parsed and inserted into the data warehouse. Evaluation of import speeds suggest feasibility of the provided solution for real-time processing of HL7 messages. By using the presented approach of standardized data import, i2b2 can be used as a plug and play data warehouse, without the hurdle of customized import for every clinical information system or electronic medical record.
Medical documentation is getting more and more computerized. Therefore specialized Emergency Medicine Information-Management-Systems (EMIS) are commercial available. One problem of EMIS is the discontinuity between pre-hospital and hospital emergency services. The EMIS use the standardized protocol MIND 2 by the German Interdisciplinary Association of Critical Care Medicine whereas is non-standardized documentation and parameterization in the hospitals. Aim of this the development of a prototype of a generic interface between EMIS and Hospital Information Systems (HIS) or other information systems like patient data management systems (PDMS).
Due to the increasing functionality of medical information systems, it is hard to imagine day to day work in hospitals without IT support. Therefore, the design of dialogues between humans and information systems is one of the most important issues to be addressed in health care. This survey presents an analysis of the current quality level of human-computer interaction of healthcare-IT in German hospitals, focused on the users’ point of view.
Die meisten heute auf dem Markt verfügbaren Patientendatenmanagementsysteme (PDMS) haben ihre Gebrauchstauglichkeit über Pilotinstallationen bewiesen. Hauptgrund gegen die Einführung eines PDMS sind die damit verbundenen hohen Kosten. Hier sind neben den Beschaffungskosten (Hardware, Softwarelizenzen, Dienstleistung) auch die Eigenleistungen (Personalkosten) zu berücksichtigen. Eigenleistungen führen neben dem technologischen Kompetenzerwerb auch zu einer strukturierten Reflexion der eigenen Strukturen und Prozesse.
Das Überleben kritisch kranker Patienten auf der Intensivstation hängt wäh-rend des Intensivaufenthaltes wesentlich vom schnellen Erkennen und Therapieren von oftmals wechselnden Krankheitsbildern ab. Neben akuten lebensbedrohlichen Zuständen, wie z.B. einem Herz-Kreislauf-Stillstand, sind es vor allem auch die subakuten Erkrankungen, wie die Sepsis, das Acute Respiratory Distress Syndrome (ARDS) sowie das Nieren- und Leberversagen, bei denen das Outcome ganz entscheidend von der Reaktionszeit abhängt. Daher spricht man bei diesen Erkrankungen auch von der „Golden Hour“.
Prospective observational study to assess the impact of two different sampling strategies on the score results of the NEMS, used widely to estimate the amount of nursing workload in an ICU.
Supporting medical processes is among the most difficult endeavors. In contrast to uniform and unvaried workflows, the complexity and dynamics of patient treatment processes prevents the application of standard methodologies and tools, such as workflow systems. Despite long-term research in flexible and adaptive workflows as well as computerized clinical guidelines there are hardly any applications used in clinical routine. However, Standard Operation Procedures are a key element for any hospital to continuously improve their processes with regard to quality of patient care as well as resources required. Based on a three-level representation of know-how about patient care and treatment, we present a methodology for a stepwise formalisation and automation of clinical guidelines embedded into a patient data management system.
Nach den Publikationen zuden allgemeinen Empfehlungen und Anforderungenzur Implementierung von DV-Systemen in Anäs-thesie, Intensivmedizin, Notfallmedizin und Schmerz-therapie sowie den speziellen Empfehlungen undAnforderungen zur Implementierung von DV-Systemen in der Intensivmedizin durch die Arbeits-gruppe EDV des Forums “Qualitätsmanagement undÖkonomie“ der Deutschen Gesellschaft für Anäs-thesiologie und Intensivmedizin (DGAI) und desBerufverbandes Deutscher Anästhesisten (BDA) fol-gen nun die speziellen Empfehlungen und Anfor-derungen zur Implementierung eines Anästhesie-Informations-Management-Systems (AMS). Aufbau-end auf den allgemeinen Empfehlungen, werden indieser Arbeit die Aspekte Dokumentationsinhalte,Funktionalität, Anwenderoberfläche, Schnittstellensowie Reporting und Auswertungen diskutiert.
At the University Hospital Giessen, an anesthesia information management system (AIMS) is used for online record keeping of perioperative patient care, but preoperative anaesthesia assessments were still being recorded on paper and subsequently entered into the AIMS. Personal digital assistants (PDAs) seem to be useful instruments to establish a seamless digital anesthesiological documentation.
Nach der Publikation der all-gemeinen Empfehlungen und Anforderungen zurImplementierung von DV-Systemen in Anästhesie,Intensivmedizin, Notfallmedizin und Schmerztherapiedurch die Arbeitsgruppe EDV des Forums “Qualitäts-management und Ökonomie“ der Deutschen Gesell-schaft für Anästhesiologie und Intensivmedizin(DGAI) und des Berufverbandes Deutscher Anäs-thesisten (BDA) folgen nun die speziellen Empfeh-lungen und Anforderungen zur Implementierung vonDV-Systemen in der Intensivmedizin. Bei diesen spe-ziellen klinischen Informations- und Arbeitsplatz-systemen handelt es sich um sog. intensivmedizini-sche Informations-Management-Systeme (IMS).Aufbauend auf den allgemeinen Empfehlungen wer-den in dieser Arbeit die Aspekte Inhalte des Doku-mentationssystems, Funktionalität, Anwenderober-fläche, Schnittstellen sowie Reporting undAuswertungen diskutiert.
Within surgical departments, a large amount of antibiotics is used for perioperative prophylaxis. Despite the existence of several guidelines and recommendations for administering antibiotic prophylaxis, mistakes still do occur and have an unknown impact on outcome severity. Based on the electronic anaesthesia records of 4304 patients undergoing defined surgical procedures requiring perioperative antibiotic prophylaxis, a matched pairs approach was used to evaluate the impact of inadequate antibiotic prophylaxis on hospital mortality and prolonged length of stay on intensive care. Stepwise regression models were developed to predict the impact of inadequate antibiotic prophylaxis on outcome measures. An inadequate antibiotic prophylaxis was found for a total of 877 cases. 77.9% of cases were successfully matched, leading to 683 cases and controls each. The crude mortality ratio of cases to controls was 1.5 (cases = 21 versus controls = 14; P = 0.19). The case group had a significantly (P < 0.01) prolonged stay on ICU when analysed as a metric variable. Using logistic regression analysis, we could determine that inadequate antibiotic prophylaxis had no impact on either hospital mortality or prolonged length of stay on ICU (>1 day; yes or no).
In einem ersten Schritt hat die Arbeitsgruppe EDV des Forums “Qualitätsmanagement und Ökonomie“ der Deutschen Gesellschaft für Anästhesiologie und Intensivmedizin (DGAI) und des Berufsverbandes Deutscher Anästhesisten (BDA) ein Basispapier formuliert, welches allgemeine Empfehlungen und Anforderungen zur Implementierung von DV-Systemen in Anästhesie, Intensivmedizin, Notfallmedizin und Schmerztherapie beschreibt. Weitere Publikationen zu den speziellen Anforderungen der einzelnen Bereiche werden folgen. Die meisten Kriterien betreffen nicht nur Patienten-Daten-Management-Systeme,sondern z.T. auch datenbankbasierte Belegleser-Systeme. Vor der Beschaffung eines DV-Systems muss ein Pflichtenheft erstellt werden als Basis für die Ausschreibung und die Angebote der einzelnen Anbieter. Es dient weiterhin als Grundlage für das später zu erstellende Einführungs- und Schulungskonzept. In diesem Pflichtenheft werden alle Anforderungen an das künftige EDV-System definiert, welches die Punkte Systemanforderungen, Einbindung in die IT-Landschaft, Anforderungen der Anwender und Administration beinhalten sollte.
The goal of this paper is to describe the clinical needs and the informational methodology which led to the realization of a realtime shared patient chart. It is an integral part of the communications infrastructure of the Patient Data Management System (PDMS) ICUData which is in routine use at the intensive care unit (ICU) of the Department for Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. ICUData utilizes a four tier system architecture consisting of modular clients, message forwarders, application servers and a relational database management system. All layers communicate with health level seven messages. The innovative aspect of this architecture consists of the interposition of a message forwarder layer which allows for instant exchange of patient data between the clients without delays caused by database access. This works even in situations with high workload as in patient monitoring. Therefore a system with many workstations acts a blackboard for patient data allowing shared access under realtime conditions. Realized first as an experimental feature, it has been embraced by the clinical users and served well during the documentation of more than 18000 patient stays.
Die Austauschbarkeit der in klinischen Subsystemen erhobenen Daten gewinnt angesichts neuer Dokumentationsvorgaben von Seiten des Gesetzgebers (DRG, Zusatzentgelte für die intensivmedizinische Behandlung) sowie der klinischen Forschung (Telematikplattform) zunehmend an Bedeutung. Dies betrifft heutzutage nicht nur Fragen der Nachrichtensyntax, die inzwischen insbesondere durch die Verbreitung des HL7-Kommunikationsstandards [1] einer Lösung nähergebracht wurden, sondern auch der Semantik für die sich verschiedenste Lösungsmöglichkeiten anbieten [2]. Im Gießener Universitätsklinikum wurde 1999 mit der Einführung eines Patienten Daten Management Systems (PDMS) auf der operativen Intensivstation begonnen [3]. Seitdem wurde dieses System auf allen Intensivstationen eingeführt und dient der weitgehend papierlosen Dokumentation der intensivmedizinischen Behandlungsabläufe. Das PDMS ist eng in den Kommunikationsablauf des gesamten KIS integriert und somit sowohl Konsument als auch Lieferant vieler Daten [4]. Dies wird durch die weitgehend strukturierte und auf einem abteilungsspezifischen Vokabular basierende Dokumentation erleichtert. Ziel dieser Arbeiten ist die Untersuchung, inwieweit sich das im US amerikanischen Sprachraum zunehmend etablierte LOINC Vokabular [5] eignet, die semantische Interoperabilität des PDMS gegenüber anderen Subsystemen zu erhöhen.
The most recent approach to estimate nursing resources consumption has led to the generation of the Nine Equivalents of Nursing Manpower use Score (NEMS). The objective of this prospective study was to establish a completely automatically generated calculation of the NEMS using a patient data management system (PDMS) database and to validate this approach by comparing the results with those of the conventional manual method.
The major intent of this article was to describe the design principles of the drug-therapy documentation module of the Patient Data Management System (PDMS) ICUData, in routine use at the intensive care unit (ICU) of the Department of Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. The new drug management system has been in routine use since March 2000. Until 8 January 2001, 1140 patients have been documented using this approach. It could be demonstrated that it was possible to transform the formerly unstructured text-based documentation into a detailed and structured model. The mediated benefit resulted in the automatic calculation of fluid balance. Further, detailed statistical analyses of therapeutic behavior in drug administration are now possible.
The objective of this study was to establish a complete computerized calculation of the Simplified Acute Physiology Score (SAPS) II within 24 hours after admission to a surgical intensive care unit (ICU) based only on routine data recorded with a patient data management system (PDMS) without any additional manual data entry. Score calculation programs were developed using SQL scripts (Structured Query Language) to retrospectively compute the SAPS II scores of 524 patients who stayed in ICU for at least 24 hours between April 1, 1999 and March 31, 2000 out of the PDMS database. The main outcome measure was survival status at ICU discharge. Score evaluation was modified in registering missing data as being not pathological and using surrogates of the Glasgow Coma Scale (GCS). Computerized score calculation was possible for all investigated patients. The 459 (87.6%) survivors had a median SAPS II of 28 (interquartile range (IQR) 13) whereas the 65 (12.4%) deceased patients had a median score of 43 (IQR 16; P<0.001). Of the physiological variables for SAPS II score calculation, bilirubin was missing in 84%, followed by PaO2/FiO2ratio (34%), and neurological status (34%). Using neurological diagnoses and examinations as surrogates for the GCS, a pathological finding was seen in only 8.8% of all results. The discriminative power of the computerized SAPS II checked with a receiver operating characteristic (ROC) curve was 0.81 (95% confidence interval (CI): 0.74–0.87). The Hosmer-Lemeshow goodness-of-fit statistics showed good calibration (H=5.55, P=0.59, 7 degrees of freedom; C=5.55, P=0.68, 8 degrees of freedom). The technique used in this study for complete automatic data sampling of the SAPS II score seems to be suitable for predicting mortality rate during stay in a surgical ICU. The advantage of the described method is that no additional manual data recording is required for score calculation.The objective of this study was to establish a complete computerized calculation of the Simplified Acute Physiology Score (SAPS) II within 24 hours after admission to a surgical intensive care unit (ICU) based only on routine data recorded with a patient data management system (PDMS) without any additional manual data entry. Score calculation programs were developed using SQL scripts (Structured Query Language) to retrospectively compute the SAPS II scores of 524 patients who stayed in ICU for at least 24 hours between April 1, 1999 and March 31, 2000 out of the PDMS database. The main outcome measure was survival status at ICU discharge. Score evaluation was modified in registering missing data as being not pathological and using surrogates of the Glasgow Coma Scale (GCS). Computerized score calculation was possible for all investigated patients. The 459 (87.6%) survivors had a median SAPS II of 28 (interquartile range (IQR) 13) whereas the 65 (12.4%) deceased patients had a median score of 43 (IQR 16; P<0.001). Of the physiological variables for SAPS II score calculation, bilirubin was missing in 84%, followed by PaO2/FiO2ratio (34%), and neurological status (34%). Using neurological diagnoses and examinations as surrogates for the GCS, a pathological finding was seen in only 8.8% of all results. The discriminative power of the computerized SAPS II checked with a receiver operating characteristic (ROC) curve was 0.81 (95% confidence interval (CI): 0.74–0.87). The Hosmer-Lemeshow goodness-of-fit statistics showed good calibration (H=5.55, P=0.59, 7 degrees of freedom; C=5.55, P=0.68, 8 degrees of freedom). The technique used in this study for complete automatic data sampling of the SAPS II score seems to be suitable for predicting mortality rate during stay in a surgical ICU. The advantage of the described method is that no additional manual data recording is required for score calculation.The objective of this study was to establish a complete computerized calculation of the Simplified Acute Physiology Score (SAPS) II within 24 hours after admission to a surgical intensive care unit (ICU) based only on routine data recorded with a patient data management system (PDMS) without any additional manual data entry. Score calculation programs were developed using SQL scripts (Structured Query Language) to retrospectively compute the SAPS II scores of 524 patients who stayed in ICU for at least 24 hours between April 1, 1999 and March 31, 2000 out of the PDMS database. The main outcome measure was survival status at ICU discharge. Score evaluation was modified in registering missing data as being not pathological and using surrogates of the Glasgow Coma Scale (GCS). Computerized score calculation was possible for all investigated patients. The 459 (87.6%) survivors had a median SAPS II of 28 (interquartile range (IQR) 13) whereas the 65 (12.4%) deceased patients had a median score of 43 (IQR 16; P<0.001). Of the physiological variables for SAPS II score calculation, bilirubin was missing in 84%, followed by PaO2/FiO2ratio (34%), and neurological status (34%). Using neurological diagnoses and examinations as surrogates for the GCS, a pathological finding was seen in only 8.8% of all results. The discriminative power of the computerized SAPS II checked with a receiver operating characteristic (ROC) curve was 0.81 (95% confidence interval (CI): 0.74–0.87). The Hosmer-Lemeshow goodness-of-fit statistics showed good calibration (H=5.55, P=0.59, 7 degrees of freedom; C=5.55, P=0.68, 8 degrees of freedom). The technique used in this study for complete automatic data sampling of the SAPS II score seems to be suitable for predicting mortality rate during stay in a surgical ICU. The advantage of the described method is that no additional manual data recording is required for score calculation.
Ziel dieser retrospektiven Analyse war es zu prüfen, inwieweit Daten des Anästhesie-Informations-Management-Systems (AIMS) detailliertere Informationen für eine Verbrauchsanalyse und Bedarfsplanung liefern, ohne auf eine personalintensive Inventur zurückgreifen zu müssen. Hierzu wurden exemplarisch für den orthopädischen Operationsbereich die von der Materialwirtschaft im Jahr 2000 in Rechnung gestellten Kosten für anästhesierelevante Katheter und Tuben mit den über das AIMS dokumentierten Artikeln verglichen.
We sought to identify factors that are associated with hypotension after the induction of spinal anesthesia (SpA) by using an anesthesia information management system. Hypotension was defined as a decrease of mean arterial blood pressure of more than 30% within a 10-min interval, and relevance was defined as a therapeutic intervention with fluids or pressors within 20 min. From January 1, 1997, to August 5, 2000, data sets from 3315 patients receiving SpA were recorded on-line by using the automatic anesthesia record keeping system NarkoData. Hypotension meeting the predefined criteria occurred in 166 (5.4%) patients. Twenty-nine patient-, surgery-, and anesthesia-related variables were studied by using univariate analysis for a possible association with the occurrence of hypotension after SpA. Logistic regression with a forward stepwise algorithm was performed to identify independent variables (P < 0.05). The discriminative power of the logistic regression model was checked with a receiver operating characteristic curve. Calibration was tested with the Hosmer-Lemeshow goodness-of-fit test. The univariate analysis identified the following variables to be associated with hypotension after SpA: age, weight, height, body mass index, amount of plain bupivacaine 0.5% used for SpA, amount of colloid infusion before puncture, chronic alcohol consumption, ASA physical status, history of hypertension, urgency of surgery, surgical department, sensory block height of anesthesia, and frequency of puncture. In the multivariate analysis, independent factors for relevant hypotension after SpA consisted of three patient-related variables (“chronic alcohol consumption,” odds ratio [OR] = 3.05; “history of hypertension,” OR = 2.21; and the metric variable “body mass index,” OR = 1.08) and two anesthesia-related variables (“sensory block height,” OR = 2.32; and “urgency of surgery,” OR = 2.84). The area of 0.68 (95% confidence interval, 0.63–0.72) below the receiver operating characteristic curve was significantly greater than 0.5 (P < 0.01). The goodness-of-fit test showed a good calibration of the model (H = 4.3, df = 7, P = 0.7; C = 7.3, df = 8, P = 0.51). This study contributes to the identification of patients with a high risk for hypotension after SpA induction, with the risk increasing two- or threefold with each additional risk factor.
To investigate a fully automated and modified APACHE II score calculation exclusively based on routine data supplied by patient data management system, the ICUData, and to assess the predictive performance of this score using analysis of discrimination and calibration at an operative ICU.
In our department, we have been using an Anesthesia Information Management System (AIMS) for five years. In this study, we tested to what extent data extracted from the AIMS could be suitable for the supervision and time-management of operating rooms.
To evaluate the discriminative power on mortality of a modified Sequential Organ Failure Assessment (SOFA) score and derived measures (maximum SOFA, total maximum SOFA, and delta SOFA) for complete automatic computation in an operative intensive care unit (ICU).
We used an anesthesia information management system (AIMS) to devise a score for predicting antiemetic rescue treatment as an indicator for postoperative nausea and vomiting (PONV) in the postanesthesia care unit (PACU). Furthermore, we wanted to investigate whether data collected with an AIMS are suitable for comparable clinical investigations. Over a 3-yr period (January 1, 1997, to December 31, 1999), data sets of 27,626 patients who were admitted postoperatively to the PACU were recorded online by using the automated anesthesia record keeping system NarkoData(R) (IMESO GmbH, Hüttenberg, Germany). Ten patient-related, 5 operative, 15 anesthesia-related, and 4 postoperative variables were studied by using forward stepwise logistic regression. Not only can the probability of having PONV in the PACU be estimated from the 3 previously described patient-related (female gender, odds ratio [OR] = 2.45; smoker, OR = 0.53; and age, OR = 0.995) and one operative variables (duration of surgery, OR = 1.005), but 3 anesthesia-related variables (intraoperative use of opioids, OR = 4.18; use of N(2)O, OR = 2.24; and IV anesthesia with propofol, OR = 0.40) are predictive. In implementing an equation for risk calculation into the AIMS, the individual risk of PONV can be calculated automatically.
The development of the ICUData patient data management system (PDMS) for intensive care units (ICU), by IMESO GmbH, Hüttenberg, Germany, was based on the assumption that processes and therapies at ICU are the most complex with the highest data density compared with those in other wards. Based on experience with the system and on a survey conducted among users at our pain clinic, we evaluated whether the concept of the present software architecture, which sufficiently reproduces processes and data at an ICU, is suitable as a PDMS for general wards. The highly modular and client-centric approach of the PDMS is founded on a message-based communications architecture (HL7). In the beginning of the year 2000, the system was implemented at the pain management clinic (12 beds) of our hospital. To assess its user friendliness, we conducted a survey of medical staff (n=14). From April 1st 2000 to August 31st 2000, all clinical and administrative data of 658 patients at the pain management clinic were recorded with the PDMS. From the start, all users had access to data and information of other connected data management systems of the hospital (e.g. patient administrative data, patient clinical data). Staff members found the system mostly useful, clearly presented, practical, and easy to learn and use. Users were relatively satisfied with stability and performance of the program but mentioned having only limited knowledge of the program’s features. The need for external support during a computer crash was rated negatively. Despite the need for further usage training and improved program performance, the software architecture described seems to be a promising starting point for the construction of a PDMS for general wards.
The aim of this project was to develop a cost-effective, standard-based and scalable clinical information system for use in Intensive Care Units (ICUs). The development started in 1998 at the University Giessen, Germany. Since its introduction as the basic documentation system at the ICU ward of the Department of Anesthesiology and Intensive Care Medicine in January 1999, all relevant clinical data of 1723 patients have been recorded. The implementation of the system in two further ICUs is scheduled for the year 2000. The following article describes some of the principal design goals of the system, including the medical vision that drove its interface design, and focuses on the technological underpinnings of the overall system architecture.
The influence of methods for record keeping on the documentation of vital signs was assessed for the Anesthesia Information Management System (AIMS) NarkoData. We compared manually entered blood-pressure readings with automatically collected data. These data were stored in a data-base and subsequently evaluated and analyzed. The data sets were split into two groups, “manual” and “automatic”. We evaluated the effect of automatic data collection on the incidence of corrected data, data validity and data variation. Blood-pressure readings of 37,726 data sets were analyzed. We could assess that the method of documentation did influence the data quality. It could not be assessed whether the incorrectness of data during automatic data gathering was caused by artefacts or by the anesthesiologist.
Der zunehmende Einsatz computergestützter Arbeitsplätze zur Dokumentation in der Anästhesie und Intensivmedizin erweitert die ärztlichen Kommunikationsmöglichkeiten inner- und außerhalb einer anästhesiologischen Abteilung. Über vernetzte Computer kann durch den Einsatz von Telekommunikationssoftware standortunäbhangiges medizinisches Expertenwissen zur Verfügung gestellt werden (Telekonsultation). Im Rahmen eines von der hessischen Landesregierung geförderten Projektes (Hessen Media, Telemedizin in der Anästhesie [1]) konnte an der Abteilung Anästhesiologie und Operative Intensivmedizin ein System zur internen Telekonsultation zwischen den 146 klinischen Arbeitsplätzen sowie für externe Telekonsile etabliert werden. Für die interne Telekonsultation wurde die Remote Control Software Timbuktu Pro® (Netopia, Alameda, USA) verwendet, die es unabhängig vom Standort erlaubt, über eine gemeinsame Sicht auf die aktuelle computergestützte Patientendokumentation (NarkoData®, ICUData®) dem am Narkose- oder Intensivarbeitsplatz tätigen Anästhesisten Unterstützung zu leisten. Eine Videokonferenzsoftware ermöglicht via ISDN z.B. bei Verlegung von Patienten der Intensivstation eine direkte Verbindung zu externen Kliniken.
In der Abteilung Anaesthesiologie und Operative Intensivmedizin der JLU Gießen erfolgt die Narkosedokumentation bereits seit 1995 mit einem Anasthesie-Informations-Management-System [1]. Seit Januar 1999 wird darüber hinaus auf 14 Routinebetten der Operativen Intensivstation die komplette Patientenakte mit dem neu entwickelten Patienten-Daten-Management-System (PDMS) “ICUdata” (Imeso GmbH, Huttenberg) erfasst. Da eine wesentliche Forderung an eine digitale Patientenakte die vollständige Integration aller zum Patienten erhobenen Befunde ist, sollte eine vollautomatische Konnektion der beiden Dokumentationssysteme mit der Übernahme aller relevanten Daten in die entsprechenden Befundkategorien des PDMS realisiert werden. Die Applikation “NarkoBatch” (Imeso GmbH, Hüuttenberg) überprüft die AIMS-Datenbank jede Minute, ob ein Protokoll mit einer neuen Operations-ID eingefügt wurde. Ist dies der Fall, wird ein definierter Auszug des Anästhesieprotokolls aus den Datenbankeinträgen zusammengestellt und als HL7-Nachricht über einen zusätzlichen Serverdienst in die äquivalenten Felder der PDMS-Datenbank geschrieben. Die Verbindung zweier Daten-Management-Systeme unterschiedlicher Struktur konnte so durch ein zusätzliches Programmodul erreicht werden, welches die Vermittlung zwischen den Datenbanken der Systeme automatisiert. Seit der Einführung des Importmoduls im Juni 1999 sind die Daten aus mehr als 13.000 Narkoseprotokollen in das PDMS integriert worden und stehen bei Aufnahme des entsprechenden Patienten zur Verfügung. Der automatisierte Datenimport aus dem Narkoseprotokoll erhöht die Datenqualität, stellt eine Erleichterung für das Personal der Intensivstation dar und vermeidet eine redundante Dateneingabe.
In this study, an Anesthesia Information Management System (AIMS) is used for the comparison of manually recorded adverse events with automatically detected events from anesthesiological procedures. In 1998, data from all anesthesia procedures, including the data set for quality assurance defined by the German Society of Anesthesiology and Intensive Care Medicine (DGAI), were recorded online with the documentation software NarkoData 4 (IMESO GmbH, Hüttenberg, Germany) followed by storage into a relational database (Oracle Corporation). The occurrence of manually recorded adverse events, as defined by the DGAI, is compared with automatically detected events. Automated detection was done with SQL-statements. The following adverse events were selected: hypotension, hypertension, bradycardia, tachycardia and hypovolemia. Data obtained from 16,019 electronic anesthesia records show that in 911 patients (5.7%), one of the selected adverse events was documented manually whereas in 2,996 patients (18.7%) a adverse event was detected automatically. The incidence of automatically detected events is obviously higher compared to manually recorded events. With the help of an AIMS, automatic detection proved significant deficiencies in the manual documentation of adverse events.
Im Rahmen der Evaluierung eines validierten Fragebogens zur Erfassung der Zufiiedenheit und Erwartungen der Anwender von Patienten-Daten-Management-Systemen (PDMS) wurde eine erste umfangreiche Version an vier operativen Intensivstationen in Kliniken der Maximalversorgung versandt. In dieser Arbeit sollen erste Ergebnisse dieser Befragung präsentiert werden. Im Dezember 1998 wurde ein Fragebogen mit 207 Fragen erstellt (Version 1), der allgemeine Fragen zur Person, zum PC-Arbeitsplatz, zur Anwenderoberflache ebenso wie spezielle Fragen zum Einfluß des Computersystems auf pflegerische, ärztliche und administrative Tätigkeiten enthält. Insgesamt wurden 84 Fragebögen ausgewertet. Die Rücklaufquote der beantworteten Fragebögen von den einzelnen Kliniken war sehr unterschiedlich (22-70%). 60,2% der Fragebögen wurden von Männern und 39,8% von Frauen beantwortet. 45,7% waren Pflegekräfte und 52,4% aus dem ärztlichen Bereich. Die Erfahrung mit der Arbeit an einem Computer wurde wie folgt angegeben: bis 2 Jahre 23,5%, 3 bis 7 Jahre 29,6% und über 7 Jahre 46,9%. Die Frage, ob die allgemeinen Erwartungen an ein Dokumentationssystem erfüllt wurden, beantworteten die Anwender überwiegend negativ. Bei den Fragen zu dem bestehenden PDMS bewerteten die Befragten die Systeme als nicht überflüssig und fanden sie leicht erlernbar. Die Anwender beurteilten die Aspekte Efiizienz, Arbeitserleichterung, Übersichtlichkeit und Zeiterspamis eher negativ; Fragen nach dem Arbeitsplatz und der Hardware wurden positiv beantwortet. Mit der Bildschirmauflösung, der Programmstabilität, der Bedienung mit Tastatur und Maus waren die Befragten zufrieden, jedoch nicht mit der Performance der Programme. Bei der Erhebung der Wünsche und Erwartungen an ein neues PDMS standen eine einfachere Präsentation der Ergebnisse, eine schnelle Behebung von Fehlem, die Bedienerfreundlichkeit, die Arbeitserleichterung sowie die Zeiterspamis im Vordergrund. Durchweg hoch bewertet wurde vor allem der Datenimport unterschiedlichster Parameter aus anderen DV-Systemen sowie die automatische Dateniibemahme aus verschiedenen technischen Geräten.
Main requirements for an Anesthesia Information Management System (AIMS) are the supply of additional information for the anesthesiologist at his workstation and complete documentation of the anesthetic procedure. With the implementation of an AIMS (NarkoData) and effective user support, the quality of documentation and the information flow at the anesthesia workstation could be increased. Today, more than 20,000 anesthesia procedures are annually recorded with the AIMS at 112 decentralized workstations. The network for data entry and the presentation and evaluation of data, statistics and results directly available at the clinical workstation was made operational.
Von Januar 1997 bis Juni 1999 wurde der komplette Aufenthalt aller tageschirurgischen Patienten, von der Aufnahme bis zur Entlassung, mit einem computergestützten Dokumentationssystem erfasst. Einzelne Aspekte und Ergebnisse aus dem Routinebetrieb sollen mit der Fragestellung vorgestellt werden, ob ein Anästhesie-Informations-Management-System (AIMS) geeignet ist, auch im Bereich des “Ambulanten Operierens” eine umfassende und qualitativ hochwertige Dokumentation zu gewährleisten.
The aim of this paper is the presentation of a new version of the anesthesia documentation software, NarkoData, that has been used in routine clinical work in our department as part of an anesthesia information management system (AIMS) since 1995. The performance of this software is presented along with requirements for future development of such a system. The originally used version, NarkoData 3.0, is an online anesthesia documentation software established by the software company ProLogic GmbH. It was primarily developed as a disk-based system for the MacOS operating system (Apple Computer Inc.). Based on our routine experience with the system, a catalogue of requirements was developed that concentrated on improvement in the sequence of work, administration and data management. In 1996, the concepts developed in our department, in close co-operation with medical personnel and the software company, led to a considerable enlargement of the program functions and the subsequent release of a new version of NarkoData. Since 1997, more than 20 000 anesthesia procedures have been recorded annually with this new version at 115 decentralized work stations at our university hospital.
In der Literatur wird die schwierige oder unmögliche Intubation je nach Definition und Patientengut mit einer Inzidenz von 0,04 % bis 13 % angegeben.Die präoperative Bestimmung des thyreo-und sternomentalen Abstandes oder die Erhebung komplexer Scores zur Vorhersage einer schwierigen Intubation gehen mit einem zusätzlichen Zeitaufwand einher.
Einflussfaktoren und Prädiktoren für die ungeplante stationäre Aufnahme tageschirurgischer Patienten
Für eine optimale und effektive Planung des täglichen Betriebs einer operativen Tagesklinik ist es unerlässlich, die Gründe für eine ungeplante stationäre Aufnahme ambulanter Patienten zu kennen. Ziel dieser Untersuchung war es, Einflussfaktoren und Prädiktoren für eine ungeplante stationäre Aufnahme für Patienten unserer Operativen Tagesklinik zu evaluieren. Dies erfolgte anhand von 3.152 Datensätzen tageschirurgischer Patienten, deren Aufenthalt computergestützt online erfasst wurde.
A deficit is suspected in the manual documentation ofadverse events in quality assurance programs in anesthesiology. In order toverify and quantify this, we retrospectively compared the incidence ofmanually recorded perioperative adverse events with automatically detectedevents.
The aim of this study was to utilize an anesthesiainformation management system (AIMS) in investigating the effects of hypnoticagents used to induce anesthesia on blood pressure, heart rate and arterialoxygen saturation. The characteristics of these agents, known from previousstudies, were compared to the effects documented in this study during routineclinical use.
The Experience of Three Years of Routine Operation of an Anaesthesia Information Management System (AIMS) at the University Hospital Giessen.
Das Anästhesie-Informations-Management-System (AIMS) unserer Abteilung liefert seit 1997 umfassendes Datenmaterial (DGAI-Kerndatensatz, Vitalparameter, Beatmungsparameter, Materialverbrauch usw.), das in einer relationalen Datenbank gespeichert wird. Die Verarbeitung der Daten mittels SQL-Abfragen (Structured Query Language) war nur wenigen Personen mit entsprechenden Fachkenntnissen vorbehalten. Ziel dieser Arbeit war es, ein Auswertungs-Tool zu erstellen, das es jedem Abteilungsmitglied ermöglicht, jederzeit aktuelle Abfragen zu entsprechenden Themen wie Leistungserfassung, Qualitätssicherung, Ausbildung und Wissenschaft selbständig durchzuführen und gleichzeitig in einer angemessenen Form zu präsentieren.
Die Abteilung Anästhesiologie und Operative Intensivmedizin der Justus-Liebig-Universität Giessen hat sich 1994 für den Aufbau eines Anästhesie-Informations-Management-Systems (AIMS) entschieden, um die bisherige manuelle Papierdokumentation abzulösen. Einzelne Aspekte und Ergebnisse dieses Datenpools sollen mit der Fragestellung vorgestellt werden, wie das System in seiner jetzigen Form geeignet ist, die Qualitätsdokumentation nach den Vorgaben der Deutschen Gesellschaft für Anästhesiologie und Intensivmedizin (DGAI) zu gewährleisten.
Die Abteilung Anaesthesiologie und Operative Intensivmedizin der Justus-Liebig-Universität Giessen hat sich 1994 für den Aufbau eines Anästhesie-Informations-Management-Systems (AIMS) entschieden, um die bisherige manuelle Papierdokumentation abzulösen. Einzelne Aspekte und Ergebnisse dieses Datenpools sollen mit der Fragestellung vorgestellt werden, wie das System in seiner jetzigen Form geeignet ist, die Qualitätsdokumentation nach den Vorgaben der Deutschen Gesellschaft für Anästhesiologie und Intensivmedizin (DGAI) zu gewährleisten.
The following paper analyses the transition from a host centered to a client-server based Hospital Information System (HIS) which takes place at the University Hospital of Gießen, Germany, since 1995. It focuses on the overall concept and practical realisation of the cornerstone within this transition, the Macintosh Mosaic Software Architecture (MMSA) which delivers now more than 90% of the clinical data which is entered on the wards and within the operating rooms. Besides the technical problems of the project, the paper also describes some aspects of the organisational challenges which took place within a typical German University Hospital during the last 4 Years and which mainly affect the daily management of a large HIS.
New functions have been integrated in the Giessen Hospital Information System WING to support the classification of all intensive care patients into the Therapeutic Intervention Scoring System TISS. The use of those functions has been pushed by legal requirements which made it essential to assess the staffing of intensive care wards in comparison with the accumulated TISS scores. This paper describes the experiences made within two years of TISS scoring. We think that TISS gave a major impact to the construction and implementation of nursing documentation into our HIS.
The following article describes strategies for the implementation of a decision monitoring system within the heterogeneous computer environment of a german university hospital. After a short discussion about different types of computerized decision support in medicine, it presents selection criteria for the integrated commercial software and design goals for the main components of the decision support software. At last, the current state of the system implementation is described.
At the Giessen University Hospital electronic data processing systems have been in routine use since 1975. In the early years developments were focused on ADT functions (admission/discharge/transfer) and laboratory systems. In the next decade additional systems were introduced supporting various functional departments. In the mid-eighties the need to stop the ongoing trend towards more and more separated stand-alone systems was realized and it was decided to launch a strategic evaluation and planning process which sets the foundation for an integrated hospital information system (HIS). The evaluation of the HELP system for its portability into the German hospital environment was the first step in this process. Despite its recognized capabilities in integrating decision support and communication technologies, and its powerful HIS development tools, the large differences between American and German hospital organization, influencing all existing HELP applications, and the incompatibility of the HELP tools with modern software standards were two important factors forcing the investigation of alternative solutions. With the HELP experience in mind, a HIS concept for the Giessen University Hospital was developed. This new concept centers on the idea of a centralized relational patient database on a highly reliable database server, and clinical front-end applications which might be running on various other computer systems (mainframes, departmental UNIX satellites or PCs in a LAN) integrated into a comprehensive open HIS network. The first step towards this integrated approach was performed with the implementation of ADT and results reporting functions on care units.
Data aquisition in pathology, like in every other clinical field, must be based on a structured representation of medical objectives if automated data analyzation should be possible. A system is described, which supports structured pathological data aquisition based on objects stored in a central medical data dictionary. The programs run on local workstations which are installed at the offices of pathologists. They are connected by a LAN to the hospital information systems central Tandem database computer.