Benchmarking von Krankenhausinformationssystemen – eine vergleichende Analyse deutschsprachiger Benchmarkingcluster

Benchmarking is a method of strategic information management used by many hospitals today. During the last years, several benchmarking clusters have been established within the German-speaking countries. They support hospitals in comparing and positioning their information system’s and information management’s costs, performance and efficiency against other hospitals. In order to differentiate between these benchmarking clusters and to provide decision support in selecting an appropriate benchmarking cluster, a classification scheme is developed. The classification scheme observes both general conditions and examined contents of the benchmarking clusters. It is applied to seven benchmarking clusters which have been active in the German-speaking countries within the last years. Currently, performance benchmarking is the most frequent benchmarking type, whereas the observed benchmarking clusters differ in the number of benchmarking partners and their cooperation forms. The benchmarking clusters also deal with different benchmarking subjects. Assessing costs and quality application systems, physical data processing systems, organizational structures of information management and IT services processes are the most frequent benchmarking subjects. There is still potential for further activities within the benchmarking clusters to measure strategic and tactical information management, IT governance and quality of data and data-processing processes. Based on the classification scheme and the comparison of the benchmarking clusters, we derive general recommendations for benchmarking of hospital information systems

An innovative telemedical network to improve infectious disease management in critically ill patients and outpatients: a stepped-wedge, cluster randomized controlled trial (TELnet@NRW)

Marx G, Greiner W, Juhra C, et al. An innovative telemedical network to improve infectious disease management in critically ill patients and outpatients: a stepped-wedge, cluster randomized controlled trial (TELnet@NRW). Journal of Medical Internet Research . 2022.BACKGROUND: Evidence-based infectious disease and intensive care management is more relevant than ever. Medical expertise in the two disciplines is often geographically limited to university institutions. In addition, the interconnection between inpatient and outpatient care is often insufficient (e.g., no shared electronic health record, no digital transfer of patient findings).; OBJECTIVE: To establish and evaluate a telemedical inpatient-outpatient network based on expert teleconsultations to increase treatment quality in intensive care medicine and infectious diseases.; METHODS: We performed a multicentre, stepped-wedge cluster randomised trial (Feb 2017 - Jan 2020) to establish a telemedicine inpatient-outpatient network among university hospitals, hospitals, and outpatient physicians in North Rhine Westphalia, Germany. Patients ≥ 18 years of age in the intensive care unit (ICU) or consulting with a physician in the outpatient setting were eligible. We provided expert knowledge from intensivists and infectious disease specialists through advanced training courses and expert teleconsultations with 24/7/365 availability on demand resp. once per week to enhance treatment quality. The primary outcome was adherence to the ten Choosing Wisely recommendations for infectious disease management. Guideline adherence was analysed using binary logistic regression models.; RESULTS: Overall, 159,424 patients (10,585 inpatients, 148,839 outpatients) from 17 hospitals and 103 outpatient physicians were included. There was a significant increase in guideline adherence in the management of Staphylococcus aureus infections (OR 4.00 [95% CI 1.83, 9.20], P<.01) and in sepsis management in critically ill patients (OR 6.82 [95% CI 1.27, 56.61], P=.04). There was a statistically non-significant decrease in sepsis related mortality from 28.8% (19/66) in the control group to 23.8% (50/210) in the intervention group. Furthermore, the extension of treatment with prophylactic antibiotics after surgery was significantly less likely (OR 9.37 [95% CI 1.52, 111.47], P=.04). Patients treated by outpatient physicians, who were regularly taking part in expert teleconsultations, were also more likely to be treated according to guideline recommendations regarding antibiotic therapy for uncomplicated upper respiratory tract infections (OR 1.34 [95% CI 1.16, 1.56], P<.01) and asymptomatic bacteriuria (OR 9.31 [95% CI 3.79, 25.94], P<.01). For the other recommendations, we found no significant effects, or we had too few observations to generate models. Key limitations of our study include selection effects due to the applied on-site triage of patients as well as the limited possibilities to control for secular effects.; CONCLUSIONS: Telemedicine facilitates a direct round-the-clock interaction over broad distances between intensivists or infectious disease experts and physicians who care for patients in hospitals without ready access to these experts. Expert teleconsultations increase guideline adherence and treatment quality in infectious disease and intensive care management creating added value for critically ill patients.; CLINICALTRIAL: ClinicalTrials.gov, NCT03137589, https://clinicaltrials.gov/ct2/show/NCT03137589

Smart Medical Information Technology for Healthcare (SMITH): Data integration based on interoperability standards

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. “Smart Medical Information Technology for Healthcare (SMITH)” is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH’s goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. Objectives: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. Governance and Policies: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals’ Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. Methodology and Architectural Framework: To share medical and research data, SMITH’s information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM2 for enterprise architecture modeling supports a consistent development process. The DIC reference architecture determines the services, applications and the standardsbased communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. Use Cases: The methodological use case “Phenotype Pipeline” (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case “Algorithmic Surveillance of ICU Patients” (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a “hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections” (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design. Discussion: SMITH’s strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals’ information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy

Introduction The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure.Methods and analysis In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested.Ethics and dissemination Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals.Trial registration number DRKS00014330