Summary 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. 3LGM 2 for enterprise architecture modeling supports a consistent development process. The DIC reference architecture determines the services, applications and the standards-based 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 ...

Abstract:Information processing in hospitals, especially in university hospitals, is currently faced with two major issues: low-cost hardware and progress in networking technology leads to a further decentralization of computing capacity, due to the increasing need for information processing in hospitals and due to economic restrictions, it is necessary to use, commercial software products. This leads to heterogeneous hospital information systems using a variety of software and hardware products, and to a stronger demand for integrating these products and, in general, for a dedicated methodology for the management of hospital information systems to support patient care and medical research. We present a three-level graph-based model (3LGM) to support the systematic management of hospital information systems. 3LGM can serve as a basis for assessing the quality of information processing in hospitals. 3LGM distinguishes between a procedural level for describing the information procedures (and their information interchange) of a hospital information system and thus its functionality, a logical tool level, focusing on application systems and communication links, and a physical tool level with physical subsystems (e.g., computer systems) and data transmission. The examples that are presented have been taken from the Heidelberg University Hospital Information System.

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BackgroundSmartphones and related applications are increasingly gaining relevance in the healthcare domain. We previously assessed the demands and preferences of medical students towards an application accompanying them during a course on general practice. The current study aims to elucidate the factors associated with adoption of such a technology. Therefore we provided students with a prototype of an application specifically related to their studies in general practice.MethodsA total estimation among students participating in a general practice examination at the Leipzig Medical School was conducted in May 2014. Students were asked to answer a structured self-designed questionnaire. Univariable comparisons were made to identify significant differences between those students who reported to have used the application frequently and those who did not. Multivariable binary logistic regression was used to reveal independent predictors of frequent application usage.ResultsThe response rate was 99.3 % (n = 305/307). The majority (59 %, n = 180/305) were female students. The mean age was 24.5 years and 79.9 % (n = 243/304) owned a smartphone or tablet computer. Regarding the usage of the provided application, 2.3 % (n = 7/303) did not use the app while 68.0 % (n = 206/303) replied to have used it more than five times. Frequent users significantly differed from non-frequent users with regard to being female rather than male, higher mobile device ownership, more frequent exchange about obtaining the course certificate, higher personal interest in new technologies, larger enjoyment of the technology, lower intention to not use smartphone applications in the future, better opinion towards smartphone applications for the profession of a doctor, higher perceived importance of medical applications on the job, higher compatibility of smartphone applications with personal work style, higher perceived relevance of university support and personal benefit of use. Multivariable analysis revealed a set of four variables independently predicting frequent usage: being female, a higher perceived benefit of the supplied application, a higher personal interest in new technologies, and a higher perceived impact of previous experiences on smartphone adoption (Pseudo-R2Nagelkerke = 0.245).ConclusionsUnderstanding medical students’ adoption of smartphone applications used for educational purposes may provide useful information to guide the implementation process as well as the design of respective applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12909-015-0377-3) contains supplementary material, which is available to authorized users.