Anesthesia Information Management Systems: Evolution of the Paper Anesthetic Record to a Multisystem Electronic Medical Record Network That Streamlines Perioperative Care.

Initially devised in the 1890s, the traditional anesthetic record comprises physiological changes, crucial anesthetic or surgical events, and medications administered during the perioperative period. The timely collection of quality data facilitates situational awareness and point-of-care clinical decision making. The burgeoning volume and complexity of data in conjunction with financial incentives and the push for improved clinical documentation by regulatory bodies have prompted the transition away from paper records. Anesthesia Information Management Systems (AIMS) are specialized electronic health record networks that allow the anesthesia record to interface with hospital clinical data repositories, resulting in improvements in quality of care, patient safety, operations management, reimbursement, and translational research. Like most new technological advances, adoption was slow at first due to the challenges of integrating complex systems into daily clinical practice, questions about return on investment, and medicolegal liability. Recent technological advances, coupled with government incentives, have allowed AIMS adoption to reach an acceleration phase among US academic medical centers; widespread utilization of AIMS by 84% of US academic medical centers is expected by 2018-2020. Adoption among nonacademic US and European medical centers still remains low; information concerning Asian countries is limited to literature describing only single-hospital center experiences.

[Challenges of Digital Medicine]. / Herausforderungen der digitalen Medizin.

Challenges of Digital Medicine Abstract. Digitization is increasingly covering more and more sectors, including medicine. To ensure medical operation 365 × 24 hours, progressively more human and financial resources are necessary. The transformation of patient histories from paper into electronic patient records focused initially on documentation. Today, hospital information systems are increasingly used as a platform for the communication of all professionals involved in the patient process - in Switzerland, however, so far without providing patients direct access to their data. Digititizing processes intend to increase efficiency, but also to enhance clinical and administrative decision support and quality assurance. The introduction of the electronic patient record in Switzerland in 2020 is expected to provide cross-company, more complete documentation of patient care. Multimorbid patients, often treated in different institutions and by different specialists, should benefit from this in particular. Advances in artificial intelligence offer new opportunities in medicine. Challenges include ensuring reliable data protection, and better interoperability of the systems involved. Semantically structured, machine-readable data exchange is a necessity for both networked services and internationally competitive research.

[Relevance of big data for molecular diagnostics]. / Bedeutung von Big Data für die molekulare Diagnostik.

Big data analysis raises the expectation that computerized algorithms may extract new knowledge from otherwise unmanageable vast data sets. What are the algorithms behind the big data discussion? In principle, high throughput technologies in molecular research already introduced big data and the development and application of analysis tools into the field of rheumatology some 15 years ago. This includes especially omics technologies, such as genomics, transcriptomics and cytomics. Some basic methods of data analysis are provided along with the technology, however, functional analysis and interpretation requires adaptation of existing or development of new software tools. For these steps, structuring and evaluating according to the biological context is extremely important and not only a mathematical problem. This aspect has to be considered much more for molecular big data than for those analyzed in health economy or epidemiology. Molecular data are structured in a first order determined by the applied technology and present quantitative characteristics that follow the principles of their biological nature. These biological dependencies have to be integrated into software solutions, which may require networks of molecular big data of the same or even different technologies in order to achieve cross-technology confirmation. More and more extensive recording of molecular processes also in individual patients are generating personal big data and require new strategies for management in order to develop data-driven individualized interpretation concepts. With this perspective in mind, translation of information derived from molecular big data will also require new specifications for education and professional competence.

[Big data from clinical routine]. / Big Data aus dem klinischen Alltag.

BACKGROUND: Over the past 100 years, evidence-based medicine has undergone several fundamental changes. Through the field of physiology, medical doctors were introduced to the natural sciences. Since the late 1940s, randomized and epidemiological studies have come to provide the evidence for medical practice, which led to the emergence of clinical epidemiology as a new field in the medical sciences. Within the past few years, big data has become the driving force behind the vision for having a comprehensive set of health-related data which tracks individual healthcare histories and consequently that of large populations. OBJECTIVES: The aim of this article is to discuss the implications of data-driven medicine, and to examine how it can find a place within clinical care. MATERIALS AND METHODS: The EU-wide discussion on the development of data-driven medicine is presented. RESULTS: The following features and suggested actions were identified: harmonizing data formats, data processing and analysis, data exchange, related legal frameworks and ethical challenges. For the effective development of data-driven medicine, pilot projects need to be conducted to allow for open and transparent discussion on the advantages and challenges. The Federal Ministry of Education and Research ("Bundesministerium für Bildung und Forschung," BMBF) Arthromark project is an important example. Another example is the Medical Informatics Initiative of the BMBF. DISCUSSION AND CONCLUSION: The digital revolution affects clinic practice. Data can be generated and stored in quantities that are almost unimaginable. It is possible to take advantage of this for development of a learning healthcare system if the principles of medical evidence generation are integrated into innovative IT-infrastructures and processes.

[Electronic decision support to promote medication safety]. / Elektronische Entscheidungsunterstützung zur Annäherung an eine sichere Arzneimitteltherapie.

Because of its inherent complexity, it is a considerable challenge to tailor drug treatment to a prevalent disease and its subgroups, which are increasingly defined by genomic variability (personalized medicine) and require consideration of context information such as co-morbidity, co-medication, patient preferences, and the specific characteristics of the healthcare sector. Thus, optimum treatment decisions might not be taken intuitively any longer, because decisions must be made both rapidly and increasingly based on analyses of complex relations of numerous variables that exceed the processing performance of a human brain. Hence, computer support is indispensable to ensure error-free high-performance medicine. A key step in computer-supported medication safety is to implement a computerized physician order entry (CPOE) system that compiles a patient's medication in a structured and coded format enabling the link to clinical decision support (CDS) systems. Implementing a CPOE is hence a strategic step for a hospital, which is crucial to exhaustingly and consistently prevent medication errors. Thereby, the best performance of a CPOE is achieved if it is deeply integrated into an electronic patient record thus enabling access to relevant patient information, which again has to be structured to allow processing. To efficiently support drug treatment, CDS systems must fulfill high-quality standards with regard to underlying data, integration, and user-interaction to ensure that they support but do not impede the provision of care.

[E-Health and reality - what are we facing in patient care?] / E-Health und die Realität ­ was sehen wir heute schon in der Klinik?

The terms e­Health and digitization are core elements of a change in our time. The main drivers of this change - in addition to a dynamic market - are the serious advantages for the healthcare sector in the processing of tasks and requirements. The large amounts of data, the intensively growing medical knowledge, the rapidly advancing technological developments and the goal of a personalized, customized therapy for the patient, make the application absolutely necessary. While e­Health describes the use of information and communication technologies in healthcare, the concept of digitization is associated with the underlying processes of change and innovation. Digital technologies include software and hardware based developments. The term clinical data intelligence describes the property of workability and also characterizes the collaboration of clinically relevant systems with which the medical user works. The hierarchy in digital processing maps the levels from pure data management through clinical decision support to automated process flows and autonomously operating units. The combination of patient data management and clinical decision support proves its value in terms of error reduction, prevention, quality and safety, especially in drug therapy. The aim of this overview is the presentation of the existing reality in medical centers with perspectives derived from the point of view of the medical user.

Clinical Information Systems - From Yesterday to Tomorrow.

OBJECTIVES: To review the history of clinical information systems over the past twenty-five years and project anticipated changes to those systems over the next twenty-five years. METHODS: Over 250 Medline references about clinical information systems, quality of patient care, and patient safety were reviewed. Books, Web resources, and the author's personal experience with developing the HELP system were also used. RESULTS: There have been dramatic improvements in the use and acceptance of clinical computing systems and Electronic Health Records (EHRs), especially in the United States. Although there are still challenges with the implementation of such systems, the rate of progress has been remarkable. Over the next twenty-five years, there will remain many important opportunities and challenges. These opportunities include understanding complex clinical computing issues that must be studied, understood and optimized. Dramatic improvements in quality of care and patient safety must be anticipated as a result of the use of clinical information systems. These improvements will result from a closer involvement of clinical informaticians in the optimization of patient care processes. CONCLUSIONS: Clinical information systems and computerized clinical decision support have made contributions to medicine in the past. Therefore, by using better medical knowledge, optimized clinical information systems, and computerized clinical decision, we will enable dramatic improvements in both the quality and safety of patient care in the next twenty-five years.