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Digitalisierung, Künstliche Intelligenz und Big Data als Motor für Wandel in Pflege und Gesellschaft
(2022)
This new edition of the classic textbook on health informatics provides readers in healthcare practice and educational settings with an unparalleled depth of information on using informatics methods and tools. However, this new text speaks to nurses and -- in a departure from earlier editions of this title -- to all health professionals in direct patient care, regardless of their specialty, extending its usefulness as a textbook. This includes physicians, therapists, pharmacists, dieticians and many others. In recognition of the evolving digital environments in all healthcare settings and of interprofessional teams, the book is designed for a wide spectrum of healthcare professions including quality officers, health information managers, administrators and executives, as well as health information technology professionals such as engineers and computer scientists in health care. The book is of special interest to those who bridge the technical and caring domain, particularly nurse and medical informaticians and other informaticians working in the health sciences. Nursing Informatics: An Interprofessional and Global Perspective contains real-life case studies and other didactic features to illustrate the theories and principles discussed, making it an ideal resource for use within health and nursing informatics curricula at both undergraduate and graduate level, as well as for workforce development. It honors the format established by the previous editions by including a content array and questions to guide the reader. Readers are invited to look out of the box through a dedicated global perspective covering health informatics applications in different regions, countries and continents.
Der primäre Einsatzzweck von Reifegradmodellen besteht zumeist in der reinen Inventarisierung der vorhandenen IT-Komponenten. Das vorliegende Kapitel gibt IT-Entscheider*innen in Krankenhäusern Empfehlungen, wie Reifegradmodelle für eine kontinuierliche Weiterentwicklung, Umsetzung und Evaluation von Digitalisierungsstrategien eingesetzt werden können. Als Prüfschema für die Auswahl geeigneter Verfahren werden neun Anforderungen an die Entwicklung und den Einsatz von Reifegradmodellen formuliert. Entlang von drei strategischen Handlungsfeldern – dem klinischen Anwendungsfeld, dem Informationsmanagement und dem organisatorischen Umfeld – werden dem Leser generische Digitalisierungsziele und dazugehörige Beispielindikatoren zur Erfolgskontrolle bereitgestellt.
Background and purpose:
Clinical information logistics is a construct that aims to describe and explain various phenomena of information provision to drive clinical processes. It can be measured by the workflow composite score, an aggregated indicator of the degree of IT support in clinical processes. This study primarily aimed to investigate the yet unknown empirical patterns constituting this construct. The second goal was to derive a data-driven weighting scheme for the constituents of the workflow composite score and to contrast this scheme with a literature based, top-down procedure. This approach should finally test the validity and robustness of the workflow composite score.
Methods:
Based on secondary data from 183 German hospitals, a tiered factor analytic approach (confirmatory and subsequent exploratory factor analysis) was pursued. A weighting scheme, which was based on factor loadings obtained in the analyses, was put into practice.
Results:
We were able to identify five statistically significant factors of clinical information logistics that accounted for 63% of the overall variance. These factors were “flow of data and information”, “mobility”, “clinical decision support and patient safety”, “electronic patient record” and “integration and distribution”. The system of weights derived from the factor loadings resulted in values for the workflow composite score that differed only slightly from the score values that had been previously published based on a top-down approach.
Conclusion:
Our findings give insight into the internal composition of clinical information logistics both in terms of factors and weights. They also allowed us to propose a coherent model of clinical information logistics from a technical perspective that joins empirical findings with theoretical knowledge. Despite the new scheme of weights applied to the calculation of the workflow composite score, the score behaved robustly, which is yet another hint of its validity and therefore its usefulness.
Information Technology (IT) continues to evolve and develop with electronic devices and systems becoming integral to healthcare in every country. This has led to an urgent need for all professions working in healthcare to be knowledgeable and skilled in informatics. The Technology Informatics Guiding Education Reform (TIGER) Initiative was established in 2006 in the United States to develop key areas of informatics in nursing. One of these was to integrate informatics competencies into nursing curricula and life-long learning. In 2009, TIGER developed an informatics competency framework which outlines numerous IT competencies required for professional practice and this work helped increase the emphasis of informatics in nursing education standards in the United States. In 2012, TIGER expanded to the international community to help synthesise informatics competencies for nurses and pool educational resources in health IT. This transition led to a new interprofessional, interdisciplinary approach, as health informatics education needs to expand to other clinical fields and beyond.
In tandem, a European Union (EU) - United States (US) Collaboration on eHealth began a strand of work which focuses on developing the IT skills of the health workforce to ensure technology can be adopted and applied in healthcare. One initiative within this is the EU*US eHealth Work Project, which started in 2016 and is mapping the current structure and gaps in health IT skills and training needs globally. It aims to increase educational opportunities by developing a model for open and scalable access to eHealth training programmes. With this renewed initiative to incorporate informatics into the education and training of nurses and other health professionals globally, it is time for educators, researchers, practitioners and policy makers to join in and ROAR with TIGER.
Background:
While aiming for the same goal of building a national eHealth Infrastructure, Germany and the United States pursued different strategic approaches – particularly regarding the role of promoting the adoption and usage of hospital Electronic Health Records (EHR).
Objective:
To measure and model the diffusion dynamics of EHRs in German hospital care and to contrast the results with the developments in the US.
Materials and methods:
All acute care hospitals that were members of the German statutory health system were surveyed during the period 2007–2017 for EHR adoption. Bass models were computed based on the German data and the corresponding data of the American Hospital Association (AHA) from non-federal hospitals in order to model and explain the diffusion of innovation.
Results:
While the diffusion dynamics observed in the US resembled the typical s-shaped curve with high imitation effects (q = 0.583) but with a relatively low innovation effect (p = 0.025), EHR diffusion in Germany stagnated with adoption rates of approx. 50% (imitation effect q = -0.544) despite a higher innovation effect (p = 0.303).
Discussion:
These findings correlate with different governmental strategies in the US and Germany of financially supporting EHR adoption. Imitation only seems to work if there are financial incentives, e.g. those of the HITECH Act in the US. They are lacking in Germany, where the government left health IT adoption strategies solely to the free market and the consensus among all of the stakeholders.
Conclusion:
Bass diffusion models proved to be useful for distinguishing the diffusion dynamics in German and US non-federal hospitals. When applying the Bass model, the imitation parameter needs a broader interpretation beyond the network effects, including driving forces such as incentives and regulations, as was demonstrated by this study.