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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.
Benchmarking, sprich die Vergleichsanalyse von Prozessen mit festgelegtem Bezugswert, findet zunehmend Einzug in die Welt der Gesundheits-IT. Dabei spielen jedoch viele Faktoren zusammen, die einen einfachen Vergleich von IT-Kosten bei Weitem übersteigen. Eine Forschungsgruppe der Hochschule Osnabrück hat mit dem IT-Benchmark Gesundheitswesen ein Analysetool vorgelegt, das auch einen Länder- vergleich ermöglicht.
Background: Clinical information logistics is the backbone of care workflows inside and outside of hospitals. Due to the great potential of health IT to support clinical processes its contribution needs to be regularly monitored and governed. IT benchmarks are a well-known instrument to optimise the availability and use of IT by guiding the decision making process. The aim of this study was to translate IT benchmarking results that were grounded on a hierarchical workflow scoring system into an appropriate visualisation concept.
Methods: To this end, a three-dimensional multi-level model was developed, which allowed the decomposition of the highly aggregated workflow composite score into score views for the individual clinical workflows concerned and for the descriptors of these workflows. Furthermore this multi-level model helped to break down the score views into single and multiple indicator views.
Results: The results could be visualised per hospital in comparison to the results of organisations of similar size and ownership (peer reference groups) and in comparison to different types of innovation adopters. The multi-level model was implemented in a benchmark of 199 hospitals and evaluated by the chief information officers. The evaluation resulted in high ratings for the comprehensibility of the different types of views of the scores and indicators.
Conclusions: The implementation of the multi-level model in a large benchmark of hospitals proved to be feasible and useful in terms of the overall structure and the different indicator views. There seems to be a preference for less complex and familiar views.
Multinational health IT benchmarks foster cross-country learning and have been employed at various levels, e.g. OECD and Nordic countries. A bi-national benchmark study conducted in 2007 revealed a significantly higher adoption of health IT in Austria compared to Germany, two countries with comparable healthcare systems. We now investigated whether these differences still persisted. We further studied whether these differences were associated with hospital intrinsic factors, i.e. the innovative power of the organisation and hospital demographics. We thus performed a survey to measure the “perceived IT availability” and the “innovative power of the hospital” of 464 German and 70 Austrian hospitals. The survey was based on a questionnaire with 52 items and was given to the directors of nursing in 2013/2014. Our findings confirmed a significantly greater IT availability in Austria than in Germany. This was visible in the aggregated IT adoption composite score “IT function” as well as in the IT adoption for the individual functions “nursing documentation” (OR = 5.98), “intensive care unit (ICU) documentation” (OR = 2.49), “medication administration documentation” (OR = 2.48), “electronic archive” (OR = 2.27) and “medication” (OR = 2.16). “Innovative power” was the strongest factor to explain the variance of the composite score “IT function”. It was effective in hospitals of both countries but significantly more effective in Austria than in Germany. “Hospital size” and “hospital system affiliation” were also significantly associated with the composite score “IT function”, but they did not differ between the countries. These findings can be partly associated with the national characteristics. Indicators point to a more favourable financial situation in Austrian hospitals; we thus argue that Austrian hospitals may possess a larger degree of financial freedom to be innovative and to act accordingly. This study is the first to empirically demonstrate the effect of “innovative power” in hospitals on health IT adoption in a bi-national health IT benchmark. We recommend directly including the financial situation into future regression models. On a political level, measures to stimulate the “innovative power” of hospitals should be considered to increase the digitalisation of healthcare.
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.
Das Thema Digitalisierung ist in aller Munde – gerade auch im Bereich Krankenhaus. Allerdings noch nicht zuverlässig und im großen Stile valuiert sind die Fragen: Wie digitalisiert ist die Gesamtheit der deutschen Krankenhäuser tatsächlich? Wie entwickelt sich der Digitalisierungsgrad über die Zeit und im Vergleich zu anderen Nationen? Welchen Maßstab sollte man anlegen? Die Autoren stellen im folgenden Artikel ihren Ansatz für eine bundesweite Erfassung der Krankenhausdigitalisierung vor. Im Ergebnis weisen die betrachteten Krankenhäuser deutliche Optimierungspotenziale auf. Diese reichen von der mobilen Verfügbarkeit elektronischer Patientendaten und IT-Funktionen bis hinzu Fragen der Integration und Interoperabilität der im Einsatz befindlichen Systeme.