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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.
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.
In September 2022, the interprofessional European Summer School on the topic “Information in Healthcare – From Data to Knowledge” was held at the University of Porto. This Summer School included the topics Interoperability, Data Protection and Security and Data Analytics and consisted of an online preparation phase and an attendance phase in Porto. The didactic concept involved problem-based learning using a case study. A variety of course materials were developed and used to achieve the learning objectives. There are plans to continue the Summer School concept at participating institutions in the future, starting with a Spring School 2023 in Osnabrück.
Interoperability, Data Protection and Security and Data Analytics are of high relevance for the future of eHealth and interprofessional care. Three online courses were therefore designed and delivered for these topics, all of which followed the same structure. A variety of materials were developed and different tools for knowledge transfer, communication and collaboration were used.
This study describes the eHealth4all@eu course development pipeline that builds upon the TIGER educational recommendations and allows a systematic development grounded on scientific and field requirements of competencies, a case/problem-based pedagogical approach and finally results in the syllabus and the course content. The pipeline is exemplified by the course Learning Healthcare in Action: Clinical Data Analytics.
The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students’ evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners. These drawbacks were obviously compensated by the positive experience of working in international and interprofessional groups and a generally welcoming environment.
Building on Rogers’ Diffusion of Innovation Theory, Bass models describe the diffusion processes distinguishing between innovation (p) and imitation (q). This study aimed at modelling the uptake of RIS, PACS and EHR systems in Germany and Finland. The Bass models revealed a quick and almost identical uptake process across all three systems for Finland. In contrast, the Bass models mirrored a slower uptake in Germany. Consequently, the Finnish “imitation” coefficients were larger than the German ones. While in Germany almost free market forces were driving the adoption through imitation but without tail wind from policy, the adoption process in Finland was centrally governed. This suggests that the diffusion process in Finland reflected a well-managed roll-out of the systems rather than imitation behaviour. Thus, in order for Bass model coefficients to be understood properly, additional contextual information is required.
Radiology has a reputation for having a high affinity to innovation – particularly with regard to information technologies. Designed for supporting the peculiarities of radiological diagnostic workflows, Radiology Information Systems (RIS) and Picture Archiving and Communication Systems (PACS) developed into widely used information systems in hospitals and form the basis for advancing the field towards automated image diagnostics. RIS and PACS can thus serve as meaningful indicators of how quickly IT innovations diffuse in secondary care settings – an issue that requires increased attention in research and health policy in the light of increasingly fast innovation cycles. We therefore conducted a retrospective longitudinal observational study to research the diffusion dynamics of RIS and PACS in German hospitals between 2005 and 2017. Based upon data points collected within the “IT Report Healthcare” and building on Rogers’ Diffusion of Innovation (DOI) theory, we applied a novel methodological technique by fitting Bayesian Bass Diffusion Models on past adoption rates. The Bass models showed acceptable goodness of fit to the data and the results indicated similar growth rates of RIS and PACS implementations and suggest that market saturation is almost reached. Adoption rates of PACS showed a slightly higher coefficient of imitation (q = 0.25) compared to RIS (q = 0.11). However, the diffusion process expands over approximately two decades for both systems which points at the need for further research into how innovation diffusion can be accelerated effectively. Furthermore, the Bayesian approach to Bass modelling showed to have several advantages over the classical frequentists approaches and should encourage adoption and diffusion research to adapt similar techniques.
Diabetic foot ulcer (DFU) is a chronic wound and a common diabetic complication as 2% – 6% of diabetic patients witness the onset thereof. The DFU can lead to severe health threats such as infection and lower leg amputations, Coordination of interdisciplinary wound care requires well-written but time-consuming wound documentation. Artificial intelligence (AI) systems lend themselves to be tested to extract information from wound images, e.g. maceration, to fill the wound documentation. A convolutional neural network was therefore trained on 326 augmented DFU images to distinguish macerated from unmacerated wounds. The system was validated on 108 unaugmented images. The classification system achieved a recall of 0.69 and a precision of 0.67. The overall accuracy was 0.69. The results show that AI systems can classify DFU images for macerations and that those systems could support clinicians with data entry. However, the validation statistics should be further improved for use in real clinical settings. In summary, this paper can contribute to the development of methods to automatic wound documentation.
Background
Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge.
Method
A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge.
Results
This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5% were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen’s d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen’s d 0.22).
Conclusions
Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.