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Characteristics of German Hospitals Adopting Health IT Systems : Results from an Empirical Study
(2011)
Hospital characteristics that facilitate IT adoption have been described by the literature extensively, however with controversial results. The aim of this study therefore is to draw a set of the most important variables from previous studies and include them in a combined analysis for testing their contribution as single factors and their interactions. Total number of IT systems installed and number of clinical IT systems in the hospital were used as criterion variables. Data from a national survey of German hospitals served as basis. Based on a stepwise multiple regression analysis four variables were identified to significantly explain the degree of IT adoption (60% explained variance): 1) hospital size, 2) IT department, 3) reference customer and 4) ownership (private vs. public). Our results replicate previous findings with regard to hospital size and ownership. In addition our study emphasizes the importance of a reliable internal structure for IT projects (existence of an IT department) and the culture of testing and installing most recent IT products (being a reference customer). None of the interactions between factors was significant.
Background: Continuous improvements of IT-performance in healthcare organisations require actionable performance indicators, regularly conducted, independent measurements and meaningful and scalable reference groups. Existing IT-benchmarking initiatives have focussed on the development of reliable and valid indicators, but less on the questions about how to implement an environment for conducting easily repeatable and scalable IT-benchmarks.
Objectives: This study aims at developing and trialling a procedure that meets the afore-mentioned requirements.
Methods: We chose a well established, regularly conducted (inter-) national IT-survey of healthcare organisations (IT-Report Healthcare) as the environment and offered the participants of the 2011 survey (CIOs of hospitals) to enter a benchmark. The 61 structural and functional performance indicators covered among others the implementation status and integration of IT-systems and functions, global user satisfaction and the resources of the IT-department. Healthcare organisations were grouped by size and ownership. The benchmark results were made available electronically and feedback on the use of these results was requested after several months.
Results: Fifty-ninehospitals participated in the benchmarking. Reference groups consisted of up to 141 members depending on the number of beds (size) and the ownership (public vs. private). A total of 122 charts showing single indicator frequency views were sent to each participant. The evaluation showed that 94.1% of the CIOs who participated in the evaluation considered this benchmarking beneficial and reported that they would enter again. Based on the feedback of the participants we developed two additional views that provide a more consolidated picture.
Conclusion: The results demonstrate that establishing an independent, easily repeatable and scalable IT-benchmarking procedure is possible and was deemed desirable. Based on these encouraging results a new benchmarking round which includes process indicators is currently conducted.
Due to the emerging evidence of health IT as opportunity and risk for clinical workflows, health IT must undergo a continuous measurement of its efficacy and efficiency. IT-benchmarks are a proven means for providing this information. The aim of this study was to enhance the methodology of an existing benchmarking procedure by including, in particular, new indicators of clinical workflows and by proposing new types of visualisation. Drawing on the concept of information logistics, we propose four workflow descriptors that were applied to four clinical processes. General and specific indicators were derived from these descriptors and processes. 199 chief information officers (CIOs) took part in the benchmarking. These hospitals were assigned to reference groups of a similar size and ownership from a total of 259 hospitals. Stepwise and comprehensive feedback was given to the CIOs. Most participants who evaluated the benchmark rated the procedure as very good, good, or rather good (98.4%). Benchmark information was used by CIOs for getting a general overview, advancing IT, preparing negotiations with board members, and arguing for a new IT project.
Background: Availability and usage of individual IT applications have been studied intensively in the past years. Recently, IT support of clinical processes is attaining increasing attention. The underlying construct that describes the IT support of clinical workflows is clinical information logistics. This construct needs to be better understood, operationalised and measured.
Objectives: It is therefore the aim of this study to propose and develop a workflow composite score (WCS) for measuring clinical information logistics and to examine its quality based on reliability and validity analyses.
Methods: We largely followed the procedural model of MacKenzie and colleagues (2011) for defining and conceptualising the construct domain, for developing the measurement instrument, assessing the content validity, pretesting the instrument, specifying the model, capturing the data and computing the WCS and testing the reliability and validity.
Results: Clinical information logistics was decomposed into the descriptors data and information, function, integration and distribution, which embraced the framework validated by an analysis of the international literature. This framework was refined selecting representative clinical processes. We chose ward rounds, pre- and post-surgery processes and discharge as sample processes that served as concrete instances for the measurements. They are sufficiently complex, represent core clinical processes and involve different professions, departments and settings. The score was computed on the basis of data from 183 hospitals of different size, ownership, location and teaching status. Testing the reliability and validity yielded encouraging results: the reliability was high with r(split-half) = 0.89, the WCS discriminated between groups; the WCS correlated significantly and moderately with two EHR models and the WCS received good evaluation results by a sample of chief information officers (n = 67). These findings suggest the further utilisation of the WCS.
Conclusion: As the WCS does not assume ideal workflows as a gold standard but measures IT support of clinical workflows according to validated descriptors a high portability of the WCS to other hospitals in other countries is very likely. The WCS will contribute to a better understanding of the construct clinical information logistics.
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.
Background: The majority of health IT adoption research focuses on the later stages of the IT adoption process: namely on the implementation phase. The first stage, however, which is defined as the knowledge-stage, remains widely unobserved. Following Rogers’ Diffusion of Innovation Theory (DOI) this paper presents a research framework to examine the possible lack of shared IT awareness-knowledge, i.e. an information gradient, of two crucial stakeholders, the Chief Information Officer (CIO) and the Director of Nursing (DoN). This study shall answer the following research questions: (1.) Does this gradient exist? (2.) Which direction does it have? (3.) Are certain health IT (HIT) attributes associated with a potential gradient? (4.) Which determinants of diffusion go along with this gradient?
Method: Results of two surveys that focused on the topic “IT support of clinical workflows” from the viewpoint of CIOs and DoNs with corresponding datasets from 75 hospitals were used in a secondary data analysis. The gradient was operationalised by measuring the disagreement of CIOs and DoNs on the availability and implementation status of 29 IT functions. HIT attributes tested were relevance and market penetration of the IT functions, determinants of diffusion were inter-professional leadership and IT service density.
Results: The analysis revealed a significant disagreement on the availability of 9 out of 29 HIT functions. In 23 HIT functions, the CIOs reported a higher implementation status than the DoNs, which pointed to a trend for a unidirectional gradient. The disagreement was significantly lower when the relevance of the IT function was high. Both determinants of diffusion correlated significantly negative with the degree of disagreement.
Conclusion: This is the first study to empirically examine shared awareness-knowledge of two IT-stakeholders that are crucial for triggering IT adoption on the frontline level in hospitals. It could be shown that a gradient and thus a lack of shared awareness-knowledge existed and was associated with certain factors. In conclusion, hospitals should implement improved cooperation between IT staff and clinicians and IT service density when establishing the prerequisites for successful IT adoption processes.
An Iterative Methodology for Developing National Recommendations for Nursing Informatics Curricula
(2016)
The increasing importance of IT in nursing requires educational measures to support its meaningful application. However, many countries do not yet have national recommendations for nursing informatics competencies. We thus developed an iterative triple methodology to yield validated and country specific recommendations for informatics core competencies in nursing. We identified relevant competencies from national sources (step 1), matched and enriched these with input from the international literature (step 2) and fed the resulting 24 core competencies into a survey (120 invited experts from which 87 responded) and two focus group sessions with a total of 48 experts (steps 3a/3b). The subsequent focus group sessions confirmed and expanded the findings. As a result, we were able to define role specific informatics core competencies for three countries.
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.
CIOs' innovation capability is regarded as a precondition of successful HIT adoption in hospitals. Based on the data of 142 CIOs, this study aimed at identifying antecedents of perceived innovation capability. Eight features describing the status quo of the hospital IT management (e.g. use of IT governance frameworks), four features of the hospital structure (e.g. functional diversification) and four CIO characteristics (e.g. duration of employment) were tested as potential antecedents in an exploratory stepwise regression approach. Perceived innovation capability in its entirety and its three sub-dimensions served as criterion. The results show that CIOs' perceived innovation capability could be explained significantly (R2=0.34) and exclusively by facts that described the degree of formalism and structure of IT management in a hospital, e.g. intensive and formalised strategic communication, the existence of an IT strategy and the use of IT governance frameworks. Breaking down innovation capability into its constituents revealed that “innovative organisational culture” contributed to a large extent (R2=0.26) to the overall result sharing several predictors. In contrast, “intrapreneurial personality” (R2=0.11) and “openness towards users” (R2=0.18) could be predicted less well. These results hint at the relationship between working in a well-structured, formalised and strategy oriented environment and the overall feeling of being capable to promote IT innovation.
Health IT adoption research is rooted in Rogers' Diffusion of Innovation theory, which is based on longitudinal analyses. However, many studies in this field use cross-sectional designs. The aim of this study therefore was to design and implement a system to (i) consolidate survey data sets originating from different years (ii) integrate additional secondary data and (iii) query and statistically analyse these longitudinal data. Our system design comprises a 5-tier-architecture that embraces tiers for data capture, data representation, logics, presentation and integration. In order to historicize data properly and to separate data storage from data analytics a data vault schema was implemented. This approach allows the flexible integration of heterogeneous data sets and the selection of comparable items. Data analysis is prepared by compiling data in data marts and performed by R and related tools. IT Report Healthcare data from 2011, 2013 and 2017 could be loaded, analysed and combined with secondary longitudinal data.