Discovering the importance of health informatics education competencies in healthcare practice. A focus group interview.

BACKGROUND: As healthcare and especially health technology evolve rapidly, new challenges require healthcare professionals to take on new roles. Consequently, the demand for health informatics competencies is increasing, and achieving these competencies using frameworks, such as Technology Informatics Guiding Reform (TIGER), is crucial for future healthcare. AIM: The study examines essential health informatics and educational competencies and health informatics challenges based on TIGER Core Competency Areas. Rather than examine each country independently, the focus is on uncovering commonalities and shared experiences across diverse contexts. METHODS: Six focus group interviews were conducted with twenty-one respondents from three different countries (Germany (n = 7), Portugal (n = 6), and Finland (n = 8)). These interviews took place online in respondents' native languages. All interviews were transcribed and then summarized by each country. Braun and Clarke's thematic analysis framework was applied, which included familiarization with the data, generating initial subcategories, identifying, and refining themes, and conducting a final analysis to uncover patterns within the data. RESULTS: Agreed upon by all three countries, competencies in project management, communication, application in direct patient care, digital literacy, ethics in health IT, education, and information and knowledge management were identified as challenges in healthcare. Competencies such as communication, information and communication technology, project management, and education were identified as crucial for inclusion in educational programs, emphasizing their critical role in healthcare education. CONCLUSIONS: Despite working with digital tools daily, there is an urgent need to include health informatics competencies in the education of healthcare professionals. Competencies related to application in direct patient care, IT-background knowledge, IT-supported and IT-related management are critical in educational and professional settings are seen as challenging but critical in healthcare.

Differential effects of electronic patient record systems for wound care on hospital-acquired pressure injuries: Findings from a secondary analysis of German hospital data.

INTRODUCTION: Despite the improvements made in recent decades, the OECD regards hospital-acquired pressure injuries (HAPI) as high priority areas for actions to ensure patient safety. This study was aimed at investigating the degree of utilization of two types of electronic patient record systems for wound care on lowering HAPI rates. Furthermore, the effect of user satisfaction with the systems and perceived alignment with clinical processes should be studied. MATERIAL AND METHODS: A regression analysis of post-stratified data from German hospitals obtained from the Hospital Quality Reports (observed/expected HAPI ratio) and the IT Report Healthcare was performed. The sample comprised 319 hospitals reporting on digital wound record systems and 199 hospitals on digital nursing record systems for system utilization and the subset of hospitals using a digital system for user satisfaction and process alignment. RESULTS: The study revealed a significant effect of hospital ownership for both types of systems and a significant interaction of ownership and system utilization for digital wound record systems: Only the for-profit hospitals benefited from a higher degree of system utilization with a lower HAPI ratio. In contrast, non-profit hospitals yielded a reversed pattern, with increasing HAPI rates matching an increased system utilization. User satisfaction (significant) and the perceived alignment of the clinical process (trend) of the digital nursing record system were related with lower HAPI ratios. DISCUSSION: These findings point to a differential effect of system utilization on HAPI ratios depending on hospital ownership, and they demonstrate that those users who are satisfied with the system can act as catalysts for better care. The explained variance was small but comparable to other studies. Furthermore, it shows that explaining quality care is a complex undertaking. Sheer utilization has no effect while a differential perspective on the facilitators and barriers might help to explain the patient outcomes.

Lessons Learned from an Interprofessional European Summer School in Health Informatics.

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.

Trust in Digitalization and AI: Findings from a Qualitative Study on Healthcare Professionals in Germany.

The acceptance and use of digital technologies depend on the trustworthiness attributed to them. Experts were interviewed about how they assign trust to digital technologies or AI (N=12). The data were analyzed applying the focused qualitative content analysis. All of the experts have experience with digital technologies, but only seven with AI. The majority of experts generally trust digital technologies, but only five experts expressed a general trust in AI. Similar reasons contributing to trust building were given for digital technologies and AI. The results show the complexity of the trust building process and the construct of trust itself. The development of explainable AI and professional training are prerequisites to support a critical and safe use of these technologies.

Adoption and Determinants of Assistive Technologies in the Real World: Results from the VdK Study.

INTRODUCTION: While there is growing evidence of the benefits of assistive technologies little is known about their adoption under real circumstances and prevalence for everyday use. OBJECTIVE: The aim of this analysis therefore was (i) to investigate the adoption rates in the real world and (ii) to identify potential determinants of their adoption by care-dependant persons and family caregivers. METHODS: The present study is a secondary analysis based on the data set of the VdK study on home care arrangements (n=53,678). The analysis of the adoption rates included 22,666 care-dependant persons and caregivers, the identification of potential determinants via binary logistic regressions included 5,275 persons. RESULTS: Emergency call systems and technical (smart) aids reached an adoption rate of 40.4 % (care-dependant persons) and 55.3 % (family caregivers). Fall detectors, orientations aids, nursing apps and monitoring systems were used in less than 5 % of the cases. Care degree and the use of an ambulatory nursing service increased the likelihood of using technical aids. CONCLUSION: It can be concluded that innovative and sophisticated types of assistive technologies are still rather scarcely used for home care arrangements in the real world despite large research efforts in the last twenty years.

Design and Implementation of an ETL-Process to Transfer Wound-Related Data into a Standardized Common Data Model.

For observational studies, which are relevant especially for chronic conditions like chronic wounds, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) offers a standardized database schema. In this study an ETL process for the transition of wound related data was developed. After understanding the data in general and mapping the relevant codes to concepts available in OMOP, the ETL process was implemented. In a first step, a generic algorithm to convert data to a csv format was implemented in Java. The resulting csv file was then processed within KNIME to be loaded into an OMOP CDM conformant database. During the whole ETL process, HL7 FHIR CodeSystem and ConceptMap resources were used for coding and mapping. First clinical test cases to retrieve data were successfully processed as an example to demonstrate the feasibility and usefulness. They concerned wound size at the first visit and the main issues of patients in the wound quality of life questionnaire (n = 24). In general, the ETL process worked well, yet some challenges arose, like post coordinated SNOMED codes or conditions, which might occur more than once.

Predilection sites of pyoderma gangrenosum: Retrospective study of 170 clearly diagnosed patients.

Pyoderma gangrenosum (PG) is a non-infectious, neutrophilic dermatosis that was difficult to diagnose in clinical practice. Today, the PARACELSUS score is a validated tool for diagnostics. Based on this score, patients with clearly diagnosed PG were examined with regard to predilection sites. In this retrospective study, the data of patients from the University Hospitals of Essen and Erlangen were analysed in whom the diagnosis of PG could be clearly confirmed using the PARACELSUS score. A total of 170 patients, 49 men (29%) and 121 women (71%) with an average age at first manifestation of 55.5 years, could be included in the analysis. The predilection sites were identified as the lower legs in 80.6% of the patients and the extensor sides in 75.2%. Other localisations of PG were the thighs in 14.1%, mammae and abdomen in 10.0% each, back and gluteal in 7.1% each, feet in 5.9%, arms in 4.7%, genital in 3.5% and head in 2.9%. This retrospective study is the first to identify a collective of PG patients with the highest data quality using the PARACELSUS score. It could be shown that PG can basically occur on the entire integument. However, the predilection sites of PG, which have now been reliably identified for the first time, are the lower legs and in particular the extensor sides.

The Representation of Trust in Artificial Intelligence Healthcare Research.

Artificial intelligence (AI) tends to emerge as a relevant component of medical care, previously reserved for medical experts. A key factor for the utilization of AI is the user's trust in the AI itself, respectively the AIt's decision process, but AI-models are lacking information about this process, the so-called Black Box, potentially affecting usert's trust in AI. This analysis' objective is the description of trust-related research regarding AI-models and the relevance of trust in comparison to other AI-related research topics in healthcare. For this purpose, a bibliometric analysis relying on 12985 article abstracts was conducted to derive a co-occurrence network which can be used to show former and current scientific endeavors in the field of healthcare based AI research and to provide insight into underrepresented research fields. Our results indicate that perceptual factors such as "trust" are still underrepresented in the scientific literature compared to other research fields.

Can Synthetic Images Improve CNN Performance in Wound Image Classification?

For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.

How Do User Participation and IT Self-Efficacy Influence User Attitudes Towards Smart Hospital Technology?

Smart hospitals aim to advance digitalization to provide better and safer care and increase user satisfaction by minimizing documentation burden. The aim of this study is to investigate the potential impact and its logic of user participation and self-efficacy on the pre-usage attitude and behavioural intention towards IT for smart barcode scanner-based workflows. A cross-sectional survey was conducted in a system of 10 hospitals in Germany that are in the process of implementing intelligent workflow technology. Based on the answers of 310 clinicians, a partial least squares (PLS) model was developed which explained 71.3% of the variance in pre-usage attitude and 49.4% of the variance in behavioural intention. User participation significantly determined pre-usage attitude through perceived usefulness and trust, while self-efficacy significantly did so through effort expectancy. This pre-usage model sheds light on how users' behavioural intention towards using smart workflow technology could be shaped. It will be complemented by a post-usage model according to the two-stage model of Information System Continuance.

Effects of Hospital Digitization on Clinical Outcomes and Patient Satisfaction: Nationwide Multiple Regression Analysis Across German Hospitals.

BACKGROUND: The adoption of health information technology (HIT) by health care providers is commonly believed to improve the quality of care. Policy makers in the United States and Germany follow this logic and deploy nationwide HIT adoption programs to fund hospital investments in digital technologies. However, scientific evidence for the beneficial effects of HIT on care quality at a national level remains mostly US based, is focused on electronic health records (EHRs), and rarely accounts for the quality of digitization from a hospital user perspective. OBJECTIVE: This study aimed to examine the effects of digitization on clinical outcomes and patient experience in German hospitals. Hence, this study adds to the small stream of literature in this field outside the United States. It goes beyond assessing the effects of mere HIT adoption and also considers user-perceived HIT value. In addition, the impact of a variety of technologies beyond EHRs was examined. METHODS: Multiple linear regression models were estimated using emergency care outcomes, elective care outcomes, and patient satisfaction as dependent variables. The adoption and user-perceived value of HIT represented key independent variables, and case volume, hospital size, ownership status, and teaching status were included as controls. Care outcomes were captured via risk-adjusted, observed-to-expected outcome ratios for patients who had stroke, myocardial infarction, or hip replacement. The German Patient Experience Questionnaire of Weisse Liste provided information on patient satisfaction. Information on the adoption and user-perceived value of 10 subdomains of HIT and EHRs was derived from the German 2020 Healthcare IT Report. RESULTS: Statistical analysis was based on an overall sample of 383 German hospitals. The analyzed data set suggested no significant effect of HIT or EHR adoption on clinical outcomes or patient satisfaction. However, a higher user-perceived value or quality of the installed tools did improve outcomes. Emergency care outcomes benefited from user-friendly overall digitization (ß=-.032; P=.04), which was especially driven by the user-friendliness of admission HIT (ß=-.023; P=.07). Elective care outcomes were positively impacted by user-friendly EHR installations (ß=-.138; P=.008). Similarly, the results suggested user-friendly, overall digitization to have a moderate positive effect on patient satisfaction (ß=-.009; P=.01). CONCLUSIONS: The results of this study suggest that hospital digitization is not an end in itself. Policy makers and hospitals are well advised to not only focus on the mere adoption of digital technologies but also continuously work toward digitization that is perceived as valuable by physicians and nurses who rely on it every day. Furthermore, hospital digitization strategies should consider that the assumed benefits of single technologies are not realized across all care domains.

What Went Wrong in eMedCare? Formative Evaluation of an IT Project in Primary Care in Two Rural Districts.

INTRODUCTION: The interaction between nurses and physicians in the primary care setting is challenging with regard to structural, process and technical barriers. In order to overcome these barriers, the eMedCare project was launched and a commercial system was implemented. OBJECTIVE: This study aimed at a formative evaluation of the project. The findings should be used retrospectively to understand the failure of the project. METHODS: To this end, two rounds of qualitative interviews with 10 respectively 8 healthcare providers were performed. RESULTS: The interviews revealed a mixed benefit. Difficulties arose because the initial aim to monitor patients shifted towards improving the communication between the providers, partly due to the poor usability of the monitoring system. Additional workload was imposed because the system was not interoperable with the institutional IT systems. CONCLUSION: Projects with an unclear or shifting vision and focus seem to be susceptible to failure. The secure communication applications could have been realised on the intended scale if the national Telematikinfrastruktur had been in place.

Developing a professional-practice-model-based nursing organizational informatics competency model.

BACKGROUND: The development of nursing informatics started late in China. There is an urgent need to develop a clinical practice model that can guide nursing managers in constructing an organizational nursing informatics competency. OBJECTIVE: The objective of this study was to develop a nursing organizational informatics competency model based on the Professional Practice Model (PPM) and to provide a reference for training in clinical nursing informatics in hospitals. METHODS: A multidisciplinary team in the hospital was first formed as the working group, consisting of nurses trained with the TIGER (Technology Informatics Guiding Education Reform) Taiwan model and had practical experience in system development. We used an exploration map to help build the prototype of the hospital nursing informatics competency model. Then, a final model was constructed by experienced out-of-hospital experts using the Delphi method. The final model was determined according to the validity analysis. RESULTS: Ten hospital stakeholders were invited to form the multidisciplinary working team to develop the prototype organizational PPM model. Two rounds of Delphi were conducted to twelve experienced nurses' informatics experts outside the hospital by e-mail. The results showed that the questionnaire return rate was 100 %, the expert authority coefficient was 0.84, the general validity of the two rounds of content was 92.46 % and 100 %, respectively, and the coefficient of variation of all items was < 0.3. The final model included five categories, including management strategy and leadership, organizational structure and operation, improvement of the environment for nursing information practice, cultivation of core competence in nursing information, and project management of the nursing information system, with 61 elements in total. CONCLUSIONS: We propose this model to help hospital nursing managers to establish a plan of action to build up organizational clinical informatics competency.

Automatic Wound Type Classification with Convolutional Neural Networks.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

Should App Self-Management Mean Self-Control? A Quantiative Study on App Supported Diabetes Self-Management.

Apps have been attested to empower patients regarding disease self-management through numerous studies. However, it is still unclear what factors determine the perception of patients whether an app is a useful tool for this purpose. A multiple regression model that was informed by the Technology Acceptance Model (TAM 2) was tested based on the answers of 235 app users with Diabetes type 1 or 2. The model accounted for 59.2% of the variance of the perceived degree of self-management. Factors belonging to the relevance-usefulness-quality complex as well as factors reflecting the patient's self-control were found to be significant in the model. Patient demographics, i.e. age, gender, app experience and type of Diabetes did not play any significant role. In conclusion, this study raises the question whether apps should be designed to strengthen self-management in the sense of self-control (e.g. own measurements, diary) as opposed to guiding and advice giving.

The eHealth4all@eu Pipeline of Course Development: TIGER Recommendations in Action.

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.

Patient Safety Informatics: Criteria Development for Assessing the Maturity of Digital Patient Safety in Hospitals.

With the start of the 21st century, patient safety as a topic of special interest has attracted increasing attention in both academia and clinical practice. As technology has continued to develop since then, questions and focal points surrounding the topic have also shifted. In particular, questions regarding the impact of digitalization on patient safety and its measurement are now of high interest. This work aims to develop a maturity assessment instrument in the form of a criteria set for measuring structural requirements for digital patient safety in hospitals. Based on the results of a literature review and a derivation of maturity objects (MO) from known maturity models, 64 criteria across 11 categories were developed. Written comments of two digital patient safety experts as well as subsequent interviews were used to evaluate and refine the criteria catalog. The resulting catalog offers hospitals guidance for detecting possible areas of structural improvements in their information systems with regard to patient safety and represents a unique instrument for assessing digital maturity in this particular area.

An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers.

Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.

Continuing Education of Nurses in Patient Handovers: Development and Evaluation of a Digitally Enabled Problem-Based Learning Course.

Communication deficits belong to the most frequent errors in patient handovers calling upon specialized training approaches to be implemented. This study aims to harness problem-based learning (PBL) methods in handover education and evaluated the learning process. A digitally enabled PBL course was developed and implemented at Klinikum Osnabrück from which eight nurses participated in the course. They agreed on the stimulating effect of the setting regarding self-directed learning and on the potential to translate the new knowledge and skills into the daily clinical practice. In conclusion, the findings are promising that a digitally enabled PBL course is a suitable learning format for handover education.

Development and Evaluation of a Bayesian Risk Stratification Method for Major Amputations in Patients with Diabetic Foot Ulcers.

The diabetic foot ulcer, which 2% - 6% of diabetes patients experience, is a severe health threat. It is closely linked to the risk of lower extremity amputation (LEA). When a DFU is present, the chief imperative is to initiate tertiary preventive actions to avoid amputation. In this light, clinical decision support systems (CDSS) can guide clinicians to identify DFU patients early. In this study, the PEDIS classification and a Bayesian logistic regression model are utilised to develop and evaluate a decision method for patient stratification. Therefore, we conducted a Bayesian cutpoint analysis. The CDSS revealed an optimal cutpoint for the amputation risk of 0.28. Sensitivity and specificity were 0.83 and 0.66. These results show that although the specificity is low, the decision method includes most actual patients at risk, which is a desirable feature in monitoring patients at risk for major amputation. This study shows that the PEDIS classification promises to provide a valid basis for a DFU risk stratification in CDSS.