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Abstract BackgroundDigitalization in the German health care system is progressing slowly, even though it offers opportunities for improvement of care. In nursing homes, most of the staff’s work is paper based. Following the pandemic, there has been a decrease in the use of telemedicine applications. To ensure long-term implementation, the views of users, in this case nurses, are of interest. ObjectiveThis cross-sectional study was conducted to describe which digital applications are already being used at inpatient care facilities, the attitude of nurses toward telemedicine, and for which areas the use of telemedicine in the facilities is considered appropriate by the participants. MethodsAll inpatient care facility staff in Schleswig-Holstein were invited to participate in the survey from August 1 to October 31, 2022. The questionnaire consists of 17 determinants that ask about the attitude, use, and possible applications of telemedicine. In addition to a descriptive analysis, the influence of the general attitude toward telemedicine on various determinants was examined using the Fisher exact test for nominal variables and Spearman correlation coefficient for metric variables. ResultsA total of 425 caregivers participated in the survey. Of these respondents, 10.7% (n=41) currently used video consultations, and 76.1% (n=321) of the respondents were in favor of video consultations being practiced in training. Furthermore, 74.8% (n=312) of the respondents would attend a training on telephone medical consultation. Respondents indicated that video consultations have a small added value compared to asynchronous telemedicine (eg, sending photos). However, video consultations were perceived as somewhat less time-consuming than other communication channels. Video consultations are perceived as most useful for clarifying urgent problems. The respondents estimated that one in five paramedic calls at their facilities could be reduced through telemedicine approaches. It was important to the participants that telemedicine is as simple as possible and that there is a high level of data security. ConclusionsAlthough many caregivers have a positive attitude toward telemedicine and perceive its advantages, communication channels such as video consultation are still used infrequently in care facilities. To promote the use of telemedicine applications, it is important to emphasize their benefits. The presumed saving of paramedic calls thus represents a benefit, and it is crucial to train caregivers in the use of telemedicine to avoid uncertainties in dealing with the newer technologies. It is important to give them enough time and repetitions of the training.

BackgroundDigital transformation offers new opportunities to improve the exchange of information between different health care providers, including inpatient, outpatient and care facilities. As information is especially at risk of being lost when a patient is discharged from a hospital, digital transformation offers great opportunities to improve intersectoral discharge management. However, most strategies for improvement have focused on structures within the hospital. ObjectiveThis study aims to evaluate the implementation of a digitalized discharge management system, the project “Optimizing instersectoral discharge management” (SEKMA, derived from the German Sektorübergreifende Optimierung des Entlassmanagements), and its impact on the readmission rate. MethodsA mixed methods design was used to evaluate the implementation of a digitalized discharge management system and its impact on the readmission rate. After the implementation, the congruence between the planned (logic model) and the actual intervention was evaluated using a fidelity analysis. Finally, bivariate and multivariate analyses were used to evaluate the effectiveness of the implementation on the readmission rate. For this purpose, a difference-in-difference approach was adopted based on routine data of hospital admissions between April 2019 and August 2019 and between April 2022 and August 2022. The department of vascular surgery served as the intervention group, in which the optimized discharge management was implemented in April 2022. The departments of internal medicine and cardiology formed the control group. ResultsOverall, 26 interviews were conducted, and we explored 21 determinants, which can be categorized into 3 groups: “optimization potential,” “barriers,” and “enablers.” On the basis of these results, 19 strategies were developed to address the determinants, including a lack of networking among health care providers, digital information transmission, and user-unfriendliness. On the basis of these strategies, which were prioritized by 11 hospital physicians, a logic model was formulated. Of the 19 strategies, 7 (37%; eg, electronic discharge letter, providing mobile devices to the hospital’s social service, and generating individual medication plans in the format of the national medication plan) have been implemented in SEKMA. A survey on the fidelity of the application of the implemented strategies showed that 3 of these strategies were not yet widely applied. No significant effect of SEKMA on readmissions was observed in the routine data of 14,854 hospital admissions (P=.20). ConclusionsThis study demonstrates the potential of optimizing intersectoral collaboration for patient care. Although a significant effect of SEKMA on readmissions has not yet been observed, creating a digital ecosystem that connects different health care providers seems to be a promising approach to ensure secure and fast networking of the sectors. The described intersectoral optimization of discharge management provides a structured template for the implementation of a similar local digital care networking infrastructure in other care regions in Germany and other countries with a similarly fragmented health care system.

BackgroundPrimary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined. ObjectiveCough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice. MethodsThe PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor. ResultsIn total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent. ConclusionsReliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning–based diagnostic algorithm in a meaningful way.

BackgroundCommunication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. ObjectiveThis scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. MethodsWe conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. ResultsThe titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. ConclusionsThe use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

In the wake of the COVID-19 pandemic, video consultation was introduced in general practice in many countries around the world as a solution to provide remote health care to patients. It was assumed that video consultation would find widespread adoption in post–COVID-19 general practice. However, adoption rates remain low across countries in Northern Europe, suggesting that barriers to its use exist among general practitioners and other practice staff. In this viewpoint, we take a comparative approach, reflecting on similarities and differences in implementation conditions of video consultations in 5 Northern European countries’ general practice settings that might have created barriers to its use within general practice. We convened at a cross-disciplinary seminar in May 2022 with researchers and clinicians from 5 Northern European countries with expertise in digital care in general practice, and this viewpoint emerged out of dialogues from that seminar. We have reflected on barriers across general practice settings in our countries, such as lacking technological and financial support for general practitioners, that we feel are critical for adoption of video consultation in the coming years. Furthermore, there is a need to further investigate the contribution of cultural elements, such as professional norms and values, to adoption. This viewpoint may inform policy work to ensure that a sustainable level of video consultation use can be reached in the future, one that reflects the reality of general practice settings rather than policy optimism.