[Digital health applications (DiGA) in the area of tension between progress and criticism : Discussion paper from the "Digital health" specialist group of the German Informatics Society]. / Digitale Gesundheitsanwendungen (DiGA) im Spannungsfeld von Fortschritt und Kritik : Diskussionsbeitrag der Fachgruppe "Digital Health" der Gesellschaft für Informatik e. V.

Since December 2019, digital health applications (DiGA) have been included in standard care in Germany and are therefore reimbursed by the statutory health insurance funds to support patients in the treatment of diseases or impairments. There are 48 registered DiGA listed in the directory of the Federal Institute of Drugs and Medical Devices (BfArM), mainly in the areas of mental health; hormones and metabolism; and muscles, bones, and joints. In this article, the "Digital Health" specialist group of the German Informatics Society describes the current developments around DiGA as well as the current sentiment on topics such as user-centricity, patient and practitioner acceptance, and innovation potential. In summary, over the past three years, DiGA have experienced a positive development, characterized by a gradually increasing availability of various DiGA and coverage areas as well as prescription numbers. Nevertheless, significant regulatory adjustments are still required in some areas to establish DiGA as a well-established instrument in long-term routine healthcare. Key challenges include user-centeredness and the sustainable use of the applications.

Circular Business Model for Digital Health Solutions: Protocol for a Scoping Review.

BACKGROUND: The circular economy reshapes the linear "take, make, and dispose" approach and evolves around minimizing waste and recapturing resources in a closed-loop system. The health sector accounts for 4.6% of global greenhouse gas emissions and has, over the decades, been built to rely on single-use devices and deal with high volumes of medical waste. With the increase in the adoption of digital health solutions in the health care industry, leading the industry into a new paradigm of how we provide health care, a focus must be put on the amount of waste that will follow. Digital health solutions will shape health care through the use of technology and lead to improved patient care, but they will also make medical waste more complex to deal with due to the e-waste component. Therefore, a transformation of the health care industry to a circular economy is a crucial cornerstone in decreasing the impact on the environment. OBJECTIVE: This study aims to address the lack of direction in the current literature on circular business models. It will consider micro, meso, and macro factors that would impact the operational validity of circular models using the digital health solutions ePaper label (medical packaging), smart wearable sensor (health monitoring devices), smart pill box (medication management), and endo-cutter (surgical equipment) as examples. METHODS: The study will systematically perform a scoping review through a database and snowball search. We will analyze and classify the studies from a predetermined set of categories and then summarize them into an evidence map. Based on the review, the study will develop a 2D framework for businesses to follow or for future research to take a standpoint from. RESULTS: Preliminarily, the review has analyzed 26 studies in total. The results are close to equally distributed among the micro (8/26, 31%), meso (10/26, 38%), and macro (8/26, 31%) levels. Circular economy studies emphasize several circular practices such as recycling (17/26, 65%), reusing (18/26, 69%), reducing (15/26, 58%), and remanufacturing (8/26, 31%). The value proposition in the examined business model is mostly dominated by stand-alone products (18/26, 69%) compared to product as a service (7/26, 27%), involving stakeholders such as health care professionals or hospitals (20/26, 77%), manufacturers (11/26, 42%), and consumers (9/26, 35%). All studies encompass societal (12/26, 46%), economic (23/26, 88%), and environmental (24/26, 92%) viewpoints. CONCLUSIONS: The study argues that each digital health solution would have to be accessed individually to find the optimal business model to follow. This is due to their differing life cycles and complexity. The manufacturer will need a layered value proposition, implementing several business models dependent on their respective product portfolios. The need to incorporate several business models implies an ecosystem perspective that is relevant to consider. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47874.

Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study.

BACKGROUND: Previous studies have revealed that users of symptom checkers (SCs, apps that support self-diagnosis and self-triage) are predominantly female, are younger than average, and have higher levels of formal education. Little data are available for Germany, and no study has so far compared usage patterns with people's awareness of SCs and the perception of usefulness. OBJECTIVE: We explored the sociodemographic and individual characteristics that are associated with the awareness, usage, and perceived usefulness of SCs in the German population. METHODS: We conducted a cross-sectional online survey among 1084 German residents in July 2022 regarding personal characteristics and people's awareness and usage of SCs. Using random sampling from a commercial panel, we collected participant responses stratified by gender, state of residence, income, and age to reflect the German population. We analyzed the collected data exploratively. RESULTS: Of all respondents, 16.3% (177/1084) were aware of SCs and 6.5% (71/1084) had used them before. Those aware of SCs were younger (mean 38.8, SD 14.6 years, vs mean 48.3, SD 15.7 years), were more often female (107/177, 60.5%, vs 453/907, 49.9%), and had higher formal education levels (eg, 72/177, 40.7%, vs 238/907, 26.2%, with a university/college degree) than those unaware. The same observation applied to users compared to nonusers. It disappeared, however, when comparing users to nonusers who were aware of SCs. Among users, 40.8% (29/71) considered these tools useful. Those considering them useful reported higher self-efficacy (mean 4.21, SD 0.66, vs mean 3.63, SD 0.81, on a scale of 1-5) and a higher net household income (mean EUR 2591.63, SD EUR 1103.96 [mean US $2798.96, SD US $1192.28], vs mean EUR 1626.60, SD EUR 649.05 [mean US $1756.73, SD US $700.97]) than those who considered them not useful. More women considered SCs unhelpful (13/44, 29.5%) compared to men (4/26, 15.4%). CONCLUSIONS: Concurring with studies from other countries, our findings show associations between sociodemographic characteristics and SC usage in a German sample: users were on average younger, of higher socioeconomic status, and more commonly female compared to nonusers. However, usage cannot be explained by sociodemographic differences alone. It rather seems that sociodemographics explain who is or is not aware of the technology, but those who are aware of SCs are equally likely to use them, independently of sociodemographic differences. Although in some groups (eg, people with anxiety disorder), more participants reported to know and use SCs, they tended to perceive them as less useful. In other groups (eg, male participants), fewer respondents were aware of SCs, but those who used them perceived them to be more useful. Thus, SCs should be designed to fit specific user needs, and strategies should be developed to help reach individuals who could benefit but are not aware of SCs yet.

Use of digital technologies by users of psychiatric inpatient services in Berlin, Germany: a cross-sectional patient survey.

Few studies and almost exclusively from the USA have recently investigated mobile phone and computer use among users of psychiatric services, which is of high relevance regarding the increasing development of digital health applications and services. OBJECTIVE, DESIGN AND SETTING: In a cross-sectional patient survey, we examined (a) rates and purposes of mobile phone, computer, internet and social media use, and (b) the role of social and clinical predictors on rates of utilisation among psychiatric inpatients in Berlin, Germany. PARTICIPANTS AND RESULTS: Descriptive analyses showed that among 496 participants, 84.9% owned a mobile phone and 59.3% a smartphone. Among 493 participants, 68.4% used a computer regularly. Multivariate logistic regression models revealed being homeless, diagnosis of a psychotic illness, being of older age and a lower level of education to be significant predictors for not owning a mobile phone, not using a computer regularly or having a social media account, respectively. CONCLUSIONS: Users of psychiatric services may have access to mobile phones and computers, although rates are lower than in the general population. However, key barriers that need to be addressed regarding the development of and engagement with digital health interventions are factors of social exclusion like marginalised housing as well as clinical aspects like psychotic illness.

Patients' and healthcare professionals' perceived facilitators and barriers for shared decision-making for frail and elderly patients in perioperative care: a scoping review.

BACKGROUND: Shared decision-making (SDM) in perioperative care, is an organizational approach to instituting sharing of information and decision-making around surgery. It aims at enabling patient autonomy and patient-centered care. Frail and elderly patients suffering from multiple health conditions and increased surgical vulnerability might particularly benefit from SDM. However, little is known about the facilitators and barriers to implementing SDM in perioperative care for the specific needs of frail and elderly patients. Our objective is twofold: First, we aim at collecting, analyzing, categorizing, and communicating facilitators and barriers. Second, we aim at collecting and mapping conceptual approaches and methods employed in determining and analyzing these facilitators and barriers. METHODS: The search strategy focused on peer-reviewed studies. We employed a taxonomy which is based on the SPIDER framework and added the items general article information, stakeholder, barriers/facilitators, category, subcategory, and setting/contextual information. This taxonomy is based on preceding reviews. The scoping review is reported under the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews. Based on the databases MEDLINE, Embase, CINAHL, and Web of Science, we screened 984 articles, identified, and reviewed 13 original studies. RESULTS: Within this review, two primary facilitators concerning patients' willingness to participate in SDM emerged: Patients want to be informed on their medical condition and procedures. Patients prefer sharing decisions with healthcare professionals, compared to decision-making solely by patients or decision-making solely by healthcare professionals. Communication issues and asymmetric power relationships between patients and clinical healthcare professionals are barriers to SDM. Regarding the methodological approaches, the evaluation of the conceptual approaches demonstrates that the selected articles lack employing a distinct theoretical framework. Second, the selected studies mainly used surveys and interviews, observational studies, like ethnographic or video-based studies are absent. CONCLUSION: Diverging findings perceived by patients or clinical healthcare professionals were identified. These imply that SDM research related to elderly and frail patients should become more encompassing by employing research that incorporates theory-based qualitative analysis, and observational studies of SDM consultations for understanding practices by patients and clinical healthcare professionals. Observational studies are particularly relevant as these were not conducted. TRIAL REGISTRATION: https://osf.io/8fjnb/.

[The maturity model for the public health service-a tool for assessing and improving the digital maturity level of German public health agencies]. / Das Reifegradmodell für den Öffentlichen Gesundheitsdienst ­ Ein Instrument zur Erfassung und Verbesserung des digitalen Reifegrades von deutschen Gesundheitsämtern.

The COVID 19 crisis has highlighted the key role of the public health service (PHS), with its approximately 375 municipal health offices involved in the pandemic response. Here, in addition to a lack of human resources, the insufficient digital maturity of many public health departments posed a hurdle to effective and scalable infection reporting and contact tracing. In this article, we present the maturity model (MM) for the digitization of health offices, the development of which took place between January 2021 and February 2022 and was funded by the German Federal Ministry of Health. It has been applied since the beginning of 2022 with the aim of strengthening the digitization of the PHS. The MM aims to guide public health departments step by step to increase their digital maturity to be prepared for future challenges. The MM was developed and evaluated based on qualitative interviews with employees of public health departments and other experts in the public health sector as well as in workshops and with a quantitative survey. The MM allows the measurement of digital maturity in eight dimensions, each of which is subdivided into two to five subdimensions. Within the subdimensions a classification is made on five different maturity levels. Currently, in addition to recording the digital maturity of individual health departments, the MM also serves as a management tool for planning digitization projects. The aim is to use the MM as a basis for promoting targeted communication between the health departments to exchange best practices for the different dimensions.

[The costs of preoperative anemia in hip joint revision surgery]. / Die Kosten der präoperativen Anämie bei Hüftgelenkrevisionsoperationen.

BACKGROUND: Anemia is highly prevalent in patients before hip joint revision surgery (HJRS) and is associated with an increased complication rate. This paper is the first to investigate costs, real diagnosis-related group (DRG) revenues and case coverage of preoperative anemia in elective HJRS. METHODS: Medical data, transfusions, costs, and revenues of all patients undergoing HJRS at two campuses of the Charité -Universitätsmedizin Berlin between 2010 and 2017 were used for subgroup analyses and linear regressions. RESULTS: Of 1187 patients included 354 (29.8%) showed preoperative anemia. A total of 565 (47.6%) patients were transfused with a clear predominance of anemic patients (72.6% vs. 37.0%, p < 0.001). Costs (12,318€ [9027;20,044€] vs. 8948€ [7501;11,339€], p < 0.001) and revenues (11,788€ [8992;16,298€] vs. 9611€ [8332;10,719€], p < 0.001) were higher for preoperatively anemic patients and the coverage was deficient (-1170€ [-4467;1238€] vs. 591€ [-1441;2103€], p < 0.001). In anemic patients, case contribution margins decreased with increasing transfusion rates (p ≤ 0.001). Comorbidities had no significant economic impact. CONCLUSION: Preoperative anemia and perioperative transfusions in HJRS are associated with increased treatment costs and a financial undercoverage for healthcare providers and health insurance companies. Concepts for the treatment of preoperative anemia (e.g. patient blood management) could reduce treatment costs in the medium term.

The medical profession transformed by artificial intelligence: Qualitative study.

Background: Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear. Objective: We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns. Methods: We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed. Results: Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization. Conclusions: The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.

The Prediction of Fall Circumstances Among Patients in Clinical Care - A Retrospective Observational Study.

Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We developed a boosting algorithm to predict both recurrent falls and the severity of fall injuries. The model was trained on a dataset including extensive information on fall events of patients who had been admitted to Charité - Universitätsmedizin Berlin between August 2016 and July 2020. The data were recorded according to the German expert standard for fall documentation. Predictive power scores were calculated to define optimal feature sets. With an accuracy of 74% for recurrent falls and 86% for injury severity, boosting demonstrated the best overall predictive performance of all models assessed. Given that our data contain initially rated risk scores, our results demonstrate that well trained ML algorithms possibly provide tools to substantially reduce fall risks in clinical care settings.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.

BACKGROUND: Artificial intelligence (AI) holds the promise of supporting nurses' clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. OBJECTIVE: This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. METHODS: Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. RESULTS: The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. CONCLUSIONS: The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care-specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

Telemedicine in Intensive Care Units: Scoping Review.

BACKGROUND: The role of telemedicine in intensive care has been increasing steadily. Tele-intensive care unit (ICU) interventions are varied and can be used in different levels of treatment, often with direct implications for the intensive care processes. Although a substantial body of primary and secondary literature has been published on the topic, there is a need for broadening the understanding of the organizational factors influencing the effectiveness of telemedical interventions in the ICU. OBJECTIVE: This scoping review aims to provide a map of existing evidence on tele-ICU interventions, focusing on the analysis of the implementation context and identifying areas for further technological research. METHODS: A research protocol outlining the method has been published in JMIR Research Protocols. This review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A core research team was assembled to provide feedback and discuss findings. RESULTS: A total of 3019 results were retrieved. After screening, 25 studies were included in the final analysis. We were able to characterize the context of tele-ICU studies and identify three use cases for tele-ICU interventions. The first use case is extending coverage, which describes interventions aimed at extending the availability of intensive care capabilities. The second use case is improving compliance, which includes interventions targeted at improving patient safety, intensive care best practices, and quality of care. The third use case, facilitating transfer, describes telemedicine interventions targeted toward the management of patient transfers to or from the ICU. CONCLUSIONS: The benefits of tele-ICU interventions have been well documented for centralized systems aimed at extending critical care capabilities in a community setting and improving care compliance in tertiary hospitals. No strong evidence has been found on the reduction of patient transfers following tele-ICU intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19695.

The social construction of the patient-physician relationship in the clinical encounter: Media frames on shared decision making in Germany.

The literature on healthcare management has noted that shared decision-making (SDM) - a practice of organizing joint decisions between healthcare professionals and patients - should improve healthcare outcomes through patient engagement and autonomy, fostering patient-centeredness. While SDM projects are implemented across Europe and the US, the diffusion of the practice remains partial, and its' conceptualization scattered. Healthcare management literature explores SDM on the underlying assumption that its limited diffusion results from an information problem, implying objective criteria and rational behavior. The purpose of this research is to study the social construction of SDM within the clinical setting and the underlying rationales using the case of one of the largest healthcare markets worldwide - Germany. To capture the complexity of SDM, a frame analysis is conducted on its medial representations. News media is both influential in shaping public opinion, as well as in generating public discourse. This analysis enables one to elaborate different facets of the construct of SDM, to capture inherent patterns of facilitating and obstructing aspects and to explore consequences for the diffusion of SDM. Three facilitating and three obstructive frames on the implementation of SDM were identified. The polarities of these frames range from the questioning of one's decision-making authority to the perception of individual competence and decision-making agency. Moreover, this study reflects on how physicians' and patients' role for SDM is conceived.

Sharing Frailty-related information in perioperative care: an analysis from a temporal perspective.

BACKGROUND: Especially patients older than 65 years undergoing surgery are prone to develop frailty-related complications that may go far beyond the index hospitalization (e.g., cognitive impairment following postoperative delirium). However, aging-relevant information are currently not fully integrated into hospitals' perioperative processes. METHODS: We introduce a temporal perspective, which focuses on the social construction of time, to better understand existing barriers to the exchange of frailty-related data, targeting complexity research. Our chosen context is perioperative care provided by a tertiary hospital in Germany that has implemented a special track for patients over 65 years old undergoing elective surgery. The research followed a participatory modelling approach between domain and modelling experts with the goal of creating a feedback loop model of the relevant system relationships and dynamics. RESULTS: The results of the study show how disparate temporal regimes, understood as frameworks for organizing actions in the light of time constraints, time pressure, and deadlines, across different clinical, ambulant, and geriatric care sectors create disincentives to cooperate in frailty-related data exchanges. Moreover, we find that shifting baselines, meaning continuous increases in cost and time pressure in individual sectors, may unintentionally reinforce - rather than discourage - disparate temporal regimes. CONCLUSIONS: Together, these results may (1) help to increase awareness of the importance of frailty-related data exchanges, and (2) impel efforts aiming to transform treatment processes to go beyond sectoral boundaries, taking into account the potential benefits for frail patients arising from integrated care processes using information technology.