Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. “Smart Medical Information Technology for Healthcare (SMITH)” is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH’s goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. Objectives: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. Governance and Policies: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals’ Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. Methodology and Architectural Framework: To share medical and research data, SMITH’s information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM² for enterprise architecture modeling supports a consistent development process. The DIC reference architecture determines the services, applications and the standards-based communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. Use Cases: The methodological use case “Phenotype Pipeline” (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case “Algorithmic Surveillance of ICU Patients” (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a “hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections” (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design. Discussion: SMITH’s strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals’ information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

The study aims to (1) investigate current levels of patient acceptance of mHealth in Germany; (2) determine the influencing factors of patients' intention to use, and (3) test the influence of prescription and reimbursement status on patient acceptance. Online survey with 1349 participants, of which 1051 were complete and included for statistical analysis, from a broad cross-section of the German population, addressing both users of mobile health (mHealth) applications and people without prior experience. SEM modeling based on a combination of two theoretical frameworks: the extended Unified Theory of Acceptance and Use of Technology and Health Protective Behavior Theories were used to assess acceptance. Users of mHealth in Germany are mostly patients between the ages of 30 – 50 with mental health or endocrine conditions. General willingness to use mHealth apps / DiGAs (mHealth apps fully reimbursed by social health insurance) is high at 76%, especially if they are governmentally certified, however only 27% of respondents were willing to pay out of pocket. With the exception of a spike in performance expectancy and data security, DiGAs lack a clear differentiation from mHealth apps. Perceived self-efficacy and performance expectancy are significant predictors of willingness to use digital health interventions; with age, attitude, and e-literacy being key demographic predictors. A key takeaway for regulators, providers of mHealth apps/ DiGAs, and other stakeholders involved in mHealth adoption is the importance of addressing negative beliefs early on, targeted communication around effortless usage of mHealth services across age groups and demographics, and focus on highlighting expected benefits of mHealth app/ DiGA usage.

Extensive research has shown the potential value of digital health solutions and highlighted the importance of clinicians’ adoption. As general practitioners (GPs) are patients’ first point of contact, understanding influencing factors to their digital health adoption is especially important to derive personalized practical recommendations. Using a mixed-methods approach, this study broadly identifies adoption barriers and potential improvement strategies in general practices, including the impact of GPs’ inherent characteristics – especially their personality – on digital health adoption. Results of our online survey with 216 GPs reveal moderate overall barriers on a 5-point Likert-type scale, with required workflow adjustments (M = 4.13, SD = 0.93), inadequate reimbursement (M = 4.02, SD = 1.02), and high training effort (M = 3.87, SD = 1.01) as substantial barriers. Improvement strategies are considered important overall, with respondents especially wishing for improved interoperability (M = 4.38, SD = 0.81), continued technical support (M = 4.33, SD = 0.91), and improved usability (M = 4.20, SD = 0.88). In our regression model, practice-related characteristics, the expected future digital health usage, GPs’ digital affinity, several personality traits, and digital maturity are significant predictors of the perceived strength of barriers. For the perceived importance of improvement strategies, only demographics and usage-related variables are significant predictors. This study provides strong evidence for the impact of GPs’ inherent characteristics on barriers and improvement strategies. Our findings highlight the need for comprehensive approaches integrating personal and emotional elements to make digitization in practices more engaging, tangible, and applicable.

One of the key problems in the drug therapy of patients with chronic conditions is drug adherence. In 2010 the initiative iNephro was launched (www.inephro.de). A software to support regular and correct drug intake was developed for a smartphone platform (iOS). The study investigated whether and how smartphone users deployed such an application. Together with cooperating partners the mobile application “Medikamentenplan” (“Medication Plan”) was developed. Users are able to keep and alter a list of their regular medication. A memory function supports regular intake. The application can be downloaded free of charge from the App Store™ by Apple™. After individual consent of users from December 2010 to April 2012 2042338 actions were recorded and analysed from the downloaded applications. Demographic data were collected from 2279 users with a questionnaire. Overall the application was used by 11688 smartphone users. 29% (3406/11688) used it at least once a week for at least four weeks. 27% (3209/11688) used the application for at least 84 days. 68% (1554/2279) of users surveyed were male, the stated age of all users was between 6–87 years (mean 44). 74% of individuals (1697) declared to be suffering from cardiovascular disease, 13% (292) had a previous history of transplantation, 9% (205) were suffering from cancer, 7% (168) reported an impaired renal function and 7% (161) suffered from diabetes mellitus. 69% (1568) of users were on <6 different medications, 9% (201) on 6 – 10 and 1% (26) on more than 10. A new smartphone application, which supports drug adherence, was used regularly by chronically ill users with a wide range of diseases over a longer period of time. The majority of users so far were middle-aged and male.

Background: Digital competence is listed as one of the key competences for lifelong learning and is increasing in importance not only in private life but also in professional life. There is consensus within the health care sector that digital competence (or digital literacy) is needed in various professional fields. However, it is still unclear what exactly the digital competence of health professionals should include and how it can be measured. Objective: This scoping review aims to provide an overview of the common definitions of digital literacy in scientific literature in the field of health care and the existing measurement instruments. Methods: Peer-reviewed scientific papers from the last 10 years (2013-2023) in English or German that deal with the digital competence of health care workers in both outpatient and inpatient care were included. The databases ScienceDirect, Scopus, PubMed, EBSCOhost, MEDLINE, OpenAIRE, ERIC, OAIster, Cochrane Library, CAMbase, APA PsycNet, and Psyndex were searched for literature. The review follows the JBI methodology for scoping reviews, and the description of the results is based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Results: The initial search identified 1682 papers, of which 46 (2.73%) were included in the synthesis. The review results show that there is a strong focus on technical skills and knowledge with regard to both the definitions of digital competence and the measurement tools. A wide range of competences were identified within the analyzed works and integrated into a validated competence model in the areas of technical, methodological, social, and personal competences. The measurement instruments mainly used self-assessment of skills and knowledge as an indicator of competence and differed greatly in their statistical quality. Conclusions: The identified multitude of subcompetences illustrates the complexity of digital competence in health care, and existing measuring instruments are not yet able to reflect this complexity.

Medication adherence is crucial for success in the management of patients with chronic conditions. This study analyzes whether a mobile application on a tablet aimed at supporting drug intake and vital sign parameter documentation affects adherence in elderly patients. Patients with coronary heart disease and no prior knowledge of tablet computers were recruited. They received a personal introduction to the mobile application Medication Plan, installed on an Apple iPad. The study was conducted using a crossover design with 3 sequences: initial phase, interventional phase (28 days of using the app system), and comparative phase (28 days of using a paper diary). Users experienced the interventional and comparative phases alternately. A total of 24 patients (12 males; mean age 73.8 years) were enrolled in the study. The mean for subjectively assessed adherence (A14-scale; 5-point Likert scale, from “never” to “very often” which results in a score from 0 to 56) before the study was 50.0 (SD = 3.44). After both interventions there was a significant increase, which was more pronounced after the interventional phase (54.0; SD = 2.01) than after the comparative phase (52.6; SD = 2.49) (for all pairs after both interventions, P <0.001). Neither medical conditions nor the number of drug intake (amount and frequency of drug taking) per day affected subjective adherence. Logging data showed a significantly stronger adherence for the medication app than the paper system for both blood pressure recordings (P <0.001) and medication intake (P = 0.033). The majority of participants (n = 22) stated that they would like to use the medication app in their daily lives and would not need further assistance with the app. A mobile app for medication adherence increased objectively and subjectively measured adherence in elderly users undergoing rehabilitation. The findings have promising clinical implications: digital tools can assist chronic disease patients achieve adherence to medication and to blood pressure measurement. Although this requires initial offline training, it can reduce complications and clinical overload because of nonadherence.