ABSTRACT
As healthcare demands exceed outpatient physicians' capacities, telemedicine holds far-reaching potential for both physicians and patients. It is crucial to holistically analyze physicians' acceptance of telemedical applications, such as online consultations. This study seeks to identify supporting and constraining factors that influence outpatient physicians' acceptance of telemedicine.We develop a model based on the unified theory of acceptance and use of technology (UTAUT). To empirically examine our research model, we conducted a survey among German physicians (n = 127) in 2018-2019. We used the partial least squares (PLS) modeling approach to test our model, including a mediation analysis. The results indicate that performance expectancy (ß = .397, P < .001), effort expectancy (ß = .134, P = .03), and social influence (ß = .337, P < .001) strongly impact the intention to conduct online consultations and explain 55% of its variance. Structural conditions regarding data security comprise a key antecedent, associating with performance expectancy (ß = .193, P < .001) and effort expectancy (ß = .295, P < .001). Regarding potential barriers to usage intentions, we find that IT anxiety predicts performance (ß = -.342, P < .001) and effort expectancy (ß = -.364, P < .001), while performance expectancy fully mediates (ßdirect = .022, P = .71; ßindirect = -.138, P < .001) the direct relationship between IT anxiety and the intention to use telemedical applications.This research provides explanations for physicians' behavioral intention to use online consultations, underlining UTAUT's applicability in healthcare contexts. To boost acceptance, social influences, such as personal connections and networking are vital, as colleagues can serve as multipliers to reach convergence on online consultations among peers. To overcome physicians' IT anxiety, training, demonstrations, knowledge sharing, and management incentives are recommended. Furthermore, regulations and standards to build trust in the compliance of online consultations with data protection guidelines need reinforcement from policymakers and hospital management alike.
Subject(s)
Physicians , Telemedicine , Humans , Models, Theoretical , Intention , Patient Acceptance of Health Care , Surveys and QuestionnairesABSTRACT
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.
Subject(s)
COVID-19 , Public Health , Humans , Germany , Public Sector , Health ServicesABSTRACT
BACKGROUND: General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care. OBJECTIVE: This study aims to understand GPs' attitudes toward AI-enabled systems in medical diagnosis. METHODS: We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs' attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants. RESULTS: We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs' thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders' perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model. CONCLUSIONS: This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care.
