This study does not focus on a single disease or health problem. The five categories of stakeholders from which key informants will be recruited are healthcare providers (subdivided into physicians and representatives of healthcare companies), representatives of patient rights organizations, representatives of regulatory institutions, developers of AI-enabled diagnostics models, and independent experts (e.g., general AI researchers, experts on AI in medical diagnostics, experts on data securit
Conditions
Interventions
Group 1: Conducting and analyzing qualitative interviews represents the prospective component of the study. Key informants from several stakeholder groups will be invited to participate in a semi-stru
Sponsors
Heidelberg Institute of Global Health
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: Interviewees who fit one of the five relevant categories of stakeholders, are over eighteen years old, are able to provide informed consent, and actually provide informed consent could be included into the study population. The five categories of stakeholders from which key informants will be recruited are healthcare providers (subdivided into physicians and representatives of healthcare companies), representatives of patient rights organizations, representatives of regulatory institutions, developers of AI-enabled diagnostics models, and independent experts (e.g., general AI researchers, experts on AI in medical diagnostics, experts on data security, and experts on regulatory aspects). Additionally, since the study focuses specifically on the German health system, an inclusion criterion will be that the stakeholders’ experiences or products apply to or are expected to influence developments in Germany.
Exclusion criteria
Exclusion criteria: Inversely, not fitting into one of the five relevant stakeholder groups, being under 18 years old, inability to provide informed consent, and lacking informed consent represent exclusion criteria. Moreover, stakeholders whose experiences or products do not apply to the German health system or will likely not influence developments in Germany will also be excluded.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| As a qualitative study, this study has no endpoint. The primary outcome of the prospective component of this study is to identify and explore barriers and enablers to the implementation of AI-enabled clinical decision support systems for diagnostics in the German healthcare system. This will occur through analyzing semi-structured in-depth interviews with key informants, after completion of a sufficiently large number of interviews. The scoping review of literature from databases will have the same aim. | — |
Secondary
| Measure | Time frame |
|---|---|
| Secondary outcomes of the prospective component include describing the current adoption and diffusion landscape, as well as envisioning future scenarios regarding AI-enabled clinical decision support systems for diagnostics in the German healthcare system. This, along with insights on the primary outcome, will inform the theoretical component and design process for policies and interventions in the later stages of the study. | — |
Countries
Germany, United States
Contacts
Public ContactSimiao Chen
Heidelberg Institute of Global Health
Outcome results
None listed