Abstract
Background: Health care systems worldwide are struggling to keep rising costs at bay with only modest outcome improvement among many diseases. Digitization with technologies like Artificial Intelligence or Machine Learning algorithms might address this. Although digital technologies have been successfully applied in clinical studies the effect on the overall health care system so far was limited. The regulatory ecosystem or data privacy might be responsible, but other reasons may also predominate. Objective: We analyzed how the digitization of the German health care market is currently perceived among different stakeholders and investigated reasons for its slow adaption. Methods: This was a mixed methods study split into a qualitative Part A using the conceptual approach of the Grounded Theory and a quantitative Part B using the Delphi method. For Part A we interviewed experts in the health care system and converted the results into 17 hypotheses. The Delphi method consisted of an online survey which was sent to the participants via email and was available for three months. For the assessment of the 17 hypotheses, the participants were given a six-point Likert scale. The participants were grouped into patients, physicians, and providers of services within the German health care market. Results: There was a strong alignment of opinions on the hypotheses between experts (N=21) and survey participants (N=733), with 70.5% overall agreement on 12/17 hypotheses. Physicians demonstrated the lowest level of agreement with the expert panel at 88% (15/17) disagreement, with the hypotheses "H8: Digitization in the health care system will free up jobs," and "H6: Digitization in the health care system will empower the patients," perceived to be in profound disagreement (P=.036 and P
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Hansen, A., Herrmann, M., Ehlers, J. P., Mondritzki, T., Hensel, K. O., Truebel, H., & Boehme, P. (2019). Perception of the progressing digitization and transformation of the German health care system among experts and the public: Mixed methods study. JMIR Public Health and Surveillance, 5(4). https://doi.org/10.2196/14689
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