Perception of the Progressing Digitization and Transformation of the German Health Care System Among Experts and the Public: Mixed Methods Study
Bibliographic record
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<.001, respectively). CONCLUSIONS: Despite the firm agreement among participants and experts regarding the impact of digitization on the health care system, physicians demonstrated a more negative attitude. We assume that this might be a factor contributing to the slow adoption of digitization in practice. Physicians might be struggling with changing power structures, so future measures to transform the market should involve them to a larger degree.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".