Patient Adoption of Digital Use Cases in Family Medicine and a Nuanced Implementation Approach for Family Doctors: Quantitative Web-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: Digital use cases describe the application of technology to achieve specific outcomes. Several studies in health care have examined patients' overall attitudes toward digitalization and specific use cases. However, these studies have failed to provide a comparison of patient acceptance criteria between inherently different digital use cases in family medicine. OBJECTIVE: To address this research gap, this paper aimed to assist family doctors in selecting digital use cases by comparing the underlying patient adoption factors and in driving usage of these use cases by presenting a differentiated implementation approach. METHODS: Adapting an established Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire to 4 digital use cases in family medicine, we surveyed a large cross-sectional sample of adults living in Germany. The results of the web-based survey were then analyzed via descriptive statistics, ANOVA, and hierarchical regression models to compare the effects of sociodemographic and technology acceptance factors on the intention to use a specific use case. RESULTS: =53.441; P<.001). Regarding sociodemographic characteristics, only digital literacy demonstrated a significant effect on the intention to use for all use cases, particularly scheduling doctor appointments online (B=0.322, SE 0.033; β=.408; P<.001). Furthermore, performance expectancy was the strongest predictor of the intention to use for all use cases, while further effects of technology acceptance factors depended on the use case (receiving medical consultations via video: B=0.603, SE 0.049; β=.527; P<.001; scheduling doctor appointments online: B=0.566, SE 0.043; β=.513; P<.001; storing personal medical information via electronic health records: B=0.405, SE 0.047; β=.348; P<.001; and providing personal information before consultation digitally [digital anamnesis]: B=0.434, SE 0.048; β=.410; P<.001). To illustrate, perceived privacy and security had an effect on the intention to use electronic health records (B=0.284, SE 0.040; β=.243; P<.001) but no effect on the intention to use video consultations (B=0.068, SE 0.042; β=.053; P=.10). CONCLUSIONS: In the selection and implementation of digital use cases, family doctors should always prioritize the perceived value of the digital use case for the patient, and further criteria might depend on the digital use case. Practice owners should therefore always harmonize the introduction of digital use cases with their own patient care strategies. Not every digital innovation fits every strategy and therefore every practice.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".