The Influence of Physician Information on Patients’ Choice of Physician in mHealth Services Using China’s Chunyu Doctor App: Eye-Tracking and Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) is becoming more popular as a way of sharing medical information. For the patient, it saves time, reduces the need for travel, reduces the cost of searching for information, and brings medical services "to your fingertips." However, it also brings information overload and makes the patient's choice of physician more difficult. OBJECTIVE: This study aimed to identify the types of physician information that play a key role in patients' choice of physician and to explore the mechanism by which this information contributes to this choice. METHODS: Based on the stimulus-organism-response (SOR) model and online trust theory, we proposed a research model to explain the influence of physician information on patients' choice of physician. The model was based on cognitive trust and affective trust and considered the moderating role of patient expertise. Study 1 was an eye-tracking experiment (n=42) to identify key factors affecting patients' choice of physician. Study 2 was a questionnaire study (n=272); Partial Least Squares Structural Equation Modeling was used to validate the research model. RESULTS: The results of Study 1 revealed that seven types of physician information played a key role in patients' choice of physician. The results of Study 2 revealed that (1) physicians' profile photo information affected patients' choice of physician by positively influencing affective trust (P<.001); (2) physicians' nonprofile photo information affected patients' choice of physician by positively influencing cognitive trust (P<.001); (3) patient-generated information affected patients' choice of physician by positively affecting cognitive trust (P<.001) and affective trust (P<.001), and patient expertise played a positive moderating role on both (P=.04 and P=.01, respectively); and (4) cognitive trust and affective trust both positively affected patients' choice of physician, with affective trust playing a more significant role (P<.001 and P<.001, respectively). CONCLUSIONS: Seven types of physician information were mainly used by patients when choosing physicians offering mHealth services; trust played an important role in this choice. In addition, the level of patient expertise was an important variable in moderating the influence of physician information and patients' trust. This paper supports the theoretical basis of information selection and processing by patients. These findings can help guide app developers in the construction of medical apps and in the management of physician information in order to facilitate patients' choice of physician.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".