The Doctors, Their Patients, and the Symptom Checker App: Qualitative Interview Study With General Practitioners in Germany
Bibliographic record
Abstract
BACKGROUND: Symptom checkers are designed for laypeople and promise to provide a preliminary diagnosis, a sense of urgency, and a suggested course of action. OBJECTIVE: We used the international symptom checker app (SCA) Ada App as an example to answer the following question: How do general practitioners (GPs) experience the SCA in relation to the macro, meso, and micro level of their daily work, and how does this interact with work-related psychosocial resources and demands? METHODS: We conducted 8 semistructured interviews with GPs in Germany between December 2020 and February 2022. We analyzed the data using the integrative basic method, an interpretative-reconstructive method, to identify core themes and modes of thematization. RESULTS: Although most GPs in this study were open to digitization in health care and their practice, only one was familiar with the SCA. GPs considered the SCA as part of the "unorganized stage" of patients' searching about their conditions. Some preferred it to popular search engines. They considered it relevant to their work as soon as the SCA would influence patients' decisions to see a doctor. Some wanted to see the results of the SCA in advance in order to decide on the patient's next steps. GPs described the diagnostic process as guided by shared decision-making, with the GP taking the lead and the patient deciding. They saw diagnosis as an act of making sense of data, which the SCA would not be able to do, despite the huge amounts of data. CONCLUSIONS: GPs took a techno-pragmatic view of SCA. They operate in a health care system of increasing scarcity. They saw the SCA as a potential work-related resource if it helped them to reduce administrative tasks and unnecessary patient contacts. The SCA was seen as a potential work-related demand if it increased workload, for example, if it increased patients' anxiety, was too risk-averse, or made patients more insistent on their own opinions.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".