Study on safety performance and condition‑suggestion accuracy of the symptom assessment mobile applications
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Objective To compare the breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of the 8 symptom assessment mobile applications (APPs) available on the Chinese market. Methods The APPs were assessed using 200 primary care vignettes and were measured against the vignettes′ standard. The primary outcome measures were proportion of conditions covered by an APP, proportion of vignettes with the correct primary diagnosis, and proportion of safe urgency advice. Results For APPs assessed, condition‑coverage was from 29.0%(58/200)to 99.5%(199/200), top‑3 suggestion accuracy was from 8.5%(17/200) to 61.5%(123/200), the proportion of safe urgency advice was from 84.8%(167/197) to 99.5% (198/199). Conclusions The APPs showed a wide range of coverage, safety performance and condition‑suggestion accuracy. Symptom assessment APPs with good performance could be used by general practitioners as supporting tools. However, even symptom assessment APPs with excellent performance need to be further assessed in a real clinical environment.
Translated title of the contribution | Study on safety performance and condition‑suggestion accuracy of the symptom assessment mobile applications |
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Details
Original language | Chinese |
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Pages (from-to) | 288-294 |
Number of pages | 7 |
Journal | Chinese Journal of General Practitioners |
Volume | 22 |
Issue number | 3 |
Publication status | Published - Mar 2023 |
Peer-reviewed | Yes |
External IDs
Mendeley | 138edc3c-1de1-3108-8b92-f9a770b86165 |
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ORCID | /0000-0002-1997-1689/work/169175779 |
Keywords
ASJC Scopus subject areas
Keywords
- Artificial intelligence, Clinical, Decision support systems, Diagnosis, differential