Study on safety performance and condition‑suggestion accuracy of the symptom assessment mobile applications

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Zhu Shanzhu - , Fudan University (Author)
  • Jiang Sunfang - , Fudan University (Author)
  • Shou Juan - , Fudan University (Author)
  • Pan Zhigang - , Fudan University (Author)
  • Zhang Yu - , Fudan University (Author)
  • Peng Minghui - , Fudan University (Author)
  • Yang Hua - , Fudan University (Author)
  • Stephen Gilbert - , Else Kröner Fresenius Center for Digital Health (Author)

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

Details

Original languageChinese
Pages (from-to)288-294
Number of pages7
JournalChinese Journal of General Practitioners
Volume22
Issue number3
Publication statusPublished - Mar 2023
Peer-reviewedYes

External IDs

Mendeley 138edc3c-1de1-3108-8b92-f9a770b86165
ORCID /0000-0002-1997-1689/work/169175779

Keywords

Keywords

  • Artificial intelligence, Clinical, Decision support systems, Diagnosis, differential