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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Zhu Shanzhu - , Fudan University (Autor:in)
  • Jiang Sunfang - , Fudan University (Autor:in)
  • Shou Juan - , Fudan University (Autor:in)
  • Pan Zhigang - , Fudan University (Autor:in)
  • Zhang Yu - , Fudan University (Autor:in)
  • Peng Minghui - , Fudan University (Autor:in)
  • Yang Hua - , Fudan University (Autor:in)
  • Stephen Gilbert - , Else Kröner Fresenius Zentrum für Digitale Gesundheit (Autor:in)

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.

Titel in Übersetzung
Study on safety performance and condition-suggestion accuracy of the symptom assessment mobile applications

Details

OriginalspracheChinesisch
Seiten (von - bis)288-294
Seitenumfang7
FachzeitschriftChinese Journal of General Practitioners
Jahrgang22
Ausgabenummer3
PublikationsstatusVeröffentlicht - März 2023
Peer-Review-StatusJa

Externe IDs

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

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

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