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How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs.
Gilbert, Stephen; Mehl, Alicia; Baluch, Adel; Cawley, Caoimhe; Challiner, Jean; Fraser, Hamish; Millen, Elizabeth; Montazeri, Maryam; Multmeier, Jan; Pick, Fiona; Richter, Claudia; Türk, Ewelina; Upadhyay, Shubhanan; Virani, Vishaal; Vona, Nicola; Wicks, Paul; Novorol, Claire.
  • Gilbert S; Ada Health GmbH, Berlin, Germany science@ada.com.
  • Mehl A; Ada Health GmbH, Berlin, Germany.
  • Baluch A; Ada Health GmbH, Berlin, Germany.
  • Cawley C; Ada Health GmbH, Berlin, Germany.
  • Challiner J; Ada Health GmbH, Berlin, Germany.
  • Fraser H; Brown Center for Biomedical Informatics, Brown University, Rhode Island, USA.
  • Millen E; Ada Health GmbH, Berlin, Germany.
  • Montazeri M; Ada Health GmbH, Berlin, Germany.
  • Multmeier J; Ada Health GmbH, Berlin, Germany.
  • Pick F; Ada Health GmbH, Berlin, Germany.
  • Richter C; Ada Health GmbH, Berlin, Germany.
  • Türk E; Ada Health GmbH, Berlin, Germany.
  • Upadhyay S; Ada Health GmbH, Berlin, Germany.
  • Virani V; Ada Health GmbH, Berlin, Germany.
  • Vona N; Ada Health GmbH, Berlin, Germany.
  • Wicks P; Ada Health GmbH, Berlin, Germany.
  • Novorol C; Ada Health GmbH, Berlin, Germany.
BMJ Open ; 10(12): e040269, 2020 12 16.
Article in English | MEDLINE | ID: covidwho-979613
ABSTRACT

OBJECTIVES:

To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.

DESIGN:

Vignettes study.

SETTING:

200 primary care vignettes. INTERVENTION/COMPARATOR For eight apps and seven general practitioners (GPs) breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes' gold-standard. PRIMARY OUTCOME

MEASURES:

(1) Proportion of conditions 'covered' by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of 'safe' urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative).

RESULTS:

Condition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users in alphabetical order, Ada 99.0%; Babylon 51.5%; Buoy 88.5%; K Health 74.5%; Mediktor 80.5%; Symptomate 61.5%; Your.MD 64.5%; WebMD 93.0%. Top-3 suggestion accuracy was GPs (average) 82.1%±5.2%; Ada 70.5%; Babylon 32.0%; Buoy 43.0%; K Health 36.0%; Mediktor 36.0%; Symptomate 27.5%; WebMD 35.5%; Your.MD 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs-Ada 97.0%; Babylon 95.1%; Symptomate 97.8%. One app had a safety performance within 2 SDs of GPs-Your.MD 92.6%. Three apps had a safety performance outside 2 SDs of GPs-Buoy 80.0% (p<0.001); K Health 81.3% (p<0.001); Mediktor 87.3% (p=1.3×10-3).

CONCLUSIONS:

The utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: General Practitioners Type of study: Prognostic study Limits: Humans Language: English Journal: BMJ Open Year: 2020 Document Type: Article Affiliation country: Bmjopen-2020-040269

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Full text: Available Collection: International databases Database: MEDLINE Main subject: General Practitioners Type of study: Prognostic study Limits: Humans Language: English Journal: BMJ Open Year: 2020 Document Type: Article Affiliation country: Bmjopen-2020-040269