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Cited by 183Mobile Health and mHealth ApplicationsArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 28 of the 30 references it cites were found in a registry or an index.

How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs

Open access
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·2020·BMJ Open
DOI 10.1136/bmjopen-2020-040269PMC PMC7745523

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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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Crossref · 10.1136/bmjopen-2020-040269

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Figures

Figure 1 Overview of the study methodology including: (1) vignette creation; (2) vignettes review and answer setting; (3) testing of general practitioners (GPs); and (4) adjudication of matching of condition suggestions to the gold standard.
Figure 1 Overview of the study methodology including: (1) vignette creation; (2) vignettes review and answer setting; (3) testing of general practitioners (GPs); and (4) adjudication of matching of condition suggestions to the gold standard.CC BY-NC · PubMed Central
Figure 2 App breadth of coverage—that is, the proportion of vignettes for which condition suggestions and levels of urgency advice were provided. When condition suggestions or urgency were not provided, the principal reason for this is shown, or alternatively ‘no results’ when no reason was given. conditions given—condition suggestions were provided by the app; mental health—mental health vignettes where no condition suggestions/urgency advice was provided; no results—the app provided a clear statement that no condition suggestion results were found for the vignette (the reason why the app failed to give a condition suggestion for these vignettes is uncertain, but generally these vignettes relate to minor conditions, and in most cases it seems that the app does not have a matching condition modelled); generic—the app gave a generic answer rather than a condition, for example, ‘further assessment is needed’; symptom not found—a directly or appropriately matching symptom to the presenting complaint could not be found in the app so the vignette could not be entered; severity—the app did not to give condition-suggestions for very serious symptoms—for example, the app stated only ‘Condition causing severe (symptom)’; pregnant, vignettes for which no condition-suggestions/urgency advice was provided by the app as the patient was pregnant; under age—vignettes for which no condition suggestions/urgency advice was provided by the app as the patient was under its specified age limit; advice given—level of urgency advice was provided by the app; not recorded—one app-entry-Dr (#4) did not fully record the levels of urgency advice, and there were no corresponding source data verification screenshots for this subset of data (see online supplemental table 6 for a subanalysis of the 150 vignettes with complete source-data-verified data for K Health on levels of urgency advice); no conditions—no condition suggestions were provided by the app, and, as a result of this, the app did not provide urgency advice. See online supplemental tables 8–10 for details.
Figure 2 App breadth of coverage—that is, the proportion of vignettes for which condition suggestions and levels of urgency advice were provided. When condition suggestions or urgency were not provided, the principal reason for this is shown, or alternatively ‘no results’ when no reason was given. conditions given—condition suggestions were provided by the app; mental health—mental health vignettes where no condition suggestions/urgency advice was provided; no results—the app provided a clear statement that no condition suggestion results were found for the vignette (the reason why the app failed to give a condition suggestion for these vignettes is uncertain, but generally these vignettes relate to minor conditions, and in most cases it seems that the app does not have a matching condition modelled); generic—the app gave a generic answer rather than a condition, for example, ‘further assessment is needed’; symptom not found—a directly or appropriately matching symptom to the presenting complaint could not be found in the app so the vignette could not be entered; severity—the app did not to give condition-suggestions for very serious symptoms—for example, the app stated only ‘Condition causing severe (symptom)’; pregnant, vignettes for which no condition-suggestions/urgency advice was provided by the app as the patient was pregnant; under age—vignettes for which no condition suggestions/urgency advice was provided by the app as the patient was under its specified age limit; advice given—level of urgency advice was provided by the app; not recorded—one app-entry-Dr (#4) did not fully record the levels of urgency advice, and there were no corresponding source data verification screenshots for this subset of data (see online supplemental table 6 for a subanalysis of the 150 vignettes with complete source-data-verified data for K Health on levels of urgency advice); no conditions—no condition suggestions were provided by the app, and, as a result of this, the app did not provide urgency advice. See online supplemental tables 8–10 for details.CC BY-NC · PubMed Central
Figure 3 Required answer approach showing the performance metrics (M1, M3, M5, comprehensiveness and relevance—as defined in table 2) of the eight apps and seven tested general practitioners (GPs). App performance is coloured in light green, average (mean) app performance is in dark green, average (mean) tested-GP performance in dark blue, and individual tested-GP performance in light blue. Statistical significance of the difference between the app with highest performance and all other apps/tested GPs is shown with the * symbol indicating: p<0.05. For one of these apps (Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 7 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here.
Figure 3 Required answer approach showing the performance metrics (M1, M3, M5, comprehensiveness and relevance—as defined in table 2) of the eight apps and seven tested general practitioners (GPs). App performance is coloured in light green, average (mean) app performance is in dark green, average (mean) tested-GP performance in dark blue, and individual tested-GP performance in light blue. Statistical significance of the difference between the app with highest performance and all other apps/tested GPs is shown with the * symbol indicating: p<0.05. For one of these apps (Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 7 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here.CC BY-NC · PubMed Central
Figure 4 Provided-answer approach showing the performance metrics (M1, M3, M5, comprehensiveness and relevance—as defined in table 2) of the eight apps and seven tested general practitioners (GPs). App performance is coloured in light orange, average (mean) app performance is in dark orange, average (mean) tested-GP performance in dark purple, and individual tested-GP performance in light purple. Statistical significance of the difference between the app with highest performance and all other apps/tested GPs is shown with the * symbol indicating: p<0.05. For one of these apps (Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 7 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here.
Figure 4 Provided-answer approach showing the performance metrics (M1, M3, M5, comprehensiveness and relevance—as defined in table 2) of the eight apps and seven tested general practitioners (GPs). App performance is coloured in light orange, average (mean) app performance is in dark orange, average (mean) tested-GP performance in dark purple, and individual tested-GP performance in light purple. Statistical significance of the difference between the app with highest performance and all other apps/tested GPs is shown with the * symbol indicating: p<0.05. For one of these apps (Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 7 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here.CC BY-NC · PubMed Central
Figure 5 Accuracy of urgency advice displayed as a stacked bar chart centred on the gold standard triage. For two of these apps (K Health & Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 6 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here. GP, general practitioner.
Figure 5 Accuracy of urgency advice displayed as a stacked bar chart centred on the gold standard triage. For two of these apps (K Health & Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 6 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here. GP, general practitioner.CC BY-NC · PubMed Central
Figure 6 Weighted confusion matrices showing the detailed triage assignments for each app. For two of these apps (K Health & Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 6 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here. Amb, ambulance; ED, emergency department; GP, general practitioner; PC, primary care
Figure 6 Weighted confusion matrices showing the detailed triage assignments for each app. For two of these apps (K Health & Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 6 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here. Amb, ambulance; ED, emergency department; GP, general practitioner; PC, primary careCC BY-NC · PubMed Central
Figure 7 Summary plot of the urgency advice performance of each app. The urgency advice coverage of each app (with respect to app average) is plotted against the percentage of safe advice (with respect to the general practitioner (GP) average). For two of these apps (K Health & Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 6 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here.
Figure 7 Summary plot of the urgency advice performance of each app. The urgency advice coverage of each app (with respect to app average) is plotted against the percentage of safe advice (with respect to the general practitioner (GP) average). For two of these apps (K Health & Your.MD), one app-entry-Dr (#4) did not record all screenshots needed for source data verification—see online supplemental table 6 for a subanalysis of fully verified data, which shows the same trend of results and no significant difference to the data recorded here.CC BY-NC · PubMed Central

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Authors
Gilbert, S., Mehl, A., Baluch, A., Cawley, C., Challiner, J., Fraser, H., Millen, E., Montazeri, M., Multmeier, J., Pick, F., Richter, C., Türk, E., Upadhyay, S., Virani, V., Vona, N., Wicks, P., & Novorol, C.
Affiliations
Brown University (US)
Corresponding author
Gilbert, Stephen
ORCID
Venue
BMJ Open
Publisher
BMJ
Published
2020-12
Volume
10
Issue
12
Pages
e040269
ISSN
2044-6055 (ISSN-L)
DOI
10.1136/bmjopen-2020-040269
PMC
PMC7745523
OpenAlex
W3112185662
Cited by
183
References
30
Citations by year
2026: 20 · 2025: 25 · 2024: 31 · 2023: 37 · 2022: 40 · 2021: 27 · 2020: 3
FWCI
27.92
Type
journal article
Language
en
Open access
Licence
Locations
  • BMJ Open (publishedVersion, cc-by-nc)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • HighWire Press Open Archive (submittedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
symptom checkers, clinical vignettes, general practitioners, Ada, Babylon, buoys, WebMD, primary care, diagnostic accuracy, patient safety, user demographics, mobile health apps, telemedicine, primary diagnosis
MeSH terms
Humans, Primary Health Care, General Practitioners, Symptom Assessment, Mobile Applications

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