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

• Published in: BMJ open Vol. 10; no. 12; p. e040269
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 16.12.2020; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: Accuracy; Coronaviruses; COVID-19; Diabetes; Diagnostics; Family physicians; General Practitioners; health informatics; Humans; Hypotheses; information technology; Mobile Applications; Patients; Primary care; Primary Health Care; Regulatory approval; Symptom Assessment; world wide web technology; United Kingdom > UK; primary care; health informatics; information technology; world wide web technology
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2020-040269

ObjectivesTo compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.DesignVignettes study.Setting200 primary care vignettes.Intervention/comparatorFor 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).ResultsCondition-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).ConclusionsThe 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.