Fiona Pick
Research Subjects
Research Interests
Fiona Pick has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Adel Baluch
Ada Health GmbH
1 shared publication - Alicia Mehl
Ada Health GmbH
1 shared publication - Caoimhe Cawley
Ada Health GmbH
1 shared publication - Claire Novorol
Ada Health GmbH
1 shared publication - Claudia Richter
Ada Health GmbH
1 shared publication - Elizabeth Millen
Ada Health GmbH Berlin Germany
1 shared publication
Latest Publications
- Open Access
Original research: How accurate are digital symptom assessment apps for suggesting conditions and urgency advice?: a clinical vignettes comparison to GPs
Objectives: To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of 8 popular symptom assessment apps with each other and with 7 General Practitioners. Design: Clinical vignettes study. Setting: 200 clinical vignettes representing real-world scenarios in primary care. Intervention/comparator: Condition coverage, suggested condition accuracy, and urgency advice performance was measured against the vignettes’ gold-standard diagnoses and triage level. Primary outcome measures: Outcomes included (i) proportion of conditions “covered” by an app, i.e. not excluded because the patient was too young/old, pregnant, or comorbid, (ii) proportion of vignettes in which the correct primary diagnosis was amongst the top 3 conditions suggested, and, (iii) proportion of “safe” urgency level advice (i.e. 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%. The top-3 suggestion accuracy (M3) of GPs was on average 82.i±5.2%. For the apps it was - 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 exclude certain user groups (e.g. younger users) or certain conditions - for these apps condition-suggestion performance is generally greater with exclusion of these 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 S.D. of the GPs (mean) - Ada: 97.0%; Babylon: 95.i%; Symptomate: 97.8%. One app had a safety performance within 2 S.D.s of GPs - Your.MD: 92.6%. Three apps had a safety performance outside 2 S.D.s of GPs - Buoy: 80.0% ( p p p =1.3×10 -3 ). Conclusions: The utility of digital symptom assessment apps relies upon 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.
Open AccessOriginal research: How accurate are digital symptom assessment apps for suggesting conditions and urgency advice?: a clinical vignettes comparison to GPs
Objectives: To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of 8 popular symptom assessment apps with each other and with 7 General Practitioners. Design: Clinical vignettes study. Setting: 200 clinical vignettes representing real-world scenarios in primary care. Intervention/comparator: Condition coverage, suggested condition accuracy, and urgency advice performance was measured against the vignettes’ gold-standard diagnoses and triage level. Primary outcome measures: Outcomes included (i) proportion of conditions “covered” by an app, i.e. not excluded because the patient was too young/old, pregnant, or comorbid, (ii) proportion of vignettes in which the correct primary diagnosis was amongst the top 3 conditions suggested, and, (iii) proportion of “safe” urgency level advice (i.e. 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%. The top-3 suggestion accuracy (M3) of GPs was on average 82.i±5.2%. For the apps it was - 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 exclude certain user groups (e.g. younger users) or certain conditions - for these apps condition-suggestion performance is generally greater with exclusion of these 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 S.D. of the GPs (mean) - Ada: 97.0%; Babylon: 95.i%; Symptomate: 97.8%. One app had a safety performance within 2 S.D.s of GPs - Your.MD: 92.6%. Three apps had a safety performance outside 2 S.D.s of GPs - Buoy: 80.0% ( p p p =1.3×10 -3 ). Conclusions: The utility of digital symptom assessment apps relies upon 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.