TY  - JOUR
AU  - Peven, Kimberly
AU  - Wickham, Aidan P
AU  - Wilks, Octavia
AU  - Kaplan, Yusuf C
AU  - Marhol, Andrei
AU  - Ahmed, Saddif
AU  - Bamford, Ryan
AU  - Cunningham, Adam C
AU  - Prentice, Carley
AU  - Meczner, András
AU  - Fenech, Matthew
AU  - Gilbert, Stephen
AU  - Klepchukova, Anna
AU  - Ponzo, Sonia
AU  - Zhaunova, Liudmila
PY  - 2023
DA  - 2023/12/5
TI  - Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study
JO  - JMIR Mhealth Uhealth
SP  - e46718
VL  - 11
KW  - women's health
KW  - symptom checkers
KW  - symptom checker
KW  - digital health
KW  - chatbot
KW  - accuracy
KW  - eHealth apps
KW  - mobile phone
KW  - mobile health
KW  - mHealth
KW  - mobile health app
KW  - polycystic ovary syndrome
KW  - gynecology
KW  - digital health tool
KW  - endometriosis
KW  - uterus
KW  - uterine
KW  - uterine fibroids
KW  - vignettes
KW  - clinical vignettes
AB  - Background: Reproductive health conditions such as endometriosis, uterine fibroids, and polycystic ovary syndrome (PCOS) affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5% to 40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased health care costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions. Objective: This study aimed to evaluate the agreement between clinicians and 3 symptom checkers (developed by Flo Health UK Limited) in assessing symptoms of endometriosis, uterine fibroids, and PCOS using vignettes. We also aimed to present a robust example of vignette case creation, review, and classification in the context of predeployment testing and validation of digital health symptom checker tools. Methods: Independent general practitioners were recruited to create clinical case vignettes of simulated users for the purpose of testing each condition symptom checker; vignettes created for each condition contained a mixture of condition-positive and condition-negative outcomes. A second panel of general practitioners then reviewed, approved, and modified (if necessary) each vignette. A third group of general practitioners reviewed each vignette case and designated a final classification. Vignettes were then entered into the symptom checkers by a fourth, different group of general practitioners. The outcomes of each symptom checker were then compared with the final classification of each vignette to produce accuracy metrics including percent agreement, sensitivity, specificity, positive predictive value, and negative predictive value. Results: A total of 24 cases were created per condition. Overall, exact matches between the vignette general practitioner classification and the symptom checker outcome were 83% (n=20) for endometriosis, 83% (n=20) for uterine fibroids, and 88% (n=21) for PCOS. For each symptom checker, sensitivity was reported as 81.8% for endometriosis, 84.6% for uterine fibroids, and 100% for PCOS; specificity was reported as 84.6% for endometriosis, 81.8% for uterine fibroids, and 75% for PCOS; positive predictive value was reported as 81.8% for endometriosis, 84.6% for uterine fibroids, 80% for PCOS; and negative predictive value was reported as 84.6% for endometriosis, 81.8% for uterine fibroids, and 100% for PCOS. Conclusions: The single-condition symptom checkers have high levels of agreement with general practitioner classification for endometriosis, uterine fibroids, and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and health care providers, innovative health apps and symptom checkers hold the potential to improve care pathways. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2023/1/e46718
UR  - https://doi.org/10.2196/46718
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38051574
DO  - 10.2196/46718
ID  - info:doi/10.2196/46718
ER  -