title: Validation of a phone app for epilepsy diagnosis in India and Nepal.
abstract: OBJECTIVE
  Untreated epilepsy is a major global public health problem with more than 20 million people not being treated for an easily treatable disease. In part this is due to a lack of trained doctors. There are many more non-medical health workers than doctors and they could have an important role in diagnosis and treatment of epilepsy if they had some tools. We have previously described such a tool to distinguish epileptic episodes from other causes of altered consciousness and here present its validation in three new populations.
  
  
  METHODS
  The tool was presented as a phone app where the answers to 11 questions provided a probability score which indicated whether episodes might be due to epilepsy or not. It was applied either by non-medical volunteers, health workers, or inexperienced doctors to 132 patients in three separate populations in India and Nepal and compared with the "gold standard" diagnosis of a neurologist with expertise in epilepsy.
  
  
  RESULTS
  There was good agreement between the app score and the neurologists' diagnoses (weighted kappa=75.3%). An app score of 90 or greater had a sensitivity of 88% and a specificity of 100% for diagnosing epilepsy. The app was easy to use with little training and took about 5min to administer.
  
  
  CONCLUSIONS
  A tool presented as a phone app can be used by non-medical health workers to identify episodes as epileptic or not with good accuracy. It needs to be evaluated more widely but has the potential to play a part in reducing the epilepsy treatment gap.
authors.affiliation: Dhulikhel Hospital, Kavre, Nepal. Electronic address: vhp498@gmail.com.
authors.forename: Victor
authors.initials: V
authors.lastname: Patterson
authors.affiliation: All India Institute of Medical Sciences, New Delhi, India.
authors.forename: Mamta
authors.initials: M
authors.lastname: Singh
authors.affiliation: Kathmandu Model Hospital, Kathmandu, Nepal.
authors.forename: Hemav
authors.initials: H
authors.lastname: Rajbhandari
authors.affiliation: All India Institute of Medical Sciences, New Delhi, India.
authors.forename: Sreenivas
authors.initials: S
authors.lastname: Vishnubhatla
mesh_terms: D000465:Algorithms; D003244:Consciousness Disorders / Q000175:diagnosis; D003937:Diagnosis, Differential; D004827:Epilepsy / Q000175:diagnosis*; D006282:Health Personnel; D006801:Humans; D007194:India; D063731:Mobile Applications; D009390:Nepal; D011336:Probability; D012640:Seizures / Q000175:diagnosis; D012680:Sensitivity and Specificity; D000068997:Smartphone; D061665:Time-to-Treatment; D014838:Volunteers
keywords: Bayesian; Epilepsy treatment gap; Health workers; Phone app; Untreated epilepsy; mHealth