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A digital community information detection tool

Primary Investigator: Anvita Bhardwaj

Co-Investigators: Brandon Kohrt

In order to facilitate detection and referral of people with mental health problems from the community, a paper-based detection tool called CIDT was developed, where vignettes are presented alongside the pictures depicting symptoms of a particular mental disorder. The Female Community Health Volunteers (FCHVs) use the tool to identify hidden cases of mental health problems in the community. Upon identification, the FCHVs provide a referral slip to the person and encourage to seek mental health services at the health facilities. Although this paper based CIDT has already been tested and has shown promising results in detecting and engaging people with mental health problems to care, there remains few shortcomings especially on the grounds that: a) clients often fail to visit the health post due to the loss of referral slip; b) there is limited communication between the FCHV and the health facility regarding the new referral and uptake of services; and c) poor documentation. With increasing access to different technology and most importantly the decreasing cost and higher penetration level of mobile devices and internet over the years, mobile Health (mHealth) has started to revolutionize health care especially the access to information and communication. One aspect of mHealth was to look at tools that directly support health workers ranging from providing information in a more up to date manner to enabling more effective communication with clients and patients seven classifications of mHealth interventions; health information delivery, reminders, communication platform, data collection platform, test result turnaround, peer or group support and psychological interventions. Thus, this project aimed to extend this work by testing a digital version of the CIDT workflow designed to strengthen the referral chain while maintaining or improving the accuracy of the case- finding procedure.

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