Objective To increase hand sanitizer usage among healthcare workers by developing and implementing a low-cost intervention using RFID and wireless mesh networks to provide real-time alarms for increasing hand hygiene compliance during opportune moments in an open layout Intensive Care Unit (ICU). Method A wireless, RFID based system was developed and deployed in the ICU. The ICU beds were divded into an intervention arm (n=10) and a control arm (n=14). Passive RFID tags were issued to the doctors, nurses and support staff of the ICU. Long range RFID readers were positioned strategically. Sensors were placed beneath the hand sanitizers to record sanitizer usage. The system would alert the HCWs by flashing a light if an opportune moment for hand sanitization was detected. Results A significant increase in hand sanitizer use was noted in the intervention arm. Usage was highest during the early part of the workday and decreased as the day progressed. Hand wash events per person hour was highest among the ancilliary staff followed by the doctors and nurses. Conclusion Real-time feedback has potential to increase hand hygiene compliance among HCWs. The system demonstrates the possibility of automating compliance monitoring in an ICU with an open layout.

Electronic Health Record (EHR) use in India is generally poor, and structured clinical information is mostly lacking. This work is the first attempt aimed at evaluating unstructured text mining for extracting relevant clinical information from Indian clinical records. We annotated a corpus of 250 discharge summaries from an Intensive Care Unit (ICU) in India, with markups for diseases, procedures, and lab parameters, their attributes, as well as key demographic information and administrative variables such as patient outcomes. In this process, we have constructed guidelines for an annotation scheme useful to clinicians in the Indian context. We evaluated the performance of an NLP engine, Cocoa, on a cohort of these Indian clinical records. We have produced an annotated corpus of roughly 90 thousand words, which to our knowledge is the first tagged clinical corpus from India. Cocoa was evaluated on a test corpus of 50 documents. The overlap F-scores across the major categories, namely disease/symptoms, procedures, laboratory parameters and outcomes, are 0.856, 0.834, 0.961 and 0.872 respectively. These results are competitive with results from recent shared tasks based on US records. The annotated corpus and associated results from the Cocoa engine indicate that unstructured text mining is a viable method for cohort analysis in the Indian clinical context, where structured EHR records are largely absent.

Background Of every 10 women in rural India, 1 suffers from a common mental disorder such as depression, and untreated depression is associated with significant morbidity and mortality. Several factors lead to a large treatment gap, specifically for women in rural India, including stigma, lack of provider mental health workforce, and travel times. There is an urgent need to improve the rates of detection and treatment of depression among women in rural India without overburdening the scarce mental health resources. Objective We propose to develop, test, and deploy a mental health app, MITHRA (Multiuser Interactive Health Response Application), for depression screening and brief intervention, designed for use in women’s self-help groups (SHGs) in rural India. Methods We will use focus groups with SHG members and community health workers to guide the initial development of the app, followed by iterative modification based on input from a participatory design group consisting of proposed end users of the app (SHG members). The final version of the app will then be deployed for testing in a pilot cluster randomized trial, with 3 SHGs randomized to receive the app and 3 to receive enhanced care as usual. Results This study was funded in June 2021. As of September 2022, we have completed both focus groups, 1 participatory design group, and app development. Conclusions Delivering app-based depression screening and treatment in community settings such as SHGs can address stigma and transportation-related barriers to access to depression care and overcome cultural and contextual barriers to mobile health use. It can also address the mental health workforce shortage. If we find that the MITHRA approach is feasible, we will test the implementation and effectiveness of MITHRA in multiple SHGs across India in a larger randomized controlled trial. This approach of leveraging community-based organizations to improve the reach of depression screening and treatment is applicable in rural and underserved areas across the globe. International Registered Report Identifier (IRRID) DERR1-10.2196/42919