Democratization of Intelligent Sensor Network for Low-Connected Remote Healthcare Facilities—A Framework to Improve Population Health & Epidemiological Studies

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Conference Proceeding

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Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC), Volume 1


Kohei Arai, Rahul Bhatia



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Healthcare associated infections (HAI), or infections that are acquired in health-care settings are the most common detrimental events in health-care delivery worldwide. Millions of patients are affected by HAI worldwide each year, leading to high mortality rates and financial losses. Out of every 100 patients that are hospitalized at a particular time, 7 in developed and 10 in developing countries will be affected by at least one HAI. These infections are responsible for approximately 2 million cases and around 80,000 deaths per year in developing countries. The prevalence of HAI in rural areas is more frequent and acute than that of in the urban areas. One chief reason: “Connectivity gap”. Unlike many urban healthcare facilities where the providers usually have Dedicated Internet Access (DIA) with greater bandwidth and better reliability to aid acute services, the healthcare facilities in rural areas have low or no connectivity and are less equipped to prevent containment of HAI. This research paper provides an innovative and low-cost alternative to overcome “Connectivity” obstacle by developing de-centralized intelligent sensor network, based on MQTT, that bring connected intelligence to non-connected healthcare facilities. Thereby overcoming “Connectivity gap” barrier. The paper presents a prototype solution design, its application and a few experimental results.


And outpatient, Association rule mining, EHR, Electronic health records, Health, Healthcare associated infections (HAI), Internet of things, IoT, IoT architecture, MQTT, MQTT-SN, Naïve bayes classifier, Preventive healthcare, Real-time stream analytics, Sanjeevani electronic health records, Supervised machine learning


Computer Engineering