Product/Service Development

Combining multiple data sets from HBGDKi using ML tools for prediction, classification and topic discovery may yield new insights for adverse birth outcomes and intermediate outcomes of interest.

Intend to adopt unconventional data analytical techniques to explore the multiple dimensions of child undernutrition in India, utilizing existing national surveys and the HBGDKi database. The novel and sophisticated analytical methods that will be used include geospatial analysis, data triangulation through statistical matching, and multilevel modeling. The idea is unconventional because India does not have a single granular and multidimensional Health and Nutrition survey that can be analyzed at the district and sub-district level to provide precision insights to public health policy.

The study aims to calculate cut-offs using data provided by HBGDki and datasets with SAS, SJRI where weight, height, and age are available for children below five years in combination with other outcomes such as death, morbidity or hospitalization. Using WHO standards, weight for height, height for age and weight for age will be calculated, and these metrics will be used as determinants for risk of death, morbidity/hospitalization.

The solution proposes to develop, pilot and assess the feasibility of an integrated mobile-based application tool to improve knowledge on India’s Universal Immunization Programme, uptake of routine immunization services, and data access targeting beneficiaries/care-givers, health care providers and the health system.

IMMUNOCHAIN, is to build a big data and blockchain technologies powered, mobile/web enabled vaccine traceability solution for immunization programs in India. It is proposed to be vendor neutral, scalable, replicable and reusable across different immunization programs in any geographical context. The goal of the Immunochain is to address the problem of traceability of immunization vaccines from manufacturing facility, through the distribution mechanisms in cold storage network and the vaccine handling/administration facilities, till the end consumer.

Proposing an infrastructure that embraces cloud and fog computing and involves a novel platform with machine-to-machine (MM) messaging, seamless data management, and the use of data fusion and decision fusion to facilitate immunization data coverage.

The initial pilot seeks to create data connectors that will translate MCTS and E-Vin data into a common format and store it onto the blockchain. A blockchain database will be the secure, decentralized, common backbone for recording data from disparate sources like MCTS and e-VIN to allow the tracking of vaccinations from the factory to the beneficiary level by using Aadhaar/UPI.

This proof of concept proposal aims at improving the immunization data quality (accuracy, validity, consistency, completeness and timeliness) through four approaches: (1) Aadhar (biometry/QR scan) and mobile number linkages of beneficiaries; (2) bridging ANMOL and eVIN platforms for data convergence; (3) user-friendly monitoring dashboards to empower the program managers for tracking and improving performance; and (4) generating timely and appropriate data inputs for HMIS.

The proposed solution aims to access, interface/integrate population coverage (HMIS/ANMOL) data and vaccination consumption (eVIN) data on a GIS server platform and to track and triangulate the datasets in near real time and enable quantitative analysis of these data sets and visualize the relevant KPI (Key Performance Indicators) through dynamic dashboards and active maps.

EVaccination app to ensure each newborn’s vaccination through trained semi-literate local people as incentivized Community Health Workers (CHWs) to find, counsel and accompany new mothers to public hospitals for vaccination