UPCHAR (Upcountry Community Health Access & Referrals)

As per WHO, out-of-pocket health spending of 10% or more of their household budget can lead to 930 million people worldwide falling into the risk of poverty. There is an urgent need to scale interventions related to primary health care across marginalized populations of LMICs to save 60 million lives and increase average life expectancy by 3.7 years by 2030. These interventions require innovation and the potential to reach the remotest areas. This situation is alarming for the 104 million tribals of India, who suffer disproportionately from infectious diseases, high maternal and child mortality rates, malnutrition, and an increasing prevalence of non-communicable diseases like diabetes and cardiovascular disorders compared to the national averages. Without targeted healthcare improvements, India risks further marginalizing these communities, undermining its efforts to fulfill SDG 2 (Zero Hunger) and SDG 3 (Good Health and Well-being).

The problems are compounded by a critical shortage of public health facilities and qualified medical professionals in tribal regions. The Rural Health Statistics 2022 highlights a notable lack of specialists in Community Health Centers in these areas. Additionally, language barriers prevent tribal populations from fully accessing and understanding healthcare services, exacerbating their isolation.

Furthermore, the absence of reliable, tribe-specific health data hampers the development of effective policies and interventions tailored to the unique needs of these diverse communities. This data deficiency ranges from insufficient information on disease burden in tribal regions to a lack of understanding about which health interventions are most effective in these communities and why.

 The specific problem addressed by the proposed solution concerns the inefficiencies faced by ASHA (Accredited Social Health Activists) workers in India, particularly in terms of time management and service delivery backed up by robust evidence synthesis to guide research design and policy;  and cost-effectiveness model. The solution aims to enhance the efficiency and effectiveness of these frontline health workers by digitizing their workflows and integrating real-time data collection and analysis into their daily tasks. The solution would also strengthen the supply side of the healthcare delivery system by identifying the gaps in the infrastructure and creating a case for improvement, engagement, and empowerment of the community for seeking healthcare, and performance of key indicators like patient outcomes, resource utilization, and wait times shall be backed up by real-time data.

 The scale of the Problem

ASHA workers are critical to India's primary healthcare system, especially in rural and underserved communities. There are approximately 600,000 ASHA workers across India, each managing about 1,000 individuals, covering 68% of the population. This translates to a significant portion of the population relying on the services provided by these workers.

 Factors Contributing to the Problem

Several key factors contribute to the inefficiencies faced by ASHA workers:

1. Time Management: ASHA workers spend nearly a third of their time travelling to households, which reduces their efficiency.

2. Service Redundancy: Multiple visits to hospitals with each patient lead to wastage of time and resources.

3. Multiple Responsibilities: The burden of multiple roles dilutes their effectiveness in service delivery.

https://drive.google.com/drive...

The proposed solution involves creating a digital Health and Demographic Surveillance System (HDSS) for an equitable enumeration of individuals and households in a rural tribal community, assigning unique identifiers to each. The efficacy of this digital transformation will be assessed through a cluster randomized controlled trial (RCT) involving 18 clusters—nine for the intervention and nine as control—with each cluster representing a "coolie line," similar to a village of 150-200 households and 500-700 individuals.

The intervention will be implemented in clusters separated by at least 2 kilometers to mitigate spillover effects. The intervention is aimed to test the hypothesis of a 15% improvement in health service utilization from a baseline of 40%. This design incorporates a fixed cluster size of 500 individuals, a two-tailed alpha of 0.05, and an intra-cluster correlation coefficient of 0.05.

The digital intervention has the following components:

1. Digital Workflows: Automating the creation of work plans for community health workers via the HDSS enhances efficiency and ensures equity.
2. Streamlining Operations: By digitizing routine tasks, community health workers spend less time on manual data entry and provide more patient care.
3. Reducing Gaps: Digital tools bridge the gap between healthcare demand and supply by providing real-time data, thus minimizing unnecessary healthcare visits.
4. Improving Service Delivery: Digital platforms facilitate better time management and prioritization for ASHA workers, ensuring focus on patient care rather than administrative duties.
5. Interactive Voice Response System (IVRS): This system will be implemented to report illnesses, and enable real-time disease surveillance, and other issues thus allowing for timely health system responses.
6. Monitoring and Analysis Tool: A real-time dashboard will be developed to identify emerging community health risks, supporting data-driven decision-making at both local and district levels.

Research components include an Evidence & Gap Map (EGM) and a Systematic Review (SR). The EGM is a visual matrix of intervention categories and outcome domains. For instance, the map would show studies linking digital tools with primary healthcare delivery metrics. The EGM will be a comprehensive and systematic search of Digital Health Interventions spanning the demand and supply chain of healthcare delivery systems in low and middle-income countries (LMICs) to identify research gaps and also to collate evidence that supports the effectiveness of such interventions. The SR will complement the RCT by identifying barriers and facilitators in the implementation process and accessibility of healthcare, employing a qualitative thematic analysis. This shall also guide the design of the intervention study.

Lastly, the project encompasses a health economic evaluation, comparing the costs involved in developing, deploying, and training for the digital health intervention (DHI) against the health outcomes obtained, both short-term and long-term. Metrics such as increased reporting of ailments, timely healthcare responses, and referrals will be assessed in the short term, whereas long-term measures will look at changes in the health system response and disease burden. This financial analysis will provide crucial insights into the cost-effectiveness of the intervention, comparing it against other health initiatives to determine the most efficient allocation of resources.

https://drive.google.com/drive...

The proposed healthcare solution is designed for the tribal populations residing in the economically challenged tea gardens of Banarhat, Matiali, and Nagrakata blocks in Jalpaiguri district, West Bengal. These areas, which include regions at the foothills of the Himalayas and on gentler slopes, house approximately 56,343 individuals, predominantly from Scheduled Tribes working as laborers in tea gardens now facing closure due to economic, management, or demand issues. Such closures have left the infrastructure, including healthcare, in decay, exacerbating challenges like geographical isolation, socio-economic disparity, and specific occupational health risks.

Challenges in Closed Tea Gardens:

1. Healthcare Access: The closure of tea gardens results in deteriorating healthcare facilities, further limited by the region’s remote location, inadequate infrastructure, and a shortage of culturally competent healthcare professionals.
2. Economic Hardship: Unemployment fosters poverty, reducing access to nutritious food and healthcare services, thereby increasing malnutrition and related health conditions.
3. Increased Disease Prevalence: Poor sanitation and clean water supply and hazardous working conditions, such as pesticide exposure, increase disease rates.
4. Mental Health Strain: Economic and job uncertainties contribute to mental health issues like depression and anxiety.
5. Cultural and Linguistic Barriers: Significant cultural and linguistic differences from mainstream health services hinder effective healthcare delivery.
6. Nutritional Deficiencies: Economic constraints lead to poor nutrition, compounded by a lack of nutritional education.

Solution Overview:

The intervention leverages digital technology to enhance healthcare delivery:

1. Digitization of Health System: Digital tools like mobile apps and Interactive Voice Response Systems (IVRS) streamline the workflow of ASHA workers, facilitating real-time data management and reducing manual data entry.
2. Prioritization of Workflow: Digital platforms enable ASHA workers to prioritize high-risk cases, improving workflow management in challenging environments like Jalpaiguri's tea gardens.
3. Robust Data Capture: Integrating health event data with national programs ensures comprehensive health monitoring, which is crucial for strategic health planning and intervention.
4. Training and Cultural Competence: ASHA workers receive training on digital tools and cultural competence, ensuring effective engagement with tribal communities.
5. Efficient Healthcare Delivery: By minimizing travel and enhancing task efficiency, ASHA workers can offer improved healthcare services and cover more households.
6. Increased transparency: By the use of dashboards, the health system is geared to understand the end-to-end requirements to strengthen the healthcare supply chain. Evidence-driven indicators can support the DHI to inform policymakers on issues on both the demand and supply sides.
7. Equity: The system would ensure no marginalized individual is left behind.

Impact and Strategic Improvements:

• Enhanced Intervention Strategies: Evidence & Gap Maps (EGMs) from the project improve RCT designs, leading to more effective technology interventions and better health outcomes.
• Access to Healthcare: Reduced logistical inefficiencies ensure quicker healthcare access, enabling ASHA workers to serve more households efficiently.
• Quality of Care: Real-time monitoring and data collection enhance disease management and overall care quality.
• Resource Optimization: Effective use of digital tools maximizes limited healthcare resources, extending benefits to a larger population segment.

This solution fundamentally transforms healthcare service delivery to Jalpaiguri’s tribal populations, ensuring timely and efficient care despite the inherent geographic and socio-economic challenges.

MANT, is a non-profit, non-racial, non-political, and non-religious organisation that, since 1960, has been working in development, livelihood promotion, research, advancing community action for the preservation of local ecology, promoting the adoption of community media like community radio, health service delivery, and health behaviour change to reduce the massive health burden in the poor and marginalized sections of society. We primarily work in the eastern and northeastern parts of India and have a team of 120 trained workers and 12 volunteers. We have been touching almost 1.5 million lives, mostly tribal, annually through our different activities. 

 Since 2007, MANT has been providing primary health care services in closed and sick tea gardens. Every year, a dedicated team comprising 28 members, including six doctors, serves nearly 200,000 people in the target area. MANT collaborates closely with the District Health Authority and maintains a robust network with key stakeholders, all of whom could play a role in the implementation of the proposed project.

 MANT takes pride in its exceptional team, a harmonious blend of seasoned development professionals, medical doctors, and PhD experts from diverse fields such as Environment, Earth Sciences, Management, Economics, Anthropology, Statistics, Climate Change, and Public Health. This wealth of expertise equips MANT to tackle complex challenges from various perspectives, making us a reliable partner for your philanthropic endeavours.

Centre for Digital and Public Health - CDPH (unit of Udaan): The team of epidemiologists, public health professionals, software developers, and cloud architects won the MIT-COVID19 hackathon to present a solution for developing new ways to deliver primary care in a post-COVID-19 world. This technology is what we are pitching for this proposal as well. The team has credible experience with creating real-time dashboards (www.covidtoday.in) and has handled global-scale projects supporting WHO in its GCAT initiative collating 80+ indicators across 234 countries and territories during COVID-19. The solution presented in the MIT hackathon is what we propose.

Details on the technology that was previously awarded:

- https://afmcalumni.org/f/MIT-COVID-19-Hackathon-2020---AFMC-wins-Innovative-Tech-Solutions-Challenge-6003?source=any

- https://covid19challenge.mit.edu/india/ - Track E.  

CSA: Campbell South Asia (CSA), an institute of international repute with a diverse team of economists, public health professionals, epidemiologists, and sociologists is trained and equipped to carry out high-quality evidence synthesis products. The research team has delivered extensive training to researchers, policymakers, and practitioners on how to produce systematic reviews and evidence and gap maps using rigorous methods, as well as on evidence use and uptake by a range of stakeholders. This team will support MANT and CDPH is building and testing the intervention in Jalpaiguri.

https://campbellsouthasia.org/

The researchers in MANT, CDPH, and CSA have experience in catering public health services through an HDSS model. However, the system was manually developed and sustained over the last decade. Given the rise in technology, we wanted to leverage the power of data pipelines, dashboards, IVRS systems, and AI to capture real-time issues in the community and also ensure improved efficiency of the healthcare workers catering to the population.

As mentioned before the team won an award in the MIT COVID-19 hackathon while presenting solutions to improve primary health care service delivery. The solution won as a credible and potential proof-of-concept. In addition, the team has experience building digital ecosystems especially for the WHO during COVID-19 to streamline the data collation of 80+ indicators from over 230 countries and member states daily. These technologies have been tested and as a team, the expertise has only grown to leverage this knowledge into other problems in public health, in this case, the problem of equity.
 

We have identified the following areas of potential challenges and have outlined respective risk mitigation strategies that Solve can guide us through:

1) Stakeholder buy-in: We will ensure effective stakeholder engagement using our previous contacts and collaboration. Solve can guide us in mapping all stakeholders at the local, national, and regional scale through it's existing partnerships.

2) Data reliability. Availability/ accessibility/ timeliness /measurement biases/ validation and quality assurance are potential issues here. We will use a team of epidemiologists and data scientists to dissect the data sources and prepare a list of solutions that can be shared with the stakeholders. In addition, our dashboard will alert relevant individuals who are collating data if any data point is missing or incorrect. Solve can mentor our approach through their team of data scientists and guide us from a neutral and broader lens.

3) Scale-up: We will leverage our existing contacts and the support from Solve to boost our chances. We will also present the data findings to the stakeholders in a customized manner as we believe that each views data differently. First scale-up will be attempted within different districts in a state, and in different states across India and then we will contact health bodies in Nepal, Bangladesh, and China from our previous collaborations. Scaling up is a barrier without the right support. Solve can support us with market and policy research in territories that we understand from a research lens and not an implementation lens.

4) Cultural barriers: Our solution can be scaled across multiple languages and contexts with the necessary modifications and adequate pilot in the NLP models to adapt in terms of language and cultural acceptability. In addition, we believe Solve can guide us to build the subsequent phases of development of this technology through seminars and networking opportunities wherein we would like to create a responsive and real-time health system where all points in the healthcare delivery supply chain in mapped. While we agree, the challenge remains in the heterogeneity, our team believes AI and digitization tools can alleviate the same seamlessly.

The innovative aspects of the solution are centered on digitizing the healthcare system to enhance the efficiency and effectiveness of ASHA workers in India, ensuring 100% coverage by bringing all individuals within the HDSS and leveraging/strengthening the existing Ayushman Bharat Digital mission ecosystem, and creating a demand-supply tracking system for efficient health system response. The key innovative features are:

1. Real-Time Data Monitoring: Unlike traditional methods that rely on periodic paper records, this solution utilizes real-time data collection and monitoring which enables timely health system response and transparency.
   

2. Integration of Digital Tools: The solution integrates interactive voice response systems (IVRS) and mobile applications specifically designed for ASHA workers. This technology supports the workers by streamlining their workflows, reducing the time spent on administrative tasks, and allowing them to focus on patient care.

3. Cloud Data Utilization: All patient data are captured and stored on the cloud, allowing for secure, scalable, and accessible data management. This comprehensive data collection is crucial for informed decision-making at both the local and national levels. This model would leverage the existing Ayushman Bharat Digital Mission’s objective to create a real-time and responsive health system.

4. Cost-Effective and Scalable Model: The solution outlines a cost-effective model that charges a minimal amount per day per ASHA worker, making it financially feasible for widespread implementation. Its design, supported by the findings of the EGM, can allow scalability across contexts.
   

5. Training and Empowerment: ASHA workers receive specific training on how to use the new digital tools, which empowers them to perform their duties more effectively. Their incentive design would be more referrals/coverage/program activity and thus more remuneration. Time is money.
   

6. Impact on National Health Programs: By integrating with national health databases, the solution ensures that data collected by ASHA workers can directly improve national health programs by tracking community-based indicators. This integration allows for a holistic approach to healthcare, bridging the gap between local issues and national health strategies.

7. Horizontalization of vertical programs: This system can be a single point where all data across different programs can be collected and integrated with the cloud. While national program operations can happen vertically, their budgeting can be immensely saved by leveraging existing resources and improving their work efficiency with program-specific work plans. Tracking and dashboards can still be program-specific as the data can be mapped accordingly in the cloud through innovative pipelines.

8. Innovative Revenue Model: The project explores innovative revenue models, such as enabling advertisements on the app, especially for health communication and health-related information, and seeking government funding, to ensure the sustainability and expansion of the project without burdening the users.

These innovations address critical barriers in the current healthcare delivery model for ASHA workers, particularly in remote and underserved areas. By leveraging technology, the solution not only enhances the efficiency of healthcare workers but also the quality of healthcare services. In short, it bridges the gap between the demand and supply sides of the health system through innovative digital design and context-specific mixed methods research. 

Theory of Change for the Solution:

Activities:

1. Digitization of Health Records and Workflows: Implementing digital tools such as mobile apps and cloud-based systems for real-time data collection, monitoring, and management.

2. Training ASHA Workers: Providing comprehensive training sessions for ASHA workers to effectively use these digital tools.

3. Integration with National Health Programs: Linking the collected data with national health databases to ensure alignment with broader health objectives.

Immediate Outputs:

1. Enhanced Efficiency of ASHA Workers: Reduced travel and wait times due to better scheduling and route planning enabled by digital tools.

2. Improved Data Quality and Accessibility: Real-time data entry and retrieval that improve the quality and timeliness of health data. In-built data checks can improve the quality over time and training.
   

3. Increased Capacity for Case Management: ASHA workers can manage more cases effectively due to reduced administrative burdens and can work with the additional remuneration as an incentive by covering more individuals promptly.
   

Short-term Outcomes:

1. Increased Coverage of Healthcare Services: More households receive regular visits and health check-ups due to the improved efficiency of ASHA workers.

2. Better Health Monitoring: Real-time data allows for immediate action and follow-up, which is crucial for managing infectious diseases and chronic conditions. In addition, the supply side of the health system would also be geared to cater to the needs from the community.
   

3. Enhanced Decision-Making: Health officials and ASHA workers make informed decisions based on accurate, up-to-date information.

Long-term Outcomes:

1. Improved Health Indicators: Reduction in disease prevalence and improvement in health outcomes due to more proactive and timely healthcare interventions.

2. Strengthened Primary Healthcare System: The overall health system becomes more responsive and resilient, benefiting from streamlined operations and data-driven insights.

Evidence to Support Links:

• Efficiency Studies: Research has shown that digitization of healthcare records and workflows can reduce the time health workers spend on administrative tasks by up to 30%.

• Impact Evaluations: Pilot projects similar to this initiative have demonstrated a 25% increase in household coverage by health workers post-implementation of digital tools.

• Data from Interviews: Feedback from ASHA workers during initial trials indicated significant improvements in job satisfaction and reduction in work-related stress due to easier data handling and less redundancy.

This theory of change outlines how specific activities will lead to the desired changes in the healthcare delivery system for ASHA workers, supported by evidence from various sources. By laying out this structured approach, stakeholders can see the logical progression from activities to long-term health improvements, making the case for the project's implementation and scaling.

The impact goals for the solution focus on enhancing the efficiency and effectiveness of ASHA workers, which is expected to improve primary healthcare delivery across underserved regions in India significantly. More importantly, the equity angle of the health system would also be addressed by increasing transparency and reporting over the entire supply chain of the health care delivery system in India.

Impact Goals:

1. Increase the Efficiency of ASHA Workers: Reduce the time spent on administrative tasks and travel, allowing ASHA workers to focus more on patient care.

2. Increase the coverage rates in the population: The digital HDSS would ensure no individual would be left behind.

3. Improve Health Outcomes: By ensuring more frequent and timely healthcare interventions, the overall health outcomes in the communities served by ASHA workers should improve.

4. Strengthen Data-Driven Decision Making: Enhance the capability of health systems to make informed decisions based on real-time data.

Measurement of Progress:

To track the effectiveness of the solution and its impact on the target population, the implementers will use a combination of quantitative and qualitative indicators, which may include:

1. Number of Households Visited Per Day by Each ASHA Worker: This metric will help quantify any increase in ASHA workers' efficiency due to reduced travel times and less time spent on paperwork. This will be tracked by GIS location trackers inbuilt in the software.
   

2. Frequency of Health Check-Ups: An increase in the frequency of health check-ups due to more efficient scheduling and data management would indicate success in improving health service delivery. We will ensure erroneous IVRS calls are reduced by adopting AI-based chatbots that would be trained by NLP to understand and respond to the queries of the individuals. Multiple errors that lead to forced home visits would be black-marked by the system. Such individuals would be enquired over the phone only in the future.
   

3. Incidence and Management of Communicable Diseases: Tracking the incidence of diseases like tuberculosis and how quickly they are addressed after being identified can show improvements in health outcomes.

4. Usage of the Digital Tools: Metrics such as daily active users and the number of data entries made can help assess how well the digital tools are being adopted by ASHA workers.

5. ASHA Worker Satisfaction and Feedback: Data on the satisfaction levels of ASHA workers with the new system will be collected through surveys and interviews, providing qualitative evidence of the project’s impact on their work lives.

6. Community Health Indicators: Improvement in community health indicators, such as child immunization rates and maternal health outcomes, can be linked to better data management and service delivery. This would be a testament to the automated workflows generated from the digital HDSS.
   

The core technology comprises the following:

1) A cloud-based database

2) IVRS system

3) Asha Ki Kiran mobile application

4) Evidence gap maps

1) Cloud-based database: This will be leveraging the existing Ayushman Bharat Digital Mission's ecosystem to add more individuals and households from Jalpaiguri. Each individual and household would be uniquely identified and a family tree would be created based on the relationship to the head of the household. This information would be carried out as part of the Ayushman Bharat Digital Mission enrollment drive. Each household will have a contact mobile number. If not, an individual residing in the community would be identified to assist households without mobile phone access. We will attempt to leverage Meghraj cloud server under NIC to develop this for better data encryption and security.

2) IVRS system: An automated helpline number with IVRS will be created wherein sick individuals can call and register their symptoms by punching numbers. Before doing so, a NLP-based chatbot would ensure that the users understand the system and only legitimate responses are captured. We foresee a few mishaps and misinterpreted calls from the community. But we believe this would not be more than 20% of an ASHA worker's target area.

3) Asha Ki Kiran - This application would have all the individuals and households under an ASHA worker. In addition, a workflow would be generated from the cloud HDSS to guide the ASHA worker on home visits for ANC care, immunization visits, TB care, etc. Necessary modules specific to the national program will be developed so that ASHA workers do not have to maintain registers. This will ensure real-time data capture and indicators from the community. With respect to the sick calls from the community, the ASHA worker will be alerted at various levels to understand the level of referral and treatment sought. This indicates the delay in referral or initiation of treatment if the individual reaches a higher facility for care. We will ensure automated call-in from Chatbot to cross-verify the information filled by ASHA workers in a random 10% of cases every month to ensure that the system is reliant and not capturing false data.

4)Evidence-gap maps - It is also one of the innovative tools in evidence synthesis, particularly in the field of research and policy-making that addresses the complex challenges of navigating and synthesizing the rapidly expanding body of research literature.  The visual representation of existing evidence and gaps in knowledge through graphical and spatial representations, EGM provides a clear overview of what is known and what is not known in a particular field or topic area. This visual aspect representing diverse types of evidence including primary research studies, systematic reviews, grey literature, and expert opinions makes them accessible and understandable to a wide range of stakeholders, including policymakers, researchers, and practitioners. Their systematic and transparent approach and rigorous methodology of identification, searching; and categorization of evidence make the methodology more robust and enhance the reliability of synthesized evidence. 

From MANT:

1. Dr Nirmalya Mukherjee

2. Dr Paramita Bhattachariya, PhD, Health Economist

3. Dr Sajda Khatoon, PhD, GIS specialist and Qualitative Research

4. Dr Biswajit Mahapatra, PhD- MIS and Quantitative Data Analysis

From UDAAN: 

1. Dr. Giridara Gopal, MD, PhD

2. Mrs. Prasidha Ramnathan, M.Sc

3. Mr. Devbalaji, M.Tech

4. Mr. Devarsh Patel, B.Tech

From Campbell South Asia:

1. Dr Howard White, Economist

2. Dr. Bhumika TV, Public Health and evidence synthesis expert

3. Dr. Neha Gupta, Phd, Nutrition Epidemiology and Evidence Synthesis Specialist

4. Dr. Swati Mantri, PhD, Social science research and evidence synthesis specialist

MANT has been working in Jalpaiguri for over a decade serving their primary healthcare needs and has maintained a demographic system using manual registers and records. The team in CDPH has been part of several initiatives to develop digital HDSS and other related public health solutions leveraging technology. Asha Ki Kiran was developed in 2021 but the prototype could not be piloted. CSA has been working in their evidence gap maps for over 5 years and have successfully driven policy changes from these maps.

At our consortium, the diversity of our leadership team is a testament to our commitment to embodying the principles outlined in MIT Solve's Diversity, Equity, and Inclusion Statement. Our leaders, including Dr. Nirmalya Mukherjee,  Dr. Giridara Gopal, Dr. Howard White, Dr. Bhumika T V, Dr. Neha Gupta, Dr. Swati Mantri, Dr. Paramita Bhattacharya, Dr. Sajda Khatoon, and Dr. Biswajit Mahapatra, each bring a wealth of diverse experiences and backgrounds that enrich our strategic directions and operational capabilities.

Leadership Team Diversity:

• Our leadership team's diverse academic and professional backgrounds—from public health and nutrition epidemiology to anthropology and geographic information systems—reflect a broad spectrum of social, cultural, and identity-based attributes. This diversity enhances our ability to innovate and address complex global challenges through varied perspectives and approaches.

Goals for Increasing Diversity, Equity, and Inclusion:

• We are dedicated to becoming even more diverse, equitable, and inclusive. Our goals include increasing representation from underrepresented groups in all levels of our organization, particularly in leadership roles. We aim to create equitable access to opportunities for professional and personal growth, ensuring that all team members can thrive.

Actions to Achieve DEI Goals:

• Recruitment and Hiring: We've implemented recruitment strategies that emphasize diversity, aiming to attract candidates from varied demographic and geographic backgrounds. This includes enhancing our outreach to institutions and organizations that serve underrepresented populations.
• Professional Development: We invest in tailored development programs that cater to the unique needs of our staff, ensuring equitable access to training and advancement opportunities.
• Policy Making: Our policies are consciously designed to address and remove systemic barriers that historically have hindered the full participation of marginalized groups. This includes flexible work arrangements and comprehensive support systems that consider the diverse needs of our team members.
• Inclusive Culture: We are continuously working on fostering an inclusive workplace where all employees feel respected, supported, and valued. Initiatives include regular diversity and inclusion training, the establishment of employee resource groups, and active solicitation of feedback on our DEI efforts.

By actively pursuing these strategies, we strive to create an environment where diversity is not only appreciated but integral to our organizational identity and success. This commitment aligns with MIT's definitions of diversity, equity, and inclusion, ensuring that we aim for not only a diverse workforce but also a truly equitable and inclusive community.

MANT provides services to the tribal communities through government funding alone and close partnerships. In addition to this majority source of funding, the organization is supported by philanthropists and donors which attribute around 5-10% of the funding.

MANT has a strong presence in the local community and in government circles working closely with the Government of West Bengal in delivering primary health care services through their network of mobile health vans for the tribal communities in Jalpaiguri.

The government is keen to introduce new interventions to improve the coverage of healthcare service delivery and MANT is confident that cost-effective solutions like UPCHAR can be scaled within the government framework and funding at ease. In addition to this, the teams supporting MANT namely CSA and CDPH have got stakeholder partnerships to drive funding through implementation research grants that can kickstart the pilot of this technology in the early stages of development.

While the capital costs would be one time, the system is capable to self-sustain as it requires only server operational and maintenance charges which can be potentially tapped from our idea to horizontalize vertical systems and saving costs. The population coverage through IVRS and HDSS also gives an opportunity to generate revenue through advertisements from health-related applications, and other products that need a market for product delivery. More importantly, the HDSS can also be tapped by R&D organizations which can use the data for planning therapies and other digital technologies for pilot and scale. Thus, in addition to government funding, research grants we feel these revenue streams can support and sustain the technology.

Since there would be a holistic HDSS and a comprehensive population-level dataset, we will explore social entrepreneurship models, micro-financing models for small-scale businesses, and public-private partnership models with laboratory networks and the pharmaceutical industry to support the system through overhead costs. Last but not least, we can tap into effective CSR grants and large-scale capital industries to support this activity effectively.