| Table of Contents | ||
|---|---|---|
|
Background & Purpose:
In 2021, over half of the global population lacked full access to essential health services. This highlights the importance of achieving universal health coverage (UHC), i.e. that all people have access to the full range of quality health services they need, when and where they need them, without financial hardship, by 2030. Limited financial resources for healthcare due to various ongoing crises make it crucial to implement strategic approaches and strengthen health systems, with primary healthcare (PHC) playing a significant role. PHC enables health systems to support a person’s health needs throughout their life course – from health promotion to disease prevention, treatment, rehabilitation, palliative care and more. PHC is considered the most inclusive and cost-effective method for achieving UHC, enabling significant cost efficiencies and enhancing the resilience of health systems. Digital approaches further improve access to care by reducing costs and providing real-time healthcare delivery, especially in low-resource settings.
Self-help groups (SHGs) have emerged as powerful tools for poverty alleviation and empowerment in low- and middle-income economies. These voluntary associations bring together economically disadvantaged individuals from similar socio-economic backgrounds with the aim towards addressing common issues through self-help initiatives and community action. By pooling resources through collective action, SHGs can alleviate financial constraints and make healthcare services more affordable. At the same time, they engage in educational programmes that disseminate important information about preventative measures to promote healthy habits in their communities.
As technology becomes increasingly accessible, integrating AI tools into SHG programs emerges as the next evolutionary step. The integration of AI into SHG programs holds immense potential to revolutionise how these groups operate and deliver services, ultimately leading to improved health outcomes and empowerment of their members.
This project aims to understand and deploy a pioneering AI digital public service to enhance access to PHC for members of SHGs and their families, with a particular focus on low-resource settings.
The research project aims to understand how AI can reduce barriers such as limited healthcare infrastructure, geographical constraints, and socioeconomic factors often hindering access to essential health services. By understanding the unique challenges faced by SHG members and their families as well as the opportunities, the project seeks to identify AI-driven solutions tailored to their needs, including telemedicine platforms, mobile health applications, and community health worker support systems. Through research and experimentation, the project aims to demonstrate the feasibility and effectiveness of AI in enhancing PHC access and improving health outcomes for underserved populations in low-income settings.
Artificial Intelligence, or AI, is increasingly recognised as having significant importance to the provision of PHC. AI can enable remote consultations through telemedicine platforms, offer personalised health advice via chatbots and virtual assistants, aid in early diagnosis through symptom analysis, analyse healthcare data for resource allocation, and enhance capacity building through educational tools and training programs. These AI-driven solutions help overcome geographical barriers, empower individuals to manage their health effectively, and optimise healthcare delivery to meet the needs of underserved communities.
Case Scenario:
South Asia is currently undergoing an epidemiological transition with significantly increasing prevalence rates of non-communicable diseases (NCDs).
India is the most significant contributor to the NCD burden.
Several studies conducted over the last two decades have highlighted the high overall burden of diabetes, hypertension and dyslipidaemia in India.
According to Lancet estimates, 11.4% of the Indian population was living with diabetes in 2023, and 15.3% were prediabetic.
Evidence shows that patient-centred PHC, in line with a chronic care model, ensures optimal diabetes self-management support and improves long-term clinical and health outcomes in diabetes patients. While public PHC in India provides free services to patients, it lacks patient-centred care. This undermines diabetes self-management education and support.
In addition, factors such as patients' lack of knowledge about diabetes, suboptimal medication adherence, persistent clinical inertia and lack of data for monitoring and evaluation through clinical trials deteriorate the standards of diabetes care in India's PHC. Establishing guidelines, supporting health care professionals´ knowledge and skills in prediabetes and diabetes care, and implementing interprofessional referral pathways can enhance prediabetes detection and care precedence in primary health care. AI tools can support healthcare professionals with these tasks.
| Info |
|---|
Case Study Scenario:South Asia is currently undergoing an epidemiological transition with significantly increasing prevalence rates of both communicable and non-communicable diseases (NCDs). India is the most significant contributor to the NCD burden. Several studies conducted over the last two decades have highlighted the high overall burden of diabetes, hypertension and dyslipidaemia in India. According to Lancet estimates, 11.4% of the Indian population was living with diabetes in 2023, and 15.3% were prediabetic. An example case design that will be sketched for the proof of concept is that for diabetes. Evidence shows that patient-centred PHC, in line with a chronic care model, ensures optimal diabetes self-management support and improves long-term clinical and health outcomes in diabetes patients. While public PHC in India provides free services to patients, it lacks patient-centred care. This undermines diabetes self-management education and support. In addition, factors such as patients' lack of knowledge about diabetes, suboptimal medication adherence, persistent clinical inertia and lack of data for monitoring and evaluation through clinical trials deteriorate the standards of diabetes care in India's PHC. Establishing guidelines, supporting health care professionals´ knowledge and skills in prediabetes and diabetes care, and implementing interprofessional referral pathways can enhance prediabetes detection and care precedence in primary health care. Deep learning models can support healthcare professionals with these tasks. |
Background & Purpose:
In 2021, over half of the global population lacked full access to essential health services. This highlights the importance of achieving universal health coverage (UHC), i.e. that all people have access to the full range of quality health services they need, when and where they need them, without financial hardship, by 2030. Limited financial resources for healthcare due to various ongoing crises make it crucial to implement strategic approaches and strengthen health systems, with primary healthcare (PHC) playing a significant role. PHC enables health systems to support a person’s health needs throughout their life course – from health promotion to disease prevention, treatment, rehabilitation, palliative care and more. PHC is considered the most inclusive and cost-effective method for achieving UHC, enabling significant cost efficiencies and enhancing the resilience of health systems. Digital approaches further improve access to care by reducing costs and providing real-time healthcare delivery, especially in low-resource settings.
Self-help groups (SHGs) have emerged as powerful tools for poverty alleviation and empowerment in low- and middle-income economies. These voluntary associations bring together economically disadvantaged individuals from similar socio-economic backgrounds with the aim towards addressing common issues through self-help initiatives and community action. By pooling resources through collective action, SHGs can alleviate financial constraints and make healthcare services more affordable. At the same time, they engage in educational programmes that disseminate important information about preventative measures to promote healthy habits in their communities.
As technology becomes increasingly accessible, integrating AI tools into SHG programs emerges as the next evolutionary step. The integration of AI into SHG programs holds immense potential to revolutionise how these groups operate and deliver services, ultimately leading to improved health outcomes and empowerment of their members.
This project aims to understand and deploy a pioneering AI digital public service to enhance access to PHC for members of SHGs and their families, with a particular focus on low-resource settings.
The research project aims to understand how AI can reduce barriers such as limited healthcare infrastructure, geographical constraints, and socioeconomic factors often hindering access to essential health services. By understanding the unique challenges faced by SHG members and their families as well as the opportunities, the project seeks to identify AI-driven solutions tailored to their needs, including telemedicine platforms, mobile health applications, and community health worker support systems. Through research and experimentation, the project aims to demonstrate the feasibility and effectiveness of AI in enhancing PHC access and improving health outcomes for underserved populations in low-income settings.
Artificial Intelligence, or AI, is increasingly recognised as having significant importance to the provision of PHC. AI can enable remote consultations through telemedicine platforms, offer personalised health advice via chatbots and virtual assistants, aid in early diagnosis through symptom analysis, analyse healthcare data for resource allocation, and enhance capacity building through educational tools and training programs. These AI-driven solutions help overcome geographical barriers, empower individuals to manage their health effectively, and optimise healthcare delivery to meet the needs of underserved communities.
Beneficiary Workflow:
...
Information about the disease focused on disease progression, incubation and recovery
The localised data set about the disease
Possible treatment plans - self-administered, symptom observation schedules
A course of Action to receive treatment from healthcare providers
Affiliated health care insurance of the patient, government subsidy on the treatment plans
Phase 3: The Patient Case Summary then awaits a human review so that the patient beneficiary can receive or self-administer treatment related to the diagnosis.
The human review is conducted by an approved and registered health worker. This member accesses the processor side of the Sentient Bot AI service.
When the health worker reviews the inferred diagnosis, the Patient Case Summary is logged into the beneficiary relationship management database. This database is available for stakeholder systems integration (an organisation where a health care worker is affiliated, e.g. Hospitals, Clinics, Non-profit organisations), and public health care systems using secure API (Application programming interface) standards.
Health workers act on different levels of the embedded knowledge base:
When reviewing any inferred diagnosis
Changing the state of the ticket logged with the Patient Case Summary
Patient Case Summary is added to the larger pool of the database to fine-tune the core diagnosis model.
Specific use cases of disease prevention and mitigation in primary health care:
The intended premises of the default sets of data models generated out of this research is:
Self-treatment, treatment routing model, Tracking and preventing spread of Infectious disease
Treatment routing model for non-infectious diseases, specifically cardiometabolic disease, where rehabilitation and recovery require tracking health data over the recovery period. the disease
Possible treatment plans - self-administered, symptom observation schedules
A course of Action to receive treatment from healthcare providers
Affiliated health care insurance of the patient, government subsidy on the treatment plans
Phase 3: The Patient Case Summary then awaits a human review so that the patient beneficiary can receive or self-administer treatment related to the diagnosis.
The human review is conducted by an approved and registered health worker. This member accesses the processor side of the Sentient Bot AI service.
When the health worker reviews the inferred diagnosis, the Patient Case Summary is logged into the beneficiary relationship management database. This database is available for stakeholder systems integration (an organisation where a health care worker is affiliated, e.g. Hospitals, Clinics, Non-profit organisations), and public health care systems using secure API (Application programming interface) standards.
Health workers act on different levels of the embedded knowledge base:
When reviewing any inferred diagnosis
Changing the state of the ticket logged with the Patient Case Summary
Patient Case Summary is added to the larger pool of the database to fine-tune the core diagnosis model.
Specific use cases of health promotion, disease prevention, treatment, and rehabilitation in primary healthcare encompass a diverse range of applications. Through our research, we aim to develop default sets of data models tailored to address these needs effectively. These models are intended to support self-administered treatment and optimize treatment routing for communicable diseases to track and prevent their spread. Additionally, they will facilitate treatment routing for non-communicable diseases, with a particular focus on cardiometabolic diseases, where rehabilitation and recovery necessitate continuous monitoring of health data throughout the recovery period.
Following default data models are involved in the project activity:
Diagnosis Inference Model for a collection of data on local disease burden - the ability to generate a diagnosis inference without human intervention, and human intervention is only required for treatment routing approval.
Treatment Routing and Self-Administration Model - the ability to tell when self-administered treatment is enough or when routing to a nearby healthcare facility is needed.
Following default data models are involved in the project activity:
...
Key Status Milestone in setting up project infrastructure and project cohort:
The platform subnet is designed to be HIPAA compliant(attach URL of the contract with Atlassian, Google Cloud and AWS) https://support.atlassian.com/organization-administration/docs/the-hipaa-implementation-guide/
Tracks the carbon footprint of the platform itself (attach reports from Open Treasury).
https://govern.muellnersfoundation.org/open-bank-protocols/carbon
...
Literature Review: Conduct a comprehensive review of existing literature on AI applications in healthcare, specifically focusing on primary healthcare delivery and its implications for low-income settings. This review also encompasses studies examining the role of SHGs in healthcare access and outcomes.
Needs Assessment: Perform a needs assessment within SHGs and their communities to identify specific healthcare challenges, preferences, and barriers to access. This may involve surveys, interviews, focus group discussions, and observations to gather insights from SHG members, community leaders, healthcare providers, and other stakeholders.
Technology Assessment: Evaluate the suitability and feasibility of different AI technologies for addressing the identified healthcare needs and challenges. This assessment should consider factors such as affordability, accessibility, usability, scalability, and cultural appropriateness within the context of low-income settings and SHG dynamics.
Pilot Implementation: Implement pilot interventions using selected AI technologies to enhance access to PHC within SHGs and their communities. This may involve deploying telemedicine platforms, mobile health applications, or AI-driven decision support systems, depending on the identified needs and priorities.
Mixed-Methods Evaluation: Use a mixed-methods approach to evaluate the effectiveness and impact of the pilot interventions. Quantitative methods such as surveys, health metrics analysis, and usage statistics can assess changes in healthcare access, utilisation, and outcomes. Qualitative methods such as interviews, focus groups, and case studies can provide insights into user experiences, perceptions, and contextual factors influencing intervention effectiveness.
Participatory Action Research: Engage SHG members, community leaders, and healthcare providers as active participants throughout the research process. Foster collaboration, co-design, and co-evaluation to ensure that interventions are responsive to community needs, culturally sensitive, and sustainable beyond the project duration.
Ethical Considerations: Adhere to ethical guidelines and principles throughout the research process, including informed consent, privacy protection, confidentiality, and respect for cultural values and beliefs. Ensure that interventions prioritize the well-being and autonomy of SHG members and their communities.
Dissemination and Knowledge Sharing: Share findings, lessons learned, and best practices with relevant stakeholders through workshops, conferences, policy briefs, and academic publications. Foster knowledge exchange and collaboration to promote the scale-up and replication of successful AI-driven interventions for enhancing PHC access in low-income settings and SHG contexts.
Timeline
...
Aug -
...
Sept 2024:
...
Activate an organized project team to commence with roles and responsibilities.Resource setup and planning
Conduct a kickoff meeting to
deliberate on research objectives, methodology, and timeline
and document a detailed project plan
encompassing milestones, deliverables, and budget allocation.
Undertake a literature review on AI applications in healthcare and SHG dynamics.
Extend invitations for partnerships with local
organisations, SHGs, and healthcare providers
.
...
Oct -
...
Nov 2024: Needs Assessment and Technology AssessmentConduct
Carry out needs assessment activities
such as surveys, interviews, and focus group discussions with SHG members and community stakeholders.
Analyse data collected from the needs assessment to
pinpoint key healthcare challenges and priorities.
Assess Open Constitution AI network technologies for suitability
in addressing identified needs.
Select appropriate AI tools
based on assessment results for pilot implementation.
...
Dec 2024 - January 2025: Pilot Intervention Development and PreparationDevelop and customize
Customise selected AI interventions, including telemedicine platforms
...
& decision support systems using human augmentation.
Conduct training sessions for project
...
volunteers & healthcare providers on using AI technologies.
Establish data collection
...
& monitoring mechanisms to track intervention
...
outcomes.
Obtain necessary approvals
...
from relevant authorities for pilot implementation.February
Feb -
...
Apr 2025: Pilot Intervention Implementation
Launch pilot interventions in selected
...
communities by deploying the AI model within the virtual private network of Open Constitution. Provide ongoing support
...
& troubleshooting for users
...
during implementation.
...
Monitor intervention usage
...
& effectiveness through data collection
...
methods. Hold regular stakeholder meetings to review progress
...
.
May -
...
Jun 2025: Evaluation
...
And Analysis
Collect quantitative
...
& qualitative data on intervention outcomes, including changes in healthcare access
...
& health outcomes.
...
Analyse data using appropriate statistical
...
methods.
...
Synthesise findings to assess the impact of AI interventions
...
within
...
communities.
...
Identify
...
lessons learned, success factors,
...
areas
...
of improvement, and model finetuning.July
Jul 2025: Dissemination and Knowledge SharingPrepare
preparing project reports, presentations, and publications
summarising findings and recommendations.
Additionally, organising dissemination events
such as workshops, conferences, and stakeholder meetings
to share project outcomes with relevant audiences is required.
Facilitating knowledge exchange and collaboration among stakeholders to promote the adoption and scale-up of successful AI-driven interventions
is also part of the responsibilities. Furthermore, developing a sustainability plan for maintaining and expanding AI interventions beyond the project period, including securing funding and institutional support, is essential.
Throughout the project, maintain regular communication and collaboration among project team members, partners, and stakeholders to ensure alignment with project goals and objectives. Adjust the implementation plan as needed based on emerging insights, challenges, and opportunities encountered during project execution.
...
By systematically tracking these short-term and long-term outcomes and evaluating them against predefined criteria for success, the project can assess its effectiveness in achieving its objectives and generating positive impacts on healthcare access and outcomes within SHG communities.
Commercialization potential:
The project uses parts of the social finance platform - Finscale AI, where the beneficiary comes from a rural household. Using the digital platform, the health of the crop can also be tracked using the same computing platform.
The rural household beneficiary is the target group for the commercialization of this project.
The base model is supposedly open-source (OCL v1). The authors do not wish to patent the research outcome. The research outcome necessarily establishes standards, and these standards can be adhered to if the resultant commercialized service is deployed on the defined Open Constitution AI network.
This project aims to find a data model to deploy on the subnet of this Open Constitution AI network.
The controls and checks have been deployed on the defined Open Constitution AI network, and its service delivery model is described in the private network’s governance model.
The foundation has established relationships with Google Cloud and AWS Cloud to scale the Sentient Bot service through the AWS Activate Program and Google Cloud Not-for-Profit program. The platform has implemented standards like HIPAA at a foundational level.
The SHG bank linkage programme can further give entry to scalable commercial aspects of the AI service.
Methodology to undertake Proof of Concept :
The research team plans to organize an operational usage of the AI service for a 1 month period amongst the group of network participants, who activate and enable the health care service.
The team plans to do so in cooperation with the Muellners Foundation, which onboards NPOs and medical research universities in the northeast and south of the country, where SHGs are linked to plantation workers and daily wage labourers as the target beneficiaries.
Preliminary findings in the Global Health program of the Foundation:
Digital approaches can further strengthen access to care in low-resource and low-income settings.
A base qualitative and quantitative field study/survey and data model has been identified due to support from the Muellners Foundation, which uses the network of self-help groups in South Asia for the implementation and deployment of a health services platform.
The research team has identified the beneficiary profile in semi-rural, semi-urban and rural settings for the planned rollout or delivery of an autonomous digital public service.
The research team has also identified the income levels of the beneficiary.
The research team has also identified the livelihood settings and disease burden in the households.SHG communities.
Preliminary findings for deploying a pilot AI deep learning model:
In the preliminary findings, the research group has documented and generated classified public data sets to be further used in this research:
Geolocation-mapped beneficiary profile in semi-rural, semi-urban and rural settings in India for the planned rollout or delivery of an autonomous digital public service using Gender, income and nutrition level demographics
Identification and mapping of the income levels of the beneficiary through the data reported in the SHG bank linkage programme.
Mapping of the livelihood and sustenance levels of the beneficiary population into the data set.
Geolocation mapped skill gap data set for health care practitioners for quality assurance and compliance review of the AI Service’s training set and human augmentation process
identified the livelihood settings and disease burden in these households.
Commercialisation potential:
The base model is supposedly open-source (OCL v1). The authors do not wish to patent the research outcome. The research outcome necessarily establishes standards, and these standards can be adhered to if the resultant commercialised service is deployed on the defined Open Constitution AI network.
The controls and checks have been deployed on the defined Open Constitution AI network, and its service delivery model is described in the private network’s governance model.
The network maintainer - Muellners Foundation, has established relationships with Google Cloud and AWS Cloud to scale the Sentient Bot service through the AWS Activate Program and Google Cloud Not-for-Profit program. The platform has implemented standards like HIPAA at a foundational level.
The SHG bank linkage programme can further give entry to scalable commercial aspects of the AI service.
Specific Engagement methodology with non-academic stakeholders to undertake Proof of Concept:
The research team plans to organise an operational usage of the AI service for a 1-3 month period amongst the group of network participants. The team plans to do so in cooperation with the Muellners Foundation, which onboards NPOs and medical research universities in the country, where SHGs are linked to daily wage women labourers as the target beneficiaries.