Primary Healthcare Performance Measurement

The conventional method to assess antenatal coverage provides a partial review of the risk stratification associated with pregnancy. This is because, the existing system relies on global benchmark indicators that assess risk based on ANC timeliness (No. of contacts during trimesters), No of visits, gestational age, service provider etc. Such indicators are found to poorly predict positive delivery outcomes or guide the primary healthcare system with proper evidence to manage complications during childbirth. The lack of an evidence-based risk assessment model creates a huge burden on the primary care system that lacks resources, equipment and modern means to save mothers when obstetric complications arise. The standard approach recommends if complications arise during childbirth in primary care settings refer women to hospitals. However, shifting the location of the childbirth may have adverse consequences on acutely ill patients and should ideally occur before not during delivery. Moreover, taking the load away from the primary setting to hospitals may increase complications, over medication, excessive cesarian and even disrespectful care due to overcrowding. For rural women, primary care should be the nucleus of maternal healthcare services with the recognized areas of expertise rather not appear ‘incapable’ and ‘handicapped’ system to handle care provision for expectant mothers.

iKure will imply a High-risk stratification model that can be effectively implemented in a primary healthcare setting using ‘Wireless Health Incident Monitoring System’ (WHIMS) with a community health worker(CHWs) with minimum education can use. WHIMS Application installed in the smartphone of each CHW can collect and store health data offline which gets stored on to a cloud and can be transmitted to PHCs for monitoring and review by certified medical personnel followed by recommendations for referral and diagnosis. The internal triggers in the App also have the potential to trigger alerts in real-time of low to high-risk health conditions, which when communicated through CHWs is able to guide the facility for appropriate medical intervention and guide the health system for positive delivery outcomes. Every PWs are registered using Digitized Health Card encrypted with a QR code. The authenticity of the patient is validated using a biometric scan and digital healthcare of the particular PW. This ensures the authenticity of technology-aided data collection up to the last mile. The unique identification number will help the primary health system track the entire continuum of care of the antenatal services, collect essential insights from the dashboard, and identify risk patients across different geographic locations.

The solution will serve the low-income community across 3 states in India including Jharkhand, Sundarbans, and West Bengal. Risk stratification is a huge challenge for poor women who are more likely to receive low-quality antenatal care that fails to provide timely screening, monitoring and treatment which makes it more challenging to predict intrapartum complications. Risk stratification is the core indicator to predict delivery outcomes and failure which results in poor obstetric care, premature delivery and maternal deaths. The indigenous capability of WHIMS enables CHWs to collect, assimilate and screen real-time health data, CHWs carrying smartphones loaded with WHIMS extend access to continuous and constant monitoring of pregnant women(PWs), provide accurate and timely information about pregnancy, and childbirth, generate alerts in real-time of the potential risk.

With the existing primary care clinics repeatedly showing poor quality of maternal care and negative outcomes compared to giving birth at home they are still deemed appropriate for low-risk birth in low-income countries. Evidence-based Risk assessment tool will guide primary care setting to maintain and track pregnant women, work with couples on birth planning and coordinate care with higher-level facilities. Through the risk stratification model, the facility will be better attuned to detect and mitigate maternal risks including Anemia, Malaria, HIV, multiple pregnancies and the like. The model will use primary care through a community health worker programme across 8 consecutive visits (Recommended by WHO), and follow-ups which will include:
• History taking that identifies PWs based on systemic illness, previous stillbirth and miscarriage
• Physical examination that assesses PWs based on a vital collection such as Pulse, Respiratory rate, BP, BMI, Fetal growth and movement, swelling of hands and feet, persistent headache & vaginal bleeding.
• Laboratory investigation to assess PWs based on Thyroid, HIV, Blood sugar, Haemoglobin, HCV Antibodies, HBsAg, Malaria and Dengue test and COVID-19
• Counselling to voice for the supply of IFA, TT injection, All screening tests, diet & rest and preparing for complication readiness
• Abdominal examination investigation to assess fundal height, foetal movements, lie & presentation.

Further, primary care will be strengthened through linkages with advanced care facilities allowing sharing of care across the continuum and seamless communication. Also, we envisage the advanced care facility with linked primary care will create a collaborative learning ecosystem that interacts regularly, reviews complex cases and offers better services to save more number of women during childbirth.
The model will create demand for higher level maternity care by informing women and their families through strategic Information, Education and Communication. Community outreach and social marketing through radio, television and community street plays will be deployed for increasing demand for quality and improve the existing primary care.

Our team is made up of trained Community health workers, Statisticians, Technology experts, operational managers, Doctors, and researchers who have immense expertise working with field team members, international institutes, and different State Governments of India. The tool has been tested and validated and pilot tested. The model will be a big potential for growth and scale up beyond India. The tool has been developed with a collaborative effort of the international client, State Government, researchers and leader’s community health.

The primary healthcare model faces an immense challenge due to:

• Lack of resources, high level of attribution creating challenges both for patient and provider
• Pregnant women lack essential support from the primary care settings due to a lack of resources, equipment and modern technology to provide timely care provision for the needy.
• Lack of data-driven evidence to guide and inform decisions that are fatal both for pregnant women and their families.
The challenge will help us improve the model for population scale deployment to save more no. of mothers during childbirth. Also, Big data analytics and artificial intelligence will improve our capacity to scale up the model beyond India. We aim to enrich our learnings through MIT solve mentorship and fellowship program that will aid iKure’s capacity to deliver its mission to create zero mortality in primary healthcare.

Our solution adopts the WHO recommendation for 8 ANC visits and quantifies the qualitative measures of the antenatal services with standard ANC coverage. Then using the routine last mile real data from the catchment areas, the analytics calculates and categorizes pregnant women into low, medium and high risk. Using a digital health card each patient is registered in WHIMS. The CHWs register the PWs and direct them for early registration in the primary health centre with the support of ASHA workers. The CHWs validate the identity of the PWs using a biometric scan. Using the unique identification number each PWs is mapped according to the health facilities they registered with and ASHA workers to help them connect with the health facility and immediate screening of the PWs starts using the ANC application integrated with WHIMS. The application will flag CHWs with schedules for ANC checkup, monitors CHWs with live location GPS sharing, and stratify risks according to different levels. The facility is able to screen risk patients and provide them with teleconsultation (using iKure’s teleconsultation application), periodically monitors them and connects them with an effective care regime.

Our solution will have an impact on the way performance to assess antenatal care is provided by primary healthcare. It will develop the relationship between Antenatal care coverage and risk stratification of pregnant women for better delivery outcomes. Insufficient data on risk have made the existing ANC model incapable to measure positive outcomes and assess the readiness of primary care to provide optimum care for pregnant women. The availability of such a risk stratification model will enable primary care for safe delivery and impacts the health and wellbeing of the mothers, family and community. The model will improve access and safety of primary healthcare to handle more positive maternal health outcomes using an evidence-based approach.

The impact goals will be measured using the risk stratification model for systematically categorizing pregnant women based on their assigned risk(here risk refers to clinical risk or likelihood of adverse clinical outcomes) level to make better use of limited resources, anticipated needs and proactively manage patient population at a primary healthcare level. The strategy will involve an assessment derived from a two-step algorithm. 

Step (1) involves sorting PWs into one of three risk groups (high, medium, low) based on objective data taking clues from electronic health records captured on WHIMS. The determination will be based presence and absence of factors such as the history of pregnancy, comorbidity, clinical investigations, demographic details including age, social and economic structure, lifestyle behaviour and health-seeking behaviour. iKure’s WHIMS application will calculate the risk score automatically based on this data. The determination can be adjusted on the requirement of subjective considerations based on ethnicity, location, culture and norms. In the second step, the PWs will be assigned to a six-risk level based on how the physician or CHW answer the following question:

• Is the PW healthy with no medical problems? If her clinical metrics are in or out of range?
• Does the PW have any chronic conditions, but she is doing well?
• Does the PWs have chronic conditions that are out of control but without any complications?
• Does the PW have complications of chronic diseases?
• Is the PW have the potential risk of death or life-threatening conditions?

Risk-Stratification Algorithm

The risk stratification model will help assess the risk of PWs using a dynamic process based on PWs' current status, for example in case of sudden vaginal bleeding, or infection with COVID-19  would promptly guide the CHW or a physician to change a risk score. The model will give a complete integrated view of the PW and make each PWs risk level readily available to the primary health system to assign an appropriate care regime. The entire ANC coverage will be assessed through the lens of the risk score, such as: 

• Prompt immediate telemedicine  & schedule longer consultations with the high level of risks
• Strategically deploy health workers to manage chronic conditions
• Identify patients who would benefit from additional services such as home visits, disease education, referral tracking and additional follow-up. 

Risk-Stratification Algorithm:

In frontend we're using ReactJs and Kotlin. In the backend, we have distributed systems mainly developed with Node.js and rabbitmq as the message broker. For logging we're using ELK . We're using AWS EKS, S3 for deploying our code.

Primary data is collected by iKure using the following:
1. Wireless Health Incident Monitoring Systems: iKure has a proprietary medical collaboration platform called -WHIMS that captures both clinical and non-clinical data through trained community health workers.
2. Capturing real-time data: Community Healthcare Workers, together with ASHA workers captures data via WHIMS, approaching them at primary health centres, doorstep survey, community mobilization, cluster meetings & health camps.
3. Medical Device Integration: WHIMS is integrated with non-invasive point of care devices that capture data seamlessly from iKure’s catchment areas.
4. Data Analytics: The real-time data undergoes preprocessing and advanced data analysis to score PWs according to high-low-medium risk patterns.
5. AI-enabled model: iKure’s integrated model together with Machine Learning algorithm and Artificial Intelligence will convert data into meaningful insights enabling different stakeholders to have a more logical approach to diagnosis and treatment of PWs.
6. Collaborative Ecosystem framework: iKure’s collaborative research ecosystem with research institutes will offer mentorship for an in-depth insight for further research intervention in antenatal care.
Output:
1. Population Health Management System: This module will be used for maintaining and tracking the care plan of pregnant women with the following components:
a. Apps for use by ASHAs, ANMs, Patients & their families, Doctors
b. Electronic Medical Record (EMR) for maintaining the patient health records
c. Program Management Dashboard: The dashboard will provide valuable information and insights to the public health system for Program monitoring and evaluation and decision making.

i. Data Management: Data will be captured and stored in iKure’s database created through the cloud-hosted platform ‘Population Health Management System’, which will be accessed by selected staff members to conduct data entry, cleaning, visualization, and data interpretation which will be done through Jupyter Notebook (Python). iKure has strict data management guidelines, which the research team will adhere to. The proposed programme will meet the highest standard of ethical code of conduct. The data and insights generated through the proposed intervention will create a platform for precision health decision support which will create new possibilities for research interventions in low-income communities, initiating an evidence-based approach to understand the occurrence, prevention and interventions to diagnose, treat and manage NCDs during and post-pandemic.
ii. Ethical Consideration: iKure’s Institutional Ethic Community (IEC) comprising of prominent senior members across platforms like medical practitioners, philosophers, academicians, and lawyers will ensure the intervention meets the regulatory norms and conditions and is carried out ethically in the field. The committee will look after the development of the project to ensure the milestones are achieved and undertake a continual evaluation to ensure smooth execution of the project and address the unexpected challenges in case if arises during the programme. This committee will be open to connecting with technology partners and intellectual property across sectors to bring newer innovations into practice during the programme to bring added value to the project beneficiaries. They will also be responsible to manage partnerships, knowledge mobilization and regulating field activities.

Our team considers gender, age and diversity of culture and ethnicity as an inclusive criteria. It blends diversity, inclusion and equity.

iKure is a population health management company that meets primary healthcare and prevention needs through a unique combination of health outreach initiatives, skills development and technology intervention. iKure focuses on addressing the primary health care needs across all settings – rural, semi-urban and urban areas. iKure has impacted 14 M populations, treated 3 M people, and touched 6, 200 villages across 10 states in India. The social venture also offers technology solutions in MENA countries, Sub-Saharan Africa and South-East Asia.

1. The programme will aim to build sustainable and healthier communities by empowering grassroots community members and frontline health workers to serve the PWs in their community.  

2. The programme will lay a strong foundation for a robust supply chain system, which will be both community-responsive and inclusive.

3. The initiative will facilitate CHWs to earn incentives based on early registration of PWs,  enabling pathology tests, diagnostic services, and antenatal services facilitated by the PHCs.  The initiative will have the potential to engage with local NGOs to make future programmes more successful and contribute to better health, economic empowerment, peace and harmony in the region

Leveraging the methodical livelihood generation schemes offered by iKure, iKure has empowered a cadre of Community health workers that earn livelihood beyond the stipend period.  The CHW promote various products and services through mobile-based supply chain application and make them accessible in hard-to-reach communities. The initiative helps them with an income of 5000 to 10,000INR per month.  We envisage creating a collaborative approach to build self-sustainability of the CHWs to act as Entrepreneurs through various supply chain products to serve three sections of the community; women community members to develop capacity and skills to earn livelihood locally, village community members to benefit from enhanced ANC coverage facilitated through primary healthcare delivery and local vendors through supply chain products. The intervention will bring new frontiers to the frontline, bringing awareness, advocacy and actions at the primary care level in bringing sustained impact on health and socio-economic outcomes of the vulnerable pregnant women in the region.