Elythea: AI to Prevent Maternal Mortality in LMICs

Postpartum hemorrhage is the #1 killer of mothers globally. The CDC estimated that 75+% of these mortalities were preventable if there were better predictive models allowing for earlier preparation/clinical coordination. The second leading cause of preventable maternal mortality is that moms themselves are not aware of the red-flag symptoms to consider if they're high risk and often do not go to the hospital until it is too late. In low-resource settings where provider bandwidth is limited and there is a high barrier for rural moms to travel to the hospital unless it is "worth it", there is a strong desire to solely identify and inform high-risk moms earlier on.

If patients who need a blood transfusion don't have blood prepped, the doctors need to rush to get blood during their delivery, which can take 10-30 minutes (up to 2-6+ hours in rural/developing regions), risking death. In African countries, you need to have a family member donate a unit of blood before you can receive one, which adds extra time when a mother is hemorrhaging. These hospitals lack the resources to assess blood type/have blood prepared for everyone but do have the capacity to prepare blood for the few high-risk patients. By predicting months in advance, at the point-of-care, we give doctors/moms months to get type/cross-matched blood ready before labor even happens.

When we interviewed 80+ obstetric providers and asked them what complication they were most worried about/wanted to prevent, >90% pointed us to Postpartum hemorrhage and hypertensive eclampsia. >97% of providers reported being able to do something in advance if they knew patients were at risk, affirming that earlier knowledge of at-risk patients would tangibly help change their clinical management and improve their odds of saving their patient's life. 

Doctors get burnt out spending extra time on preventable complications and extending patient backlogs, hospital systems lose millions in funding from having higher mortality rates/poor resource allocation, and insurance companies pay $10B extra (US alone) on preventable complications. In African countries, government systems (specifically the Ministry of health) lose $1B+/year paying for these costly complications. 

Globally, 140M births occur, where >80% of patients have a mobile device/are patients at a clinic with a mobile device capable of using our platform. 14M moms have PPH annually, causing 70,000 maternal deaths globally, >60% of which are preventable (WHO). Up to 11M moms have eclampsia, globally causing 50,000 maternal deaths each year. >25M moms have an unexpected, emergency c-section each year.

>99% of maternal deaths occur in developing countries, yet there are no widely adopted prediction models and most facilities rely purely on clinical judgment.

African obstetricians currently have medications (oxytocin/TXA) and intervention mechanisms (tighter follow-up, active management of labor, specialized maneuvers/MFM attendings) to prevent/treat these complications, but just don't have enough resources/time to allocate for every patient--just the high-risk ones. 

But, doctors in developing African countries have as low as ~1-3% accuracy rates (NIH, Reproductive Health) in detecting hemorrhage, and developed hospitals have red-flag rubric point-scoring systems that have <50% accuracy rates. 

Elythea is a machine learning mobile/web app platform that obstetricians in any setting use to predict risk for pregnancy complications (postpartum, hemorrhage, eclampsia, preterm-labor, etc) before they happen. We use ML models that are better at dealing with multidimensional medical data than linear models/red-flag checklist systems.

We use sociodemographic/clinical history information that is available at the point-of-care, so we can make the prediction as early as the 1st trimester, allowing doctors months to prepare in advance! Our platform can intake manual input of patient features in an easy-to-use mobile question flow which is especially helpful for nurses/providers in low-resource regions. Every doctor we interviewed across 5 African countries affirmed that a mobile app (1-time download through mobile-data) would be easily accessible for them and their care staff. NCBI publications substantiate this, supporting that >90% of Sub-Saharan African doctors owned a mobile device. 

By predicting adverse outcomes, we allow doctors to allocate their finite resources (medicine, time, specialized consult) for the patients that truly need it. For moms at risk of hemorrhaging, doctors can coordinate family members to donate blood months before delivery and have blood-type-matched blood ready before the mom even goes into labor. During/prior to labor, oxytocin can be prophylactically administered, along with tighter clinical followup. Moms who are aware they are high risk can get clinical consults on specific red-flag symptoms to watch out for, can have diet/exercise regimen modifications, and are more likely to travel to hospitals when they detect concerning symptoms. 

No existing models predicting PPH have been prospectively tested in rural/African cohorts, despite 99% of PPH deaths occurring in low-resource regions like Sub-Saharan Africa.

We prospectively tested our models on 2,000+ women enrolled from 13 sites across Cameroon, Nigeria, Uganda, Texas, and Rhode Island in predicting complications like PPH, c-section, and eclampsia. We have demonstrated a higher accuracy, F1, and AUC ROC metrics than all other externally validated gold-standard models/judgments used in clinical practice. 

Of existing models, we address the following limitations of the status quo: 

1) Current models are trained on low number of patients (<10-50k): 

We trained our models on >10M patients

2) Current models have a low number of "positive samples" to train their models on. ~1% of patients in the data need a transfusion, which is really bad for training models, causing poor true positive detection rates:

We used proprietary models, hyperparameters, data augmentation/feature engineering techniques, and weight scaling algorithms, which took ~2 years to develop and are state-of-the-art and utility patent-pending.

3) Current models can't be used until later in the pregnancy (when it's too late/the mother has already developed complications of pregnancy) and require expensive/invasive blood tests: 

Our models can be used *at the point of care*. We primarily use clinical history/demographic information (ex: number of previous pregnancies, smoking status, education-level, age, etc), most of which is available and can be used for prediction as early as the first trimester! This makes it accessible to developing countries and low-resource rural regions. 

Elythea demo:

We serve moms in developing countries and rural regions. Currently, these moms face the highest complication burden and constitute 99% of the global maternal mortality. 

These moms come from widely different backgrounds -- ranging all the way from unplanned teenage moms to moms of advanced age, they span different education levels, different income statuses, and different levels of familial/spousal support. 

Yet, they all share multiple unfortunate and inequitable burdens -- they typically live far away from healthcare facilities. It is a prohibitive barrier to travel all the way to the nearest pregnancy care facility. There are typically local clinics with midwives that staff these clinics, but moms that have complications (like hemorrhage or eclampsia) must be transported (while they are complicating) to advanced facilities (typically tertiary care clinics closer to the main cities). 

Because they are unaware of their risk status, many high-risk moms ignore red-flag symptoms (that they have not been counseled to watch out for) because of the potential financial and logistical burden that traveling to the nearest hospital imposes. Many high-risk moms also go to their local community clinics expecting a normal pregnancy, and have precious hours wasted while they suffer through life-threatening complications like hemorrhaging or eclampsia as they are transported to a tertiary care facility with the resources/doctors to actually take care of them. 

Moms at risk of hypertensive disorders like eclampsia have defined prophylactic antihypertensive medication, exercise regimen, and diet modification,  that they can undergo to drastically reduce their risk of their complication (by 80%!). In potentially life-threatening cases, earlier induction of labor can be scheduled to optimize the likelihood of the mom and baby surviving. Currently, because doctors have poor accuracy in predicting these complications, these moms have to wait until they have their complications and must wait to be treated, risking death.

Elythea provides promise for these moms to live through their complications (and even prevent their complications in many cases). Given that >80% of Africans own mobile phones (UN 2016), moms in these regions are able to input their basic clinical history and demographic information to see if they are at risk of complications. This gives moms a tangible reason to get advanced medical consult, initiate prophylactic diet/exercise/medication, schedule deliveries at better-resourced tertiary care facilities, and allows moms to get better clinical consulting, allowing them to watch out for red-flag symtoms better.

Doctors/midwives in these low resource regions (>95% of which own a mobile device), will be able to prescribe better treatment, have improved clinical coordination, and can know which high risk patients to schedule their limited experienced attendings for.

My mother had a bleeding complication following a cancer-related hysterectomy--this opened my eyes to the field of hemorrhage and prompted us to conduct our user interviews with 80 obstetric providers across 5 countries (4 of which were African countries). We spoke to doctors that worked in different socioeconomic strata of Africa -- ranging from doctors in the developed capital of Lagos, Nigera to doctors working in rural Uganda. We made a sincere effort to speak to as many African providers as possible to understand what complications were their biggest concern, where the true pain lay, what resource limitations they were working with, what the most important criteria in a predictive model would be, and what cultural considerations to integrate. 

Melissa Bime is our key partner and trusted advisor. She is a former Cameroonian nurse who grew up in Cameroon herself, being intimately acquainted with the African healthcare ecosystem and its structural/cultural environment. She founded a YC-backed clinical research startup initially focusing on blood transfusions/blood bank infrastructure working with African hospitals (gaining deep knowledge of African medical systems). We were able to leverage her existing network to conduct our African trials, coordinating patient recruitment/ethics, and PI recruitment. 

Reetam (Founder and CEO) got into medical school at Brown when he was 17 and was studying to be an OBGYN, but dropped out to focus on Elythea full time. His specific research emphasis was developing models to detect hard-to-predict outcomes in women's health (from recurrent cancer to postoperative mortality). He has authored >15 accepted medical publications, including 1st author papers accepted in multiple Nature Journals. 

Prior to Elythea, Reetam previously founded a global biology education company, which has scaled to 10,000+ students across 30+ countries, and has special consultative status with the UN. With this nonprofit, he developed a novel model to reach non-English speaking students in rural African regions previously unreachable by Western nonprofits through a grassroots movement, by working with student communities in the capital to reach out and teach to students in rural villages. He has deep experience working with local African communities and expanding to rural African regions.

Our MVP platform, use time, variables included, UI, etc have all been screened across the dozens of African providers that we have been able to interview. We have incorporated cultural/logistical changes they have requested and are making an active effort to work with these communities. 

We just began a randomized control trial spanning >1,500 moms and dozens of African providers across 4 African countries. We are recruiting currently, >300 African moms have been through our platform. We have active  relationships with African obstetric PI's who are excited and willing to help inform the implementation of our platform. 

We are also working with rural clinics in Bihar to deliver our predictive models to screen at-risk pregnant patients. We are working with the rural patients, doctors, and clinic operators to develop and integrate our solution, ground up and fully informed by the people directly impacted by our platform. 

In General: 

The majority of locations (>70%) use clinical judgment and developed settings use high-low rubric scores with <50% accuracy and 0.52 AUC. We are able to predict hemorrhage with 78% accuracy and 3x higher sensitivity. 

Competitor Companies are in 2 Buckets:

1) Physical Devices:

Ex: NUVO and the Oli Device. 

They use a physical monitoring product moms must wear to record pregnancy data and try to predict complications based on physiologic data. These are expensive, inaccessible in low-resource/rural regions, and require long-time use/physical wearing of device. 

2) Blood-Based Biomarkers:

Ex: Mirvie and Sera Prognostics.

 Use RNA-sequencing data to predict major complications of pregnancy--it is extremely useful but requires invasive testing, is expensive, takes 1-2 weeks to get results, and is completely inaccessible and unaffordable to rural regions where >95% of the hemorrhage/eclampsia burden lies.

Here's why that's bad:

Patients are reluctant to use invasive tests and pay the high bills; there is very low clinical utility to predict at late pregnancy stages (at which point it is too late for the doctors to clinically intervene/prevent complications, especially in low-resource settings). 

Here's how we address that:

We can make our predictions as early as the first trimester, only take 60 seconds to use, can be used in any location with mobile phones (>85% of patients in developing countries), and require NO physical/blood tests. Our SaaS model lets us scale rapidly without having to process blood draws, and lets us give risk scores in <1 second versus 1-4 weeks like competitors. 

Any venture using AI will hit the class imbalance issue: low positives/low proportion of minority patients will lead to poor AUC/accuracy. If only 1% of your patients bleed, your models will erroneously predict "Healthy" for most patients. If <10% of your training data is from black moms, your models will fundamentally be biased against minorities, which is unacceptable from a lens of health equity. 

Our propietary models/weights/hyperparameters/data-augmentations and our hyperparameter weight-scaling algorithms are our advantage. By being able to expand the model's distribution, we drastically improve how our model predicts for hemorrhaging moms and minorities.

Here's how we will change the market:

Right now, there isn't a huge emphasis on "point-of-care" predictive models. Most need late-stage variables that are inaccessible. We want to change that. As we gain market adoption, we want to sway the market to follow suit and make their diagnostic/predictive devices emphasize earlier detection/diagnosis to actually allow doctors/patients to take measures to intervene/prevent complications. 

We also want to push the predictive tech market to use generative AI to augment models. As medical care gets increasingly advanced, we will hit a “diminishing positives” issue, where lower rates of serious complications make it increasingly difficult for models to predict these complications. We want to pave the way for all models to implement algorithms assigning higher weighting to low-representation outcome variables and minority patient data, truly and algorithmically promoting health equity. 

Elythea's purpose is to reduce preventable maternal mortality (a noncommunicable form of premature mortality) by catching these life-threatening complications sooner and allowing for earlier intervention. 

For SGD 3, Elythea is able to promote good health and well-being at the most critical time in a mother's life: her pregnancy. It's no secret that maternal mortality is on the rise and devastates a family when it occurs. Life-threatening pregnancy complications (that were avoidable, yet still not caught until the mom experienced severe complications) can lead to life-long chronic complications/morbidity that impairs a mother's health, and her child and family's health and well being as a result. 

By empowering moms to know that they are at risk of complications before they happen, informing them to be able to see a doctor before their complication becomes life-threatening, and being able to prevent 2/3 severe maternal complications and up to 80% of all maternal mortalities is the greatest fulfillment of being able to promote healthy lives among one of the most vulnerable populations. 

Elythea’s algorithm is trained from >15M patients, uniquely sourced from multiple sources, including the CDC, as well as proprietary surgical registries inaccessible commercially. Reetam, the Elythea founder had access to through his surgical research affiliation at Brown. Elythea also has unique data from mothers participating in their clinical trials from across the globe. Building the algorithm involved a non-intuitive mix of unique model weights, model architecture, data augmentation, weight scaling algorithms, hyperparameters, and optimization techniques that took Reetam the better part of 2 years and his medical expertise to beat the current gold standard.

• Data Protection: Elythea adheres to strict data protection and privacy regulations, safeguarding all patient information with robust encryption and access controls. We have signed BAAs with Google and AWS to ensure that the backend is HIPAA compliant and we are going through compliance software to ensure that our infrastructure is compliant with the highest standards of patient safety. 
• Anonymization: Patient data is anonymized to protect individual identities, all data we collect is data that authorities like the CDC release publicly -- showing that we are in line with the gold standard for de-identification. Our models don't need and therefore, don't collect identifying info like name, address, zipcode, etc, so 100% of the information collected is deidentified. 
• Patient Involvement via Consent: We prioritize informed consent and patient involvement in the data-sharing process. Patients have the option to consent to data usage for predictive purposes, ensuring that their rights and autonomy are respected. 100% of patients in our studies have given and signed for informed consent. 
• Clinical Decision Support: Elythea is not predictive, we do not replace doctors and we do not override clinical judgment. We augment/support clinical decisionmaking by identifying complications before doctors can identify them and provide recommended interventions to ease provider burnout, enhance clinical management, and improve outcomes to ensure ethical use. 
• We are conducting penetration tests and model inversion attacks to ensure that the platform is robust and is not compromiseable. 

Key metrics: number of moms reached, number of hospitals using Elythea, number of complications accurately diagnosed, number of preventable deaths avoided.

1-Year Goals:

Within 1 year we want to conclude our clinical trials, having hard, statistically valid evidence demonstrating the statistically significant decrease in costs, death, and maternal morbidity from Elythea usage.

We have already finished up prospective trials demonstrating that Elythea models have higher accuracy, AUC ROC, and recall metrics than existing methods and have funded and submitted ethics clearance for our randomized control trial. We already have established partnerships with the nurses and PI's conducting the trials, and have the infrastructure in place once we get approval within the next month. 

Based on the contracts we have signed, within the next year,  we will reach a total of >100,000 mothers across the world, and be able to catch complications for 25,000+ moms, preventing 10,000+ avoidable deaths/complications. 

5-Year Goals:

In 5 years, we want to reach >1,000,000 pregnant moms globally, sign on 100 obstetric facilities in the US and 250 obstetric hospitals across African, South American, and Asian countries to use our platform, accurately predict complications for 500,000+ moms, and prevent 100,000+ avoidable complications/deaths.

We hope to have our prediction accuracy be >90%+ with the incoming data and advanced deep learning augmentation that our models have been fine-tuned under once we collect tens of thousands more data points from African patients. All this collected data will be proprietary and will directly go toward helping us tune our models to perform equitably and predict optimally for an African patient base. 

Currently, no such openly accessible large-scale databases exist for obstetric African patient cohorts to train ML models upon specifically tracking hemorrhage/eclampsia.  Our vision is to source hundreds of thousands of African data points, be able to understand which populations are highest risk and why, understand the best culturally-informed intervention methods to reduce morbidity/mortality, and use this data to drive positive change by having models best equipped to detect for the populations of moms that need it the most. We hope to be uniquely situated to be able to do that in 5 years!

Full time: 1, Advisors: 5, contractors: 20

2 years

Our team is diverse -- we span BIPOC, genderqueer/LGBTQIA folks, and native African women who have grown up/worked in the African healthcare system. Our founder's family came from rural India and are personally acquainted with the healthcare challenges and maternal challenges that take place in these regions.

We took conscious action to include African women with healthcare experience to make sure we amplified their voices first and foremost and integrated their suggestions/perspectives deeply into the platform.

We want to include more women who have grown up in rural regions and have personally gone through pregnancy complications like hemorrhage, who can give personal insight into what the problem looks like from the mom's perspective. We also hope to add team members who have worked in digital health startups operating in developing countries and people with a cultural/anthropologic background who bring the business network and diverse perspective to complement our technical team. 

How we promote diversity:

1) Clinical judgment has been shown, time and time again, to have biases against historically underrepresented women, immigrants, and LGBTQIA+ folks. By having objective, ML-predicated models (which fit complex statistical equations to millions of patients' worth of data), we get objective metrics, agnostic to any racial biases, to make predictions off of. This helps to combat racial/systemic prejudices doctors may have. It's no secret that black women are overlooked by medical professionals -- we hope to provide objective means to amplify their voices. 

2) Women in rural/developing countries have a 6-fold higher chance of mortality than women in developing countries when giving birth. This is because they lack the advanced healthcare facilities/technology to be able to predict adverse outcomes. Our technology requires NO lab tests, genomic tests, and doesn't even require the woman to be at a late stage of pregnancy. It can be used at the point of care, anywhere, for cheap. 

3) The biggest reason why current models have biases against minorities/LGBTQIA+ folks is because there is limited training data. If less than 0.5% of your patients are transgender, if only <10% of your patients are black women, etc then it's no wonder why current models will perform poorly for these types of patients. Our generative neural network generates synthetic patient records to provide more training data to racial minorities and LGBTQIA+ folks, which have boosted our models' performances for historically underrepresented populations. 

4) We are working with African nurses, midwives, and doctors who truly and genuinely understand the space. They grew up in African cities themselves, have been working in the healthcare systems for decades, and are the best individuals to understand the intricate cultural dynamics/considerations. As we expand our trials, our team/healthcare pool expands, growing the number of input points we receive to make sure that the platform is best tuned for the people who will benefit most from it. 

1. Team Organization:

• Elythea has a dedicated and multidisciplinary team, including AI and machine learning experts, healthcare professionals, data scientists, and African clinical trial experts. 
• The team is organized into specialized units focusing on AI model development, data management, user interface design, clincial trials and engagement with key stakeholders.

2. Stakeholder Engagement:

• Healthcare Providers: We actively engage with obstetricians, our founding advisor is an obstetric attending at Beth Israel from Harvard Medical School who is helping us conduct our clinical trials at Beth Israel.
• We are working with our African clinical trial partners to expand into African healthcare ecosystems, since the hospitals we worked with for our trials will be best positioned to understand the cost savings and outcomes improvement. 
• One of our key advisors is a health equity director for a managed care organization that manages a state Medicaid program in the US, this will be key for securing a large pilot and coordinating expansion for millions of low income women across the US!
• We have signed contracts to cover >150,000 pregnant moms across the world, the outcomes and improvements from these pilots will be the key to expanding beyond to larger government systems and delivering ROI. 
• Implementing Partners: We collaborate with healthcare institutions, hospitals, and clinics to implement Elythea within their systems. This involves close coordination to ensure seamless integration and user training once we have efficacy data from our pilots/trials.

4. Access to Tools:

• Elythea ensures access to the necessary tools and resources for successful development and implementation. This includes access to state-of-the-art AI technologies, secure data storage infrastructure, and tools for continuous monitoring of model performance -- as YCombinator alumni, we have access to >$200k credits for GCP and AWS infrastructure to build and host our models, all the models were made natively and in supercomputers our team already has access to, the clinical trial data and patients are all underway, which we have been able to access with our clinical research partners. All tools needed have already been acquired to build and validate our models, the only thing left is accelerating our growth!

In the past 3 months of commercializing, in the US, we have signed a contract and 2 LOIs spanning a $1.2M ACV spanning 55,000 patients. We also signed a contract in rural India spanning a $1.2M ECV for the pilot (til end of 2024) which automatically converts to $6M contract volume. 

We are a software as a service -- in hospitals with an EHR, we can sync into the EHR and provide instantaneous predictions for each patient. In settings without an EHR, we are a mobile app accessible by the majority of healthcare staff and moms. 

We expect the costs to be minimal -- it costs us <$1 per patient, while we charge hospitals $25-50+ per mom. Our revenue will be enough to sustain us. 

Additional go to market strategy:

We have initiated the largest randomized control trial ever conducted in this space across 1,500+ moms across 3 countries to demonstrate a tangible reduction in maternal mortality, costs, and adverse outcomes through publishing our results in a peer reviewed obstetric journal. We would then use these published results as ethos to then sell to hospital administrators and obstetric facilities across the country/world. Peer-reviewed publications are the gold standard way that all currently used models have been scaled and marketed, so our next steps forward have deep precedent.

We will start by converting our clinical trial hospitals, and will directly market to hospital admin from referrals and at OB conferences.  Elythea scales as a software-as-a-service platform. In developed settings, we sell to hospital administration and seamlessly integrate with all EHR systems. In developing settings, we are offered as a mobile app (accessible to >95% of African doctors); distribution through android/IOS app stores. 

Obstetric hospitals in the US get "graded" by the Joint Commission (overarching organization governing hospital funding/rules) based on key criteria like maternal mortality, hemorrhage, c-section rates--if hospitals have poor rates, they get funding taken away and if they have good rates, they receive additional funding. Besides these funding incentives, we will also work with the Joint Commission to target the highest-need hospitals with the worst mortality/hemorrhage rates that would maximally benefit from Elythea. This is strategically beneficial, it helps to maximize early market penetration with the hospitals that would need us the most and provides an incentive for their competitor hospital systems to use Elythea to avoid losing patients/funding. 

We can also work with large health plans in the US to charge on a per-member-per-month basis to help reduce complication rates (and thus reduce costs that the health plan incurs for the patient). 

We will employ a similar strategy in developing countries by working with the governments that lose the most money/pay the most for postpartum hemorrhage and maternal mortality. There is precedent for these government systems mandating the use of certain procedures/tools that have been shown to reduce mortality/costs, which we hope to leverage for Elythea. 

Currently, $12k/month operational costs

Projected burn next year: $16k/month operational costs

Costs include salaries, clinical trials, data infrastructure, and computational capacity

We are at an inflection point and we need Cure's capital to be able to overcome our biggest barrier: we request $86,000.

We use inclusive models that can function at the point-of-care to solve the most pressing issue: moms dying from preventable causes. We have finished up conducting clinical trials across 10+ sites in Nigeria and Cameroon proving that our models work with 3x greater high-risk sensitivity when compared to current risk assessments. 

The one and only burden of proof required by hospital/governments before adopting our platform would be a randomized clinical trial demonstrating that Elythea usage has a statistically significant impact on hospital outcomes. Peer-reviewed publications are the gold standard way that all currently used models have been scaled and marketed, so our next steps forward have deep precedent. 

Currently, we are $60,000 away from fully financing our randomized control trial to show that hospitals that use Elythea have lower rates of mortality/costs/complication rates than hospitals that do not use Elythea. We would then use these published results as ethos to sell to hospital administrators and obstetric facilities across the world. We have already signed multiple contracts and LOIs showing that hospital systems around the world desperately want this -- the commercial need is clear, they just want clinical validation before they sign on to use us. These clinical trials pave the way for government systems to adopt us at larger scales, particularly in developing countries. Since our clinical trials span thousands of moms from 4 developing countries, it is the biggest randomized clinical trial (highest rigor clinical trial possible) using AI ever conducted in the maternal health space. 

Funding our trials is the one barrier standing between us and commercial adoption-- The Cure Challenge's prize will help us recruit >1500 moms across 6 sites in Uganda, Ghana, Cameroon, Tanzania and Kenya, and will have the potential to help us reach 100,000+ African moms within the next 2 years, and uniquely allow for intervention that would save >10,000+ moms from life-threatening complications that would have otherwise been missed. 

The money will go directly to: patient recruitment, secure and encrypted data storage, and healthcare providers that will directly monitor and follow at-risk moms over the course of their pregnancy.

We also signed a contract to reach 1 million patients in rural Bihar over the next year, scaling up to 5 million patients by 2028. We are expanding our predictive models beyond pregnancy, and encompassing cancer, stroke, and other severe outcomes that are often missed in rural regions that have no access to healthcare. The funding will be vital for us to support scanning patients, at the point of care, using door-to-door healthcare workers using our advanced models that take in "bare minimum" data. The funding for the computational capacity to scale the models here ($8,000) and for the healthcare workers to visit patients in the villages, door-to-door to catch them before complications occur so that doctors can intervene in the clinics ($18,000) will be vital.

Right now, our biggest needs are capital, guidance/intros to relevant stakeholders in government systems and health plans, and education on how to sell to larger health plans/understanding the complex dynamics of the healthcare system and considerations when scaling AI models in healthcare. 

1. Mentorship: Mentorship from experienced professionals in healthcare and is the #1 way we've been able to connect with major stakeholders (CEOs of one of the biggest health plans in US, medical directors from one of the biggest state Medicaid programs focused on maternal health, etc). We are trying to get experienced advisors who are in the health plan space/related to healthcare entrepreneurship, help us navigate sales cycles, and provide warm intros to relevant stakeholders/potential clients to help expand the patients we are able to impact and generate more validation data for future hospitals/governments. 
2. Networking Opportunities: Networking within the Cure Residency would provide us with access to a broader community of innovators, potential partners, investors, and healthcare providers. These connections would facilitate strategic collaborations and partnerships, further accelerating our impact. Our second biggest way of getting more customers/expanding our reach is networking with healthcare-adjacent startups, speaking to other advisors at networking events, and meeting new, relevant stakeholders at these events. 
3. Seed Funding: Seed funding is crucial for the continued development and refinement of our AI models, the expansion of our solution's reach, and the ability to reach our patients that we have signed contracts to cover.

4. Educational Programming: Educational programming would enable our team to stay updated on the latest trends in AI, healthcare, and regulatory compliance, ensuring that Elythea remains at the cutting edge of maternal healthcare innovation, and also understand the more intricate dynamics of the healthcare system, modify our GTM, and get a clearer understanding of how to scale our models sustainably, since most of our founder's experience has been through academic research.