iGESTO

Hospitalizations due to primary care-sensitive conditions (HPCSC) constitute an important indicator for monitoring the quality of primary healthcare. HPCSC represent avoidable hospitalizations, including uncontrolled diabetes, uncontrolled hypertension, asthma, malnutrition, anemia, epilepsy, bacterial pneumonia and urinary tract infection1. 

Particularly among children in middle-income countries (like Brazil), the proportion of hospital admissions due to primary care-sensitive conditions are between 55-85% of all hospitalizations (in adults these statistics are between 15-35%). Among children in Brazil, 70-90% of HPCSC occur due to infectious causes and these causes are mostly seen in poor communities of Midwest, North and Northeast1. Among other low- and middle-income countries these statistics are superimposable2-4.

Researchers estimate that 30-50% of HPCSC in children / adolescents are associated with inborn errors of immunity (IEI)2. In fact, several genetic variants associated with immune dysregulation (deficiency in interferon production, complement overactivation, etc.) have been described among children, adolescents and young adults with severe infections3-6.

In parallel, IEIs are a heterogeneous group of chronic diseases of the immune system (406 different phenotypes) characterized by recurrent infections, including those caused by opportunistic agents, as well as autoimmunity and atopy7,8. A study conducted by the Immune Deficiency Foundation (IDF) showed that 60-90% of patients were not diagnosed with IEI until adulthood, even with chronic or severe illnesses prior to diagnosis8. The delay in diagnosing IEIs results in higher expenses for the health system and increased morbidity, HPCSC and mortality7. The Jeffrey Modell Foundation launched, in 1994, the 10 Warning Signs for Immunodeficiencies9. However, recent studies have questioned the promotion of these signs as predictors of IEIs and have shown that, despite being widely disseminated, these signs have low specificity and there is little population evidence to support their use10,11. According to international data and those of our group, the family pattern of serious / recurrent infections is sufficient to raise suspicion and treatment in the setting of Primary Health Care.

In this way, identifying family clusters with severe infections can help to identify families most likely to have IEIs. Despite the reduction in mortality from infectious causes in Brazil in the last 3 decades, pneumonia, gastrointestinal infections, sepsis and meningitis are still among the 10 biggest causes of infant mortality, occurring in 4.13 / 1,000 live births12. Although these deaths are also related to lack of access to health in the acute phase of infections, there are ways to minimize the risk of infections with accessible counseling and therapies7,8.

The central problem is the lack of public health mechanisms to identify families at-risk for severe infections and the lack of referral mechanisms to specialists. The faster children with suspected IEI are identified, the faster they will be treated and less expensive treatment will be. 

After deep immersion in the healthcare area, conducting interviews with patients, healthcare workers and stakeholders, Clarity Healthcare understood that relevant outcomes (HPCSC, quality of life, healthcare spending) both among children and adults are influenced by the lack of surveillance mechanisms at risk factor-level. Although children and adults share a common problem, these groups show different predictors for hospitalizations.

To address these problems, Clarity understands the development of surveillance systems for adults and children is an unmet need, but it is necessary to consider different data entry processes and different concepts in terms of health priorities and preferences. 

Thus, Clarity developed PENEIRA:IMUNO (Platform for Early recognition, intervention and tracking iNborn ErROrs of IMMUNity) with two purposes: (i) to improve recognition, locate in the territory and intervene in cases of probable IEI, facilitating the referral to pediatric immunology services or telemedicine care; (ii) integrate a predictive model for death or serious infection, allowing prioritization of surveillance actions on children with a higher risk of death and greater propensity for serious infections. 

In parallel, Clarity is developing an online platform (called iGESTO) to track vulnerabilities among adults, as well as the control of chronic diseases and major risk factors for poor quality of life and hospitalizations.

iGESTO and PENEIRA-IMUNO are an online computational platform that uses a decentralized data collection process and identifies individuals at-risk for hospitalizations due to primary care-sensitive conditions (HPCSC) by using four types of inputs: (i) information given by healthcare professionals directly in an online system (which works as a primary electronic health record [EHR]); (ii) information extracted via a General Data Protection Law (GDPR)-compliant API from other EHRs available in Brazilian market (eSUS, MV, CFM, AGHUse etc); (iii) inputs from patient’s mobile phones, including patient-reported outcomes; (iv) information from official hospitalization records (the SIH/DATASUS database) which allow us to geolocate communities and families with higher occurrence of HPCSC.

In summary, the iGESTO platform is a clinical and administrative decision support tool that connects information from patients, healthcare teams and healthcare managers at different hierarchical levels. It focuses on understanding the perspective of the person who experiences online health services. As a patient-centric platform we aim to design an interface that does not stigmatize any group of users, accommodates a wide range of individual preferences and abilities, that is easy to understand (regardless of the user's experience, knowledge, language skills, or current concentration level), and develop a responsive system in order to accommodate all screens for every kind of devices (tablet, mobile, desktop). To achieve the inclusive communities we aspire to, we commit to design computational interfaces that work for everyone.

From technology perspective, the prototype is based on a Web and Mobile platform, structured with back-end services and front-end friendly interfaces, relational database, distributed computational engines, code object storage service in the cloud infrastructure that offers scalability and code versioning with Github. We apply Agile Methodology for system developing to increase team performance, improve customer satisfaction and increase project versatility. Organizations that have adopted Agile methodologies are able to respond to market dynamics. 

As said, the primary health care data come from healthcare professionals, patients and GDPR-compliant API from existing EHRs. We designed a system with low band requirements that gives healthcare professionals and patients immediate compensation: ready, structured and filtered information from medical records. 

The system provides dashboards and plain text with most important medical data from individual patients for healthcare professionals, optimizing time during consultation, in parallel with access to predictions for the risk of hospitalization, the risk of rare (like IEI) and common illnesses by using machine learning algorithms developed by our team. In addition, the health team and managers have access to dashboards with population health diagnoses, making it possible to identify patients with uncontrolled risk factors for hospitalization and optimize care. This approach improves the efficiency and quality of care, reducing claims and generating savings for companies, healthcare systems, care providers and patients.

The GDPR-compliant API from existing EHRs is the only part of the solution requiring higher band needs as we use natural language processing (NLP using BioBERTpt - Bidirectional Encoder Representation from Transformers-based models on Portuguese clinical and biomedical corpora) for capturing unstructured health data and converting into structured data. This feature permits automatic filling of most iGESTO EHR data. Although this mechanism is not obligatory, it further optimizes time during consultations.

The PENEIRA:IMUNO suit uses the same resources as iGESTO and is specifically dedicated to detect the risk of severe infectious disease among children and young adults (<20 years-old). The difference compared to iGESTO is that the input variables are specific to the pediatric population. 

The platform shares educational data and makes information related to individual and family risks available for patients in communities, healthcare teams and healthcare managers, empowering all actors in the healthcare chain.

We primarily target patients in communities and the ones who receive care. Our main goal is to empower these subjects with self-information, predictions and education. We also target to improve patient communication with the healthcare teams, and, at the end, we also expect to improve direct care by allowing stakeholders and healthcare teams to access population summaries and dashboards. 

This patient-centric design targets to solve a major unmet medical need: access to high-quality information, access to specialists and the identification of hidden/unknown medical conditions (such as inborn errors of immunity [IEI] and other chronic conditions that increase the risk of hospitalizations).

In parallel, iGESTO and PENEIRA:IMUNO also facilitate more specific interventions in communities by healthcare professionals and managers. As the technology is able to recognize and locate in the territory (geolocation) cases of probable IEI, other rare disorders (such as amyloidosis, Brugada syndrome etc) and uncontrolled chronic conditions (such as hypertension, diabetes, hypercholesterolemia), it also resolves a major need for these actors. Today most of these interventions need task forces that manually collect data from individual EHRs (frequently doing so by non-GDPR compliant methods), which is costly and time consuming. Finally, the use of accessible risk prediction tools is not only a mechanism for surveillance and prevention but also a mechanism that makes the demands for hospital beds and medium and high complexity resources more foreseeable.

In that sense, we also consider iGESTO supplies unmet needs of (i) medical insurance companies (who usually do not have access to individual EHRs), (ii) companies that want to improve workers' quality of life and reduce absenteeism.

With a long-term background as a Family physician, Clarity’s Executive Director (L Sérgio Carvalho) has a deep connection with primary care healthcare workers and patients. During the last 12 years, Carvalho has maintained a close connection with poor communities around the Federal District of Brazil, in the cities of Taguatinga, Gama, Sobradinho and Planaltina. Since then, he actively participates in professional conferences / workshops and patient-professional (“social involvement”) councils.

With yearly interviews in the last 5 years with primary healthcare workers, we identified a high heterogeneity in the incidence of clinical outcomes (hospitalizations due to primary care-sensitive conditions [HPCSC]) across different communities (sometimes neighbor communities showed large disparities). However, managers and stakeholders in the public sector have limited resources to track outcomes and to propose timely interventions. It means that several cases of preventable strokes, dialysis and severe infections were not avoided due to the lack of ability to follow the control of chronic diseases, associated with the inability to assess clinical outcomes at the individual-level. These needs motivated in 2019 a task force in Sobradinho (DF) to identify individuals at risk for HPCSC. The task force was successful in its short-term aims, but demanded a lot of manual work and had only a 6-months follow-up period. The program was discontinued during the COVID-19 pandemic. 

As a computational specialist, Marta D. Fernandez was technically responsible for the Orienta Covid Project which guided and assisted the Brazilian population in times of COVID-19 pandemic. Several cities (from all regions) were part of the project and incorporated the practice of call centers for health inquiries with qualified specialists to provide correct information about the disease, creating remote channels to make calls such as telephones, video calls and applications that do not require the crowding of people at health units. During tele orientation, health professionals collected patient data, their complaints and doubts to determine, through the classification of symptoms, what would be the guidelines to be passed. The call center performed by health professionals during the project was essential to answer patients' doubts as well as being able to better guide them on how to proceed in suspected or confirmed cases of the new coronavirus and thus contribute to reducing the demand for urgent and emergency services. The data were analyzed and the results were used for decision making. 

In the pandemic scenario, Marta Fernandez led another technological project involving health professionals working at the Pediatrics Outpatient Clinic of Hospital de Clínicas da Faculdade Estadual de Campinas - Unicamp, who organized a system of Teleconsultations carried out by pediatric doctors. This communication channel aimed to respond to the demand of patients who were looking for information, as well as actively searching for patients who had missed the appointment due to closing activities. During this period, approximately 4,000 consultations/month previously scheduled at the Pediatrics Outpatient Clinic were no longer performed. This project allowed the continuity of care for patients with complex clinical conditions and in need of more intense attention.

In parallel, during the COVID-19 pandemic until recent months, we continuously heard from patients that several families were affected by severe COVID and non-COVID infections. Typically, when severe infections occurred in some families, 2-3 or more members of the same family had severe infections too. Looking back at these family histories, we discovered a propensity for severe infections and designed an intervention with pediatric immunologists (Adriana Riccetto and Marcos Nolasco). We started collecting patient data after an IRB (Institutional Ethics Review Board) approval and designed the seed of the PENEIRA-IMMUNE system. The close contact with the communities in the “social involvement” councils (Conselhos de Saúde) crucially facilitated the system review processes, permitting monthly inputs collected data from patients. As stakeholders are also part of these “social involvement” councils, it is possible to use this mechanism (which is obligatory in every macroregion [20.000-50.000 inhabitants] in Brazilian public healthcare system) to draw a convergence of agendas, share scientific results, get inputs from different perspectives and design a meaningful application to most actors involved in disease prevention.

As we develop our solution, we usually share the advances in these “social involvement” councils. The most exciting moment came up when we showed the regional map of Sobradinho with data on HPCSC concentration among a few neighborhoods. From this moment on, it was clear that both patients and managers need the information we provide and want to be part of it.

Knowing the medical history carefully, seeing the patient as a person, recognizing how much the patient knows or wants to know about their disease, the possibility of treatment and chances of cure, tend to favor the patient's trust and engagement. The iGESTO Engagement Plan involves: 

• Implementation of a Care Plan in common agreement between the physician and the patient; 

• Patient Evolution Communication Plan in order to stimulate the understanding of their goals and desires; 

• Communication of Results Plan in a transparent manner and aimed at improving care; 

• Offer structured and organized knowledge to the patient, thus collaborating with their education.

• We understand that the better informed, the more chances of engagement; 

• Feedback Plan to patients with the objective of seeking to improve care according to their demands. 

• We also offer Mobile and Online Tools to optimize services and provide convenience - anywhere, always available and intuitive.

This Challenge may provide ways to overcome financial and market barriers. We believe that if our products are associated with institutions such as MIT and Bill & Melinda Gates Foundation, we should gain visibility and attract more potential investors.

Besides several cultural barriers, low- and middle-income countries' health care markets over the world are complex, some of them highly regulated, fragmented and uneven, with major discrepancies among competitors in terms of infrastructure, technology, organization, training, processes and human resources. This challenge may offer proper conditions to address those barriers. 

A close look at the local scenario shows that Brazil has made huge strides towards building a public health system, enshrining provision as a constitutional right, yet the scale of opportunities and challenges continues to provide a complex balance. The Brazilian constitutional right to healthcare has provided a beacon for the benefits of universal health coverage around the world. Yet despite a promising outlook, building on this is becoming more difficult, and the health system now needs to embrace the power of data and digital health services to drive up quality and serve the most remote and poor parts of the country. 

The big opportunities for Brazilian healthcare are to harness the power of its data to drive quality and to harness the potential of digital services to reach remote and poor communities. Alongside systematic collection and analysis of data, digital tools need to be brought to primary care and the family health programme. Family doctors and the local health teams should have all of a family's information in front of them on digital devices. This would enable them to provide prescriptions and book appointments, greatly improving the chances of addressing health problems early. Brazil now needs to use smart phone technology to provide access to care, particularly among poor and remote communities, and to encourage people to take greater responsibility for their own health. With WhatsApp already a routine way of communicating for somewhere in the region of two-thirds of Brazilians, the potential is obvious. 

This project is a great opportunity to address this scenario and improve digital health services in the Brazilian primary health sector. Beside that, it encourages people to manage their own health.

We believe our solution creates a paradigm shift in the delivery of care and in the patient's attitude. We think iGESTO forces a change in the relationship between patients and health services, with more empowered patients and more proactive health teams to mitigate health problems. This shift in the key actors of the health-disease process will bring a positive change in the attitude from others in the healthcare sector: we may truly force stakeholders and practitioners to employ primary and secondary prevention strategies to anticipate diseases and anticipate complications of diseases. 

The iGESTO also generates useful data for public health studies, epidemiology and artificial intelligence. Our project essentially creates multiple gateway to information, not limited to manual data access from electronic medical records, as it allows the collection of information from semi-structured or structured questionnaires, extraction of information from photos in any format or pdf, and other resources.

The scalability of iGESTO is actually one of the main ingredients to justify its highly innovative potential. There is currently no computer system available in Latin America that performs tasks such as: (i) natural language processing from free text in electronic medical records; (ii) provision of tools to track rare, neglected diseases and mental health disorders; (iii) visualization of the history of chronic disease control based on information provided by the patient and the health team; (iv) capture of patient-reported outcomes; (v) capture of outcomes of interest (examples: death, heart attack, hospitalizations for conditions sensitive to primary care) from existing databases; (v) offering visualization of data on the control of chronic diseases; (vi) predictions for the risk of relevant clinical outcomes.

The iGESTO project also includes a scalar business model, which will include a program of financial incentives, inclusion of a marketplace as a new service, and also scale in terms of offering services to different customer sectors. After primarily focusing on Government (B2G) and Organizations (B2B), our plan is to offer services to individual consumers or stakeholders (B2C) and even the offer of a digitally-based healthcare insurance.

By launching iGESTO / PENEIRA:IMUNO platform for the next year we expect to:

• Track down a population of 25.000 children and their families

• Enable early referral of individuals with suspected innate errors of immunity (IEI) to specialists and diagnosing at least 120 individuals with these conditions.

• Reduce in 20% the rate of hospitalizations due to primary care sensitive conditions among children

• Collect information reported by patients in a non-face-to-face manner of at least 50% of the engaged population

• Collect clinical outcomes and geolocation of suspected cases of 100% of the covered population

In five years we expect to:

• Track down a population of 1 million children and their families

• Enable early referral of individuals with suspected innate errors of immunity (IEI) to specialists and diagnosing at least 5000 individuals with these conditions.

• Reduce in 80% the rate of hospitalizations due to primary care sensitive conditions among children

• Reduce in 30% the rate of hospitalizations due to primary care sensitive conditions among adults

• Radically change the relationship between patients and healthcare services, with more empowered patients and more proactive healthcare staff

• Early identify other rare, neglected diseases and mental health disorders

• Enable early interventions in people with chronic diseases, rare diseases, neglected diseases and mental health disorders

• Enhance the Natural Language Processing capabilities available today

• Maintain scientific talents in the country

• Reinvestment in R&D and Research in Artificial Intelligence

In PENEIRA:IMUNO, our primary indicator is the rate of hospitalizations due to primary care sensitive conditions among children and adults. And we also measure as secondary outcomes: (i) proportion of children with the diagnosis of malnutrition, (ii) proportion of children with anemia, (iii) proportion of children with chronic conditions, (iv) local infant and child mortality, (v) proportion of children using prescription medications, (vi) healthcare utilization among children.

In iGESTO we also also measure: (i) the number of individuals with new diagnoses of innate errors of immunity; (ii) mean and standard deviation of systolic blood pressure for the last 4 weeks in the population; (iii) mean and standard deviation of diastolic blood pressure for the last 4 weeks in the population; (iv) number of new diagnoses of arterial hypertension and total number of people with hypertension in the population; (v) mean and standard deviation of blood glucose for the last 4 weeks in the population; (vi) number of new diabetes diagnoses and total number of people with diabetes in the population; (vii) mean and standard deviation of LDL cholesterol over the last 12 weeks in the population; (viii) mean weight trend in the last 6 months in the population; (ix) number of individuals with creatinine > 1.5 mg/dL in the population; (x) number of individuals with urinary albumin/creatinine ratio > 30 mg/g; (xi) number of individuals with stage IV and V chronic kidney disease; (xii) number of individuals with chronic kidney disease on dialysis; (xiii) number of individuals diagnosed with congestive heart failure receiving adequate medical treatment; (xiv) number of individuals diagnosed with liver cirrhosis or chronic liver failure; (xv) number of individuals with chronic obstructive pulmonary disease receiving adequate medical treatment; (xvi) number of individuals hospitalized for primary care sensitive conditions; (xvii) mean value of the EQ5D questionnaire in the population; (xviii) mean value of the EQ5D questionnaire among diabetics in the population; (xix) mean value of the EQ5D questionnaire among people with chronic diseases in the population, (xx) proportion of women with complete prenatal assessments.

We are also able to assess the impact of major health determinants on communities. Every 4-6 months, we generate a report on the impact of gender, ethnicity, education, access to adequate nutrients, access to clear water, employment and income level on primary and secondary outcomes. The aim of these auxiliary reports is to promote an agenda to build actions around equity and sustainability.

The iGESTO / PENEIRA:IMUNO platform is a clinical and administrative decision support tool that connects information from patients, healthcare teams and healthcare managers at different hierarchical levels using a decentralized data collection process. 

The prototype is based on a Web and Mobile platform, structured with microservices Back-end in JavaScript/Python/R languages, and Front-end interfaces developed with Angular framework. MySQL as a relational database. Firebase as Backend-as-a-Service (BaaS) app development platform that provides hosted backend services such as a realtime database, authentication, crash reporting, machine learning, remote configuration, and hosting for static files. Code object storage in the cloud service that offers scalability and code versioning management with Github. We also implement BigQuery, a scalable, distributed engine and managed data warehouse for data analyses with built-in features like machine learning and geospatial analytics. We apply Agile Methodology for system development based on life cycles to meet the specifications requirements of User Cases.

Information security in systems computing that involves health data is an essential requirement to be met. It was defined policies, processes and tools to be implemented. In the Policies Layer, it involved definition of access grants and data modification rules, design of the Recovery, Disasters and Continuity Plan; developing the strategic plan to safeguard the data. In the People Layer: it was defined as training to prevent phishing, social engineering, and other attacks targeting users and guidance on choosing secure passwords. In the Process Layer, implementation of deletion policy of patient data and unnecessary information. Through the implementation of patches suggested by manufacturers, we can keep software on computer equipment up to date to minimize vulnerabilities. Data security relies on encryption and follows the Brazilian protocols for the protection of personal and sensitive data, with availability, data deletion, back-up and inviolability guaranteed in accordance with the laws that govern medical records. 

From an Artificial Intelligence technology perspective, we apply clustering techniques (K-means, K-Medoids, NMF, DBSCAN, GMM) on longitudinal data. Depending on some criteria, such as data quality, we use dimensionality reduction (such as Principal Component Analysis with and without functions kernel or Canon Correlation Analysis). To anomaly detection, Jackknifing and Mahalanobis distance comparison is applied. To dimension elimination, Sobol Sensitivity or Tree Ensemble are possible methods. Considering imputation to fill missing data, we apply different types of Regression techniques. 

Concerning Machine Learning classification methods: because they are frequently used methods for calculating individual health risk, the following parametric and non-parametric methods will be used for the classification and prediction of risks of future hospitalization: Ordinary Linear Regressions (with different regularizers) and Bayesian, Poisson regression, conditional independence trees [CTREE], CART, M5, Bagged and Boosted Trees, Gaussian Processes, Artificial Neural Network, methods Bayesians of association of latent variables and non-stationary processes of Poisson and exponentially distributed.

Considering Long-term predictive models – survival with competing risks: for long term outcomes term, we will use the following survival algorithms with competitive risks: risk model Cause-specific Cox proportionals (CS-Cox), proportional under-distribution risk model Fine-Gray (Fine-Gray), Deep Multitasking Gaussian Process (DMGP) and DeepHit. CS-Cox and the Fine-Gray assume linear proportional risks, the DMGP assumes that the underlying stochastic process follows a Gaussian process and DeepHit employs a network architecture that makes no assumptions about the relationship between predictors and outcomes.

iGESTO / PENEIRA-IMUNO uses a decentralized data collection process with four types of inputs: (i) information given by healthcare professionals directly in an online system (which works as a primary electronic health record [EHR]); (ii) information extracted via a General Data Protection Law (GDPR)-compliant API from other EHRs available in Brazilian market (eSUS, MV, CFM, AGHUse etc); (iii) inputs from patient’s mobile phones, including patient-reported outcomes; (iv) information from official hospitalization records (the SIH/DATASUS database) which allow us to geolocate communities and families with higher occurrence of HPCSC.

Patients get the following incentives to collect data: (i) access to educational materials tailored to their health condition, (ii) access to resources related to risk prediction for hospitalizations, (iii) access to care goals, complying or not with the medical guidelines, (iv) social and political inclusion driven by periodic reports and open discussions of the social determinants of health in their respective communities.

Healthcare workers also receive the following incentives: (i) the system provides dashboards and plain text with the most important medical data from individual patients, optimizing time during consultation; (ii) access to predictions for the risk of hospitalization, the risk of rare (like IEI) and common illnesses by using machine learning algorithms; (iii)  access to dashboards with population health diagnoses, making it possible to identify patients with uncontrolled risk factors for hospitalization and optimize care.

The iGESTO platform is all about inclusion and equity. The platform was created to assess disparities and targets to mitigate them: (i) the system has a decentralized data collection process, which contributes to increasing the chances of identifying the most vulnerable groups; (ii) iGESTO is designed to stimulate the creation of policies and interventions to target vulnerable individuals and to promote equity; (iii) the platform elicits education and health literacy for all; (iv) periodic reports related to social determinants of health are also made public, which promotes social and political empowerment of communities.

Clarity Health's Founders are composed of a woman from the Northeast, and a black man from a modest family from the Brazilian Midwest, in both cases political minorities. Thus, it is easy to understand how much the company is committed to diversity, inclusion and equity.

The iGESTO project includes a scalar business model in terms of offering new services at each moment and offering services to different customer sectors. We primarily focus on delivering a Web-based platform to Government (B2G) and Organizations (B2B) using a freemium plan and service subscription where the companies or government pay per individual covered. We plan to implement this business model in January-2024. 

In January-2025 we plan to launch new services, including a program of financial incentives and a marketplace to motivate individuals to achieve their care goals. With these new products, our plan is to offer services to individual consumers or stakeholders (B2C). Up to 2026, we plan to launch digital health insurance targeting both B2C and B2B segments.

We designed a staged plan to achieve financial sustainability. As shown above, revenues are expected to exceed expenses in 2025.

Stage 1 (research and MVP development - 2022-2023): estimated cost of R$ 1,900,000, of which 1,500,000 are guaranteed through research and innovation agreements with private companies and financial support from FAPESP, FAPDF, CNPq and Sebrae. We are still searching for other sources to cover the full needs of this project. The financial support we received so far include the hiring of Information Technology (IT) fellows, consultants and infrastructure necessary for research in Artificial Intelligence and product development.

Stage 2 (Year 1 of commercialization - 2024): launch of the MVP focused in B2B and B2G markets. R&D will target the development of the incentive program and healthcare marketplace. We estimate 1-3 service contracts to governments (covering 5.000 lives) and to cover an additional 5,000 lives in B2B contracts. We forecast annual revenue of R$1.66 million and total development cost. and maintenance of the business of R$2.39 million, requiring financing of an additional R$ 730,000.00. Investment capital will be raised through economic subsidy and research grants from FINEP and CNPq.

Stage 3 (Year 2 - 2025): iGESTO 2.0 launch (financial incentives + healthcare marketplace). R&D will focus on the development of the B2C interface. We estimate the iGESTO platform will cover 30.000 lives. Revenue forecast is estimated at R$5.37 million and total development cost. and business maintenance at R$4.96 million. 

Stage 4 (Year 3 - 2026): iGESTO 3.0 launch (B2B and B2C). R&D will prepare the system for internationalization to Portugal and Latin America. We estimate the iGESTO platform will cover 90.000 lives. Revenue forecast is estimated at R$16.09 million and total development cost. and business maintenance at R$9.87 million.

Stage 5 (Year 4 - 2027): iGESTO 3.0 international launch. R&D will prepare the environment for offering a B2B health insurance plan. We estimate the iGESTO platform will cover 180.000 lives. Revenue forecast is estimated at R$32.19 million and total development cost. and business maintenance at R$19.43 million.

Stage 6 (Year 5 - 2028): iGESTO 4.0 launch. R&D will prepare the environment for offering a B2C health insurance plan. We estimate the iGESTO platform will cover 180.000 lives and the health insurance will cover 15.000 lives. Revenue forecast is estimated at R$71.54 million and total development cost. and business maintenance at R$32.16 million.

The iGESTO platform is being developed, tested and implemented in communities with research grants from FAP-DF (number 00193-00001032/2021-17 / 371/2021, R$ 400,000), CNPq (437413/2018-7 and 310718/2021-0), Sebrae (number 29083-111, R$ 116,000), FAPESP (2019/09068-3, R$ 200,000) and investment from the Aramari Apo Institute (0001/2021, R$ 790,000).