Awards

Andrew Maranhão Ventura Dadario of Hospital Israelita Albert Einstein in Brazil will test and evaluate LLMs for their ability to structure and anonymize clinical notes. Such structured notes would promote data interoperability, which would improve patient care, research, and health policies for the diversity of Brazilian public health patients; and establishing standardized evaluation of LLMs for public health would promote their future applications. They will prepare a representative dataset by extracting, processing, and annotating clinical notes, and then test and compare a set of existing LLMs in extracting information from text, including their ability to preserve patient privacy, their applicability to the Portuguese language, and their suitability to primary healthcare in Brazil encompassing equitable performance across patients stratified by gender, race, ethnicity, and age. The best performing LLM will be used to transform free text from clinical notes, and the structured product will be assessed by health managers for accuracy and usefulness.

Hugo Fernandes of Universidade Federal de São Paulo in Brazil will create a university center focused on using AI to support primary healthcare professionals in surveillance and clinical decision making for complex cases of interpersonal violence in vulnerable populations in Brazil. AI systems could help in diagnosis and classification, risk determination, recommendation of interventions, referrals to relevant care providers, socio-emotional support plans, support for perpetrators in avoiding repeated offenses, mandatory notifications, as well as generalization across contexts for improved understanding of patterns of violence. They will aggregate information technology at a campus site, train a Large Language Model (LLM) with appropriate data and create a prototype tool, and gather relevant experts to judge the quality of the tool for its potential contributions to Brazil’s health system in targeting interpersonal violence.

Cecília Claudia Costa Ribeiro of Universidade Federal do Maranhão in Brazil will develop an LLM to help frontline healthcare workers identify risks and take action to prevent NCDs in the first 1000 days of life. There are connections between NCDs in early childhood that are affected by social inequities as well as pregnancy-associated factors. They will apply machine learning algorithms to data for women during and shortly after pregnancy to build risk prediction models of NCDs for children in the BRISA Cohort of São Luís, with predictor variables encompassing socioeconomic, behavioral, and biological stressors. The estimated risks will be input to an LLM that incorporates the best available scientific evidence on each NCD. This LLM, when given data from women patients, can then determine the risk of NCDs for the patient’s children, along with an explanation to help CHWs effectively communicate these risks and make evidence-based recommendations in real-time for prevention of NCDs.

Elisdete Maria Santos de Jesus of Universidade Estadual de Campinas in Brazil will train an LLM to read Brazilian medicine package leaflets and generate accurate, up-to-date summaries of how to use the medicine that is easier to understand for patients who take the medicine and for healthcare professionals who prescribe, dispense, and administer it. They will do a literature review, including articles in English and Portuguese, to better understand the types of information in leaflets and how understandable it is to users of the medicine. Subsequently, medicine leaflet information will be collected, processed, and used to train an LLM to create summaries in plain language, after which the summaries will be evaluated for their quality. The trained LLM could be used to help develop additional health education tools.

Zilma Silveira Nogueira Reis of Universidade Federal de Minas Gerais in Brazil will develop a Large Language Model (LLM) that can generate personalized instructions for taking medications that are tailored to a user’s level of literacy, cultural context, and special needs to increase equitable access to medications and promote safe and effective patient self-care. They will determine the contexts that medicines are prescribed in the Portuguese-speaking countries Brazil, Mozambique, and Portugal; and they will train an LLM with a Brazilian public corpus of medical leaflets, social media text, and other relevant documents. A team of prescribing professionals will evaluate the LLM product for use as a web-based platform, including its ability to use an individual patient profile to generate personalized text instructions with supporting pictograms, such as those indicating relevant body parts, dose, unit of measurement, and mechanism of administration.

João Paulo Papa of Sao Paolo State University in Brazil will develop a platform in which Large Language Models (LLMs) will extract information from published research articles and generate one-page fact sheets highlighting the potential implications for public health policy in an accessible form for researchers and health system managers. They will integrate into the LLM training a methodology for knowledge translation that has been used to train health system researchers to communicate results in a form they can most readily be used.

Sandra Maria do Valle Leone de Oliveira of Fiocruz in Brazil will develop an early warning system for infectious diseases that integrates community leader engagement and a Large Language Model (LLM)-based tool for health-related event-based surveillance in two Brazilian cities on the Bioceanic Route transcontinental railway. Monitoring information from formal and informal internet sources about health-related events can lead to quicker detection and response for disease outbreaks. They will develop the system with leaders across diverse roles in the community, who will advise on the list of reportable events and priority health conditions and be trained in reporting through the system. They will create an online reporting form and a process for escalating alerts to local or national health authorities for analysis to quickly enable decisions about potential responses to disease outbreaks. Data accumulated through this system will be used to train an LLM to extract patterns and generate accurate and relevant predictions.

Shirley da Silva Jacinto de Oliveira Cruz of Universidade Federal de Pernambuco in Brazil will develop an LLM-based chatbot and use it in a pilot study to support post-discharge follow-up communication with surgical wound patients to promote a faster and safer recovery. A chatbot would provide a personalized and accessible mechanism for overcoming geographical, socioeconomic, and communication barriers between the healthcare team and the patient, including care reminders and regular check-ins via text and voice to acquire data on the patient's recovery and alert healthcare professionals to signs of complications. They will survey the needs at the Hospital das Clínicas de Pernambuco pilot site, train an LLM using electronic patient records from the hospital, and perform a series of evaluations using feedback from surgery patients and hospital staff to assess the usefulness and relevance of the information the chatbot provides.

Ida Viktoria Kolte of Fiocruz in Brazil will adapt ChatGPT for use in the languages of indigenous populations in Brazil (Guarani-Kaiowá communities) and Mozambique (Echuwabo-speaking communities in Nicoadala) to support community health workers (CWAs) in reducing the burden of tuberculosis. Translating information on tuberculosis from Portuguese would enable its integration with the traditional medicine practiced by indigenous populations particularly vulnerable to the disease, helping overcome language and cultural barriers and a history of marginalization. They will train a translation model using existing translated texts and validated common questions and answers about tuberculosis in the literature. They will test and improve the model through assessment of its accuracy and cultural appropriateness by CHWs, traditional medicine practitioners, and community members; and they will perform a two-month pilot trial of its use by CHWs in Brazil and Mozambique.

This project aims to understand and analyze the determinants of vaccination coverage in the Brazilian territory by assessing its association with socio-economic factors, public health spending, coverage of primary health care and the Bolsa Família Program and the influence of patterns of content dissemination on immunization on social media. The results will be disseminated through virtual games, podcasts, interactive panels, infographics, an e-book for municipal managers, a webinar for undergraduate students in the health field and a seminar on World Immunization Day.