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Adoption précoce de l'IA dans les programmes de planification familiale

Paving the Way for Gains in Program Impact and Efficiency


Accroître les investissements dans les technologies émergentes à faible- and middle-income countries have created unprecedented opportunities to leverage numérique innovations to enhance voluntary family planning programs. En particulier, l'utilisation de l'intelligence artificielle (IA) to gain new insights into family planning and optimize decision-making can have a lasting impact on programs, prestations de service, and users. Current advances in AI are just the beginning. As these approaches and tools are refined, practitioners should not miss the opportunity to apply AI to expand the reach of family planning programs and strengthen their impact.

Potential Uses for AI in Family Planning Programs

By applying the USAID-developed framework of AI use in health care, we can classify AI’s potential application in family planning programs into four categories:

  1. Population health.
  2. Individual health (care routing and care service).
  3. Health systems.
  4. Pharma and medtech.

Below are examples of AI use relevant to family planning programs for select subcategories from the USAID framework.

Santé de la population

Intervention selection. Specific family planning methods are recommended based on an examination of the characteristics of a given population at risk of unmet need for family planning and what is likely to be most effective and efficient for meeting their needs.

Individual Health—Care Routing

Self-referral. Based on patient-entered, real-time data, an AI-enabled system provides recommendations to the patient on the care needed.
Personalized outreach. Real-time patient data is captured and analyzed to identify patterns to generate personalized, direct patient outreach (par exemple, messages from health care providers and chatbots, care recommendations).

Individual Health—Care Services

Behavioral change. Individuals receive real-time, targeted information or customized guidance on family planning options.
Data-driven diagnosis. Diagnose conditions by analyzing symptoms and other data provided by patients.
Clinical decision support. Health workers receive real-time guidance on best-practice family planning care based on patient data.
AI-facilitated care. Patients receive guidance on best practices for self-care for family planning based on their symptoms and situations.
Compliance monitoring. Alert users or providers about medication compliance based on patient-use data.

Systèmes de santé

Capacity planning and personnel management. Examine data on facility-level care needs and the availability of health workers to help predict and plan resources.
Quality assurance and training. Analyze past decisions and identify where errors may have been made to improve the quality and efficiency of the provided family planning services.
Medical records. Assist in creating electronic medical records to limit the time providers spend on the task.
Coding and billing. Support provider finance functions by analyzing medical notes to ensure proper coding; billing strategies are also optimized.

Health care worker entering patient information
Crédit: Ncamsile Maseko and Lindani Sifundza/USAID in Africa

Pharma and Medtech

Supply chain and planning optimization. Improve family planning supply chain management and resource planning by automating the process.

Applications of AI in Family Planning Programs

Family planning programs have not yet implemented some of these uses of AI, but the technology is expected to create efficiencies in how family planning services are delivered and increase affordability and coverage. According to the IT consulting firm Accenture, AI-powered health applications may result in annual cost savings of $150 billion for the U.S. health care economy by 2026. Experts also recognize the potential savings en basse- et pays à revenu intermédiaire. Early lessons can be drawn from family planning projects that have used AI, demonstrating both the opportunity for its use and its potential impact, highlighted here.

Individual Health—Care Routing

Personalized outreach

  • Palindrome Data, a data science firm, and Jhpiego partnered on the Post Pregnancy Family Planning (PPFP) Choices study in Kenya and Indonesia. The study’s two main objectives were to predict the likelihood of family planning uptake among women six months following delivery and identify groups who were more likely or less likely to take up family planning immediately after delivery. In Indonesia, the AI model they developed predicted a family planning method uptake at six months postpartum with an accuracy of 62% (64% specificity and 63% sensitivity). Using the model, they categorized women’s profiles into higher, average, and lower PPFP method uptake groups. The development of these models demonstrates immediate, actionable insights to plan and design interventions for pregnant, delivering, and postpartum women to improve the content of counseling messages and ultimately support women to achieve their reproductive goals and their uptake of a family planning method when desired.

“The development of these models demonstrates immediate, actionable insights to plan and design interventions for pregnant, delivering, and postpartum women to improve the content of counseling messages and ultimately support women to achieve their reproductive goals and their uptake of a family planning method when desired.”

  • Dans 2020, IT firm Quilt.AI, using an AI tool called Culture AI, analyzed digital content from four social media platforms to understand young people’s knowledge, croyances, motivations, and attitudes toward family planning in the Indian states of Uttar Pradesh and Bihar. Quilt.AI grouped internet users ages 16 pour 24 into eight categories based on their online behaviors related to family planning, such as conformists, spiritualists, skeptics, and activists. They also identified the unique skews on topics related to family planning across different social media platforms. The information allows those working in behavior-change communications to tailor their messages to appeal to distinct youth groups. In making the optimal platform available for use, they can influence attitudes and behaviors regarding family planning.
  • Data science firm AIfluence has partnered with MSI Reproductive Choices, psi, and Jhpiego to support sexual and reproductive health-focused social behavior change campaigns in Côte d’Ivoire, Kenya, Nigeria, Aller, et Ouganda. Using AI, they identify the appropriate influencers to communicate with different audiences on social media by measuring and analyzing an influencer’s affinity to the campaign, looking at how positive their connection to their network is and how much meaningful engagement their posts generate. Par exemple, Alfluence worked with MSI Reproductive Choices on a social media campaign to promote testing for HIV and other sexually transmitted infections in Eastleigh, Nairobi, Kenya. They collaborated with 38 influencers to regularly post content to their social media accounts over a six-week period to drive more adolescents to these services and try to understand the barriers to accessing preventative health services, y compris la planification familiale, within the community. The marketing campaign reached more than 1.5 million people on social media, a quarter of whom were youth and almost a third of whom were male. The project demonstrated success in partnering with influencers to drive demand for and uptake of preventative services.

“In Indonesia, the AI model they developed…demonstrates immediate, actionable insights to plan and design interventions for pregnant, delivering, and postpartum women to…ultimately support women to achieve their reproductive goals.”

Individual Health—Care Services

Behavioral change

  • 9ja Girls Big Sista, developed by PSI under the A360 project, interacts with Nigerian girls via a chatbot available on Facebook. Big Sista delivers contenu about family planning and reproductive health in bite-sized messages, including the advantages and disadvantages of each method and frequently asked questions.
  • SnehAI, developed by the Population Foundation of India, is an AI chatbot (a software application used to conduct online conversations) designed to educate and inspire adolescents and young adults to live healthy lives, promote sexual and reproductive health, and advocate for the health and well-being of women and girls. le chatbot provides a safe space for youth to have conversations on taboo topics, offers accurate information on safe sex contraceptive choices, and addresses mental health concerns. SnehAI represents an innovative educational intervention that enables vulnerable and hard-to-reach groups to discuss sensitive topics.

Data-driven diagnosis

  • InData Labs, a data science and AI company, partnered with the company Flo to implement a neural network—a computer program that identifies and recognizes patterns—to make better predictions for irregular menstrual cycles and ovulation based on the information users enter into a consumer-facing app. le current version of Flo’s neural network by InData Labs can improve predictions of irregular cycles by up to 54%. Improved knowledge about one’s menstrual cycles can help users identify when and which family planning methods may best meet their needs.

Pharma and Medtech

Supply chain and planning optimization

  • Macro-Eyes, an AI company, is developing an AI model to forecast the contraceptive supply chain and ensure health service delivery sites have the necessary supplies when needed by improving availability and efficiency and reducing waste. Macro-Eyes is now testing its model in Côte d’Ivoire. It is drawing on early lessons from the STRIATA project in Tanzania, whose forecasting of vaccine supply and demand helped achieve a 26% decrease in vaccine costs over one year in the Arusha region.

These projects provide early insights into the potential opportunities to incorporate AI tools and technologies to advance family planning programs for decision-makers and program managers designing new solutions or looking to scale tested solutions. While the integration of AI-based solutions will ultimately be based on country context, capacities, and specific needs, innovators and other stakeholders need to continue sharing lessons learned to advance the field.

Where Else Is AI Being Used?

Do you have an AI (or other digital health technology) for a family planning project serving a low- or middle-income country to share? To promote learning on AI for family planning, along with other digital health innovations, la Projet PACE à PRB developed the Compendium sur la santé numérique. The compendium is managed by The Medical Concierge Group and aims to consolidate emerging information and data on applications of digital technology in family planning programs to inform the adoption and scale-up of successful approaches. Contactez-nous for the opportunity to get your project featured in the Digital Health Compendium.

les femmes sur les ordinateurs
Krissy Celentano

Propriétaire, Koralaide Conseil

Krissy Celentano, propriétaire de Koralaide Consulting, est un chef de projet de santé numérique axé sur les résultats et un expert technique avec plus de dix ans de travail sur les politiques, la gouvernance, coordination, assistance technique, et planification stratégique en haute, faible, et pays à revenu intermédiaire. Elle a auparavant été conseillère principale en systèmes d'information sur la santé aux États-Unis.. Agence pour le développement international (USAID) au Bureau du VIH / SIDA. Elle a présidé le groupe de travail sur l’informatique de la santé de l’Agence, dirigé les efforts de renforcement des capacités internes, a géré la communauté de pratique des champions du domaine de la santé numérique, fourni une assistance technique au pays, et soutenu le développement d'une stratégie de santé numérique. Krissy a également supervisé un système de données soutenant la collecte et l'analyse de données interinstitutions sur le VIH / SIDA pour éclairer les décisions politiques et de financement.. Avant de rejoindre l'USAID, Krissy a occupé plusieurs postes au bureau du coordonnateur national des technologies de l'information sur la santé aux États-Unis.. Département de la santé et des services sociaux. Krissy est actuellement professeur adjoint d'informatique de la santé à l'Université George Washington et au Massachusetts College of Pharmacy and Health Services., aussi bien que, membre émérite du conseil consultatif du Global Digital Health Network.

Toshiko Kaneda, Doctorat

Associé de recherche principal, Programmes internationaux, Bureau de référence démographique (PRB)

Toshiko Kaneda est chercheur associé principal dans les programmes internationaux au Population Reference Bureau (PRB). Elle a rejoint PRB en 2004. Kaneda a 20 années d’expérience dans la recherche et l’analyse démographique. Elle a écrit de nombreuses publications politiques et des articles évalués par des pairs sur des sujets tels que la santé reproductive et la planification familiale., les maladies non transmissibles, vieillissement de la population, et accès aux soins de santé. Kaneda dirige l'analyse des données pour la Fiche de données sur la population mondiale et fournit des conseils techniques sur les méthodes démographiques et statistiques au sein du PRB, ainsi qu'aux partenaires externes. Elle dirige également le programme de formation en communication politique au PRB, soutenu par les National Institutes of Health. Avant de rejoindre PRB, Kaneda était boursier Bernard Berelson au Population Council. Elle détient un doctorat. en sociologie de l'Université de Caroline du Nord à Chapel Hill, où elle était également stagiaire prédoctorale au Carolina Population Center.

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