Work

Duke Global Health Innovation CenterResearch · AI for global health2026

AI for Global Health

Researching how AI-enabled health innovations move from promising technology into real-world delivery.

Research assistant · interviews, evidence review, case-study research and writing

Context
Summer research on AI-enabled global-health innovation (GHIG9 Learning Lab)
Role
Research assistant; led three of six innovator case studies
Focus
Implementation, evidence, delivery, scale
Outputs
An NCD access-program analysis section and three authored innovator case studies

The project

The summer research began with an analysis of pharmaceutical-company-funded programs for noncommunicable disease (NCD) access. Three students worked on it, screening programs and analyzing 19 of them. The study drew on existing frameworks, including the WHO health-system building blocks, the Levesque access framework, and logic-model thinking about outputs, outcomes, and impact. I worked on screening and analysis, and I was responsible for the contributions to health-system-strengthening section.

Later in the same summer, the work shifted to a series of case studies on AI-enabled global-health innovators: Simprints, mDoc, Bive, Jacaranda Health, Munai, and Sevamob. Six cases, split between two students.

The two stages sat inside one summer project rather than forming a single line of inquiry. One insight from the NCD analysis stayed with me: high activity and output counts do not by themselves demonstrate meaningful access or sustained health impact.

What I did

I led three of the six innovator case studies — Munai, Jacaranda Health, and Bive — from interview and evidence collection through research, drafting, feedback, and revision.

  1. Interview
  2. Research
  3. Fact check
  4. Draft
  5. Feedback
  6. Revision

I conducted the interviews for the three cases I owned. From there the work moved through supporting research, evidence collection and fact checking, drafting, review with a supervisor, and revision.

Each of us peer-reviewed the other's cases, so every draft was read twice before it went further. Much of the day-to-day research process was student-led, from preparing for interviews and gathering supporting evidence to drafting and revising the final case studies.

Three implementation contexts

The three cases I worked on each raised a different question about how technology reaches care. I summarize them here in my own words; the company figures and evaluation results below are reported by the case studies and the evaluations they cite, not by me.

01

Munai

Hospital / clinical workflow

How does clinical data become actionable information, and how does an alert become clinical action?

Munai, a health technology company in Curitiba, Brazil, works on a specific version of a common problem: a patient's condition changes through several small signals held in different systems, and no single view brings them together. The case study describes INTI, the company's clinical intelligence platform, connecting existing hospital data sources and delivering alerts, dashboards, and decision support inside the electronic health record rather than in a separate tool.

The case study reports more than 30 hospital partners, roughly 18 million medical encounters processed, and a multicenter validation of its clinical deterioration prediction across six hospitals. It also describes a gap that matters for implementation: the people who use the alerts and the people who buy the platform are not the same. Clinicians act on a signal; administrators decide whether the platform is worth paying for, and the case study describes the financial case being anchored in the reported cost of an avoidable deterioration event.

A prediction is not the same as an action. What turns an insight into care is the workflow around it, who is accountable for responding, and whether the hospital's incentives line up with the clinical value.

Source: Innovator Case Study — Munai: From Clinical Insight to Hospital Value (GHIG9 Learning Lab, July 2026).

02

Bive

Community delivery

How does identifying an eligible patient become actual access to care?

Bive works in rural Colombia, connecting underserved communities to preventive services. In this project it added an AI-supported eligibility step to an existing outreach model: provider data goes in, women eligible for cervical cancer screening are identified, automated voice calls reach them where connectivity and smartphones are limited, and community health workers follow up in person so that the existing provider delivers the care.

An evaluation by Universidad de Caldas reported large shifts in the participating community: attitudes toward HPV vaccine safety at 95.9%, up from 63.5%; HPV vaccination among girls aged 9 to 13 at 87.6%, up from 28.5%; and Pap smears at 97.9%, up from 67.2%. The more consequential finding was about ownership. Participating providers handed over their population databases and relied on Bive to run identification, contact, and follow-up rather than integrating the tool into their own routines. The project worked; the capability did not necessarily stay behind.

A project can function well while support is present without the capability becoming part of the local system. That raises questions about who owns the outreach, who maintains it, and whether the sustainable answer is software, training, or a managed service that combines technology, people, and coordination.

Source: Innovator Case Study — Bive: From Project Delivery to Business Model Transformation (GHIG9 Learning Lab, July 2026).

03

Jacaranda Health

Public-system scale

Once an intervention has evidence, who owns, funds, staffs, and maintains it at scale?

Jacaranda Health is a nonprofit working on maternal and newborn health with public health systems. Its PROMPTS program sends guidance and support to mothers by SMS, while a companion program strengthens frontline providers' emergency skills. The case study reports that by November 2021, at the start of its peer-reviewed evaluation period, PROMPTS had enrolled more than 750,000 women through over 900 health facilities in Kenya. A cluster-randomized trial across 40 facilities in eight counties found modest but statistically significant improvements in five of six evaluated domains.

The implementation story is less about whether the intervention works than about what scaling requires. Expanding from Kenya to Ghana surfaced the need for clearly defined institutional roles, coordination with mobile network operators, and explicit decisions about who funds message volume and support over time. The case study describes work with government and telecom partners, and an interest in bringing government in earlier so those arrangements can be built rather than retrofitted.

Evidence answers whether something can work. It does not answer who keeps it running, which is a question about ownership, staffing, and financing as much as about technology.

Source: Innovator Case Study — Jacaranda Health: Scaling AI-Enabled Maternal Health Through Public Systems (GHIG9 Learning Lab, July 2026).

Technology was only one part of the implementation story.

Evidence · Workflow · Trust · Ownership · Financing

What kept appearing across the work

These are questions that kept coming up across the cases, and my own reading of them. They are not an official framework from the project.

Evidence
What has actually been shown, and what is still assumed.
Workflow
Whether a tool fits the steps people already take, and who responds when it fires.
Trust
Whether patients and staff accept it, which is often built through people, not software.
Ownership
Who runs and maintains the capability after the project support ends.
Financing
Who pays, for what, and for how long.
  1. Promising technology
  2. Evidence
  3. Workflow and delivery
  4. Trust and local context
  5. Institutional ownership
  6. Financing
  7. Sustained implementation

A sequence I noticed while reading back through the cases. It is a way of ordering the questions, not a validated model.

What I took from it

The project gave me a closer look at what happens after a health technology appears promising: how evidence, workflows, community trust, institutional ownership, and financing shape whether it actually becomes part of healthcare delivery.

It also shaped what I want to work on next. The interesting problems are rarely only about building something; they are about whether it holds up in the setting it was built for.

Sources and notes

The case studies below are internal project documents and are not published here. Figures and evaluation results quoted on this page are attributed to those documents and the evaluations they cite.

  1. Innovator Case Study — Munai: From Clinical Insight to Hospital Value. GHIG9 Learning Lab, July 2026.
  2. Innovator Case Study — Bive: From Project Delivery to Business Model Transformation. GHIG9 Learning Lab, July 2026.
  3. Innovator Case Study — Jacaranda Health: Scaling AI-Enabled Maternal Health Through Public Systems. GHIG9 Learning Lab, July 2026.
  4. NCD access-program analysis, contributions to health-system strengthening (team analysis of 19 programs). GHIG9 Learning Lab, 2026.

None of the company results described here are my results. I interviewed people, gathered and checked evidence, and wrote the case studies; the outcomes belong to the organizations and the evaluations that measured them.

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