EXTEND Toolbox: An Adapted Solution for Nigerian Sickle Cell Care (2026-2027)
Background
Sickle cell disease is one of the most common inherited blood disorders in the world. In high resource settings such as the United States, evidence-based guidelines from the National Heart, Lung and Blood Institute have improved life expectancy by reducing deaths from stroke, infection and delayed treatment. However, in low and middle income countries, adult life expectancy for people with sickle cell disease remains 20 to 30 years shorter.
Nigeria has the highest burden of sickle cell disease globally and has developed national treatment guidelines to support health care providers. Yet many clinicians are unaware of these guidelines or find them difficult to use in busy clinical environments. To address similar challenges in the U.S., researchers created the SCD Toolbox — a set of clinical decision algorithms to support point-of-care decisions. Nigerian clinicians participating in the EXTEND Study have expressed interest in adapting this Toolbox to the Nigerian clinical context to improve access to guideline-based care.
Project Description
This project team will adapt the SCD Toolbox to create the EXTEND Toolbox, a digital decision support tool designed specifically for sickle cell disease care in Nigeria. Students will work closely with Duke faculty, the IVAN Research Institute in Nigeria and clinical collaborators across six Nigerian regions.
The team will begin by analyzing interview data from the EXTEND Study using two implementation science frameworks. This analysis will help identify necessary changes to both the clinical algorithms and the strategies needed to implement them successfully in Nigeria. Once these adaptations are identified, students will revise the existing algorithm documents and work with clinical partners to refine their terminology, layout and workflow.
Students will then help develop a protocol for implementing the EXTEND Toolbox in clinical practice, mapping strategies to established implementation methods. Team members will also support the design of a clickable prototype of the adapted algorithms using Figma and will help create interactive user testing simulations in UXtweak.
Clinicians in Nigeria will test the adapted algorithms using simulated sickle cell disease scenarios. Feedback on usability, acceptability and user experience will inform future improvements. The project will contribute to an evidence-based digital tool that can increase access to high quality care for sickle cell disease in settings with limited clinical resources.
Anticipated Outputs
- Adapted pediatric and adult clinical algorithms
- Interactive digital prototype of the EXTEND Toolbox
- Implementation protocol for clinical use
- Co-designed clinical case scenarios for user testing
- Student presentation with collaborators in Nigeria
- At least one publication
Student Opportunities
Ideally, this project team will include 2 graduate students and 10 undergraduate students. Students from computer science, nursing, medicine, global health, biological sciences, implementation science and related fields are encouraged to apply. Students with interests in algorithm design, graphic design, health systems in low resource settings or hematology may find this project particularly rewarding.
Team members will develop skills in qualitative data analysis, implementation science, tool design, user testing and global health collaboration. Students will gain hands-on experience adapting a clinical decision support tool for a new setting and will engage with partners at the IVAN Research Institute. Activities will include reviewing transcripts, coding data, adapting algorithms, mapping implementation strategies, designing prototypes and assisting with usability testing.
Graduate students will have opportunities to lead subteams, mentor undergraduates, practice scientific communication and develop leadership skills. A project manager has already been identified.
The team will meet weekly, with additional meetings for subteam work. While there is no travel to Nigeria, students will collaborate virtually with Nigerian researchers and clinicians throughout the year.
An optional summer component in 2027 will offer opportunities for data analysis and presentations.
Timing
Fall 2026 – Spring 2027
Fall 2026:
- Complete required onboarding
- Train student team and identify team leads
- Pilot and refine analysis templates
- Conduct qualitative analysis
- Compile algorithm modifications
- Assign roles for adaptation and protocol development
- Map implementation strategies
Spring 2027:
- Clinical review of adapted algorithms
- Develop implementation protocol
- Recruit clinicians
- Transfer adapted content to Figma and UXtweak
- Create case scenarios
- Conduct user testing
- Analyze usability data
Summer 2027 (optional):
- Continue data analysis
- Prepare presentations
Crediting
Academic credit available for fall and spring semesters