Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi:...

Area of Science:

• Neonatal Medicine
• Digital Health
• Health Informatics

Background:

• Neonatal mortality remains high in low-resource settings despite increased hospital births.
• Limited diagnostics necessitate reliance on clinical findings for neonatal diagnoses.
• Digital clinical decision support (CDS) tools can standardize care and improve neonatal outcomes.

Purpose of the Study:

• To co-develop and implement a neonatal digital CDS tool in Malawi and Zimbabwe.
• To tailor evidence-based algorithms to local resource availability and national guidelines.
• To evaluate the usability of the Neotree CDS system among healthcare professionals.

Main Methods:

• Conducted 11 diagnosis-specific workshops with international and local neonatal experts.
• Reviewed evidence and country-specific guidelines to develop algorithms.
• Programmed algorithms into Neotree, tested with healthcare professionals, and refined based on feedback.
• Implemented the Neotree system at tertiary hospitals in Malawi and Zimbabwe.
• Evaluated usability using workshops, questionnaires (PSSUQ, SUS).

Main Results:

• Developed and integrated 11 evidence-based diagnostic and management algorithms into Neotree.
• Created country-specific guidelines where national protocols differed.
• Achieved high usability scores (SUS: 75.8/100, PSSUQ: 2.28/7) among 9 participating healthcare professionals.
• Identified technical complexity as an area for iterative improvement.

Conclusions:

• Successfully developed and implemented the Neotree neonatal CDS system, a unique bedside data capture tool.
• Demonstrated the importance of collaborative, participatory design in developing healthcare tools.
• Further evaluation is planned to inform strategies for reducing neonatal morbidity and mortality in resource-limited settings.