Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique

Area of Science:

• Neonatal Medicine
• Clinical Informatics
• Evidence-Based Practice

Background:

• Neonatal guidelines often contain evidence gaps, impacting care quality.
• Digital platforms offer potential solutions for standardizing neonatal care.
• Expert consensus is crucial for developing reliable clinical decision algorithms.

Purpose of the Study:

• To determine if neonatal experts could reach consensus on clinical decision algorithms for the NeoTree digital platform.
• To address evidence gaps in local and international neonatal guidelines through expert consensus.
• To refine four key clinical algorithms: neonatal sepsis, hypoxic ischemic encephalopathy, respiratory distress, and hypothermia.

Main Methods:

• A two-round, modified Delphi technique was employed.
• Neonatal experts from high-income and low-income countries participated.
• Experts rated and revised items for four clinical decision algorithms, with consensus defined as ≥80% agreement.

Main Results:

• Consensus was reached on 52% of items after two rounds.
• The final algorithms included 62% of items, incorporating expert recommendations and WHO guidelines.
• Four algorithms were reviewed and refined, with 10 experts participating in the final round.

Conclusions:

• Expert consensus successfully refined clinical decision algorithms for a neonatal digital platform.
• The refined algorithms aim to improve consistency for healthcare workers, especially in low-resource settings.
• Clinical validation of the NeoTree algorithms is the next critical step.