To develop and implement the AQUATIC protocol for enhancing qualitative data analysis (QDA) capacity at the local level using artificial intelligence, emphasizing the significance of AI in this enhancement.
Key Findings:
AQUATIC enhances local capacity for qualitative analysis, reducing reliance on external research organizations, with specific examples of efficiency gains.
AI tools can significantly accelerate the analysis process, enabling results in minutes rather than weeks, supported by data.
The protocol emphasizes human verification to maintain analytical rigor and contextual relevance, addressing potential biases introduced by AI.
Interpretation:
The AQUATIC protocol represents a significant advancement in qualitative research methodology, leveraging AI to improve efficiency while maintaining the depth of analysis traditionally associated with qualitative methods, with a comparison to traditional methods.
Limitations:
The protocol is not designed to replace traditional interpretive methods that rely on iterative coding and researcher reflexivity, acknowledging potential biases from AI tools.
Potential challenges in training personnel to effectively use AI tools and integrate them into existing workflows.
Conclusion:
The AQUATIC protocol offers a promising framework for enhancing qualitative data analysis in public health, enabling timely and informed decision-making through AI integration, while emphasizing the importance of human oversight.
