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Background <p>In low- and middle-income countries (LMICs), unstructured patient-generated data remain underutilized in public healthcare systems, where they are often treated as isolated technical inputs rather than integrated into governance processes.</p> Objectives <p>This study develops a data-driven governance framework to support data-informed governance of healthcare quality in public hospitals, positioning patient experience (PX) as patient-generated data from which governance-relevant operational signals can be derived.</p> Methods <p>An exploratory mixed-methods design was employed, combining a conceptual governance framework with empirical analysis of 450 publicly available patient reviews collected from Google Maps (Q4 2024 - Q3 2025) in a public hospital in Viet Nam. Topic modelling was conducted using BERTopic (PhoBERT embeddings, UMAP, HDBSCAN), and aspect-based sentiment analysis (ABSA) was performed using a prompt-based large language model. Extracted aspects were mapped to SERVQUAL dimensions and interpreted through the Donabedian framework using a rule-based dictionary approach.</p> Results <p>The findings indicate how PX-derived signals can be translated into structured, interpretable, and decision-relevant insights, representing a potential form of governance-oriented public health intelligence aligned with service quality dimensions and system-level quality domains.</p> Conclusion <p>This study contributes to public healthcare systems research by redefining patient-generated data as governance-relevant inputs and reframing data-driven approaches. The findings are exploratory and context-specific but suggest a structured pathway for leveraging PX analytics to support continuous monitoring and priority-setting in healthcare governance.</p>

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A data-driven governance framework for co-creation healthcare quality in public hospitals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月28日

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  • Huu Phuc Dang,
  • Nguyen-Kim-Thao Vo

摘要

Background

In low- and middle-income countries (LMICs), unstructured patient-generated data remain underutilized in public healthcare systems, where they are often treated as isolated technical inputs rather than integrated into governance processes.

Objectives

This study develops a data-driven governance framework to support data-informed governance of healthcare quality in public hospitals, positioning patient experience (PX) as patient-generated data from which governance-relevant operational signals can be derived.

Methods

An exploratory mixed-methods design was employed, combining a conceptual governance framework with empirical analysis of 450 publicly available patient reviews collected from Google Maps (Q4 2024 - Q3 2025) in a public hospital in Viet Nam. Topic modelling was conducted using BERTopic (PhoBERT embeddings, UMAP, HDBSCAN), and aspect-based sentiment analysis (ABSA) was performed using a prompt-based large language model. Extracted aspects were mapped to SERVQUAL dimensions and interpreted through the Donabedian framework using a rule-based dictionary approach.

Results

The findings indicate how PX-derived signals can be translated into structured, interpretable, and decision-relevant insights, representing a potential form of governance-oriented public health intelligence aligned with service quality dimensions and system-level quality domains.

Conclusion

This study contributes to public healthcare systems research by redefining patient-generated data as governance-relevant inputs and reframing data-driven approaches. The findings are exploratory and context-specific but suggest a structured pathway for leveraging PX analytics to support continuous monitoring and priority-setting in healthcare governance.