Narrative Health Analytics: Integrating Empathy, Data, and Ethics in Patient-Centered Healthcare

Abstract

Data-driven healthcare has expanded the capacity to measure performance, predict risk, and optimize clinical workflows; however, many analytic systems continue to under-represent the meaning-laden dimensions of care captured in patient narratives. This paper develops the Narrative Health Analytics (NHA) Framework, a patient-centered model that integrates narrative interpretation, computational text analytics, and ethical governance to support organizational learning and quality improvement. A qualitative meta-synthesis was conducted using peer-reviewed literature published from 2015–2025, identified through structured searches of Consensus.app, PubMed, Scopus, and Web of Science across five relevant domains: narrative medicine/health humanities, clinical discourse and linguistics, natural language processing and text mining, data ethics and datafication studies, and patient-centered care research. The synthesis yields three integrative outputs: (1) a staged pathway linking narrative inputs to linguistic–emotional feature identification and NLP-based processing, (2) an explicit ethical–cultural mediation layer to reduce bias and interpretive flattening, and (3) a feedback loop that positions narrative-derived indicators as complementary inputs to existing dashboards rather than replacements for established quantitative measures. The framework emphasizes that interpretability and cultural context are necessary conditions for responsible analytics when patient experience is operationalized as data. The paper recommends future empirical testing of the framework across settings and languages, with attention to privacy, fairness, and implementation feasibility.

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