Emerging Role of electronic health record analysis in the Diagnosis of essential hypertension
DOI:
https://doi.org/10.5281/zenodo.22813624Keywords:
electronic health records, essential hypertension, phenotyping algorithms, clinical decision support, natural language processingAbstract

Background: Essential hypertension is diagnosed almost entirely through data already in the electronic health record, yet structured fields, diagnosis codes, prescribing data, and free-text notes each carry incomplete, conflicting information.
Objective: This review synthesizes the literature on electronic health record analysis for identifying essential hypertension: phenotyping algorithms, natural language processing and machine learning extraction, and decision support applications.
Methods: We conducted a narrative synthesis of peer-reviewed studies on electronic health record-based hypertension phenotyping, natural language processing extraction of blood pressure data, and randomized trials of record-linked decision support.
Findings: Algorithms combining codes, blood pressure thresholds, prescribing data, and notes mined by natural language processing outperform any single source; record-linked decision support and home-monitoring trials improve control; yet surveillance shows control has stagnated.
Conclusions: Electronic health record analysis is a genuinely useful, if imperfect, diagnostic instrument for essential hypertension, and standards-driven validation will determine how far its promise is realized.
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