Evidence map›Paper›PMID 42807620›Full record

ArticleJournal of cardiac failure - intersections2026

Challenges in Identifying Heart Failure Diagnoses from the Electronic Health Record.

Sharon Klein, Amrita Mukhopadhyay, Carine E Hamo, Samrachana Adhikari, Lipika Samal, Saul Blecker

Abstract read
In one paragraph

Article in Journal of cardiac failure - intersections, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sharon KleinLeon H. Charney Division of Cardiology, Department of Medicine, NYU Grossman School of Medicine, New York, New York.
Amrita MukhopadhyayLeon H. Charney Division of Cardiology, Department of Medicine, NYU Grossman School of Medicine, New York, New York.
Carine E HamoLeon H. Charney Division of Cardiology, Department of Medicine, NYU Grossman School of Medicine, New York, New York.
Samrachana AdhikariDepartment of Population Health, NYU Grossman School of Medicine, New York, New York.
Lipika SamalHarvard Medical School, Boston, Massachusetts.
Saul BleckerDepartment of Population Health, NYU Grossman School of Medicine, New York, New York.

Funding

Training Program in Cardiovascular SciencesT32HL098129 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Glenn I Fishman, Ira J Goldberg · 2009 to 2026
$5.9M
Generalizable prediction of medication adherence in heart failureR01HL155149 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI ADHIKARI, SAMRACHANA, BLECKER, SAUL B. · 2021 to 2025
$3.9M
NHLBI NIH HHS R01 HL155149NHLBI NIH HHS T32 HL098129
6 · The paper itself

Abstract

Background: To leverage the EHR effectively for clinical operations, quality improvement, and research, patients with a specific clinical condition such as heart failure are often identified through ICD diagnosis codes and structured data. These diagnoses are validated by clinician chart review, which is considered the gold standard for confirming true cases from the EHR. The purpose of the study was to explore obstacles that clinicians encounter when using chart review to validate heart failure diagnoses from the EHR. Methods: In prior work, a team of eight clinicians performed chart review to validate heart failure diagnosis based on the "Universal Definition of Heart Failure." A portion of the charts were reviewed by two independent reviewers, and we assessed inter-rater reliability using Cohen's kappa (κ). We conducted a content analysis of documents from the clinician chart review to understand challenges in validating EHR-based definitions of heart failure. Results: Clinicians reviewed 528 charts, with a κ of 0.62 among the 35% of charts with double review. In qualitative analysis, we identified two overarching themes related to challenges in validation of heart failure: 1) challenges in applying the Universal Definition of Heart Failure to real-world patient data, and 2) EHR interoperability and usability barriers impacting validation of heart failure diagnoses. Conclusions: Our study highlights clinical and technological limitations of clinician chart review for identifying heart failure patients. This has implications for patient care, quality improvement, and population health. Policies and digital solutions aimed at improving EHR data collection, organization, and data extraction are needed.

Indexed as

Electronic Medical RecordHealth InformaticsHeart FailureResearch Methods

Identifiers

PMID42807620
PMCPMC13618696

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.