Evidence map›Paper›PMID 39917566›Full record

ArticleThe British journal of cardiology2024

Lessons learnt from HF coding in primary care. What might best practice look like?

Amanda Crundall, Mary Crawshaw-Ralli, Ahmet Fuat, Kavita Oberoi, Jo Crossan, Sharon Jones, Jaya Authunuri

Abstract read
In one paragraph

Article in The British journal of cardiology, 2024. 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

7 authors.

Amanda CrundallLead Heart Failure Nurse Specialist.
Mary Crawshaw-RalliHeart Failure Nurse Specialist.
Ahmet FuatGeneral Practitioner and Medical Director.
Kavita OberoiManaging Director.
Jo CrossanService Development Manager.
Sharon JonesService Development Manager Oberoi Consulting, 20 St. Christopher's Way, Pride Park, Derby, DE24 8JY.
Jaya AuthunuriClinical Pharmacist Practice 3, Station Avenue, Bridlington, East Yorkshire, YO16 4LZ.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure (HF) is a prevalent and complex condition that demands precise and efficient management. This paper delves into a critical, yet often under-recognised, challenge in HF care - the accurate coding of patients on HF registers. We explore how discrepancies in coding, including missing patients and incorrect HF type classifications, can significantly impact patient management. The experiences of our healthcare system's staff are examined to shed light on the real-world implications of these issues. Clinicians and administrative staff in primary care can play a pivotal role in identifying, monitoring, and ensuring the accuracy of patient coding. By sharing their insights, we uncover the intricacies of addressing coding discrepancies and strategies suggested to optimise patient management. Furthermore, we investigate the technology and systems in place to aid staff in this endeavour. This paper aims to contribute to the broader healthcare community's understanding of the challenges related to coding accuracy in HF registers and to offer insights into potential solutions. By rectifying these coding discrepancies, we can enhance patient care, minimise potential oversights, and ultimately improve outcomes for individuals living with HF. Our findings underscore the significance of ensuring that no HF patient is missed or misclassified, emphasising the need for continued improvement in this vital aspect of HF care.

Indexed as

general practiceheart failureheart failure codingprimary care audit

Identifiers

PMID39917566
PMCPMC11795906

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.