Evidence map›Paper›PMID 42233902›Full record

ArticleJACC. Case reports2026

AI-Driven Heart Failure Decision Support in Skilled Nursing Facilities.

Emma Glajchen, Mills Reed, Takeshi Uemura, Johanna Contreras, Zoe Ozols, Annapoorna S Kini, Jeffrey Bander

Abstract readCase Reports
In one paragraph

Article in JACC. Case reports, 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

7 authors.

Emma GlajchenIcahn School of Medicine at Mount Sinai, New York, New York, USA.
Mills ReedIcahn School of Medicine at Mount Sinai, New York, New York, USA.
Takeshi UemuraIcahn School of Medicine at Mount Sinai, New York, New York, USA.
Johanna ContrerasIcahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Cardiology, Mount Sinai Fuster Heart Hospital, Mount Sinai West, New York, New York, USA.
Zoe OzolsIcahn School of Medicine at Mount Sinai, New York, New York, USA.
Annapoorna S KiniIcahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Cardiology, Mount Sinai Fuster Heart Hospital, Mount Sinai West, New York, New York, USA.
Jeffrey BanderIcahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Cardiology, Mount Sinai Fuster Heart Hospital, Mount Sinai West, New York, New York, USA; ProviderLoop, New York, New York, USA. Electronic address: jeffrey.bander@mountsinai.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure management in skilled nursing facilities (SNFs) is complicated by limited access to specialists, incomplete clinical documentation, and patients with complex comorbidities. Artificial intelligence clinical decision support systems have been developed mostly for acute hospital settings but not for SNF settings. We present ForeSight-HF, an artificial intelligence clinical decision support tool designed specifically for SNF heart failure management. Built on a large language model with HIPAA-compliant infrastructure, ForeSight-HF synthesizes unstructured clinical documentation to generate heart failure likelihood scores, exacerbation risk scores, and individualized management recommendations. We illustrate its utility through 2 contrasting cases: a Warm/Wet (congested, well-perfused) patient for whom the system recommended intensified diuresis, and a Cold/Dry (hypovolemic, hypoperfused) patient for whom it recommended diuretic de-escalation to prevent harm. ForeSight-HF may help close a persistent care gap in postacute heart failure management.

Indexed as

acute heart failureangiotensin receptor blockerschronic heart failurediastolic heart failurediureticechocardiographyhemodynamicsorthopneapreserved ejection fractionsystolic heart failure

Identifiers

PMID42233902
PMCPMC13420647

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.