Evidence map›Paper›PMID 41784150›Full record

ArticleESC heart failure2026

AI task-shifting for echocardiographic LVEF assessment in Singapore: an economic evaluation.

Aprajita Kaushik, Sameera Senanayake, Sanjeewa Kularatna, Khung-Keong Yeo, Nicholas Graves, Carolyn S P Lam, Huang Weiting, Chanchal Chandramouli, Jasper Tromp

Abstract read
In one paragraph

Article in ESC heart failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

9 authors.

Aprajita KaushikSaw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive 2, 117549  Singapore.
Sameera SenanayakeHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Sanjeewa KularatnaHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Khung-Keong YeoHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Nicholas GravesHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Carolyn S P LamHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Huang WeitingHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Chanchal ChandramouliHealth Services Research & Population Health, Duke-NUS Medical School, 8 College Rd, 169857  Singapore.
Jasper TrompSaw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive 2, 117549  Singapore.ORCID 0000-0001-6043-0713

Funding

AstraZeneca
6 · The paper itself

Abstract

backgroundAccurate assessment of left ventricular ejection fraction (LVEF) is crucial for heart failure (HF) diagnosis but requires skilled sonographers. Artificial intelligence-enabled point-of-care (AI-POC) devices may enable novices to assess LVEF, potentially reducing healthcare costs. We conducted a cost-minimization analysis comparing conventional sonographer-performed echocardiography versus novice-operated AI-POC devices.

methodsUsing a decision tree model, we compared the costs of diagnosing LVEF <50% in patients with suspected heart failure across two pathways: novice-operated AI-POC devices versus standard transthoracic echocardiogram (TTE) performed by sonographers. The model incorporated LVEF <50% prevalence, diagnostic accuracy metrics, and comprehensive cost data for both approaches. We conducted a probabilistic sensitivity analysis to test the robustness of our findings under varying assumptions.

resultsThe AI-POC pathway demonstrated substantial cost savings, averaging S$1185 [US$1422] per patient compared to S$1403 [US$1684] for conventional TTE. In a single tertiary referral centre in Singapore, implementing AI-POC devices for LVEF assessment in 100 patients resulted in savings of S$21 669 [US$26 013]. Probabilistic sensitivity analysis suggested a 99.9% probability that the AI-POC approach would be cost-saving compared to standard TTE.

conclusionsThis study provides economic evidence that task-shifting echocardiographic assessment of LVEF to novices using AI-POC devices is likely cost-saving compared to standard TTE. This task-shifting strategy offers a cost-saving alternative to conventional sonographer-led TTE.

Indexed as

Artificial IntelligenceEchocardiographyHeart FailurePoint-of-Care SystemsStroke VolumeTask ShiftingVentricular Function, LeftCost-Benefit AnalysisFemaleHumansMaleSingapore

Identifiers

PMID41784150
PMCPMC13143010

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.