Evidence map›Paper›PMID 41941065›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2026

Impact of postoperative in-hospital continuous vital sign monitoring on healthcare costs, resource utilization and labor market outcomes.

Arendse Tange Larsen, Liza Sopina, Jesper Mølgaard, Eske Kvanner Aasvang, Christian S Meyhoff, Jakob Kjellberg, Søren Rud Kristensen

Registry-linked trialAbstract read
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In one paragraph

Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04640415 (Continuous Wireless Monitoring of Vital Signs and Automated Alerts of Patient Deterioration vs. Routine Monitoring of High-risk Patients After Major Surgery. A Randomized Controlled Trial.), which is not on this 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.

NCT04640415 nacompletednot on this map

Continuous Wireless Monitoring of Vital Signs and Automated Alerts of Patient Deterioration vs. Routine Monitoring of High-risk Patients After Major Surgery. A Randomized Controlled Trial.

TypeinterventionalSponsorRigshospitalet, DenmarkRan2021 to 2023Enrolled400ConditionsComplication,PostoperativeArmsActive Alarms
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.

Arendse Tange LarsenDanish Medicines Council, Copenhagen, Denmark.
Liza SopinaDanish Center for Health Economics, University of Southern Denmark, Odense, Denmark. lsopina@health.sdu.dk.ORCID https://orcid.org/0000-0002-2680-6999
Jesper MølgaardDepartment of Anaesthesiology, Center for Cancer and Organ Diseases, Rigshospitalet, Copenhagen University, Copenhagen, Denmark.
Eske Kvanner AasvangDepartment of Anaesthesiology, Center for Cancer and Organ Diseases, Rigshospitalet, Copenhagen University, Copenhagen, Denmark.
Christian S MeyhoffDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Jakob KjellbergThe Danish Center for Social Science Research, Copenhagen, Denmark.
Søren Rud KristensenDanish Center for Health Economics, University of Southern Denmark, Odense, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous vital signs monitoring (CVSM) is increasingly being used for early complication detection in general wards, potentially reducing prolonged medical care and healthcare utilization. However, its impact on health economic outcomes, including post-discharge trajectories, remains largely unexplored. This study aimed to estimate the impact on healthcare costs, resource utilization and labor market outcomes of patients included in a postoperative randomized clinical trial (RCT) of an artificial intelligence (AI)-assisted CVSM. The WARD Surgical RCT (Clinicaltrials.gov Identifier: NCT04640415) randomized participants to monitoring by AI-assisted CVSM with real-time alerts or blinded monitoring. In this study, Generalized Linear Models and Two-part models were used to estimate the impact on healthcare costs and resource utilization across primary and secondary care, including home care, home nursing and prescription medication. Additionally, the impact on employment status, full-time employment duration and sickness leave duration were examined. The study included 382 trial participants traceable in the Danish national registries. No differences in total healthcare costs were found between the groups (EUR 21,184 vs EUR 20,621, p = 0.58 at 30 days and EUR 40,505 vs EUR 40,870, p = 0.81 at 1 year). After adjustment, the intervention group had a reduction of 2.4 weeks (95%CI (-4.5;-0.3)) on sickness leave within 3 months. No other statistically significant differences were observed. There was no statistically significant impact of AI-assisted CVSM on total healthcare costs, resource utilization, employment status or duration of full-time employment compared to blinded monitoring. Monitoring was associated with shorter duration of sickness leave within 3 months. This reduction, even if modest, may yield broader societal benefits by supporting workforce participation and reducing productivity loss.

Indexed as

Continuous vital signs monitoringHealthcare costs and utilizationLabor market outcomesRandomized Controlled TrialRegistry data

Identifiers

PMID41941065

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Registered trials

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.