Evidence map›Paper›PMID 42804759›Full record

ArticleJMIR mHealth and uHealth2026

A Cardiovascular Care Pathway Supported by Remote Patient Management in Dutch Primary Care: Cost-Effectiveness, Budget Impact, and Workload Analysis.

Jan Heijdra Suasnabar, Margot Rakers, Nicoline van Hattem, Eric Hiddink, Marcel Haas, Xuerui Zhang, Douwe Atsma, Tobias N Bonten, M Elske van den Akker-van Marle, Hendrikus J A van Os

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 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

10 authors.

Jan Heijdra Suasnabar *Section of Medical Decision Making, Biomedical Data Science, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZB, The Netherlands, 31 71 526 45 74.ORCID http://orcid.org/0000-0002-8057-1318
Margot Rakers *Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0000-0001-8233-1150
Nicoline van HattemDepartment of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0000-0001-8990-9445
Eric HiddinkDepartment of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0009-0002-2057-1954
Marcel HaasHealth Campus, Leiden University Medical Center, The Hague, The Netherlands.ORCID http://orcid.org/0000-0003-2581-8370
Xuerui ZhangDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0009-0005-1701-2512
Douwe AtsmaNational eHealth Living Lab (NeLL), Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0000-0002-9664-2985
Tobias N BontenDepartment of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0000-0002-7719-6182
M Elske van den Akker-van MarleSection of Medical Decision Making, Biomedical Data Science, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZB, The Netherlands, 31 71 526 45 74.ORCID http://orcid.org/0000-0002-5269-509X
Hendrikus J A van OsDepartment of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.ORCID http://orcid.org/0000-0002-8911-8608

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Remote patient management (RPM) that supports patient self-monitoring of vital parameters and lifestyle factors may improve cardiovascular risk management (CVRM) in primary care. However, large-scale implementation remains limited, partly due to insufficient evidence on long-term value for money, budget impact, and implications for health care professionals' workload. Objective: This study aimed to estimate the long-term cost-effectiveness, 5-year health care budget impact, and expected changes in general practitioner (GP) and practice nurse (PN) workload associated with the CVRM-Box intervention in the Netherlands. Methods: We conducted a model-based economic evaluation comparing CVRM-Box with care as usual in Dutch primary care integrated CVRM programs. CVRM-Box is a multicomponent RPM intervention comprising a digital blood pressure (BP) monitor, digital weight scale, step counter/activity tracker, and a mobile app, with measurements transferred to the GP practice for periodic review. A time-inhomogeneous cohort Markov model simulated lifetime transitions among health states, including at-risk, post-myocardial infarction (MI), poststroke, recurrent events, cardiovascular death, and noncardiovascular death. Cardiovascular risks were modeled using prediction equations (SCORE2, SCORE2-OP, and SMART2) populated with subgroup-specific risk factor profiles. Intervention effects were modeled as changes in systolic BP derived from a matched cohort study of CVRM-Box. Other key parameters (costs and utilities) were similarly obtained from the matched cohort study, or routine primary care data, and published sources. Outcomes included incremental cost-effectiveness ratios (ICERs, indicating cost per quality-adjusted life year [QALY] gained), cost-effectiveness probabilities, 5-year health care budget impact, and an exploratory workload analysis estimating annual changes in visit time and remote consultation frequency for GPs and PNs. Probabilistic and scenario uncertainty analyses were performed. Results: In the overall population, CVRM-Box increased costs and QALYs versus care as usual, yielding an ICER of €17,340/QALY gained (EUR €1=US $1.11 as of 29 December 2023) and a 60% probability of cost-effectiveness at a willingness-to-pay threshold of €20,000/QALY. Cost-effectiveness was more favorable in higher-risk subgroups (uncontrolled BP and/or prior MI/stroke), with ≥70% probability of cost-effectiveness at €20,000/QALY, whereas in the lower-risk subgroup with controlled BP and no prior MI/stroke, the intervention is unlikely to be cost-effective (ICER: €42,384/QALY). The 5-year health care budget impact was €2.9 million and €662.2 million for regional and national rollout, respectively. In a typical Dutch primary care practice, the CVRM-Box reduced PN workload by 25.9 h and 44.7 remote consultations annually, while the change in GP workload was negligible (0.5 h; 3.3 remote consultations). Conclusions: The CVRM-Box multicomponent RPM intervention is likely to be cost-effective among high-risk subgroups but not in lower-risk groups. The intervention further reduces PN workload but not GP workload. Results primarily apply to the Dutch context and may not generalize to health care systems with different payment/incentive arrangements. Additionally, long-term outcomes were modeled using prediction models rather than observed cardiovascular events. Nonetheless, our findings provide an argument for implementation in primary care for higher-risk subgroups.

Indexed as

Cardiovascular DiseasesWorkloadAgedBudgetsCost-Benefit AnalysisCost-Effectiveness AnalysisFemaleHumansMaleMarkov ChainsMiddle AgedNetherlandsPrimary Health CareRemote Patient Monitoringbudget impactcost-effectivenesspatient managementpreventive cardiologyprimary care

Identifiers

PMID42804759
PMCPMC13618991

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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.