Evidence map›Paper›PMID 35914920›Full record

ArticleBMJ open2022

Budget impact analysis of providing hospital inpatient care at home virtually, starting with two specific surgical patient groups.

Guido M Peters, Carine J M Doggen, Wim H van Harten

Abstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

3 authors.

Guido M PetersRijnstate Research Center, Rijnstate Hospital, Arnhem, The Netherlands.ORCID 0000-0002-9035-4982
Carine J M DoggenRijnstate Research Center, Rijnstate Hospital, Arnhem, The Netherlands.
Wim H van HartenDepartment of Health Technology and Services Research, Faculty of Behavioural, Management & Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands w.h.vanharten@utwente.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo determine the budget impact of virtual care.

methodsWe conducted a budget impact analysis of virtual care from the perspective of a large teaching hospital in the Netherlands. Virtual care included remote monitoring of vital signs and three daily remote contacts. Net budget impact over 5 years and net costs per patient per day (costs/patient/day) were calculated for different scenarios: implementation in one ward, in two different wards, in the entire hospital, and in multiple hospitals. Sensitivity analyses included best-case and worst-case scenarios, and reducing the frequency of daily remote contacts.

resultsNet budget impact over 5 years was €2 090 000 for implementation in one ward, €410 000 for two wards and €-6 206 000 for the entire hospital. Costs/patient/day in the first year were €303 for implementation in one ward, €94 for two wards and €11 for the entire hospital, decreasing in subsequent years to a mean of €259 (SD=€72), €17 (SD=€10) and €-55 (SD=€44), respectively. Projecting implementation in every Dutch hospital resulted in a net budget impact over 5 years of €-445 698 500. For this scenario, costs/patient/day decreased to €-37 in the first year, and to €54 in subsequent years in the base case.

conclusionsWith present cost levels, virtual care only saves money if it is deployed at sufficient scale or if it can be designed such that the active involvement of health professionals is minimised. Taking a greenfield approach, involving larger numbers of hospitals, further decreases costs compared with implementing virtual care in one hospital alone.

Indexed as

BudgetsInpatientsCost-Benefit AnalysisHospitalsHumansNetherlandsPatient Carehealth economicshealth services administration & managementtelemedicine

Identifiers

PMID35914920
PMCPMC9345035

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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.