Evidence map›Paper›PMID 28887294›Full record

ArticleJMIR medical informatics2017

Impact of At-Home Telemonitoring on Health Services Expenditure and Hospital Admissions in Patients With Chronic Conditions: Before and After Control Intervention Analysis.

Branko Celler, Marlien Varnfield, Surya Nepal, Ross Sparks, Jane Li, Rajiv Jayasena

Abstract read
In one paragraph

Article in JMIR medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

6 authors.

Branko CellerBiomedical Systems Research Laboratory, University of New South Wales, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0003-3790-2895
Marlien VarnfieldHealth and Biosecurity Business Unit, eHealth Research Program, Commonwealth Scientific and Industrial Research Organisation, Herston QLD, Australia.ORCID http://orcid.org/0000-0003-4848-0181
Surya NepalData 61, Software and Computational Systems Program, Commonwealth Scientific and Industrial Research Organisation, Marsfield, NSW, Australia.ORCID http://orcid.org/0000-0002-3289-6599
Ross SparksHealth and Biosecurity Business Unit, eHealth Research Program, Commonwealth Scientific and Industrial Research Organisation, North Ryde, NSW, Australia.ORCID http://orcid.org/0000-0001-5852-5334
Jane LiHealth and Biosecurity Business Unit, eHealth Research Unit, Commonwealth Scientific and Industrial Research Organisation, Marsfield, NSW, Australia.ORCID http://orcid.org/0000-0002-2936-2318
Rajiv JayasenaHealth and Biosecurity Business Unit, eHealth Research Program, Commonwealth Scientific and Industrial Research Organisation, Parkville, VIC, Australia.ORCID http://orcid.org/0000-0003-4943-8524

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTelemonitoring is becoming increasingly important for the management of patients with chronic conditions, especially in countries with large distances such as Australia. However, despite large national investments in health information technology, little policy work has been undertaken in Australia in deploying telehealth in the home as a solution to the increasing demands and costs of managing chronic disease.

objectiveThe objective of this trial was to evaluate the impact of introducing at-home telemonitoring to patients living with chronic conditions on health care expenditure, number of admissions to hospital, and length of stay (LOS).

methodsA before and after control intervention analysis model was adopted whereby at each location patients were selected from a list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. Test patients were supplied with a telehealth vital signs monitor and were remotely managed by a trained clinical care coordinator, while control patients continued to receive usual care. A total of 100 test patients and 137 control patients were analyzed. Primary health care benefits provided to Australian patients were investigated for the trial cohort. Time series data were analyzed using linear regression and analysis of covariance for a period of 3 years before the intervention and 1 year after.

resultsThere were no significant differences between test and control patients at baseline. Test patients were monitored for an average of 276 days with 75% of patients monitored for more than 6 months. Test patients 1 year after the start of their intervention showed a 46.3% reduction in rate of predicted medical expenditure, a 25.5% reduction in the rate of predicted pharmaceutical expenditure, a 53.2% reduction in the rate of predicted unscheduled admission to hospital, a 67.9% reduction in the predicted rate of LOS when admitted to hospital, and a reduction in mortality of between 41.3% and 44.5% relative to control patients. Control patients did not demonstrate any significant change in their predicted trajectory for any of the above variables.

conclusionsAt-home telemonitoring of chronically ill patients showed a statistically robust positive impact increasing over time on health care expenditure, number of admissions to hospital, and LOS as well as a reduction in mortality.

trial registrationRetrospectively registered with the Australian and New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030 (Archived by WebCite at http://www.webcitation.org/6sxqjkJHW).

Indexed as

BACI analysischronic disease managementhealthcare outcomestelehealthtelemonitoring

Identifiers

PMID28887294
PMCPMC5610354

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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.