Evidence map›Paper›PMID 33258781›Full record

ArticleJournal of medical Internet research2020

Capitated Telehealth Coaching Hospital Readmission Service in Australia: Pragmatic Controlled Evaluation.

Carmel Martin, Narelle Hinkley, Keith Stockman, Donald Campbell

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. 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
1.0field-weighted citation impact, top 22% of its field
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, 13 citations in OpenAlex.

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  6. LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan.International journal of environmental research and public health · 2021
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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

4 authors at 2 institutions in 1 country.

Carmel MartinMonash Health Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Australia.ORCID 0000-0001-8174-7859
Narelle HinkleyCommunity Health, Monash Health, Dandenong, Victoria, Australia.ORCID 0000-0002-8074-4960
Keith StockmanCommunity Health, Monash Health, Dandenong, Victoria, Australia.ORCID 0000-0002-1096-6453
Donald CampbellNorthern Health, Northern Hospital, Epping, Victoria, Australia.ORCID 0000-0003-0218-0917
Monash Health · AUNorthern Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMonashWatch is a telehealth public hospital outreach pilot service as a component of the Government of Victoria's statewide redesign initiative called HealthLinks: Chronic Care. Rather than only paying for hospitalizations, projected funding is released earlier to hospitals to allow them to reduce hospitalization costs. MonashWatch introduced a web-based app, Patient Journey Record System, to assess the risk of the journeys of a cohort of patients identified as frequent admitters. Telecare guides call patients using the Patient Journey Record System to flag potential deterioration. Health coaches (nursing and allied health staff) triage risk and adapt care for individuals.

objectiveThe aim was a pragmatic controlled evaluation of the impact of MonashWatch on the primary outcome of bed days for acute nonsurgical admissions in the intention-to-treat group versus the usual care group. The secondary outcome was hospital admission rates. The net promoter score was used to gauge satisfaction.

methodsPatients were recruited into an intention-to-treat group, which included active telehealth and declined/lost/died groups, versus a systematically sampled (4:1) usual care group. A rolling sample of 250-300 active telehealth patients was maintained from December 23, 2016 to June 23, 2019. The outcome-mean bed days in intervention versus control-was adjusted using analysis of covariance for age, gender, admission type, and effective days active in MonashWatch. Time-series analysis tested for trends in change patterns.

resultsMonashWatch recruited 1373 suitable patients who were allocated into the groups: usual care (n=293) and intention-to-treat (n=1080; active telehealth: 471/1080, 43.6%; declined: 485, 44.9%; lost to follow-up: 178 /1080, 10.7%; died: 8/1080, 0.7%). Admission frequency of intention-to-treat compared to that of the usual care group did not significantly improve (P=.05), with a small number of very frequent admitters in the intention-to-treat group. Age, MonashWatch effective days active, and treatment group independently predicted bed days. The analysis of covariance demonstrated a reduction in bed days of 1.14 (P<.001) in the intention-to-treat group compared with that in the usual care group, with 1236 bed days estimated savings. Both groups demonstrated regression-to-the-mean. The downward trend in improved bed days was significantly greater (P<.001) in the intention-to-treat group (Sen slope -406) than in the usual care group (Sen slope -104). The net promoter score was 95% in the active telehealth group compared with typical hospital scores of 77%.

conclusionsClinically and statistically meaningful reductions in acute hospital bed days in the intention-to-treat group when compared to that of the usual care group were demonstrated (P<.001), although admission frequency was unchanged with more short stay admissions in the intention-to-treat group. Nonrandomized control selection was a limitation. Nonetheless, MonashWatch was successful in the context of the HealthLinks: Chronic Care capitation initiative and is expanding.

Indexed as

AgedAustraliaFemaleHospitalizationHumansMalePatient ReadmissionTelemedicineevaluationfunding modelhealth services researchpotentially preventable hospitalizations, medical informaticstelehealth

Identifiers

PMID33258781
PMCPMC7738256
OpenAlexW3048635763

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.