Evidence map›Paper›PMID 23808888›Full record

ArticleTelemedicine journal and e-health : the official journal of the American Telemedicine Association2013

A retrospective study on patient characteristics and telehealth alerts indicative of key medical events for heart failure patients at a home health agency.

Kavita Radhakrishnan, Kathryn Bowles, Alexandra Hanlon, Maxim Topaz, Jesse Chittams

Abstract read
In one paragraph

Article in Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Supporting Patient-Provider Collaboration to Identify Individual Triggers using Food and Symptom Journals.CSCW : proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported Cooperative Work · 2017
    Article
  5. Observational
  6. Review
  7. Boundary Negotiating Artifacts in Personal Informatics: Patient-Provider Collaboration with Patient-Generated Data.CSCW : proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported Cooperative Work · 2016
    Article
  8. Article
  9. Article
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

5 authors.

Kavita Radhakrishnan1 School of Nursing, University of Texas at Austin , Austin, Texas.
Kathryn Bowles
Alexandra Hanlon
Maxim Topaz
Jesse Chittams

Funding

Individualized Care for At Risk Older AdultsT32NR009356 · NINR · UNIVERSITY OF PENNSYLVANIA · PI Lauren M Massimo, MARY D NAYLOR · 2007 to 2026
$7.8M
NINR NIH HHS T32NR009356
6 · The paper itself

Abstract

objectiveTo explore association of patient characteristics and telehealth alert data with all-cause key medical events (KMEs) of emergency department (ED) visits and hospitalizations as well as cardiac-related KMEs of ED visits, hospitalizations, and medication changes. MATERIALS AND

methodsA 6-month retrospective study was conducted of electronic patient records of heart failure (HF) patients using telehealth services at a Massachusetts home health agency. Data collected included patient demographic, psychosocial, disease severity factors and telehealth vital signs alerts. Association between patient characteristics and KMEs was analyzed by Generalized Estimating Equations.

resultsThe sample comprised 168 patients with a mean age of 83 years, 56% females, and 96% white. Ninety-nine cardiac-related KMEs and 87 all-cause KMEs were recorded for the subjects. Odds of a cardiac-related KME increased by 161% with the presence of valvular co-morbidity (p=0.001) and 106% with increased number of telehealth alerts (adjusted p<0.0001). Odds of an all-cause KME increased by 124% (p=0.02), 127% (p=0.01), and 70% (adjusted p<0.0001) with the presence of cancer co-morbidity, anxiety, and increased number of telehealth alerts, respectively. Overall, only 3% of all telehealth alerts were associated with KMEs.

conclusionsThe very low proportion of telehealth vital sign alerts associated with KMEs indicates that telehealth alerts alone cannot inform the need for intervention within the larger context of HF care delivery in the homecare setting. Patient-relevant data such as psychosocial and symptom status, involvement with HF self-management, and presence of co-morbidities could further inform the need for interventions for HF patients in the homecare setting.

Indexed as

Clinical AlarmsHeart FailureHome Care AgenciesTelemedicineAgedAged, 80 and overComorbidityCritical CareElectronic Health RecordsEmergency Service, HospitalFemaleHospitalizationHumansMaleMassachusettsMedical Audit

Identifiers

PMID23808888
PMCPMC3757529

What OpenQuestion holds

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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.