Evidence map›Paper›PMID 34179457›Full record

ArticleIEEE robotics and automation letters2021

Reducing Fall-related Revisits for Elderly Diabetes Patients in Emergency Departments: A Transition Flow Model.

Wenjun Zhu, Allie DeLonay, Maureen Smith, Pascale Carayon, Jingshan Li

Abstract read
PubMed Full text at PMC
In one paragraph

Article in IEEE robotics and automation letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A Soft Sensor for Bleeding Detection in Colonoscopies.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2022
    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.

Wenjun ZhuDepartment of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA.
Allie DeLonayDepartment of Population Health Sciences, University of Wisconsin, Madison, WI 53705 USA.
Maureen SmithDepartments of Population Health Sciences and Family Medicine & Community Health, University of Wisconsin, Madison, WI 53705 USA.
Pascale CarayonDepartment of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA.
Jingshan LiDepartment of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA.

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
Institutional Clinical and Translational Science AwardUL1TR000427 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI DREZNER, MARC KENNETH · 2012 to 2016
$32.2M
Engineering Safe Care Journeys for Vulnerable Older AdultsR18HS026624 · AHRQ · UNIVERSITY OF WISCONSIN-MADISON · PI SMITH, MAUREEN A, WERNER, NICOLE E · 2018 to 2021
$2.5M
AHRQ HHS R18 HS026624NCATS NIH HHS UL1 TR000427NCATS NIH HHS UL1 TR002373
6 · The paper itself

Abstract

This paper introduces a transition flow model to study fall-related emergency department (ED) revisits for elderly patients with diabetes. Five diabetes classes are used to classify patients at discharge, within 7-day revisits, and between 8 and 30-day revisits. Analytical formulas to evaluate patient revisiting risks are derived. To reduce revisits, sensitivity analysis is introduced to identify the most critical, i.e., dominant, factors whose changes can lead to the largest reduction in revisits. In addition, a case study at University of Wisconsin (UW) Hospital ED is described to illustrate the applicability of the model.

Indexed as

diabeteselder patientsEmergency departmentfallsrevisitstransition flow model

Identifiers

PMID34179457
PMCPMC8224474

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.