Evidence map›Paper›PMID 33626016›Full record

ArticleJMIR mHealth and uHealth2021

Using a Clinical Workflow Analysis to Enhance eHealth Implementation Planning: Tutorial and Case Study.

Stephanie Staras, Justin S Tauscher, Natalie Rich, Esaa Samarah, Lindsay A Thompson, Michelle M Vinson, Michael J Muszynski, Elizabeth A Shenkman

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Review
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  7. Scaling out a Digital-First Behavioral Health Care Model to Primary Care.Administration and policy in mental health · 2025
    Article
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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

8 authors.

Stephanie StarasDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0000-0002-0726-1524
Justin S TauscherBehavioral Research in Technology and Engineering Center, Department of Psychiatry, University of Washington, Seattle, WA, United States.ORCID 0000-0002-6129-9371
Natalie RichDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0000-0003-2081-2965
Esaa SamarahDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0000-0001-8643-5555
Lindsay A ThompsonDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0000-0002-9066-5131
Michelle M VinsonDepartment of Clinical Sciences, Florida State University College of Medicine Orlando Regional Campus, Orlando, FL, United States.ORCID 0000-0001-6417-8295
Michael J MuszynskiDepartment of Clinical Sciences, Florida State University College of Medicine Orlando Regional Campus, Orlando, FL, United States.ORCID 0000-0001-8111-6512
Elizabeth A ShenkmanDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0000-0003-4903-1804

Funding

Maximizing HPV vaccination: Real-time Reminders, Guidance, and RecommendationsR21CA202011 · NCI · UNIVERSITY OF FLORIDA · PI STARAS, STEPHANIE A S · 2016 to 2017
$372k
NCI NIH HHS R21 CA202011
6 · The paper itself

Abstract

eHealth apps often fail to improve clinical outcomes due to poor integration with clinical workflow-the sequence and personnel needed to undertake a series of tasks for clinical care. Our central thesis is that eHealth interventions will be more effective if the clinical workflow is studied and taken into consideration for intervention implementation. This paper aims to provide an introductory tutorial on when and how to use a clinical workflow analysis to guide the implementation of eHealth interventions. The tutorial includes a step-by-step guide to conducting a clinical workflow analysis in planning for eHealth implementation. We began with a description of why a clinical workflow analysis is best completed before the implementation of eHealth interventions. Next, we described 4 steps needed to perform the clinical workflow analysis: the identification of discrete workflow components, workflow assessment, triangulation, and the stakeholder proposal of intervention implementation. Finally, we presented a case study of a clinical workflow analysis, which was conducted during patient visits of patients aged 11 or 12 years from 4 diverse pediatric or family medicine clinics to plan the implementation of a tablet-based app for adolescent vaccination. Investigators planning the implementation of new eHealth interventions in health care settings can use the presented steps to assess clinical workflow, thereby maximizing the match of their intervention with the clinical workflow. Conducting a prospective workflow study allows for evidence-based planning, identifying potential pitfalls, and increasing stakeholder buy-in and engagement. This tutorial should aid investigators in increasing the successful implementation of eHealth interventions.

Indexed as

TelemedicineAdolescentChildHumansProspective StudiesResearch DesignResearch PersonnelWorkfloweHealthimplementation scienceprimary carestakeholder engagementworkflow

Identifiers

PMID33626016
PMCPMC8047797

What OpenQuestion holds

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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.