Evidence map›Paper›PMID 30482740›Full record

ArticleJMIR cancer2018

A Rapid Process for Identifying and Prioritizing Technology-Based Tools for Health System Implementation.

Emily R Dibble, Bradley E Iott, Allen J Flynn, Darren P King, Mark P MacEachern, Charles P Friedman, Tanner J Caverly

Abstract read
In one paragraph

Article in JMIR cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Emily R Dibble *Department of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-0516-1850
Bradley E Iott *Department of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0001-9784-0521
Allen J FlynnDepartment of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-3471-3063
Darren P KingDivision of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-3647-9677
Mark P MacEachernTaubman Health Sciences Library, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-8872-1181
Charles P FriedmanDepartment of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-3395-5199
Tanner J CaverlyDepartment of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-6560-0547

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth system decisions to put new technologies into clinical practice require a rapid and trustworthy decision-making process informed by best evidence.

objectiveThis study aimed to present a rapid evidence review process that can be used to inform health system leaders and clinicians seeking to implement new technology tools to improve patient-clinician decision making and patient-oriented outcomes.

methodsThe rapid evidence review process we pioneered involved 5 sequential subprocesses: (1) environmental scan, (2) expert panel recruitment, (3) host evidence review panel, (4) analysis, and (5) local validation panel. We conducted an environmental scan of health information technology (IT) literature to identify relevant digital tools in oncology care. We synthesized the recent literature using current evidence review methods, creating visual summaries for use by a national panel of experts. Panelists were taken through a 6-hour modified Delphi process to prioritize tools for implementation. Findings from the rapid evidence review panel were taken to a local validation panel for further rapid review during a 3-hour session.

resultsOur rapid evidence review process shows promise for informing decision making by reducing the amount of time and resources needed to identify and prioritize adoption of IT tools. Despite evidence of improved patient outcomes, panelists had substantial concerns about implementing patient-reported outcome tracking tools, voicing concerns about liability, lack of familiarity with new technology, and additional time and workflow changes such tools would require. Instead, clinicians favored technologies that did not require clinician involvement.

conclusionsHealth system leaders can use the rapid evidence review process presented here to usefully inform local technology adoption, implementation, and use in practice.

Indexed as

clinical decision supportdecision support systems, clinicalevidence-based practiceevidence reviewexpert panelhealth information technologymedical informatics applicationsoncology care modelpatient reported outcome measurespractice guidelines as topic

Identifiers

PMID30482740
PMCPMC6290266

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.