Evidence map›Paper›PMID 35649500›Full record

ArticleApplied clinical informatics2022

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care.

Katie S Allen, Elizabeth C Danielson, Sarah M Downs, Olena Mazurenko, Julie Diiulio, Ramzi G Salloum, Burke W Mamlin, Christopher A Harle

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

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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 at 5 institutions in 1 country.

Katie S AllenHealth Policy and Management, Richard M. Fairbanks School of Public Health, IUPUI, Indianapolis, Indiana, United States.
Elizabeth C DanielsonCenter for Education in Health Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States.
Sarah M DownsDivision of Internal Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States.
Olena MazurenkoHealth Policy and Management, Richard M. Fairbanks School of Public Health, IUPUI, Indianapolis, Indiana, United States.
Julie DiiulioHealth Outcomes and Biomedical Informatics, Applied Decision Science, LLC, Dayton, Ohio, United States.
Ramzi G SalloumUniversity of Florida, Gainesville, Florida, United States.
Burke W MamlinCenter for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, United States.
Christopher A HarleCenter for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, United States.
Indiana University – Purdue University Indianapolis · USUniversity of Florida · USApplied Decision Science (United States) · USIndiana University School of MedicineNorthwestern University · US

Funding

Designing User-Centered Decision Support Tools for Chronic Pain in Primary CareR01HS023306 · AHRQ · INDIANA UNIVERSITY INDIANAPOLIS · PI HARLE, CHRISTOPHER ALBERT · 2015 to 2018
$2.0M
AHRQ HHS R01 HS023306R01HS023306 Agency for Healthcare Research and Quality, Designing User-Centered Decision Support Tools for Primary Care Pain Management
6 · The paper itself

Abstract

objectivesThe Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design.

methodsWe conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts.

resultsWe identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality.

conclusionTools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

Indexed as

Chronic PainDecision Support Systems, ClinicalAnalgesics, OpioidElectronic Health RecordsHumansPrimary Health CareAnalgesics, Opioid

Identifiers

PMID35649500
PMCPMC9279015
OpenAlexW4281750559

What OpenQuestion holds

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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.