Evidence map›Paper›PMID 38151042›Full record

ArticleApplied clinical informatics2023

A Tool to Identify and Engage Patients on Risky Opioid Regimens.

Ashley C Griffin, Taryn Perez, Tigran Avoundjian, William Becker, Amanda M Midboe

Abstract readCase Reports
In one paragraph

Article in Applied clinical informatics, 2023. 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. 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.

Ashley C GriffinVeterans Affairs Palo Alto Health Care System, Palo Alto, California, United States.
Taryn PerezVeterans Affairs Palo Alto Health Care System, Palo Alto, California, United States.
Tigran AvoundjianVeterans Affairs Palo Alto Health Care System, Palo Alto, California, United States.
William BeckerVeterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States.
Amanda M MidboeVeterans Affairs Palo Alto Health Care System, Palo Alto, California, United States.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundConcerns around opioid safety for patients living with chronic pain have led to a growing number of collaborative and multimodal pain care initiatives. A major challenge in these efforts has been identifying and engaging patients on high-risk opioid regimens in a timely manner.

objectivesIn this clinical informatics case report, we describe the development and implementation of a web-based tool to support providers as they implement an integrated pain support clinical initiative at primary care clinics across three health care systems.

methodsThe tool identifies patients on risky opioid medication regimens and generates autopopulated patient outreach letters. It contains three core functions that: (1) identify patients prescribed high-dose opioids or coprescribed opioids and benzodiazepines, (2) generate automated letters for patients with an upcoming primary care appointment, and (3) allow clinic staff to write back to a database to track outreach and referrals. Qualitative stakeholder feedback was gathered through interviews and user testing to assess perceived usefulness and ease of use of the tool.

resultsOver a 24-month period, the tool identified 1,125 patients prescribed risky medication regimens and generated 1,315 total letters as some patients became reeligible. Stakeholder feedback revealed that the tool was useful to quickly find patients on risky medication regimens and efficient in generating prepopulated letters that could be mailed in large batches. Additional feedback led to iterative refinements and improved system capabilities that varied across clinics.

conclusionDeploying clinical informatics tools that prioritize, engage, and track high-risk patient populations supports reduction of risky medication regimens. Such tools can reduce workload burden on busy primary care staff, particularly during implementation studies, and enhance patient-centered care through the use of direct-to-consumer outreach.

Indexed as

Analgesics, OpioidChronic PainHumansPatient-Centered CareAnalgesics, Opioid

Identifiers

PMID38151042
PMCPMC10752654

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.