Evidence map›Paper›PMID 40355787›Full record

ArticleJournal of general internal medicine2025

The Rise of Clinical Decision Support Algorithms in Pain Management 2009-2024.

Dan Kabella, Dorie Apollonio, Halle Young, Kelly R Knight

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
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

4 authors.

Dan KabellaCenter for Tobacco Control Research and Education, University of California San Francisco, San Francisco, CA, USA.
Dorie ApollonioCenter for Tobacco Control Research and Education, University of California San Francisco, San Francisco, CA, USA.
Halle YoungDepartment of Humanities & Social Sciences, University of California San Francisco, San Francisco, CA, USA.
Kelly R KnightDepartment of Humanities & Social Sciences, University of California San Francisco, San Francisco, CA, USA. kelly.knight@ucsf.edu.ORCID 0000-0002-5838-8592

Funding

Using opioid industry documents to understand marketing of addictive medicationsR01DA058687 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Dorothy Elinor Apollonio · 2024 to 2026
$2.0M
Postdoctoral training in tobacco controlT32DA057216 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Dorothy Elinor Apollonio, Pamela May Ling · 2023 to 2026
$2.0M
Intramural Research Program, National Institute on Drug Abuse NIH R01DA058687Intramural Research Program, National Institute on Drug Abuse T32DA057216NIDA NIH HHS R01 DA058687NIDA NIH HHS T32 DA057216
6 · The paper itself

Abstract

This paper examines the rise of clinical decision support algorithms used to assess risk in pain management and the opioid industry's influence on their development and implementation. To understand this influence, we conducted a qualitative study of documents related to the development of a tool that relied on artificial intelligence (AI) to suggest modifications in opioid prescribing, called NarxCare. The study began with keyword searches of the Opioid Industry Document Archive (OIDA), which contained over 3 million documents at the time of the study, to examine the pharmaceutical industry's role in shaping the digital transformation of opioid prescribing. Our findings highlight industry-driven investments, educational campaigns, corporate policy activities, and the reliance on proprietary data that facilitated the widespread implementation of NarxCare. The increasing reliance on NarxCare raises concerns about its limited transparency, unknown reliability, and potential bias which may disproportionately affect certain patient groups based on race, socioeconomic status, or health conditions. This paper argues that the promotion of technologies like NarxCare allows the pharmaceutical industry to reinforce the narrative that opioids can be effective when prescribed responsibly, using advanced, data-driven strategies. Marketed as tools that assist clinicians in making more informed prescribing decisions, NarxCare contributes to the portrayal of the industry as a responsible actor in the regulation and distribution of opioids. Shifting attention to individual risk factors rather than systemic challenges enables the pharmaceutical industry to sidestep its role in the opioid crisis and evade scrutiny for its influence over regulation, the sponsorship of education and research, lobbying, supply chain control, and public health narratives. While NarxCare aims to improve prescribing safety, it requires critical evaluation in terms of effectiveness, ethical considerations, and the continued influence of the pharmaceutical industry in its design and implementation.

Indexed as

AlgorithmsAnalgesics, OpioidDecision Support Systems, ClinicalPain ManagementArtificial IntelligenceDrug IndustryHumansAnalgesics, Opioidalgorithmsclinical decision supportopioid industryopioid risk scorespain

Identifiers

PMID40355787
PMCPMC12343441

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.