Evidence map›Paper›PMID 34014326›Full record

ArticleJAMA network open2021

Comparison of Methods for Alcohol and Drug Screening in Primary Care Clinics.

Jennifer McNeely, Angéline Adam, John Rotrosen, Sarah E Wakeman, Timothy E Wilens, Joseph Kannry, Richard N Rosenthal, Aimee Wahle, Seth Pitts, Sarah Farkas and 12 more

Registry-linked trialOpen access · goldAbstract readComparative Study
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02963948 (A Phased-Implementation Feasibility and Proof-of-Concept Study to Assess Incorporating the NIDA CTN Common Data Elements), which is not on this map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
13.4field-weighted citation impact, top 1% 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.

NCT02963948 completednot on this map

A Phased-Implementation Feasibility and Proof-of-Concept Study to Assess Incorporating the NIDA CTN Common Data Elements (CDEs) Into the Electronic Health Record (EHR) in Large Primary Care Settings ("CDE-EHR-PC" Study), Phase 3

TypeobservationalSponsorNYU Langone HealthRan2018 to 2019Enrolled113,123ConditionsSubstance AbuseArmsMeetings with implementation leader(s), the SAAS survey, Patient surveys
3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Trial
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. 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

22 authors at 8 institutions in 2 countries.

Jennifer McNeelyDepartment of Population Health, New York University Grossman School of Medicine, New York.
Angéline AdamDepartment of Psychiatry, University Hospital Lausanne, Lausanne, Switzerland.
John RotrosenDepartment of Psychiatry, New York University Grossman School of Medicine, New York.
Sarah E WakemanDepartment of Medicine, Harvard Medical School, Massachusetts General Hospital, Boston.
Timothy E WilensDepartment of Psychiatry, Massachusetts General Hospital, Boston.
Joseph KannryDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Richard N RosenthalRenaissance School of Medicine, Stony Brook University, Stony Brook, New York.
Aimee WahleThe Emmes Company, Rockville, Maryland.
Seth PittsThe Emmes Company, Rockville, Maryland.
Sarah FarkasDepartment of Psychiatry, New York University Grossman School of Medicine, New York.
Carmen RosaNational Institute on Drug Abuse, Bethesda, Maryland.
Lauren PeccoraloDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Eva WaiteDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Aida VegaDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Jennifer KentDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Catherine K CravenDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
Tamar A KaminskiDepartment of Psychiatry, Massachusetts General Hospital, Boston.
Elizabeth FirminDepartment of Psychiatry, Massachusetts General Hospital, Boston.
Benjamin IsenbergDepartment of Psychiatry, Massachusetts General Hospital, Boston.
Melanie HarrisDepartment of Population Health, New York University Grossman School of Medicine, New York.
Andre KushnirukSchool of Health Information Science, University of Victoria, Victoria, British Columbia, Canada.
Leah HamiltonDepartment of Population Health, New York University Grossman School of Medicine, New York.
Icahn School of Medicine at Mount Sinai · USNew York University · USMassachusetts General Hospital · USEmmes (United States) · USHarvard University · USNational Institute on Drug Abuse · USStony Brook School · USUniversity of Victoria · CA

Funding

Project-003UG1DA013035 · NIDA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Aimee N Campbell, Jennifer McNeely · 2015 to 2026
$130.3M
NIDA NIH HHS UG1 DA013035
6 · The paper itself

Abstract

Importance: Guidelines recommend that adult patients receive screening for alcohol and drug use during primary care visits, but the adoption of screening in routine practice remains low. Clinics frequently struggle to choose a screening approach that is best suited to their resources, workflows, and patient populations. Objective: To evaluate how to best implement electronic health record (EHR)-integrated screening for substance use by comparing commonly used screening methods and examining their association with implementation outcomes. Design, Setting, and Participants: This article presents the outcomes of phases 3 and 4 of a 4-phase quality improvement, implementation feasibility study in which researchers worked with stakeholders at 6 primary care clinics in 2 large urban academic health care systems to define and implement their optimal screening approach. Site A was located in New York City and comprised 2 clinics, and site B was located in Boston, Massachusetts, and comprised 4 clinics. Clinics initiated screening between January 2017 and October 2018, and 93 114 patients were eligible for screening for alcohol and drug use. Data used in the analysis were collected between January 2017 and October 2019, and analysis was performed from July 13, 2018, to March 23, 2021. Interventions: Clinics integrated validated screening questions and a brief counseling script into the EHR, with implementation supported by the use of clinical champions (ie, clinicians who advocate for change, motivate others, and use their expertise to facilitate the adoption of an intervention) and the training of clinic staff. Clinics varied in their screening approaches, including the type of visit targeted for screening (any visit vs annual examinations only), the mode of administration (staff-administered vs self-administered by the patient), and the extent to which they used practice facilitation and EHR usability testing. Main Outcomes and Measures: Data from the EHRs were extracted quarterly for 12 months to measure implementation outcomes. The primary outcome was screening rate for alcohol and drug use. Secondary outcomes were the prevalence of unhealthy alcohol and drug use detected via screening, and clinician adoption of a brief counseling script. Results: Patients of the 6 clinics had a mean (SD) age ranging from 48.9 (17.3) years at clinic B2 to 59.1 (16.7) years at clinic B3, were predominantly female (52.4% at clinic A1 to 64.6% at clinic A2), and were English speaking. Racial diversity varied by location. Of the 93,114 patients with primary care visits, 71.8% received screening for alcohol use, and 70.5% received screening for drug use. Screening at any visit (implemented at site A) in comparison with screening at annual examinations only (implemented at site B) was associated with higher screening rates for alcohol use (90.3%-94.7% vs 24.2%-72.0%, respectively) and drug use (89.6%-93.9% vs 24.6%-69.8%). The 5 clinics that used a self-administered screening approach had a higher detection rate for moderate- to high-risk alcohol use (14.7%-36.6%) compared with the 1 clinic that used a staff-administered screening approach (1.6%). The detection of moderate- to high-risk drug use was low across all clinics (0.5%-1.0%). Clinics with more robust practice facilitation and EHR usability testing had somewhat greater adoption of the counseling script for patients with moderate-high risk alcohol or drug use (1.4%-12.5% vs 0.1%-1.1%). Conclusions and Relevance: In this quality improvement study, EHR-integrated screening was feasible to implement in all clinics and unhealthy alcohol use was detected more frequently when self-administered screening was used at any primary care visit. The detection of drug use was low at all clinics, as was clinician adoption of counseling. These findings can be used to inform the decision-making of health care systems that are seeking to implement screening for substance use. Trial Registration: ClinicalTrials.gov Identifier: NCT02963948.

Indexed as

Practice Guidelines as TopicAdultAgedAlcoholismBostonFemaleHumansMaleMass ScreeningMiddle AgedNew York CityPrimary Health CareSubstance-Related Disorders

Identifiers

PMID34014326
PMCPMC8138691
OpenAlexW3162618532

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.