Evidence map›Paper›PMID 33885773›Full record

Trial reportJAMA network open2021

Effect of Changing Electronic Health Record Opioid Analgesic Dispense Quantity Defaults on the Quantity Prescribed: A Cluster Randomized Clinical Trial.

Marcus A Bachhuber, Denis Nash, William N Southern, Moonseong Heo, Matthew Berger, Mark Schepis, Manu Thakral, Chinazo O Cunningham

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report 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 NCT03003832 (A Cluster-randomized Trial of Modifying Electronic Health Record Defaults to Reduce the Prescribed Quantity of Opioid Analgesics in Primary Care and Emergency Department Settings), which is not on this map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 12% 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.

NCT03003832 nacompletednot on this map

A Cluster-randomized Trial of Modifying Electronic Health Record Defaults to Reduce the Prescribed Quantity of Opioid Analgesics in Primary Care and Emergency Department Settings

TypeinterventionalSponsorMontefiore Medical CenterRan2016 to 2018Enrolled15,000ConditionsAcute PainArmsChange in electronic health record default for new opioid analgesic prescriptions
3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 16 citations in OpenAlex.

  1. Trial
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  13. Behavioral "nudges" in the electronic health record to reduce waste and misuse: 3 interventions.Journal of the American Medical Informatics Association : JAMIA · 2023
    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

8 authors at 4 institutions in 1 country.

Marcus A BachhuberDivision of General Internal Medicine, Department of Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York.
Denis NashInstitute for Implementation Science in Population Health, City University of New York, New York.
William N SouthernDivision of Hospital Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York.
Moonseong HeoDivision of General Internal Medicine, Department of Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York.
Matthew BergerMontefiore Information Technology, Montefiore Medical Center, Bronx, New York.
Mark SchepisMontefiore Information Technology, Montefiore Medical Center, Bronx, New York.
Manu ThakralCollege of Nursing and Health Sciences, University of Massachusetts Boston, Boston.
Chinazo O CunninghamDivision of General Internal Medicine, Department of Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York.
Albert Einstein College of Medicine · USMontefiore Medical Center · USCity University of New York · USUniversity of Massachusetts Boston · US

Funding

Patient and Population Health Outcomes Research SWGP30AI124414 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI Vilma Gabbay · 2017 to 2026
$28.3M
CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patientsUL1TR002556 · NCATS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KELLER, MARLA J, KIM, MIMI Y · 2018 to 2022
$26.7M
Core-010UL1TR001073 · NCATS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KELLER, MARLA J, SHAMOON, HARRY · 2013 to 2017
$21.3M
Mentoring researchers on drug abuse and HIVK24DA036955 · NIDA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI CUNNINGHAM, CHINAZO · 2014 to 2019
$911k
Leveraging the electronic health record to reduce opioid analgesic prescriptionsK08DA043050 · NIDA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI BACHHUBER, MARCUS A · 2017 to 2018
$223k
NCATS NIH HHS UL1 TR001073NCATS NIH HHS UL1 TR002556NIAID NIH HHS P30 AI124414NIDA NIH HHS K08 DA043050NIDA NIH HHS K24 DA036955
6 · The paper itself

Abstract

Importance: Interventions to improve judicious prescribing of opioid analgesics for acute pain are needed owing to the risks of diversion, misuse, and overdose. Objective: To assess the effect of modifying opioid analgesic prescribing defaults in the electronic health record (EHR) on prescribing and health service use. Design, Setting, and Participants: A cluster randomized clinical trial with 2 parallel arms was conducted between June 13, 2016, and June 13, 2018, in a large urban health care system comprising 32 primary care and 4 emergency department (ED) sites in the Bronx, New York. Data were analyzed using a difference-in-differences method from 6 months before implementation through 18 months after implementation. Data were analyzed from January 2019 to February 2020. Interventions: A default dispense quantity for new opioid analgesic prescriptions of 10 tablets (intervention) vs no change (control) in the EHR. Main Outcomes and Measures: The primary outcome was the quantity of opioid analgesics prescribed with the new default prescription. Secondary outcomes were opioid analgesic reorders and health service use within 30 days after the new prescription. Intention-to-treat analysis was conducted. Results: Overall, 21 331 patients received a new opioid analgesic prescription from 490 prescribers. Comparing the intervention and control arms, site, prescriber, and patient characteristics were similar. For the new prescription, compared with the control arm, patients in the intervention arm had significantly more prescriptions for 10 tablets or fewer (7.6 percentage points; 95% CI, 6.1-9.2 percentage points), a lower number of tablets prescribed (-2.1 tablets; 95% CI, -3.3 to -0.9 tablets), and lower morphine milligram equivalents (MME) prescribed (-14.6 MME; 95% CI, -22.6 to -6.6 MME). Within 30 days after the new prescription, significant differences remained in the number of tablets prescribed (-2.7 tablets; 95% CI, -4.8 to -0.6 tablets), but not MME (-15.8 MME; 95% CI, -33.8 to 2.2 MME). Within this 30-day period, there were no significant differences between the arms in health service use. Conclusions and Relevance: In this study, implementation of a uniform reduced default dispense quantity of 10 tablets for opioid analgesic prescriptions led to a modest reduction in the quantity prescribed initially, without significantly increasing health service use. However, during 30 days after implementation, the influence on prescribing was mixed. Reducing EHR default dispense quantities for opioid analgesics is a feasible strategy that can be widely disseminated and may modestly reduce prescribing. Trial Registration: ClinicalTrials.gov Identifier: NCT03003832.

Indexed as

Electronic Health RecordsAcute PainAdultAnalgesics, OpioidEmergency Service, HospitalFemaleHumansMaleMiddle AgedPractice Patterns, Physicians'Primary Health CareAnalgesics, Opioid

Identifiers

PMID33885773
PMCPMC8063068
OpenAlexW3154616243

What OpenQuestion holds

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LicenceCC BY
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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.