Evidence map›Paper›PMID 33851922›Full record

ArticleJournal of medical Internet research2021

Reduction in Hospital System Opioid Prescribing for Acute Pain Through Default Prescription Preference Settings: Pre-Post Study.

Benjamin Heritier Slovis, Jeffrey M Riggio, Melanie Girondo, Cara Martino, Bracken Babula, Lindsey M Roke, John C Kairys

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. 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
2.0field-weighted citation impact, top 14% 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, 12 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Observational
  4. Article
  5. Article
  6. 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

7 authors at 1 institution in 1 country.

Benjamin Heritier SlovisOffice of Clinical Informatics, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0003-4396-3512
Jeffrey M RiggioOffice of Clinical Informatics, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0002-8655-3006
Melanie GirondoInformation Services and Technology, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0002-8362-3486
Cara MartinoInformation Services and Technology, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0003-0399-4621
Bracken BabulaOffice of Clinical Informatics, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0001-7896-3972
Lindsey M RokeInformation Services and Technology, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0002-9134-8269
John C KairysOffice of Clinical Informatics, Thomas Jefferson University, Philadelphia, PA, United States.ORCID 0000-0002-8195-5233
Thomas Jefferson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe United States is in an opioid epidemic. Passive decision support in the electronic health record (EHR) through opioid prescription presets may aid in curbing opioid dependence.

objectiveThe objective of this study is to determine whether modification of opioid prescribing presets in the EHR could change prescribing patterns for an entire hospital system.

methodsWe performed a quasi-experimental retrospective pre-post analysis of a 24-month period before and after modifications to our EHR's opioid prescription presets to match Centers for Disease Control and Prevention guidelines. We included all opioid prescriptions prescribed at our institution for nonchronic pain. Our modifications to the EHR include (1) making duration of treatment for an opioid prescription mandatory, (2) adding a quick button for 3 days' duration while removing others, and (3) setting the default quantity of all oral opioid formulations to 10 tablets. We examined the quantity in tablets, duration in days, and proportion of prescriptions greater than 90 morphine milligram equivalents/day for our hospital system, and compared these values before and after our intervention for effect.

resultsThere were 78,246 prescriptions included in our study written on 30,975 unique patients. There was a significant reduction for all opioid prescriptions pre versus post in (1) the overall median quantity of tablets dispensed (54 [IQR 40-120] vs 42 [IQR 18-90]; P<.001), (2) median duration of treatment (10.5 days [IQR 5.0-30] vs 7.5 days [IQR 3.0-30]; P<.001), and (3) proportion of prescriptions greater than 90 morphine milligram equivalents/day (27.46% [10,704/38,976; 95% CI 27.02%-27.91%] vs 22.86% [8979/39,270; 95% CI 22.45%-23.28%]; P<.001).

conclusionsModifications of opioid prescribing presets in the EHR can improve prescribing practice patterns. Reducing duration and quantity of opioid prescriptions could reduce the risk of dependence and overdose.

Indexed as

Acute PainAnalgesics, OpioidHospitalsHumansPractice Patterns, Physicians'PrescriptionsRetrospective StudiesUnited StatesAnalgesics, Opioidelectronic health recordinformaticsopioidsoxycodoneprescriptions

Identifiers

PMID33851922
PMCPMC8082388
OpenAlexW3154939553

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.