Evidence map›Paper›PMID 35325663›Full record

ArticleInternational journal of medical informatics2022

Opioid2MME: Standardizing opioid prescriptions to morphine milligram equivalents from electronic health records.

Juan Antonio Lossio-Ventura, Wenyu Song, Michael Sainlaire, Patricia C Dykes, Tina Hernandez-Boussard

Abstract read
In one paragraph

Article in International journal of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Substance Use Disorder Outcomes and Treatment by Autism Status in Medicaid Enrollees With Substance Use Disorder.Autism research : official journal of the International Society for Autism Research · 2026
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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.

Juan Antonio Lossio-VenturaDepartment of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA; National Institute of Mental Health, National Institutes of Health, MD, USA. Electronic address: juan.lossio@nih.gov.
Wenyu SongDepartment of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Michael SainlaireDepartment of Medicine, Brigham & Women's Hospital, Boston, MA, USA.
Patricia C DykesDepartment of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Mass General Brigham, Boston, MA, USA.
Tina Hernandez-BoussardDepartment of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA.

Funding

Machine Learning TeamZICMH002968 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI PEREIRA, FRANCISCO · 2018 to 2025
$13.9M
Advancing Knowledge Discovery for Postoperative Pain ManagementR01LM013362 · NLM · STANFORD UNIVERSITY · PI HERNANDEZ-BOUSSARD, TINA · 2019 to 2023
$3.2M
NLM NIH HHS R01 LM013362
6 · The paper itself

Abstract

backgroundThe national increase in opioid use and misuse has become a public health crisis in the U.S. To tackle this crisis, the systematic evaluation and monitoring of opioid prescribing patterns is necessary. Thus, opioid prescriptions from electronic health records (EHRs) must be standardized to morphine milligram equivalent (MME) to facilitate monitoring and surveillance. While most studies report MMEs to describe opioid prescribing patterns, there is a lack of transparency regarding their data pre-processing and conversion processes for replication or comparison purposes.

methodsIn this work, we developed Opioid2MME, a SQL-based open-source framework, to convert opioid prescriptions to MMEs using EHR prescription data. The MME conversions were validated internally using F-measures through manual chart review; were compared with two existing tools, as MedEx and MedXN; and the framework was tested in an external academic EHR system.

resultsWe identified 232,913 prescriptions for 49,060 unique patients in the EHRs, 2008-2019. We manually annotated a sample of prescriptions to assess the performance of the framework. The internal evaluation for medication information extraction achieved F-measures from 0.98 to 1.00 for each piece of the extracted information, outperforming MedEx and MedXN (F-Scores 0.98 and 0.94, respectively). MME values in the internal EHR system obtained a F-measure of 0.97 and identified 3% of the data as outliers and 7% missing values. The MME conversion in the external EHR system obtained 78.3% agreement between the MME values obtained with the development site.

conclusionsThe results demonstrated that the framework is replicable and capable of converting opioid prescriptions to MMEs across different medical institutions. In summary, this work sets the groundwork for the systematic evaluation and monitoring of opioid prescribing patterns across healthcare systems.

Indexed as

Database management systemElectronic Health RecordsMorphine milligram equivalentNatural language processingOpioid epidemic

Identifiers

PMID35325663
PMCPMC9477978

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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.