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

doi:10.1016/j.ijmedinf.2022.104739

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

Int J Med Inform. 2022 Mar 16:162:104739. doi: 10.1016/j.ijmedinf.2022.104739. Online ahead of print.

Authors

Juan Antonio Lossio-Ventura¹, Wenyu Song², Michael Sainlaire³, Patricia C Dykes⁴, Tina Hernandez-Boussard⁵

Affiliations

¹ Department 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.
² Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
³ Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA.
⁴ Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Mass General Brigham, Boston, MA, USA.
⁵ Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA.

Abstract

Background: The 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.

Methods: In 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.

Results: We 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.

Conclusions: The 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.

Keywords: Database management system; Electronic Health Records; Morphine milligram equivalent; Natural language processing; Opioid epidemic.

Grants and funding

R01 LM013362/LM/NLM NIH HHS/United States