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

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.

Figure 1:

An overview of the framework to calculate MME values from EHR based opioid prescriptions. The parameters configuration module allows to set up the SQL module for execution. The SQL module converts the opioid prescriptions to MME values and creates a new table.

Figure 2:

Distribution of MME values per day of all opioid prescriptions. Left side (A) shows the entire distribution our all MME values calculated on the internal dataset. Right side (B) illustrates in detail the MME values per day.

Figure 3:

Example of an outlier with a MME value equal to 540. Experts agreed that this high value was due to a mistake when entering information in EHRs.

Figure 4:

Average of MME per day per patient for each year obtained on the internal dataset.

Figure 5:

A. Average of MME per day per year obtained with our framework using datasets from both academic institutes. B. Average of MME per day per year obtained with the framework and the BWH tool on the validation EHR dataset.