Complexities and approaches for deriving longitudinal daily morphine milligram equivalents using electronic health record prescription data

Abstract

Objective: To describe challenges and solutions for calculating longitudinal daily opioid dose in morphine milligram equivalents from electronic health record prescriptions for a clinical trial of voluntary opioid reduction in patients with chronic non-cancer pain.

Materials and methods: Researchers obtained opioid prescriptions for 525 participants from the National Patient-Centered Clinical Research Network datamart at three health systems. Daily opioid dose was calculated using dose conversions and summing across prescriptions after applying assumptions, reviewing suspect prescribing patterns, and removing spurious prescriptions.

Results: Out of 16 071 extracted prescriptions, 1207 (8%) were unusable, and 14 864 (92%) were analyzed.

Discussion: Numerous challenges were identified related to incomplete data, inaccurate refill dates, and overlapping or duplicate prescriptions.

Conclusion: Using electronic prescription data to calculate daily doses of opioid consumption is challenging and requires significant cleaning prior to use in research. This paper recommends steps to review and clean electronic opioid prescription data.

Keywords: chronic non-cancer pain; electronic health records; morphine milligram equivalents (MME); opioids; prescriptions.

Figure 1.

Components of a prescription [Panel A] used in the derivation methods for days’ supply for a single prescription [Panel B], daily morphine milligram equivalent (MME) for a single prescription [Panel C], total daily MME for a single participant for all opioid prescriptions in a given day [Panel D], and average MME for all opioid prescriptions over a 90-day period for a single participant [Panel E].

Figure 2.

Flow diagram of exclusion steps to develop prescription analytic dataset. *The data cleaning steps described in Table 1 and Table 2 are applicable to this starting number of opioid prescriptions.

Figure 3.

Example graphs of daily MME over the entire study period for a fictional participant, with average MME markers at the end of each 90-day period (where baseline is defined as day 1-90). Panel A depicts a spike between days 551 and 580, where there were three overlapping prescriptions for the same medication, assumed to be duplicates. Panel B shows the same participant’s graph after two of the duplicates were removed, bringing down the spike and the 18-month average by roughly 100 MME.