24-hour Oral Morphine Equivalent Based Opioid Prescribing After Surgery

Prescription opioid overdose has emerged as a leading cause of death in the general population. Opioid-based therapy represents a corner-stone of post-operative pain management. With increasing emphasis on robust pain therapy, sales of opioid medications have increased four-fold in the last decade. Parallel to this rise, opioid-associated deaths have also quadrupled. The investigators have shown that long-term opioid use occurs in up to 22% of patients following surgery. Over-prescribed opioids after surgery can create a reservoir of opioids that become available for non-medical use. Effective strategies to maximize non-opioid pain therapy and to limit such a reservoir are lacking. Thus, there is an urgent need to individualize post-operative pain therapy and reduce reliance on opioids.

Converting knowledge on actual need for opioid pain medications after surgery into tangible benefits can prevent over-prescription of opioids that become available for non-medical use. The objective of this study is to prescribe pain medications after surgery in a patient-centered fashion. The investigators will use the average amount of opioid medications (in oral morphine equivalents - OME) taken in the last 24 hours prior to discharge to inform and design a clinical decision support tool. This tool will reduce the amount of opioid medications prescribed while maintaining patient post-operative pain control. The calculated "last 24 hour OME dose" will be applied to generate a recommendation for the prescription of a cumulative opioid dose that will be shared with the provider when the post-discharge pain medication is made, e.g. via displaying the last 24-hour opioid use in the electronic health record (EHR). The rationale is that testing of such a decision-making tool will lay the groundwork to translate the findings of this project into more secure and efficient opioid prescribing practices to a system-wide level. Upon completion, the investigators expect to have developed an effective decision-making tool to help providers estimate required pain medication following patient discharge.

The investigators will perform a randomized clinical trial (RCT) of an Electronic Health Record (EHR)-based recommendation to prescribe opioids at discharge according to the recorded last 24-hour inpatient use according to current recommendations and evaluate its effect on the amount of opioids prescribed as well as pain control after discharge.

Using the University of Colorado Hospital Epic/Clarity database, the investigators will continuously identify all patients who underwent Cesarean section surgery at University Hospital. Patients will be approached regarding interest in the study prior to hospital discharge.A trained Professional Research Assistant or other provider will explain the study and obtain informed consent from prospective participants. After consent, demographics and the best contact information will be collected from those enrolled patients who indicate they are interested in completing the surveys after their hospital discharge.

The investigators expect 44 patients to complete all four weekly surveys and define the intent-to-treat sample as patients who complete one or more surveys (providing at least one observation to be used in analyses). To conservatively allow for increased variability due to attrition, 54 patients will be randomized equally electronically to intervention (prescription tool) or no intervention (prescription as usual). The investigators will use an electronic randomization scheme created so that the principal investigator and statistician can remain blind to assignment.