OBJECTIVES: Inappropriate use of prescription opioids costs insurers over $72 billion annually in direct healthcare expenses. Prescription monitoring programs (PMP) are state-operated databases which allow authorized clinicians (e.g., prescribers, pharmacists) to query a patient’s opioid dispensing history via a secured online connection, during patient care. Since the emergency department (ED) is a primary target for drug-seeking patients, this pilot study was conducted to validate a survey instrument designed to assess emergency physicians (EPs) intention to use the recently available Texas PMP. METHODS: A cross-sectional survey of EPs was conducted at a statewide emergency medicine conference.  A 34-item questionnaire, based on the Technology Acceptance Model (TAM), was developed to assess EPs intention to use the Texas PMP. Items related to technology acceptance (perceived ease of use, perceived usefulness, attitude, and intention) were assessed using 5-point Likert scale responses (1=strongly disagree to 5=strongly agree). The survey expanded on a previous exploratory survey of EPs. Correlation analyses were used to validate the survey instrument scales. RESULTS: Of the 45 respondents, most were male (68.9%), attending EPs (57.8%), with 10.8±11.1 years in emergency medicine, from a community hospital setting (55.6%), and were users of the Texas PMP (51.2%). Among those who were not registered, 39.2% reported lack of awareness as the primary reason for not being registered. Standardized Cronbach’s alphas for the constructs of perceived ease of use, perceived usefulness, attitude, and intention for PMP users were 0.88, 0.90, 0.74, and 0.84, respectively; and 0.77, 0.87, 0.84, and 0.74, respectively for PMP non-users. CONCLUSIONS: Considering the ED as a source of diversion, it is important to understand EPs utilization of PMPs. EPs use of PMPs may help to mitigate the economic burden associated with the non-medical use of prescription opioids, while improving patient outcomes. Future studies using this survey instrument are needed to further assess the predictive utility.