OBJECTIVES/GOALS: Nurses are leaving the profession at an alarming rate due to increased workload and burnout.#_msocom_1 Computational models that are reliable and reproducible are needed to quantitatively examine nursing workload and estimate potential effect of interventions. This project developed a logical data model to represent nursing EHR interactions. METHODS/STUDY POPULATION: With nursing EHR interactions as a starting point, we expand upon literature that examined the EHR workload of physicians. We conducted an exploratory analysis of nursing EHR audit log data at a large academic medical center, and explored components of nursing workload that can be extracted from other health system data. Using concepts derived from the studying temporal biomedical data patterns, we formulated a data structure that describes nurse EHR interactions, nurse intrinsic and situational characteristics, and nurse outcomes of interest in a scalable and extensible manner. RESULTS/ANTICIPATED RESULTS: Temporal machine learning models are grounded in the concept of vectors. We developed a logical data model that describes tasks performed by nurses (NTask), nurse types (NType), and nursing outcomes (NOutcome). For each nurse (k), we define a function <NTask (k, i)>, i=1 to N as a vector of dimension N, where N is the number of time periods in the study. The i component corresponds to the activity that the nurse is doing. The model will allow the quantitative classification of activity patterns for any finite number of nurses for an arbitrary set of tasks and for time at any specified resolution. The expected outcome is a set of vectors that can then be utilized to quantitatively model nurse activity trajectories and other patterns of nurse EHR interactions. DISCUSSION/SIGNIFICANCE: By instantiating the logical data model, we will demonstrate how nurse EHR interactions can be studied using temporal unsupervised learning and state-of-the-art artificial intelligence methods. We plan to simulate the potential impact of workload interventions and predict risk for nurse burnout.