Approximately 15% of older adults are admitted to skilled nursing facilities (SNFs) for post-acute care to restore function and independence following hospitalization. Independent function and intact cognition are key measures of meaningful recovery after illness, yet it can be very hard to find reliable measures in real-world data that are tracked over time. Standardized functional assessments (via the Minimum Data Set, MDS) are required upon admission and discharge to document progress and support reimbursement. However, therapists often conduct additional functional, physical, and cognitive assessments over the course of a SNF stay and occasionally in long-term residents. This project will validate the extent to which these other functional assessments are available, valid, and reliable in long-term care (LTC) electronic health record (EHR) data. This Real World Data Scholar Award will provide Dr. Lam with the necessary training and experience using the LTC Data Cooperative EHR data to: 1) Determine the number of interim functional assessments per week (i.e., functional assessments other than MDS assessments at admission and discharge) that are conducted in short-term post-acute patients and long-term residents by the type of assessment instrument used; 2) Determine the agreement between interim assessments and MDS-ADL assessments conducted within 3 days of one another; and 3) Characterize differences in assessments between short-term patients discharged to community versus hospital, nursing home, and those who died. Findings from this study will provide insight about the utility of this information to clinicians and researchers. Training and findings from this award will lay the foundation for the Scholar’s future work to further investigate if, how, and when people get better after hospitalization, examine what leads to better recovery, and develop tools to improve shared decision-making about what to do when recovery does not go as well as expected.