Abstract: Wait time is a key factor of health-care services, yet data is rare, often unreliable, and inconsistently measured across systems. The scarcity of data prevents systematic analyses of allocative inefficiency and inequality in access to health. We propose a measure of wait times - detection to treatment (DTT) - solely based on medical variables, which are both widely available and standardized. DTT records the time elapsed between the detection of a patient as being high-risk of receiving a surgery, and the date of the procedure. We use recurrent neural networks to represent patients’ high-dimensional medical trajectories as a risk profile over time. As expected for a measure of wait times, we find that DTT increases with supply constraints. Patients enrolled in more restrictive insurance plans experience longer DTT and an increase in the load of medical providers increases the wait time. Using provider loads as exogenous variation in wait times, we show that an increase in DTT results in higher medical expenditures, longer hospitalization, and increased use of addictive drugs.