TY - JOUR AU - Flaks-Manov, Natalie AU - Topaz, Maxim AU - Hoshen, Moshe AU - Balicer, Ran D. AU - Shadmi, Efrat PY - 2019 DA - 2019/06/26 TI - Identifying patients at highest-risk: the best timing to apply a readmission predictive model JO - BMC Medical Informatics and Decision Making SP - 118 VL - 19 IS - 1 AB - Most of readmission prediction models are implemented at the time of patient discharge. However, interventions which include an early in-hospital component are critical in reducing readmissions and improving patient outcomes. Thus, at-discharge high-risk identification may be too late for effective intervention. Nonetheless, the tradeoff between early versus at-discharge prediction and the optimal timing of the risk prediction model application remains to be determined. We examined a high-risk patient selection process with readmission prediction models using data available at two time points: at admission and at the time of hospital discharge. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-019-0836-6 DO - 10.1186/s12911-019-0836-6 ID - Flaks-Manov2019 ER -