Abstract
OBJECTIVES: Although hospital clinicians strive to effectively refer patients who require post-acute care (PAC), their discharge planning processes often vary greatly, and typically are not evidence-based. DESIGN: Quasi-experimental study employing pre-/postdesign. Aimed at improving patient-centered discharge processes, we examined the effects of the Discharge Referral Expert System for Care Transitions (DIRECT) algorithm that provides clinical decision support (CDS) regarding which patients to refer to PAC and to what level of care (home care or facility). SETTING AND PARTICIPANTS: Conducted in 2 hospitals, DIRECT data elements were collected in the pre-period (control) but discharging clinicians were blinded to the advice and provided usual discharge care. During the postperiod (intervention), referral advice was provided within 24 hours of admission to clinicians, and updated twice daily. Propensity modeling was used to account for differences between the pre-/post patient cohorts. MEASURES: Outcomes compared between the control and the intervention periods included PAC referral rates, patient characteristics, and same-, 7-, 14-, and 30-day readmissions or emergency department visits. RESULTS: Although 24%-25% more patients were recommended for PAC referral by DIRECT algorithm advice, the proportion of patients receiving referrals for PAC did not significantly differ between the control (3302) and intervention (5006) periods. However, the characteristics of patients referred for PAC services differed significantly and inpatient readmission rates decreased significantly across all time intervals when clinicians had DIRECT CDS compared with without. There were no differences observed in return emergency department visits. Largest effects were observed when clinicians agreed with the algorithm to refer (yes/yes). CONCLUSIONS/IMPLICATIONS: Our findings suggest the value of timely, automated, discharge CDS for clinicians to optimize PAC referral for those most likely to benefit. Although overall referral rates did not change with CDS, the algorithm may have identified those patients most in need, resulting in significantly lower inpatient readmission rates.