# Determining Barriers to Achieving Optimal Post-Acute Care Destinations > **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2021 · $37,424 ## Abstract Project Summary 13 million Medicare beneficiaries are discharged from acute care hospitals annually, and approximately 42% of these older adults receive referrals to post-acute care (PAC) services including long term acute care hospitals, inpatient rehabilitation, skilled nursing facilities, and home health care. Effective referrals that promote patient health and prevent negative outcomes rely on coordinated discharge planning. However, this coordination is difficult to achieve when interprofessional discharge planning teams frequently face time constraints, team communication issues, variance in risk tolerance in decision making, and inconsistent assessments. Therefore, significant variation in discharge planning practices exists at the individual and hospital level and there are no clinical guidelines for this common but crucial process. Without standardized discharge planning practices in place, patients are at risk for negative outcomes after discharge including social and economic disparities in PAC referral location, unnecessary treatments, unplanned hospital readmissions, and increased healthcare costs. Clinical decision support systems (CDSS) equip clinicians with evidence-based, individualized information about their patients at the point of care, and address the urgent need for standardized solutions to improve discharge planning decisions. The Discharge Referral Expert System for Care Transitions (DIRECT) is a recently developed CDSS algorithm (RO1-2-NR007674) that identifies which patients need PAC and suggests the level of care as home health care or facility-level care based on patient needs. Use of DIRECT in discharge planning is associated with a reduction in hospital readmissions, however, hospital clinicians referred 26% fewer patients to PAC than DIRECT. This discordance has been historically difficult to study due to the unstructured nature of discharge planning data in clinical notes, making data abstraction and analysis difficult to achieve. The proposed study prepares the applicant to advance the DIRECT algorithm and expand it to a new clinical setting through two specific aims: 1) Among patients discharged without PAC, compare patient characteristics and 30-day readmission rates between those identified by DIRECT as needing PAC and those where DIRECT and clinicians agreed on no referral for PAC and 2) Identify the reasons associated with discharge home without services when the DIRECT CDSS recommends PAC. The proposed study will expand an existing CDSS developed in a suburban community hospital to a new population in a large urban hospital and utilize natural language processing methods to advance the understanding of why some patients do not receive the recommended level of PAC. The findings from this study will illuminate possible implementation and algorithm refinement strategies for future prospective study, and align with the applicant’s long term research goals to improve transitions in care for older adults by deve... ## Key facts - **NIH application ID:** 10226410 - **Project number:** 1F31NR019919-01 - **Recipient organization:** UNIVERSITY OF PENNSYLVANIA - **Principal Investigator:** Erin Kennedy - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $37,424 - **Award type:** 1 - **Project period:** 2021-09-01 → 2022-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10226410 ## Citation > US National Institutes of Health, RePORTER application 10226410, Determining Barriers to Achieving Optimal Post-Acute Care Destinations (1F31NR019919-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10226410. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*