Patient Factors Linked with Return Acute Healthcare Use in Older Adults by Discharge Disposition.

OBJECTIVES: Compare patient characteristics by hospital discharge disposition (home without services, home with home healthcare (HHC) services, or post-acute care (PAC) facilities). Examine timing and rates of 30-day healthcare utilization (rehospitalization, emergency department (ED) visit, or observation (OBS) visit) and patient characteristics associated with rehospitalization by discharge location. DESIGN: Retrospective analysis of hospital administrative and clinical data. SETTING AND PARTICIPANTS: A total of 3,294 older adult inpatients discharged home with or without HHC services or to a PAC facility. MEASUREMENTS: Patient-level sociodemographic and clinical characteristics. Number of and time to occurrences of rehospitalization or ED/OBS visit within 30 days of hospital discharge. RESULTS: Most rehospitalizations and ED/OBS visits occurred within 14 days from hospital discharge. Patients who returned within 24 hours came mostly from inpatient rehabilitation facilities (IRFs). More intense levels of PAC services were linked with higher rehospitalization risk. However, specific predictors differed by discharge location. Being unemployed, being single, and having more comorbidities were most associated with rehospitalization in those who went home with or without services, whereas patients rehospitalized from IRFs were younger, with less chronic illness burden, but greater and recent functional decline. Those discharged with HHC services had more return ED/OBS visits. CONCLUSIONS: Although sicker patients were referred for more intense levels of PAC services, patients with greater chronic illness burden were still most often rehospitalized. In addition to unique patient differences, rehospitalizations from IRF within 24 hours suggest systems factors are contributory. Most return acute healthcare utilization occurred within 14 days; therefore, interventions should focus on smoothing transitions to all discharge locations. Because predictors of rehospitalization risk differed by discharge disposition, future research is necessary to study approaches aimed at matching patients' care needs with the most suitable PAC services at the right time. J Am Geriatr Soc 68:2279-2287, 2020.

Using Machine Learning on Home Health Care Assessments to Predict Fall Risk.

Falls are the leading cause of injuries among older adults, particularly in the more vulnerable home health care (HHC) population. Existing standardized fall risk assessments often require supplemental data collection and tend to have low specificity. We applied a random forest algorithm on readily available HHC data from the mandated Outcomes and Assessment Information Set (OASIS) with over 100 items from 59,006 HHC patients to identify factors that predict and quantify fall risks. Our ultimate goal is to build clinical decision support for fall prevention. Our model achieves higher precision and balanced accuracy than the commonly used multifactorial Missouri Alliance for Home Care fall risk assessment. This is the first known attempt to determine fall risk factors from the extensive OASIS data from a large sample. Our quantitative prediction of fall risks can aid clinical discussions of risk factors and prevention strategies for lowering fall incidence.

Meritorious Academic Partnership Designation: Recognizing Nursing Professional Development in Graduate Curricula.

Graduate nursing education programs' focus on preparation for academia results in many graduate nurses being unprepared to function as nursing professional development (NPD) practitioners in the practice environment. This article describes the development of an innovative collaborative partnership designation program and how a specialty organization, health system, and academia partnered to create an educational program to prepare NPD practitioners. The designation program provides a practical tool for use by NPD departments to advocate for the NPD specialty.

Nurse Generated EHR Data Supports Post-Acute Care Referral Decision Making: Development and Validation of a Two-step Algorithm.

Objective: Build and validate a clinical decision support (CDS) algorithm for discharge decisions regarding referral for post-acute care (PAC) and to what site of care. Materials and Methods: Case studies derived from EHR data were judged by 171 interdisciplinary experts and prediction models were generated. Results: A two-step algorithm emerged with area under the curve (AUC) in validation of 91.5% (yes/no refer) and AUC 89.7% (where to refer). Discussion: CDS for discharge planning (DP) decisions may remove subjectivity, and variation in decision-making. CDS could automate the assessment process and alert clinicians of high need patients earlier in the hospital stay. Conclusion: Our team successfully built and validated a two-step algorithm to support discharge referral decision-making from EHR data. Getting patients the care and support they need may decrease readmissions and other adverse events. Further work is underway to test the effects of the CDS on patient outcomes in two hospitals.

Comparison of algorithm advice for post-acute care referral to usual clinical decision-making: examination of 30-day acute healthcare utilization.

Objective: Compare patient characteristics and acute healthcare utilization between patients identified as in need of post-acute care (PAC) by the clinical decision support (CDS) algorithm yet were discharged home without services, to those where the CDS and hospital clinicians agreed on no referral. Methods: Retrospective analysis of hospital administrative and clinical data for 1,366 patients. Results: 30-day acute healthcare utilization rates are significantly higher for those patients flagged as in need of PAC referral. There are also significant differences in patient characteristics based on referral risk. Discussion: Clinicians were blinded to the algorithm enabling the comparison of usual care to decision support. Future work will examine the effect of sharing algorithm advice with clinicians on PAC referral rates and utilization. Conclusion: The CDS algorithm clearly identified patients with high-risk characteristics and those who will go on to utilize acute care resources. Providing CDS to discharge planners may improve patient outcomes.