Early Discharge Planning and Improved Care Transitions: Pre-Admission Assessment for Readmission Risk in an Elective Orthopedic and Cardiovascular Surgical Population.

BACKGROUND/METHODS: Readmission prevention is a marker of patient care quality and requires comprehensive, early discharge planning for safe hospital transitions. Effectively performed, this process supports patient satisfaction, efficient resource utilization, and care integration. This study developed/tested the utility of a predictive early discharge risk assessment with 366 elective orthopedic/cardiovascular surgery patients. Quality improvement cycles were undertaken for the design and to inform analytic plan. An 8-item questionnaire, which includes patient self-reported health, was integrated into care managers' telephonic pre-admission assessments during a 12-month period. RESULTS: Regression models found the questionnaire to be predictive of readmission (p ≤ .005; R(2) = .334) and length-of-stay (p ≤ .001; R(2) = .314). Independent variables of "lives-alone" and "self-rated health" were statistically significant for increased readmission odds, as was "self-rated health" for increased length-of-stay. Quality measures, patient experience and increased rates of discharges-to-home further supported the benefit of embedding these questions into the pro-active planning process. CONCLUSION: The pilot discharge risk assessment was predictive of readmission risk and length-of-stay for elective orthopedic/cardiovascular patients. Given the usability of the questionnaire in advance of elective admissions, it can facilitate pro-active discharge planning essential for producing quality outcomes and addressing new reimbursement methodologies for continuum-based episodes of care.

Higher 30-day and 60-day readmissions among patients who refuse post acute care services.

OBJECTIVES: To compare patients who accepted ("acceptors") post acute care services (PAC) with those who were offered services and refused ("refusers") in terms of their sociodemographic and clinical characteristics, quality of life, health-related problems, and unmet needs; and to examine the association between refusing PAC services and the risk for 30- and 60-day readmission. STUDY DESIGN: Secondary data analysis from a cross-sectional study. METHODS: Bivariate analysis and logistic regressions were used to examine the association between refusing PAC services and 30- and 60-day readmission. RESULTS: A convenience sample of 495 PAC-referred patients 55 years and older discharged from 2 large academic medical centers in the northeastern United States completed the study questionnaires, with a resulting 28% (n = 139) that refused PAC services. Refusers were significantly younger (average age 68 years vs 73 years; P < .001), as well as more likely to be married (62% vs 46%; P < .001), privately insured (35% vs 18%; P < .001), and with lower risk of mortality/severity of illness. Refusers also had shorter hospital stays (4.8 days vs 7.5 days; P < .001); higher quality of life after discharge (0.83 vs 0.73; P < .001); and fewer unmet needs after discharge. However, refusers had higher 30-day (21% vs 16%; P = .17) and 60-day (31% vs 25%; P = .18) readmission rates; with logistic regression showing about twice-higher odds of 30-day (OR [odds ratio], 2.13; 95% CI, 1.11-3.02; P = .01) and 60-day (OR, 1.8; 95% CI, 1.11-3.02; P = .02) readmission. CONCLUSIONS: PAC refusers are younger, better educated, and healthier, but they have twice-higher odds of 30- and 60-day readmissions, compared with PAC acceptors. Further investigation into reasons for PAC refusal is critical to foster enhanced patient communication regarding PAC services, improve rates of service acceptance, and ultimately decrease readmissions.

Discharge before noon: an achievable hospital goal.

BACKGROUND: Late afternoon hospital discharges are thought to contribute to admission bottlenecks, overcrowding, and increased length of stay (LOS). In January 2012, the discharge before noon (DBN) percentage on 2 medical units was 7%, below the organizational goal of 30%. OBJECTIVE: To sustainably achieve a DBN rate of 30% and to evaluate the effect of this intervention on observed-to-expected (O/E) LOS and 30-day readmission rate. DESIGN: Pre-/post-intervention retrospective analysis. SETTING: Two acute care inpatient medical units in an urban, academic medical center. PATIENTS: All inpatients discharged from the units. INTERVENTION: All staff helped create a checklist of daily responsibilities at a DBN kickoff event. We initiated afternoon interdisciplinary rounds to identify next-day DBNs and created a website for enhanced communication. We provided daily feedback on the DBN percentage, rewards for success, and real-time opportunities for case review. MEASUREMENTS: Calendar month DBN percentage, O/E LOS, and 30-day readmission rate. RESULTS: The DBN percentage increased from 11% in the 8-month baseline period to an average of 38% over the 13-month intervention (P = 0.0002). The average discharge time moved 1 hour and 31 minutes earlier in the day. The O/E LOS declined from 1.06 to 0.96 (P = 0.0001), and the 30-day readmission rate declined from 14.3% to 13.1% (P = 0.1902). CONCLUSIONS: Our study demonstrates that increased DBN is an achievable and sustainable goal for hospitals. Future work will allow for better understanding of the full effects of such an intervention on patient outcomes and hospital metrics.

Intensity of acute care services at the end of life: nonclinical determinants of treatment variation in an older adult population.

Technological advances in medicine have led to increasing complexity in health care decision making, and subsequently, greater opportunity for variation in the delivery of end-of-life care. Factors such as age, race/ethnicity, physician and hospital system characteristics, and geographic location have been found to be strong predictors of variation in acute interventions before death, even when controlling for medical acuity. The study presented here explores factors affecting health care utilization at end of life for a hospitalized older adult population in a major metropolitan area of Arizona. The study results reveal that effects of age, minority status, health plan, and hospital characteristics all affect hospital utilization and intensity of care above and beyond clinical factors.