No abstract

Background: Approximately 20% of seniors live with five or more chronic medical illnesses. Terminal stages of their lives are often characterized by repeated burdensome hospitalizations and advance care directives are insufficiently addressed. This study reports on the preliminary results of a Palliative Care Homebound Program (PCHP) at the Mayo Clinic in Rochester, Minnesota to service these vulnerable populations. Objective: The study objective was to evaluate inpatient hospital utilization and the adequacy of advance care planning in patients who receive home-based palliative care. Methods: This is a retrospective pilot cohort study of patients enrolled in the PCHP between September 2012 and March 2013. Two control patients were matched to each intervention patient by propensity scoring methods that factor in risk and prognosis. Primary outcomes were six-month hospital utilization including ER visits. Secondary outcomes evaluated advance care directive completion and overall mortality. Results: Patients enrolled in the PCHP group (n = 54) were matched to 108 controls with an average age of 87 years. Ninety-two percent of controls and 33% of PCHP patients were admitted to the hospital at least once. The average number of hospital admissions was 1.36 per patient for controls versus 0.35 in the PCHP ( p < 0.001). Total hospital days were reduced by 5.13 days. There was no difference between rates of ER visits. Advanced care directive were completed more often in the intervention group (98%) as compared to controls (31%), with p < 0.001. Goals of care discussions were held at least once for all patients in the PCHP group, compared to 41% in the controls.

Background: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools. Methods: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention. The E2C2 intervention will have two levels of care: level 1 will offer low-touch, automated self-management support for patients reporting moderate sleep disturbance, pain, anxiety, depression, and energy deficit symptoms or limitations in physical function (or both). Level 2 will offer nurse-managed collaborative care for patients reporting more intense (severe) symptoms or functional limitations (or both). By surveying and interviewing clinical staff, we will also evaluate whether the use of a multifaceted, evidence-based implementation strategy to support adoption and use of the E2C2 technologies improves patient and clinical outcomes. Finally, we will conduct a mixed methods evaluation to identify disparities in the adoption and implementation of the E2C2 intervention among elderly and rural-dwelling patients with cancer. Discussion: The E2C2 intervention offers a pragmatic, scalable approach to delivering guideline-based symptom and function management for cancer patients. Since discrete EHR-imbedded algorithms drive defining aspects of the intervention, the approach can be efficiently disseminated and updated by specifying and modifying these centralized EHR algorithms.

Objectives: Patients discharged to a skilled nursing facility (SNF) for postacute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF.Design: Retrospective cohort study.Setting: Ten independent SNFs affiliated with the postacute care practice of an integrated health care delivery system.Participants: We evaluated 6,032 patients who were discharged to a SNF for postacute care after hospitalization. Measurements:The primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver-operator curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation.

BACKGROUND: Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success. OBJECTIVE:To identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30-day readmissions. DESIGN: Retrospective cohort.PATIENTS: A total of 315 community-dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013.SETTING: Midwest primary care practice in an integrated health system. INTERVENTION:Enrollment in a CTP during acute hospitalization. MEASUREMENTS:The primary outcome was all-cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare. RESULTS:The overall 30-day readmission rate was 17.1%. The intensity of follow-up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission. CONCLUSIONS:No single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.