Peterson, Stephanie M. Peck

Background: Care transitions programs have been shown to reduce hospital readmissions. Objectives: The main objective of this study was to evaluate effects of the Mayo Clinic Care Transitions (MCCTs) Program on potentially preventable and nonpreventable 30-day unplanned readmissions among high-risk elders. Research Design: This was a retrospective cohort study of patients enrolled in MCCT following hospitalization and propensity score-matched controls receiving usual primary care. Subjects: The subjects were primary care patients, who were 60 years or older, at high-risk for readmission, and hospitalized for any cause between January 1, 2011 and June 30, 2013. Measures: Hospital readmission within 30 days. The 3M algorithm was used to identify potentially preventable readmissions. Readmissions for ambulatory care sensitive conditions, a subset of preventable readmissions identified by the 3M algorithm, were also assessed. Results: The study cohort included 365 pairs of MCCT enrollees and propensity score-matched controls. Patients were similar in age (mean 83 y) and other baseline demographic and clinical characteristics, including reason for index hospitalization. MCCT enrollees had a significantly lower all-cause readmission rate [12.4% (95% confidence interval: CI, 8.9–15.7) vs. 20.1% (15.8–24.1); P=0.004] resulting from a decrease in potentially preventable readmissions [8.4% (95% CI, 5.5–11.3) vs. 14.3% (95% CI, 10.5–17.9); P=0.01]. Few potentially preventable readmissions were for ambulatory care sensitive conditions (6.7% vs. 12.0%). The rates of nonpotentially preventable readmissions were similar [4.3% (95% CI, 2.2–6.5) vs. 6.7% (95% CI, 4.0–9.4); P=0.16]. Potentially preventable readmissions were reduced by 44% (hazard ratio, 0.56; 95% CI, 0.36–0.88; P=0.01) with no change in other readmissions. Conclusions: The MCCT significantly reduces preventable readmissions, suggesting that access to multidisciplinary care can reduce readmissions and improve outcomes for high-risk elders.

Objectives To assess the degree to which self‐reported symptoms predict unplanned readmission or emergency department ( ED ) care within 30 days of high‐risk, elderly adults enrolled in a posthospitalization care transition program ( CTP ). Design Retrospective cohort study. Setting Posthospitalization CTP at Mayo Clinic, Rochester, Minnesota, from January 1, 2013, through March 3, 2015. Participants Frail, elderly adults (N = 230; mean age 83.5 ± 8.3, 46.5% male). Measurements Charlson Comorbidity Index ( CCI ) and self‐reported symptoms, measured using the Edmonton Symptom Assessment System ( ESAS ), were ascertained upon CTP enrollment. Results Mean CCI was 3.9 ± 2.3. Of 51 participants returning to the hospital within 30 days of discharge, 13 had ED visits, and 38 were readmitted. Age, sex, and CCI were not significantly different between returning and nonreturning participants, but returning participants were significantly more likely to report shortness of breath ( P = .004), anxiety ( P = .02), depression ( P = .02), and drowsiness ( P = .01). Overall ESAS score was also a significant predictor of hospital return ( P = .01). Conclusion Four self‐reported symptoms and overall ESAS score, but not CCI , ascertained after hospital discharge were strong predictors of hospital return within 30 days. Including symptoms in risk stratification of high‐risk elderly adults may help target interventions and reduce readmissions.

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurologic disorder with predictable challenges regarding disease progression and end-of-life care. These include need for respiratory and nutritional support. Little is known about how such choices impact end-of-life health service utilization for these patients. Using OptumLabs Data Warehouse, a large administrative claims database with more than 150 million privately insured, geographically diverse enrollees, we sought to explore outcomes associated with the use of enteral nutrition (EN). Patients were of age ≥18 years, with first ALS diagnosis during calendar years 2006-2012, and 6 months of continuous health plan coverage before first diagnosis. EN use was identified using procedure codes. Data were summarized descriptively. Among 1974 patients with ALS, mean age was 60.0 ± 12.5 years, 41.8% were women, and 9.7% demonstrated use of EN. Median time from ALS diagnosis to evidence of EN was 211 days (interquartile range [IQR]: 70-426). Those receiving EN had greater aggregate comorbidity (47% with Charlson-Deyo Comorbidity Index ≥ 3 vs only 27% in non-EN subset). In total, 38.1% of patients had at least 1 hospitalization, with median time to hospitalization of 162 days. Unfortunately, the EN group ended coverage a median of 155 days after EN started (IQR: 63.5-388), thereby limiting ability to capture outcomes. Although many ALS patients were identified, EN use was lower than expected, due to being earlier in disease trajectory and lost to follow-up with transition from private insurance. As such, databases exclusively including privately insured patients may be suboptimal for detecting late complications of protracted illnesses.

ObjectiveAssess algorithms for linking patients across de‐identified databases without compromising confidentiality.Data Sources/Study SettingHospital discharges from 11 Mayo Clinic hospitals during January 2008–September 2012 (assessment and validation data). Minnesota death certificates and hospital discharges from 2009 to 2012 for entire state (application data).Study DesignCross‐sectional assessment of sensitivity and positive predictive value (PPV) for four linking algorithms tested by identifying readmissions and posthospital mortality on the assessment data with application to statewide data.Data Collection/Extraction MethodsDe‐identified claims included patient gender, birthdate, and zip code. Assessment records were matched with institutional sources containing unique identifiers and the last four digits of Social Security number (SSNL4).Principal FindingsGender, birthdate, and five‐digit zip code identified readmissions with a sensitivity of 98.0 percent and a PPV of 97.7 percent and identified postdischarge mortality with 84.4 percent sensitivity and 98.9 percent PPV. Inclusion of SSNL4 produced nearly perfect identification of readmissions and deaths. When applied statewide, regions bordering states with unavailable hospital discharge data had lower rates.ConclusionAddition of SSNL4 to administrative data, accompanied by appropriate data use and data release policies, can enable trusted repositories to link data with nearly perfect accuracy without compromising patient confidentiality. States maintaining centralized de‐identified databases should add SSNL4 to data specifications.