Identifying Patient Readmissions: Are Our Data Sources Misleading?

BackgroundThe accuracy of data is vital to identifying hospitalization outcomes for clinical trials. Patient attrition and recall bias affects the validity of patient-reported outcomes, and the growing prevalence of Medicare Advantage (MA) could mean Fee-for-Service (FFS) claims are less reliable for ascertaining hospital utilization. Statewide health information exchanges (HIEs) may be a more complete data source but have not been frequently used for research.DesignSecondary analysis comparing identification of readmissions using 3 different acquisition approaches.SettingRandomized controlled trial of heart failure (HF) disease management in 37 skilled nursing facilities (SNFs).ParticipantsPatients with HF discharged from the hospital to SNF.MeasuresReadmissions up to 60 days post-SNF admission collected by patient self-report, recorded by nursing home (NH) staff during the SNF stay, or recorded in the state HIE.ResultsAmong 657 participants (mean age 79 ± 10 years, 49% with FFS), 295 unique readmissions within 60 days of SNF admission were identified. These readmissions occurred among 221 patients. Twenty percent of all readmissions were found using only patient self-report, 28% were only recorded by NH staff during the SNF stay, and 52% were identified only using the HIE. The readmission rate (first readmission only) based only on patient self-report and direct observation was 18% rather than 34% with the addition of the enhanced HIE method.Conclusions and implicationsMore than one-quarter (34%) of HF patients were rehospitalized within 60 days post SNF admission. Use of a statewide HIE resulted in identifying an additional 153 admissions, 52% of all the readmissions seen in this study. Without use of an HIE, nearly half of readmissions would have been missed as a result of incomplete patient self-report or loss to follow-up. Thus, HIEs serve as an important resource for researchers to ensure accurate outcomes data.     

Hospital Readmissions Among Post-acute Nursing Home Residents: Does Obesity Matter?

ObjectivesTo explore profiles of obese residents who receive post-acute care in nursing homes (NHs) and to assess the relationship between obesity and hospital readmissions and how it is modified by individual comorbidities, age, and type of index hospitalizations.DesignRetrospective cohort study.Setting and participantsMedicare fee-for-service beneficiaries who were newly admitted to free-standing US NHs after an acute inpatient episode between 2011 and 2014 (N = 2,323,019).MeasuresThe Minimum Data Set 3.0 were linked with Medicare data. The outcome variable was 30-day hospital readmission from an NH. Residents were categorized into 3 groups based on their body mass index (BMI): nonobese, mildly obese, moderate-to-severely obese. We tested the relationship between obesity and 30-day readmissions by fixed-effects logit models and stratified analyses by the type of index hospitalization and residents' age.ResultsForty percent of the identified residents were admitted after a surgical episode, and the rest were admitted after a medical episode. The overall relationship between obesity and readmissions suggested that obesity was associated with higher risks of readmission among the oldest old (≥85 years) residents but with lower risks of readmission among the youngest group (65-74 years). After accounting for individual co-covariates, the association between obesity and readmissions among the oldest old residents became weaker; the adjusted odds ratio was 1.061 (P = .049) and 1.004 (P = .829) for moderate-to-severely obese patients with surgical and medical index hospitalizations, respectively. The protective effect of obesity among younger residents reduced after adjusting for covariates.Conclusions/RelevanceThe relationship between obesity and hospital readmission among post-acute residents could be affected by comorbidities, age, and the type of index hospitalization. Further studies are also warranted to understand how to effectively measure NH quality outcomes, including hospital readmissions, so that policies targeting at quality improvement can successfully achieve their goals without unintended consequences.