Cardiovascular patients who reported difficulty in accessing routine care had substantial risks of readmission within 30 days after discharge. These findings have important implications for identifying high-risk patients and developing interventions to improve access to routine medical care.

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Background Recent studies have drawn attention to nonclinical factors to better understand disparities in the development, treatment and prognosis of patients with cardiovascular disease. However, there has been limited research describing the nonclinical characteristics of patients hospitalized for cardiovascular care. Methods Data for this study come from 520 patients admitted to the Duke Heart Center from January 1, 2015 through January 10, 2017. Electronic medical records and a standardized survey administered before discharge were used to ascertain detailed information on patients’ demographic (age, sex, race, marital status and living arrangement), socioeconomic (education, employment and health insurance), psychosocial (health literacy, health self-efficacy, social support, stress and depressive symptoms) and behavioral (smoking, drinking and medication adherence) attributes. Results Study participants were of a median age of 65 years, predominantly male (61.4%), non-Hispanic white (67.1%), hospitalized for 5.11 days and comparable to all patients admitted during this period. Results from the survey showed significant heterogeneity among patients in their demographic, socioeconomic and behavioral characteristics. We also found that the patients’ levels of psychosocial risks and resources were significantly associated with many of these nonclinical characteristics. Patients who were older, women, nonwhite and unmarried had generally lower levels of health literacy, self-efficacy and social support, and higher levels of stress and depressive symptoms than their counterparts. Conclusions Patients hospitalized with cardiovascular disease have diverse nonclinical profiles that have important implications for targeting interventions. A better understanding of these characteristics will enhance the personalized delivery of care and improve outcomes in vulnerable patient groups.

Background Cardiovascular disease (CVD) is the leading cause of hospitalization in older adults and high readmission rates have attracted considerable attention as actionable targets to promote efficiency in care and to reduce costs. Despite a plethora of research over the past decade, current strategies to predict readmissions have been largely ineffective and efforts to identify novel clinical predictors have been largely unsuccessful. Objective The objective of this study is to examine a wide array of socioeconomic, psychosocial, behavioral, and clinical factors to predict risks of 30-day hospital readmission in cardiovascular patients. Methods The study includes patients (aged 18 years and older) admitted for the treatment of cardiovascular-related illnesses at the Duke Heart Center, which is among the nation’s largest and top-ranked cardiovascular care hospitals. The study uses a novel standardized survey to ascertain data on a comprehensive array of patient characteristics that will be linked to their electronic medical records. A series of univariate and multivariate models will be used to estimate the associations between the patient-level factors and 30-day readmissions. The performance of the risk models will be examined based on 2 components of accuracy—model calibration and discrimination—to determine how closely the predicted outcome agrees with the observed (actual) outcome and how well the model distinguishes patients who were readmitted and those who were not. The purpose of this paper is to present the protocol for the implementation of this study. Results The study was launched in February 2014 and is actively recruiting patients from the Heart Center. Approximately 550 patients have been enrolled to date and the study is expected to continue recruitment until February 2018. Preliminary results show that participants in the study were aged 63.6 years on average (SD 14.0), predominately male (61.2%), and primarily non-Hispanic white (64.6%) or non-Hispanic black (31.7%). The demographic characteristics of study participants were not significantly different from all patients admitted to the Heart Center during this period with an average age of 65.0 years (SD 15.3) and predominately male (58.6%), non-Hispanic white (62.9%) or non-Hispanic black (31.8%) The integration of the interview data with clinical data from the patient electronic medical records is currently underway. The study has received funding and ethical approval. Conclusions Many US hospitals continue to struggle with high readmission rates in patients with cardiovascular disease. The primary objective of this study is to collect and integrate a comprehensive array of patient attributes to develop a powerful yet parsimonious model to stratify risks of rehospitalization in cardiovascular patients. The results of this research also have the potential to identify actionable targets for tailored intervention...

To determine whether a clinical decision support system can favorably impact the delivery of emergency department and hospital services. Randomized clinical trial of three clinical decision support delivery modalities: email messages to care managers (email), printed reports to clinic administrators (report) and letters to patients (letter) conducted among 20,180 Medicaid beneficiaries in Durham County, North Carolina with follow-up through 9 months. Patients in the email group had fewer low-severity emergency department encounters vs. controls (8.1 vs. 10.6/100 enrollees, p < 0.001) with no increase in outpatient encounters or medical costs. Patients in the letter group had more outpatient encounters and greater outpatient and total medical costs. There were no treatment-related differences for patients in the reports group. Among patients <18 years, those in the email group had fewer low severity (7.6 vs. 10.6/100 enrollees, p < 0.001) and total emergency department encounters (18.3 vs. 23.5/100 enrollees, p < 0.001), and lower emergency department ($63 vs. $89, p = 0.002) and total medical costs ($1,736 vs. $2,207, p = 0.009). Patients who were ≥18 years in the letter group had greater outpatient medical costs. There were no intervention-related differences in patient-reported assessments of quality of life and medical care received. The effectiveness of clinical decision support messaging depended upon the delivery modality and patient age. Health IT interventions must be carefully evaluated to ensure that the resultant outcomes are aligned with expectations as interventions can have differing effects on clinical and economic outcomes.