Using Routinely Collected Electronic Health Record Data to Predict Readmission and Target Care Coordination.

ABSTRACT: Patients with chronic renal failure (CRF) are at high risk of being readmitted to hospitals within 30 days. Routinely collected electronic health record (EHR) data may enable hospitals to predict CRF readmission and target interventions to increase quality and reduce readmissions. We compared the ability of manually extracted variables to predict readmission compared with EHR-based prediction using multivariate logistic regression on 1 year of admission data from an academic medical center. Categorizing three routinely collected variables (creatinine, B-type natriuretic peptide, and length of stay) increased readmission prediction by 30% compared with paper-based methods as measured by C-statistic (AUC). Marginal effects analysis using the final multivariate model provided patient-specific risk scores from 0% to 44.3%. These findings support the use of routinely collected EHR data for effectively stratifying readmission risk for patients with CRF. Generic readmission risk tools may be evidence-based but are designed for general populations and may not account for unique traits of specific patient populations-such as those with CRF. Routinely collected EHR data are a rapid, more efficient strategy for risk stratifying and strategically targeting care. Earlier risk stratification and reallocation of clinician effort may reduce readmissions. Testing this risk model in additional populations and settings is warranted.

Modifiable risk factors for adherence to adjuvant endocrine therapy among breast cancer patients.

OBJECTIVE: Breast cancer incidence and mortality are declining due to improvements in early detection and treatment. One advance in treatment is the development of adjuvant endocrine therapy (AET) for women with hormone receptor positive breast cancer. Despite strong evidence linking AET to better health outcomes, AET adherence continues to be suboptimal. This study tests the hypothesis that patient beliefs about medication mediate the relationship between frequency of physician communication and AET adherence. METHODS: This cross-sectional study utilizes data from patient self-report and medical chart abstraction (N=200). Survey measures included frequency of physician communication, patient beliefs about medicine, AET adherence, and demographic characteristics. RESULTS: Necessity beliefs mediated the relationship between frequency of physician communication and medication adherence (necessity beliefs ß=.18, p<.05; physician communication ß=.13, p>.05). There was no evidence of medication concerns mediating the relationship between frequency of physician communication and medication adherence. CONCLUSION: More frequent physician communication that shapes what patients believe about AET importance may be associated with greater AET adherence; however, frequent physician communication that shapes patient concerns about side effects may not be associated with greater AET adherence. PRACTICE IMPLICATIONS: Research is needed to enhance understanding of the type of physician communication that is most consistently associated with patient beliefs about medication and AET adherence.