An electronic medical record-derived real-time assessment scale for hospital readmission in the elderly

OBJECTIVE: Readmission risk score , a 20-point, 4-dimensional tool, is generated from the electronic medical record. This study was performed to evaluate the ability of the readmission risk score to predict 30-day readmissions among older hospitalized patients. METHODS: A retrospective study was conducted utilizing data from the electronic medical record. Using a cutoff value of 7, the readmission score sensitivity was 61%, specificity was 22%, positive predictive value 12%, negative predictive value 77%. The positive and negative likelihood ratios were 0.8 and 1.8, respectively. CONCLUSION: The readmission risk score was associated with 30-day readmissions (median score of readmitted vs not readmitted patients was 8 vs. 5; P = 0.001), and it may be better at identifying those who are not at risk for readmission

Electronic data modeling to predict 30-day hospital readmission for older adults

Background/Significance: Approximately 20% of Medicare beneficiaries are readmitted within 30 days, costing $17.4 billion annually. Research predicting readmission (readmit) has focused on administrative and diagnosis data. Purpose: The aim of this study was to identify electronic health record (EHR)-based clinical factors to predict readmit for older adults. Methods: This retrospective cohort study used demographic, diagnoses, and clinical EHR data to identify readmit predictors at a large quaternary medical center. The population was limited to adults \u3e 65 years, index length of stay \u3c 30 days and those not discharged to an acute care facility or inpatient rehabilitation. Logistic regression modeling evaluated clinical predictors with diagnoses from two sources: medical history and postdischarge ICD9 coding. Univariate analysis was done for categorical and continuous variables. For multivariate logistic regression, the population was divided into derivation (70%) and validation (30%) cohorts. Results: The sample (N=4,503; mean age ± standard deviation (SD): 77 ± 8 years; female: 54%) included patients hospitalized between July 2012 and Dec. 2012. Index length of stay ± SD was 4.9 ± 4; disposition to home was 65%, to home care was 18% and to skilled nursing was 18%; readmit rate was 12.3%. Readmit predictors were: age, heart failure, COPD, depression, anxiety, gastrointestinal disease, malnutrition, chronic pain, Medicaid insurance, length of stay, smoking, respiratory symptoms, social work consult, hypertension and acute respiratory failure (ICD9 only), pneumonia and kidney disease (medical history only). The receiver-operating characteristic (ROC) C-statistic using ICD9 diagnoses was 0.64 (95% confidence interval [CI]: 0.61-0.67) for derivation and 0.63 (95% CI: 0.58- 0.67) for validation cohorts, respectively, with significant predictors being age 75-84 (odds ratio [OR]: 1.33; 95% CI: 1.04-1.71), depression (OR: 1.42; 95% CI: 1.01-2.01), hypertension (OR: 0.76; 95% CI: 0.61-0.95); smoking past (OR: 0.69; 95% CI: 0.53-0.88), length of stay = 0.95 (95% CI: 0.93-0.98). The model using medical history data produced similar findings (ROC: 0.64, 95% CI: 0.61-0.67) with somewhat different predicators. Limitations included single site and missing clinical values. Conclusion: EHR-based clinical factors were found to predict readmission. Medical history produced similar results to ICD9 coding, suggesting that risk can be predicted using clinical data available during patient care. More work is needed to isolate clinical predictors for use in creating real time scoring mechanisms

Systems-based practice to improve care within and beyond the emergency department

There is evidence that an emergency department (ED) visit signifies a period of vulnerability for older adults. Transition between the ED and community care can be fraught with challenges. There are essential elements for improved care transition from the ED to the community. Starting a new program requires buy-in from leaders, clinical team, and community. Improving care within an ED requires looking beyond the ED. Following implementation science will increase the success of program implementation and dissemination. There are successful alternative approaches that can be learned from when striving to improve care and transitions