Identifying Risk of Hospital Readmission Among Medicare Aged Patients: An Approach Using Routinely Collected Data

Abstract

Readmission provisions in the Patient Protection and Affordable Care Act of March 2010 have created urgent fiscal accountability requirements for hospitals, dependent upon a better understanding of their specific populations, along with development of mechanisms to easily identify these at-risk patients. Readmissions are disruptive and costly to both patients and the health care system. Effectively addressing hospital readmissions among Medicare aged patients offers promising targets for resources aimed at improved quality of care for older patients. Routinely collected data, accessible via electronic medical records, were examined using logistic models of sociodemographic, clinical, and utilization factors to identify predictors among patients who required rehospitalization within 30 days. Specific comorbidities and discharge care orders in this urban, nonprofit hospital had significantly greater odds of predicting a Medicare aged patient's risk of readmission within 30 days.

Risk predictors for hospital readmission in a low socio-economic and underserved population

“…[15,16] Mixed results have been procured for sex as a variable associated with readmissions, with some studies finding elevated risk of 30-day readmission in females, [26,31] and others finding the same risk elevation in males. [35] Contrary to the positive findings of previous researchers, in the current study we found no statistically significant differences in hospital readmission rates in our demographic variables. The demographic variables of age and female sex were not found to be significant predictors of 30-, 60-, or 90-day readmission, which is not out of character with the unfixed body of literature.…”

“…It has been reported that increasing age could potentially serve as a predictor of all-cause hospital readmissions, especially in patients over the age of 65. [14,35] Race and ethnicity have also been reported to be potential predictors of all-cause readmission risk, with African American and Hispanic patients facing a higher risk of early readmission than their white counterparts. [35,36] Additionally, hospitals serving primarily ethnic and racial minority patient groups experience elevated readmission rates.…”

“…[14,35] Race and ethnicity have also been reported to be potential predictors of all-cause readmission risk, with African American and Hispanic patients facing a higher risk of early readmission than their white counterparts. [35,36] Additionally, hospitals serving primarily ethnic and racial minority patient groups experience elevated readmission rates. [15,16] Mixed results have been procured for sex as a variable associated with readmissions, with some studies finding elevated risk of 30-day readmission in females, [26,31] and others finding the same risk elevation in males.…”

Factors Associated With 30-Day Hospital Readmissions Among Participants in a Care Transition Quality Improvement Program

“…Many of the included studies were based on retrospective review of medical records (e.g., Allaudeen, Vidyarthi, Maselli, & Auerbach, 2011;Marcantonio et al, 1999;Navarro, Enguídanos, & Wilber, 2012). Only a few studies used a prospective approach to identify medical, functional, and psychosocial factors associated with hospital readmissions among older adults in general or among older adults participating in a care transition intervention (see Marcantonio et al, 1999;Mudge et al, 2011;Ottenbacher et al, 2012).…”

Hospitalizations and Emergency Department Use in Mayo Clinic Biobank Participants Within the Employee and Community Health Medical Home

“…1 The design of health care exchanges within the Affordable Care Act will make the issues of risk stratification important as more patients are managed through a capitated payment system or have penalties for readmission. 2,3 One of the most commonly used methods of risk stratification is the assignment of medical tiers involving a higher tier with an increasing number of comorbid health deficits. 4,5 Risk stratification often uses data obtained from administrative billing data from an electronic medical record.…”