PURPOSE OF STUDY
The medical record is a sea of information that can reveal what patients are trying to tell us about their health condition. It can reveal hints and trends as to why veterans with congestive heart failure (CHF) are being readmitted within 30 days after hospital discharge. These hints and trends lead caregivers to key contributing variables to veterans' readmission. Furthermore, these variables can be used to predict patient outcomes such as readmission and even prognosis. This article looks at readmissions for CHF from documentation within the medical record to see what was driving the 30-day readmissions. Second, it examines whether the driving forces can be used to predict a veteran's increased risk for readmission or other poor prognosis.
PRIMARY PRACTICE SETTING(S)
The study was conducted at a rural 84-bed Veterans Health Administration hospital in the Western United States.
METHODOLOGY AND SAMPLE
A retrospective screen was performed on 1,279 veterans' admissions of which 217 were identified as having CHF as a primary or secondary diagnosis on admission. The descriptive statistics, odds ratio (OR) and multivariate logistic regression were used to examine the data. The multivariate logistic regression equation was p = 1/1 + e, which can be found in the biostatistics textbook by . developed and validated the equation and used it to screen for undiagnosed diabetic patients. The equation was refined by . The variables selected for this study were based on a literature review of 30 articles.
The probability and OR for 30-day readmissions for all ages increased as the age increased. The ORs for 30-day readmissions for the variables selected were as follows: brain natriuretic peptide 6.21 (95% CI [0.36, 108.24]), ejection fraction 1.298 (95% CI [0.68, 2.49]), hypertension 1.795 (95% CI [0.83, 3.85]), comorbid conditions 1.02 (95% CI [0.04, 25.02]), Stage III and below were protective, Stage IV 2.057 (95% CI [0.63, 9.32]), lack of discharge education 0.446 (95% CI [0.19, 6.45]). The impact of these variables on veterans with more than 3 readmissions (N = 66) was examined. In 32% of these admissions, there was insufficient data to compare the values of the variables between readmissions. In almost 26% (N = 17) of the cases as the number of variables increased, the time between admissions decreased. In 23% of the cases (N = 15), the values did not change; of these, 14 died and the one who survived had assistance with his care in the form of home health and telehealth.
IMPLICATIONS FOR CASE MANAGEMENT PRACTICE
Use of this evidence-based tool will help case managers to strategically plan care and prioritize interventions to impact the major variables and risk factors that are impacting veterans' health.
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