Hospital Care May Not Affect the Risk of Readmission

Departments of Health Systems Management, Rush University Medical Center, Chicago, Illinois 60612, USA.
Quality management in health care 04/2012; 21(2):68-73. DOI: 10.1097/QMH.0b013e31824b9297
Source: PubMed


: Thirty-day readmissions have become a focal point for reducing health care spending, because they are viewed as a marker of the quality of hospital care. However, if increased time in the hospital is associated with better care, attempts to shorten length of stay (LOS) may result in increased rates of readmission. As such, we sought to explore the association of an incremental added day in LOS with the rate of readmission.
: We examined the rate of readmission at 30 and 120 days for 4151 patients admitted to a general internal medicine unit between July 2004 and March 2006. We used binary logistic regression to examine the relationship between an incremental added day in LOS and the probability of readmission.
: Readmission rates were 8.7% at 30 days and 21.0% at 120 days, respectively. After controlling for demographic characteristics and severity of illness, we found that the probability of readmission varied little for an incremental added day in LOS.
: Our findings suggest that more hospital care may not affect the likelihood of readmission and thus denying payment for readmission may be unwarranted.

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    Journal of Asthma 06/2013; 50(8). DOI:10.3109/02770903.2013.816726 · 1.80 Impact Factor
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    ABSTRACT: Introduction: It has been estimated that re-hospitalisation may be accountable for almost half of all the hospital admissions in the elderly. Similarly studies have shown that re-hospitalisation account for up to 60% of hospital expenditure. Aim: To assess the potential reasons for re-hospitalisation of elderly medical patients and the outcome of these patients. Methodology: It was a hospital based cross-sectional observational study done from May 2011 to July 2011. All elderly (>60 years) patients readmitted to the General Medical Ward and Medical Emergency Wards were identified. Short admissions for therapeutic or diagnostic procedures were excluded. The patient’s diagnosis at time of current admission and the old records of past admissions were thoroughly scrutinized. The patient was followed up during his/her hospital stay and the outcome was assessed. Results: A total of 48 cases were identified. Fifty two percent of patients were re-hospitalised within 6 months (28% within a month, 6% in 2 months, 8% in 4 months and 10% in 6 months). Two or more comorbidities were present in 69% patients. Seventy three percent patients improved, 21% showed no change in status and 6% deteriorated. Disease related factors: 33% re-hospitalisation were found to be due to unavoidable relapse of underlying chronic disease, 25% due to failed trial with outpatient management, 17% due to complication of the underlying disease, 16% due to independent new disease, 5% due to adverse drug reaction and 4% due to decompensation of other co-morbid conditions. Patient’s related factors: 33% had perception of poor self rated general health, 27% premature discharge/inadequate rehabilitation, 24% had poor compliance to recommended prescription and 19% due to poor outpatient follow up. Conclusion: Our study shows that most of the re-hospitalisation were due to the relapses of underlying chronic diseases and were unavoidable. The other important findings were the poor perception of self rated general health, poor follow up with the outpatient clinic and non-compliance to drugs prescribed.
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    ABSTRACT: Background Hospital readmission is an adverse patient outcome that is serious, common, and costly. For hospitals, identifying patients at risk for hospital readmission is a priority to reduce costs and improve care. PurposeThe purposes were to validate a predictive algorithm to identify patients at a high risk for preventable hospital readmission within 30 days after discharge and determine if additional risk factors enhance readmission predictability. MethodsA retrospective study was conducted on a randomized sample of 598 patients discharged from a Southeast community hospital. Data were collected from the organization's database and manually abstracted from the electronic medical record using a structured tool. Two separate logistic regression models were fit for the probability of readmission within 30 days after discharge. The first model used the LACE index as the predictor variable, and the second model used the LACE index with additional risk factors. The two models were compared to determine if additional risk factors increased the model's predictive ability. ResultsThe results indicate both models have reasonable prognostic capability. The LACE index with additional risk factors did little to improve prognostication, while adding to the model's complexity. Conclusion Findings support the use of the LACE index as a practical tool to identify patients at risk for readmission.
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