Using routine inpatient data to identify patients at risk of hospital readmission

Howell, Stuart, Coory, Michael, Martin, Jennifer L. and Duckett, Stephen (2009) Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Services Research, 9 96.1-96.9. doi:10.1186/1472-6963-9-96

Author Howell, Stuart
Coory, Michael
Martin, Jennifer L.
Duckett, Stephen
Title Using routine inpatient data to identify patients at risk of hospital readmission
Journal name BMC Health Services Research   Check publisher's open access policy
ISSN 1472-6963
Publication date 2009-06
Year available 2009
Sub-type Article (original research)
DOI 10.1186/1472-6963-9-96
Open Access Status DOI
Volume 9
Start page 96.1
End page 96.9
Total pages 9
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2010
Language eng
Subject C1
920204 Evaluation of Health Outcomes
111709 Health Care Administration
Formatted abstract
Background: A relatively small percentage of patients with chronic medical conditions account for a much larger percentage of inpatient costs. There is some evidence that case-management can improve health and quality-of-life and reduce the number of times these patients are readmitted. To assess whether a statistical algorithm, based on routine inpatient data, can be used to identify patients at risk of readmission and who would therefore benefit from case-management.

Methods: Queensland database study of public-hospital patients, who had at least one emergency admission for a chronic medical condition (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes or dementia) during 2005/2006. Multivariate logistic regression was used to develop an algorithm to predict readmission within 12 months. The performance of the algorithm was tested against recorded readmissions using sensitivity, specificity, and Likelihood Ratios (positive and negative).

Several factors were identified that predicted readmission (i.e., age, co-morbidities, economic disadvantage, number of previous admissions). The discriminatory power of the model was modest as determined by area under the receiver operating characteristic (ROC) curve (c = 0.65). At a risk score threshold of 50, the algorithm identified only 44.7% (95% CI: 42.5%, 46.9%) of patients admitted with a reference condition who had an admission in the next 12 months; 37.5% (95% CI: 35.0%, 40.0%) of patients were flagged incorrectly (they did not have a subsequent admission).

Conclusion: A statistical algorithm based on Queensland hospital inpatient data, performed only moderately in identifying patients at risk of readmission. The main problem is that there are too many false negatives, which means that many patients who might benefit would not be offered case-management.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 96

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Created: Thu, 03 Sep 2009, 07:54:31 EST by Mr Andrew Martlew on behalf of School of Public Health