Abstract
BackgroundSignificant effort has been directed at developing prediction tools to identify patients at high risk of unplanned hospital readmission, but it is unclear what these tools add to clinicians’ judgment. In our study, we assess clinicians’ abilities to independently predict 30-day hospital readmissions, and we compare their abilities with a common prediction tool, the LACE index.MethodsOver a period of 50 days, we asked attendings, residents, and nurses to predict the likelihood of 30-day hospital readmission on a scale of 0–100% for 359 patients discharged from a General Medicine Service. For readmitted versus non-readmitted patients, we compared the mean and standard deviation of the clinician predictions and the LACE index. We compared receiver operating characteristic (ROC) curves for clinician predictions and for the LACE index.ResultsFor readmitted versus non-readmitted patients, attendings predicted a risk of 48.1% versus 31.1% (p < 0.001), residents predicted 45.5% versus 34.6% (p 0.002), and nurses predicted 40.2% versus 30.6% (p 0.011), respectively. The LACE index for readmitted patients was 11.3, versus 10.1 for non-readmitted patients (p 0.003). The area under the curve (AUC) derived from the ROC curves was 0.689 for attendings, 0.641 for residents, 0.628 for nurses, and 0.620 for the LACE index. Logistic regression analysis suggested that the LACE index only added predictive value to resident predictions, but not attending or nurse predictions (p < 0.05).ConclusionsAttendings, residents, and nurses were able to independently predict readmissions as well as the LACE index. Improvements in prediction tools are still needed to effectively predict hospital readmissions.
Highlights
Significant effort has been directed at developing prediction tools to identify patients at high risk of unplanned hospital readmission, but it is unclear what these tools add to clinicians’ judgment
When we calculated odds ratios to estimate the risk of readmission in patients identified as having the different characteristics, we found that there was an elevated risk of readmission when attendings believed that patients were poorly adherent to their therapies or had severe disease; or when residents believed that patients were medically complex or had a previous admission
We cannot know with certainty what accounts for the difference in our results, but possibilities include the difference in our patient population, including our inclusion of clinicians’ predictions for patients over age 18 instead of only for patients over age 65; as well as the approximately 7 years that elapsed since that previous report, during which much attention and clinician effort has been directed at reducing hospital readmissions
Summary
Significant effort has been directed at developing prediction tools to identify patients at high risk of unplanned hospital readmission, but it is unclear what these tools add to clinicians’ judgment. Thirty-day hospital readmissions are costly, and can be frustrating for both patients and clinicians As such, they are increasingly scrutinized, and significant efforts are directed at quantifying, understanding, and preventing them. They are increasingly scrutinized, and significant efforts are directed at quantifying, understanding, and preventing them One part of these efforts has been the development of risk models to help identify patients at risk for hospital readmission. A popular model has been the LACE index, due to its simplicity and comparable accuracy to other models [1,2,3] It is calculated by taking into account the length of hospitalization (L), acuity of admission (A), the. Allaudeen and colleagues addressed this question in Inability of Providers to Predict Unplanned Readmissions, where they
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