Background
As healthcare systems strive for efficiency, hospital “length of stay outliers” have the potential to significantly impact a hospital’s overall utilization. There is a tendency to exclude such “outlier” stays in local quality improvement and data reporting due to their assumed rare occurrence and disproportionate ability to skew mean and other summary data. This study sought to assess the influence of length of stay (LOS) outliers on inpatient length of stay and hospital capacity over a 5-year period at a large urban academic medical center.
Methods
From January 2014 through December 2019, 169,645 consecutive inpatient cases were analyzed and assigned an expected LOS based on national academic center benchmarks. Cases in the top 1% of national sample LOS by diagnosis were flagged as length of stay outliers.
Results
From 2014 to 2019, mean outlier LOS increased (40.98 to 45.11 days), as did inpatient LOS with outliers excluded (5.63 to 6.19 days). Outlier cases increased both in number (from 297 to 412) and as a percent of total discharges (0.98 to 1.56%), and outlier patient days increased from 6.7 to 9.8% of total inpatient plus observation days over the study period.
Conclusions
Outlier cases utilize a disproportionate and increasing share of hospital resources and available beds. The current tendency to exclude such outlier stays in data reporting due to assumed rare occurrence may need to be revisited. Outlier stays require distinct and targeted interventions to appropriately reduce length of stay to both improve patient care and maintain hospital capacity.
procedures and the subanalysis contained 91 907 GI procedures and 1440 unique physicians. Patient characteristics are described in the Table. The Figure shows that the predicted probability of being coded as having a high risk of anesthesia more than doubled for all conditions from 2005 to 2013, indicating potential upcoding. The probability for patients with sleep apnea, for example, increased from 8.8% in 2005 to 21.5% in 2011 and remained at 20.8% in 2013. A similar pattern was also found among patients without any chronic conditions. In the subanalysis, the odds of patients with similar characteristics being coded as being at high risk in 2011 were approximately twice those in 2010, more than 3 times those in 2012, and about 5 times those in 2013, with all year-to-year changes found to be statistically significant.
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