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Original Investigation |

Auditing Practice Style Variation in Pediatric Inpatient Asthma Care

Jeffrey H. Silber, MD, PhD1,2,3,4,5; Paul R. Rosenbaum, PhD5,6; Wei Wang, PhD1; Justin M. Ludwig, MA1; Shawna Calhoun, MPH1; James P. Guevara, MD, MPH5,7; Joseph J. Zorc, MD, MSCE2,8; Ashley Zeigler, BA1; Orit Even-Shoshan, MS1
[+] Author Affiliations
1Center for Outcomes Research, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
2Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
3Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia
4Department of Health Care Management, Wharton School, University of Pennsylvania, Philadelphia
5Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
6Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia
7Division of General Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
8Division of Emergency Medicine, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
JAMA Pediatr. 2016;170(9):878-886. doi:10.1001/jamapediatrics.2016.0911.
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Importance  Asthma is the most prevalent chronic illness among children, remaining a leading cause of pediatric hospitalizations and representing a major financial burden to many health care systems.

Objective  To implement a new auditing process examining whether differences in hospital practice style may be associated with potential resource savings or inefficiencies in treating pediatric asthma admissions.

Design, Setting, and Participants  A retrospective matched-cohort design study, matched for asthma severity, compared practice patterns for patients admitted to Children’s Hospital Association hospitals contributing data to the Pediatric Hospital Information System (PHIS) database. With 3 years of PHIS data on 48 887 children, an asthma template was constructed consisting of representative children hospitalized for asthma between April 1, 2011, and March 31, 2014. The template was matched with either a 1:1, 2:1, or 3:1 ratio at each of 37 tertiary care children’s hospitals, depending on available sample size.

Exposure  Treatment at each PHIS hospital.

Main Outcomess and Measures  Cost, length of stay, and intensive care unit (ICU) utilization.

Results  After matching patients (n = 9100; mean [SD] age, 7.1 [3.6] years; 3418 [37.6%] females) to the template (n = 100, mean [SD] age, 7.2 [3.7] years; 37 [37.0%] females), there was no significant difference in observable patient characteristics at the 37 hospitals meeting the matching criteria. Despite similar characteristics of the patients, we observed large and significant variation in use of the ICUs as well as in length of stay and cost. For the same template-matched populations, comparing utilization between the 12.5th percentile (lower eighth) and 87.5th percentile (upper eighth) of hospitals, median cost varied by 87% ($3157 vs $5912 per patient; P < .001); total hospital length of stay varied by 47% (1.5 vs 2.2 days; P < .001); and ICU utilization was 254% higher (6.5% vs 23.0%; P < .001). Furthermore, the patterns of resource utilization by patient risk differed significantly across hospitals. For example, as patient risk increased one hospital displayed significantly increasing costs compared with their matched controls (comparative cost difference: lowest risk, −34.21%; highest risk, 53.27%; P < .001). In contrast, another hospital displayed significantly decreasing costs relative to their matched controls as patient risk increased (comparative cost difference: lowest risk, −10.12%; highest risk, −16.85%; P = .01).

Conclusions and Relevance  For children with asthma who had similar characteristics, we observed different hospital resource utilization; some values differed greatly, with important differences by initial patient risk. Through the template matching audit, hospitals and stakeholders can better understand where this excess variation occurs and can help to pinpoint practice styles that should be emulated or avoided.

Figures in this Article


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Figure 1.
A Description of Template Matching

Each geometric shape represents a different type of patient (eg, patient with asthma admitted with different physiologic severity), and the varying sizes of each shape represent different characteristics of that patient type (eg, different ages). A template is formed from the national sample and each hospital is matched to the template using a 1:1, 2:1, or 3:1 ratio, depending on the available sample size at that hospital.

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Figure 2.
Cost by Length of Stay (LOS) by Intensive Care Unit (ICU) Utilization

The Spearman correlation coefficient r between median cost (A) and trimmed LOS (B) was highly significant (r = 0.57; P < .001), the correlation between ICU utilization (C) and trimmed LOS was significant (r = 0.41, P = .01), and the correlation between median cost and ICU utilization was not significant (r = 0.03, P = .87). The straight solid lines within the scatterplots were constructed using robust linear model m-estimation.37,38,40

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Figure 3.
In-Hospital Cost by Patient Risk Level

The x-axis of each graph represents the risk, estimated by predicted length of stay, for each template patient strata. The y-axis represents the difference in cost (focal  minus control) inside each matched pair. A point falling on the horizontal line at 0 represents no difference in cost between the 2 patients in the matched pair, and a point falling below the line suggests a lower cost for the focal vs control patient. The solid lines represent the locally weighted scatterplot smoothing (LOWESS) line.41 LOWESS 95% CI bands (shaded areas) for the central tendency line were produced using the bootstrap method. A box plot at the bottom of each graph denotes the 5%, 25%, 50%, 75%, and 95% values of predicted risk over all strata. Each graph illustrates an individual hospital.

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