Katherine A Auger1,2,3, Michael C Ponti-Zins4, Angela M Statile1,2,3, Kris Wesselkamper3,5, Beth Haberman3,6, Samuel P Hanke2,3,4,7. 1. Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. 2. James M. Anderson Center for Healthcare Improvement, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. 3. University of Cincinnati School of Medicine, Cincinnati, Ohio. 4. Center for Patient Family Experience, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. 5. Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. 6. Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. 7. Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Abstract
BACKGROUND: Readmission rates are frequently used as a hospital quality metric; yet multiple measures exist to evaluate pediatric readmission rates. We sought to assess how four different measures of pediatric readmission compare with assessment of both preventable and unplanned readmission. METHODS: Clinicians on hospital medicine, cardiology, neonatology, and neurology teams reviewed medical records for 30-day readmissions using an abstraction tool with high interrater reliability for preventability assessment. Readmissions between July 2014 and June 2016 were classified separately as preventable or not preventable and planned or unplanned. We compared the classifications to four existing readmission metrics: all-cause readmission, unplanned readmission/time flag classification, the pediatric all-condition readmission, and potentially preventable readmission. We calculated sensitivity and specificity for all readmission metrics. RESULTS: Among 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68%). On medical record review, the majority of readmissions were determined not preventable (85%). Only 15% were classified as unplanned and preventable. None of the four readmission measures had appropriate sensitivity or specificity for identifying preventable readmission. The unplanned readmission/time flag classification had the highest sensitivity (95%) and specificity (90%) in identifying unplanned readmissions. CONCLUSION: None of the existing pediatric readmission measures can reliably determine preventability. The unplanned readmission/time flag measure performed best in identifying unplanned readmissions.
BACKGROUND: Readmission rates are frequently used as a hospital quality metric; yet multiple measures exist to evaluate pediatric readmission rates. We sought to assess how four different measures of pediatric readmission compare with assessment of both preventable and unplanned readmission. METHODS: Clinicians on hospital medicine, cardiology, neonatology, and neurology teams reviewed medical records for 30-day readmissions using an abstraction tool with high interrater reliability for preventability assessment. Readmissions between July 2014 and June 2016 were classified separately as preventable or not preventable and planned or unplanned. We compared the classifications to four existing readmission metrics: all-cause readmission, unplanned readmission/time flag classification, the pediatric all-condition readmission, and potentially preventable readmission. We calculated sensitivity and specificity for all readmission metrics. RESULTS: Among 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68%). On medical record review, the majority of readmissions were determined not preventable (85%). Only 15% were classified as unplanned and preventable. None of the four readmission measures had appropriate sensitivity or specificity for identifying preventable readmission. The unplanned readmission/time flag classification had the highest sensitivity (95%) and specificity (90%) in identifying unplanned readmissions. CONCLUSION: None of the existing pediatric readmission measures can reliably determine preventability. The unplanned readmission/time flag measure performed best in identifying unplanned readmissions.
Authors: Katherine A Auger; Emily L Mueller; Steven H Weinberg; Catherine S Forster; Anita Shah; Christine Wolski; Grant Mussman; Anna J Ipsaro; Matthew M Davis Journal: J Pediatr Date: 2015-12-30 Impact factor: 4.406
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Authors: Katherine A Auger; Ronald J Teufel; J Mitchell Harris; James C Gay; Mark A Del Beccaro; Mark I Neuman; Javier Tejedor-Sojo; Rishi K Agrawal; Rustin B Morse; Pirooz Eghtesady; Harold K Simon; Richard E McClead; Evan S Fieldston; Samir S Shah Journal: Pediatrics Date: 2017-02 Impact factor: 7.124
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Authors: James C Gay; Rishi Agrawal; Katherine A Auger; Mark A Del Beccaro; Pirooz Eghtesady; Evan S Fieldston; Justin Golias; Paul D Hain; Richard McClead; Rustin B Morse; Mark I Neuman; Harold K Simon; Javier Tejedor-Sojo; Ronald J Teufel; J Mitchell Harris; Samir S Shah Journal: J Pediatr Date: 2014-12-02 Impact factor: 4.406
Authors: Katherine A Auger; Samir S Shah; Heather L Tubbs-Cooley; Heidi J Sucharew; Jennifer M Gold; Susan Wade-Murphy; Angela M Statile; Kathleen D Bell; Jane C Khoury; Colleen Mangeot; Jeffrey M Simmons Journal: JAMA Pediatr Date: 2018-09-04 Impact factor: 16.193
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Authors: Christine M White; Angela M Statile; Denise L White; Dena Elkeeb; Karen Tucker; Diane Herzog; Stephen D Warrick; Denise M Warrick; Julie Hausfeld; Amanda Schondelmeyer; Pamela J Schoettker; Pamela Kiessling; Michael Farrell; Uma Kotagal; Frederick C Ryckman Journal: BMJ Qual Saf Date: 2014-01-27 Impact factor: 7.035
Authors: Katherine A Auger; Jeffrey M Simmons; Heather L Tubbs-Cooley; Heidi J Sucharew; Angela M Statile; Rita H Pickler; Hadley S Sauers-Ford; Jennifer M Gold; Jane C Khoury; Andrew F Beck; Susan Wade-Murphy; Pierce Kuhnell; Samir S Shah Journal: Pediatrics Date: 2018-07 Impact factor: 7.124