Leora I Horwitz1,2,3, Jacqueline N Grady4, Dorothy B Cohen4, Zhenqiu Lin4, Mark Volpe4,5, Chi K Ngo5, Andrew L Masica6, Theodore Long7, Jessica Wang8, Megan Keenan5, Julia Montague5, Lisa G Suter5,9, Joseph S Ross5,10,11, Elizabeth E Drye5,12, Harlan M Krumholz5,7,11,13, Susannah M Bernheim5,10. 1. Division of Healthcare Delivery Science, Department of Population Health, New York University School of Medicine, New York, New York. 2. Center for Healthcare Innovation and Delivery Science, New York University Langone Medical Center, New York, New York. 3. Division of General Internal Medicine and Clinical Innovation, Department of Medicine, New York University School of Medicine, New York, New York. 4. Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut. 5. Yale Physician Associate Program, Yale School of Medicine, New Haven, CT. 6. Center for Clinical Effectiveness, Baylor Scott & White Health, Dallas, Texas. 7. Robert Wood Johnson Clinical Scholars Program, Department of Medicine, Yale School of Medicine, New Haven, Connecticut. 8. Yale School of Medicine, New Haven, Connecticut. 9. Section of Rheumatology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. 10. Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. 11. Department of Health Policy and Management, Yale University School of Public Health, New Haven, Connecticut. 12. Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut. 13. Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut.
Abstract
BACKGROUND: It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES: To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN: Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS: For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS: We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS: In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS: An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.
BACKGROUND: It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES: To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN: Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS: For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS: We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS: In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS: An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.
Authors: Patricia S Keenan; Sharon-Lise T Normand; Zhenqiu Lin; Elizabeth E Drye; Kanchana R Bhat; Joseph S Ross; Jeremiah D Schuur; Brett D Stauffer; Susannah M Bernheim; Andrew J Epstein; Yongfei Wang; Jeph Herrin; Jersey Chen; Jessica J Federer; Jennifer A Mattera; Yun Wang; Harlan M Krumholz Journal: Circ Cardiovasc Qual Outcomes Date: 2008-09
Authors: Leora I Horwitz; Chohreh Partovian; Zhenqiu Lin; Jacqueline N Grady; Jeph Herrin; Mitchell Conover; Julia Montague; Chloe Dillaway; Kathleen Bartczak; Lisa G Suter; Joseph S Ross; Susannah M Bernheim; Harlan M Krumholz; Elizabeth E Drye Journal: Ann Intern Med Date: 2014-11-18 Impact factor: 25.391
Authors: Peter K Lindenauer; Sharon-Lise T Normand; Elizabeth E Drye; Zhenqiu Lin; Katherine Goodrich; Mayur M Desai; Dale W Bratzler; Walter J O'Donnell; Mark L Metersky; Harlan M Krumholz Journal: J Hosp Med Date: 2011-01-05 Impact factor: 2.960
Authors: Harlan M Krumholz; Zhenqiu Lin; Elizabeth E Drye; Mayur M Desai; Lein F Han; Michael T Rapp; Jennifer A Mattera; Sharon-Lise T Normand Journal: Circ Cardiovasc Qual Outcomes Date: 2011-03
Authors: Erica S Spatz; Kasia J Lipska; Ying Dai; Haikun Bao; Zhenqiu Lin; Craig S Parzynski; Faseeha K Altaf; Erin K Joyce; Julia A Montague; Joseph S Ross; Susannah M Bernheim; Harlan M Krumholz; Elizabeth E Drye Journal: Med Care Date: 2016-05 Impact factor: 2.983
Authors: Leora I Horwitz; Susannah M Bernheim; Joseph S Ross; Jeph Herrin; Jacqueline N Grady; Harlan M Krumholz; Elizabeth E Drye; Zhenqiu Lin Journal: Med Care Date: 2017-05 Impact factor: 2.983
Authors: Hrishikesh Chakraborty; Robert Neal Axon; Jordan Brittingham; Genevieve Ray Lyons; Laura Cole; Christine B Turley Journal: Health Serv Res Date: 2016-09-28 Impact factor: 3.402
Authors: Himali Weerahandi; Li Li; Haikun Bao; Jeph Herrin; Kumar Dharmarajan; Joseph S Ross; Kunhee Lucy Kim; Simon Jones; Leora I Horwitz Journal: J Am Med Dir Assoc Date: 2019-04 Impact factor: 4.669
Authors: Nihar R Desai; Joseph S Ross; Ji Young Kwon; Jeph Herrin; Kumar Dharmarajan; Susannah M Bernheim; Harlan M Krumholz; Leora I Horwitz Journal: JAMA Date: 2016-12-27 Impact factor: 56.272
Authors: Harlan M Krumholz; Kun Wang; Zhenqiu Lin; Kumar Dharmarajan; Leora I Horwitz; Joseph S Ross; Elizabeth E Drye; Susannah M Bernheim; Sharon-Lise T Normand Journal: N Engl J Med Date: 2017-09-14 Impact factor: 91.245
Authors: Rozalina G McCoy; Stephanie M Peterson; Lynn S Borkenhagen; Paul Y Takahashi; Bjorg Thorsteinsdottir; Anupam Chandra; James M Naessens Journal: Med Care Date: 2018-08 Impact factor: 2.983