Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data

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...

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Veröffentlicht in:Journal of hospital medicine 2015-10, Vol.10 (10), p.670-677
Hauptverfasser: Horwitz, Leora I., Grady, Jacqueline N., Cohen, Dorothy B., Lin, Zhenqiu, Volpe, Mark, Ngo, Chi K., Masica, Andrew L., Long, Theodore, Wang, Jessica, Keenan, Megan, Montague, Julia, Suter, Lisa G., Ross, Joseph S., Drye, Elizabeth E., Krumholz, Harlan M., Bernheim, Susannah M.
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Sprache:eng
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Zusammenfassung: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. Journal of Hospital Medicine 2015;10:670–677. © 2015 Society of Hospital Medicine.
ISSN:1553-5592
1553-5606
1553-5606
DOI:10.1002/jhm.2416