Validation of new readmission data in the American college of surgeons national surgical quality improvement program

Morgan M. Sellers, Ryan P. Merkow, Amy Halverson, Keiki Hinami, Rachel R. Kelz, David J. Bentrem, Karl Y. Bilimoria*

*Corresponding author for this work

Research output: Contribution to journalArticle

150 Citations (Scopus)

Abstract

Background: Hospital readmissions are gathering increasing attention as a measure of health care quality and as a cost-saving target. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) recently began collecting data related to 30-day postoperative readmissions. Our objectives were to assess the accuracy of the ACS NSQIP readmission variable by comparison with the medical record, and to evaluate the readmission variable against administrative data. Study Design: Readmission data captured in ACS NSQIP at a single academic institution between January and December 2011 were compared with data abstracted from the medical record and administrative data. Results: Of 1,748 cases captured in ACS NSQIP, 119 (6.8%) had an all-cause readmission event identified, and ACS NSQIP had very high agreement with chart review for identifying all-cause readmission events (κ = 0.98). For 1,110 inpatient cases successfully matched with administrative data, agreement with chart review for identifying all-cause readmissions was also very high (κ = 0.97). For identifying unplanned readmission events, ACS NSQIP had good agreement with chart review (κ = 0.67). Overall, agreement with chart review on cause of readmission was higher for ACS NSQIP (κ = 0.75) than for administrative data (κ = 0.46). Conclusions: The ACS NSQIP accurately captured all-cause and unplanned readmission events and had good agreement with the medical record with respect to cause of readmission. Administrative data accurately captured all-cause readmissions, but could not identify unplanned readmissions and less consistently agreed with chart review on cause. The granularity of clinically collected data offers tremendous advantages for directing future quality efforts targeting surgical readmission.

Original languageEnglish (US)
Pages (from-to)420-427
Number of pages8
JournalJournal of the American College of Surgeons
Volume216
Issue number3
DOIs
StatePublished - Mar 1 2013

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Quality Improvement
Medical Records
Patient Readmission
Surgeons
Quality of Health Care
Inpatients
Costs and Cost Analysis

ASJC Scopus subject areas

  • Surgery

Cite this

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title = "Validation of new readmission data in the American college of surgeons national surgical quality improvement program",
abstract = "Background: Hospital readmissions are gathering increasing attention as a measure of health care quality and as a cost-saving target. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) recently began collecting data related to 30-day postoperative readmissions. Our objectives were to assess the accuracy of the ACS NSQIP readmission variable by comparison with the medical record, and to evaluate the readmission variable against administrative data. Study Design: Readmission data captured in ACS NSQIP at a single academic institution between January and December 2011 were compared with data abstracted from the medical record and administrative data. Results: Of 1,748 cases captured in ACS NSQIP, 119 (6.8{\%}) had an all-cause readmission event identified, and ACS NSQIP had very high agreement with chart review for identifying all-cause readmission events (κ = 0.98). For 1,110 inpatient cases successfully matched with administrative data, agreement with chart review for identifying all-cause readmissions was also very high (κ = 0.97). For identifying unplanned readmission events, ACS NSQIP had good agreement with chart review (κ = 0.67). Overall, agreement with chart review on cause of readmission was higher for ACS NSQIP (κ = 0.75) than for administrative data (κ = 0.46). Conclusions: The ACS NSQIP accurately captured all-cause and unplanned readmission events and had good agreement with the medical record with respect to cause of readmission. Administrative data accurately captured all-cause readmissions, but could not identify unplanned readmissions and less consistently agreed with chart review on cause. The granularity of clinically collected data offers tremendous advantages for directing future quality efforts targeting surgical readmission.",
author = "Sellers, {Morgan M.} and Merkow, {Ryan P.} and Amy Halverson and Keiki Hinami and Kelz, {Rachel R.} and Bentrem, {David J.} and Bilimoria, {Karl Y.}",
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T1 - Validation of new readmission data in the American college of surgeons national surgical quality improvement program

AU - Sellers, Morgan M.

AU - Merkow, Ryan P.

AU - Halverson, Amy

AU - Hinami, Keiki

AU - Kelz, Rachel R.

AU - Bentrem, David J.

AU - Bilimoria, Karl Y.

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N2 - Background: Hospital readmissions are gathering increasing attention as a measure of health care quality and as a cost-saving target. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) recently began collecting data related to 30-day postoperative readmissions. Our objectives were to assess the accuracy of the ACS NSQIP readmission variable by comparison with the medical record, and to evaluate the readmission variable against administrative data. Study Design: Readmission data captured in ACS NSQIP at a single academic institution between January and December 2011 were compared with data abstracted from the medical record and administrative data. Results: Of 1,748 cases captured in ACS NSQIP, 119 (6.8%) had an all-cause readmission event identified, and ACS NSQIP had very high agreement with chart review for identifying all-cause readmission events (κ = 0.98). For 1,110 inpatient cases successfully matched with administrative data, agreement with chart review for identifying all-cause readmissions was also very high (κ = 0.97). For identifying unplanned readmission events, ACS NSQIP had good agreement with chart review (κ = 0.67). Overall, agreement with chart review on cause of readmission was higher for ACS NSQIP (κ = 0.75) than for administrative data (κ = 0.46). Conclusions: The ACS NSQIP accurately captured all-cause and unplanned readmission events and had good agreement with the medical record with respect to cause of readmission. Administrative data accurately captured all-cause readmissions, but could not identify unplanned readmissions and less consistently agreed with chart review on cause. The granularity of clinically collected data offers tremendous advantages for directing future quality efforts targeting surgical readmission.

AB - Background: Hospital readmissions are gathering increasing attention as a measure of health care quality and as a cost-saving target. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) recently began collecting data related to 30-day postoperative readmissions. Our objectives were to assess the accuracy of the ACS NSQIP readmission variable by comparison with the medical record, and to evaluate the readmission variable against administrative data. Study Design: Readmission data captured in ACS NSQIP at a single academic institution between January and December 2011 were compared with data abstracted from the medical record and administrative data. Results: Of 1,748 cases captured in ACS NSQIP, 119 (6.8%) had an all-cause readmission event identified, and ACS NSQIP had very high agreement with chart review for identifying all-cause readmission events (κ = 0.98). For 1,110 inpatient cases successfully matched with administrative data, agreement with chart review for identifying all-cause readmissions was also very high (κ = 0.97). For identifying unplanned readmission events, ACS NSQIP had good agreement with chart review (κ = 0.67). Overall, agreement with chart review on cause of readmission was higher for ACS NSQIP (κ = 0.75) than for administrative data (κ = 0.46). Conclusions: The ACS NSQIP accurately captured all-cause and unplanned readmission events and had good agreement with the medical record with respect to cause of readmission. Administrative data accurately captured all-cause readmissions, but could not identify unplanned readmissions and less consistently agreed with chart review on cause. The granularity of clinically collected data offers tremendous advantages for directing future quality efforts targeting surgical readmission.

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JF - Journal of the American College of Surgeons

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