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study that provided data for this analysis: Florida Hospital (Tampa, FL), Mayo Clinic (Jacksonville, FL), Boston Medical Center (Boston, MA), Cooley Dickinson Hospital (Northampton, MA), UMass Memorial HealthAlliance Hospital (Leominster, MA), Cortland Regional Medical Center (Cortland, NY), Kaleida Health (Buffalo, NY), Mohawk Valley Health System (Utica, NY), Norwell Health (New Hyde Park, NY), South Nassau Communities Hospital (Oceanside, NY), Strong Memorial Hospital (Rochester, NY), White Plains Hospital (White Plains, NY), Allenmore Hospital (Tacoma, WA), and Tacoma General Hospital (Tacoma, WA). All other hospitals contributing data wished to remain anonymous. Additionally, we are grateful to the members of the PAICAP Scientific Advisory Board for their guidance and input, including Neil Fishman, MD; Patricia Grant, RN, BSN, MS, CIC, FAPIC; Richard Platt, MD, MSc; and Donald Goldmann, MD.
Financial support. This project was supported by the Agency for Healthcare Research and Quality (grant nos. K08HS025008 to C.R., T32HS000063 to H.E. H., and 2R01HS018414-06 \ to G.M.L). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Fig. 1. Bubble plot of hospital rankings by quartiles for rates of surgical site infection after colon surgery by National Healthcare Safety Network data versus claims billing data (2012–2014 combined). Bubble sizes are proportional to the number of hospitals in each matched quartile. The actual number of hospitals in each category is denoted within the bubbles. The cohort included 155 hospitals; the total number of hospitals reflected in the graph equals the number of unique hospital years (N=394) during the study period. Lower quartiles indicate better performance (ie, quartile 1=lowest SSI rates and quartile 4=highest SSI rates). The solid line indicates where all hospitals or bubbles would lie if concordance was perfect between claims and NHSN data. Bubbles above the solid line indicate quartiles that are worse by claims versus NHSN data. Bubbles below the dotted line indicate quartiles that are better by claims data versus NHSN data. τ=Kendall’s τ, a measure of the ordinal association between 2 measures (in this case, hospital rankings using NHSN vs claims data). We considered a τ<0.40 as poor agreement, 0.41–0.75 as fair– good agreement, and>0.75 as excellent agreement.
of random variation to discordant claims versus NHSN hospital rankings. We used ICD-9-CM codes, and the degree to which our findings hold true with ICD-10 codes is unknown. We were unable to exactly match the 30-day NHSN SSI time window with claims data because claims data were constrained by month. This limitation may have contributed to some of the discordance between the 2 data sources. We were unable to determine how well NHSN SSIs matched claims codes on a patient-level basis. Lastly, we examined hospital rankings prior to reimbursement policies targeting SSI-colon; additional work is needed to deter- mine whether and how the relationship between claims and NHSN data changes after VBIP implementation. In conclusion, determinations of hospital SSI-colon rates using
claims vs NHSN data are poorly concordant. This underscores the challenges in evaluating hospital quality by administrative data and the need to optimize surveillance methods in the age of public reporting and value-based payment.
Acknowledgements. We thank the hospitals for participating in the Pre- venting Avoidable Infectious Complications by Adjusting Payment (PAICAP)
Conflicts of interest. None of the authors have any conflicts to disclose. References
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