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Sensitivity of Insurance Claims Codes in Identifying Robotic Assisted Surgery.

Elizabeth Wall-Wieler1, Shih-Hao Lee1, Yuki Liu1

  • 1Global Health Economics and Outcomes Research, Intuitive Surgical, Sunnyvale, CA.

Annals of Surgery
|January 6, 2026
PubMed
Summary

Insurance claims codes have low sensitivity for identifying robotic-assisted surgery (RAS), especially in outpatient settings. This misclassification can bias studies on surgical outcomes and the generalizability of RAS research.

Keywords:
insurance claimsrobotic assisted surgerysensitivity analysis

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Area of Science:

  • Healthcare informatics
  • Surgical outcomes research
  • Health services research

Background:

  • Insurer-generated databases are crucial for studying robotic-assisted surgery (RAS) outcomes.
  • Inconsistent use of claims codes in these databases can lead to misclassification and biased study results.

Purpose of the Study:

  • To assess the sensitivity of insurance claims codes for identifying RAS.
  • To evaluate misclassification bias in RAS outcome studies.
  • To examine the generalizability of findings across different healthcare data sources.

Main Methods:

  • A retrospective cohort study compared claims-only definitions to a reference definition including free-text billing data for RAS identification (2018-2023).
  • Two U.S. datasets (Premier Healthcare Database and Merative™) were utilized, evaluating seven common procedures in inpatient and outpatient settings.
  • Misclassification bias was assessed for operative time, length of stay, conversion to open surgery, and surgical site infection.

Main Results:

  • Claims-only identification sensitivity for RAS was 0.578 in the Premier Healthcare Database.
  • Sensitivity was high for inpatient procedures but very low (<0.5 by 2021) for outpatient procedures.
  • Claims-only definitions frequently resulted in biased effect estimates for outpatient procedures, and RAS rates differed between datasets.

Conclusions:

  • The sensitivity of insurance claims data for identifying RAS is variable, depending on the procedure, setting, and time.
  • Low sensitivity leads to significant misclassification bias, affecting the accuracy of analyses on surgical modality and patient outcomes.