Jinhai Huo1, Ming Yang2, Ya-Chen Tina Shih3. 1. Department of Health Services Research, Management and Policy, The University of Florida, Gainesville, FL, USA. 2. Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 3. Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: yashih@mdanderson.org.
Abstract
BACKGROUND: The "meaningful use of certified electronic health record" policy requires eligible professionals to record smoking status for more than 50% of all individuals aged 13 years or older in 2011 to 2012. OBJECTIVES: To explore whether the coding to document smoking behavior has increased over time and to assess the accuracy of smoking-related diagnosis and procedure codes in identifying previous and current smokers. METHODS: We conducted an observational study with 5,423,880 enrollees from the year 2009 to 2014 in the Truven Health Analytics database. Temporal trends of smoking coding, sensitivity, specificity, positive predictive value, and negative predictive value were measured. RESULTS: The rate of coding of smoking behavior improved significantly by the end of the study period. The proportion of patients in the claims data recorded as current smokers increased 2.3-fold and the proportion of patients recorded as previous smokers increased 4-fold during the 6-year period. The sensitivity of each International Classification of Diseases, Ninth Revision, Clinical Modification code was generally less than 10%. The diagnosis code of tobacco use disorder (305.1X) was the most sensitive code (9.3%) for identifying smokers. The specificities of these codes and the Current Procedural Terminology codes were all more than 98%. CONCLUSIONS: A large improvement in the coding of current and previous smoking behavior has occurred since the inception of the meaningful use policy. Nevertheless, the use of diagnosis and procedure codes to identify smoking behavior in administrative data is still unreliable. This suggests that quality improvements toward medical coding on smoking behavior are needed to enhance the capability of claims data for smoking-related outcomes research.
BACKGROUND: The "meaningful use of certified electronic health record" policy requires eligible professionals to record smoking status for more than 50% of all individuals aged 13 years or older in 2011 to 2012. OBJECTIVES: To explore whether the coding to document smoking behavior has increased over time and to assess the accuracy of smoking-related diagnosis and procedure codes in identifying previous and current smokers. METHODS: We conducted an observational study with 5,423,880 enrollees from the year 2009 to 2014 in the Truven Health Analytics database. Temporal trends of smoking coding, sensitivity, specificity, positive predictive value, and negative predictive value were measured. RESULTS: The rate of coding of smoking behavior improved significantly by the end of the study period. The proportion of patients in the claims data recorded as current smokers increased 2.3-fold and the proportion of patients recorded as previous smokers increased 4-fold during the 6-year period. The sensitivity of each International Classification of Diseases, Ninth Revision, Clinical Modification code was generally less than 10%. The diagnosis code of tobacco use disorder (305.1X) was the most sensitive code (9.3%) for identifying smokers. The specificities of these codes and the Current Procedural Terminology codes were all more than 98%. CONCLUSIONS: A large improvement in the coding of current and previous smoking behavior has occurred since the inception of the meaningful use policy. Nevertheless, the use of diagnosis and procedure codes to identify smoking behavior in administrative data is still unreliable. This suggests that quality improvements toward medical coding on smoking behavior are needed to enhance the capability of claims data for smoking-related outcomes research.
Authors: Brendan T Heiden; Daniel B Eaton; Su-Hsin Chang; Yan Yan; Martin W Schoen; Li-Shiun Chen; Nina Smock; Mayank R Patel; Daniel Kreisel; Ruben G Nava; Bryan F Meyers; Benjamin D Kozower; Varun Puri Journal: Chest Date: 2021-12-14 Impact factor: 10.262
Authors: Neal S Parikh; Yongkang Zhang; Daniel Restifo; Erika Abramson; Matthew J Carpenter; Babak B Navi; Hooman Kamel Journal: Prev Med Rep Date: 2021-12-28