Limitations of pulmonary embolism ICD-10 codes in emergency department administrative data: let the buyer beware

Abstract

BackgroundAdministrative data is a useful tool for research and quality improvement; however, validity of research findings based on these data depends on their reliability. Diagnoses assigned by physicians are subsequently converted by nosologists to ICD-10 codes (International Statistical Classification of Diseases and Related Health Problems, 10th Revision). Several groups have reported ICD-9 coding errors in inpatient data that have implications for research, quality improvement, and policymaking, but few have assessed ICD-10 code validity in ambulatory care databases. Our objective was to evaluate pulmonary embolism (PE) ICD-10 code accuracy in our large, integrated hospital system, and the validity of using these codes for operational and health services research using ED ambulatory care databases.MethodsAmbulatory care data for patients (age ≥ 18 years) with a PE ICD-10 code (I26.0 and I26.9) were obtained from the records of four urban EDs between July 2013 to January 2015. PE diagnoses were confirmed by reviewing medical records and imaging reports. In cases where chart diagnosis and ICD-10 code were discrepant, chart review was considered correct. Physicians’ written discharge diagnoses were also searched using ‘pulmonary embolism’ and ‘PE’, and patients who were diagnosed with PE but not coded as PE were identified. Coding discrepancies were quantified and described.ResultsOne thousand, four hundred and fifty-three ED patients had a PE ICD-10 code. Of these, 257 (17.7%) were false positive, with an incorrectly assigned PE code. Among the 257 false positives, 193 cases had ambiguous ED diagnoses such as ‘rule out PE’ or ‘query PE’, while 64 cases should have had non-PE codes. An additional 117 patients (8.90%) with a PE discharge diagnosis were incorrectly assigned a non-PE ICD-10 code (false negative group). The sensitivity of PE ICD-10 codes in this dataset was 91.1% (95%CI, 89.4–92.6) with a specificity of 99.9% (95%CI, 99.9–99.9). The positive and negative predictive values were 82.3% (95%CI, 80.3–84.2) and 99.9% (95%CI, 99.9–99.9), respectively.ConclusionsAmbulatory care data, like inpatient data, are subject to coding errors. This confirms the importance of ICD-10 code validation prior to use. The largest proportion of coding errors arises from ambiguous physician documentation; therefore, physicians and data custodians must ensure that quality improvement processes are in place to promote ICD-10 coding accuracy.

Percutaneous versus surgical tracheostomy: timing, outcomes, and charges

“…Inaccurate reporting can result in undercoding and overcoding and may vary among institutions. IA previous study demonstrated that ICD‐10‐CM coding errors could result in a significant number of false positives and false negatives . Thus, inaccuracy due to ICD‐10‐CM coding errors may hinder our ability to identify cases of tracheostomy and complications during the visit.…”

“…IA previous study demonstrated that ICD-10-CM coding errors could result in a significant number of false positives and false negatives. 25 Thus, inaccuracy due to ICD-10-CM coding errors may hinder our ability to identify cases of tracheostomy and complications during the visit. Furthermore, this study is limited to data in New York State.…”

Changing Trends in Hospital Admissions for Pulmonary Embolism in Spain from 2001 to 2018

“…Nevertheless, our study has several limitations, some of which are inherent to the study design and the use of administrative data. First, it was based on ICD discharge codes rather than clinical criteria, which could be subject to underreporting or miscoding [ 39 ]. Nevertheless, the sensitivity and specificity of the diagnosis codes for a principal discharge diagnosis of PE are high as compared with objectively documented diseases based on medical chart review criteria [ 40 ].…”

Risk factors in progression from endometriosis to ovarian cancer: a cohort study based on medical insurance data

“…Second, although based on a large sample size (n=229,617), it lacked details on clinical characteristics such as subtypes of endometriosis, diagnostic tools, histological information of ovarian cancer and cancer stage. Furthermore, there have been a growing body of studies on the accuracy of ICD codes [ 21 22 ], and unfortunately, the study was based on ICD codes to identify diseases of interest, and was thus subject to the errors and limitations of the ICD coding system. In addition, the overall incidence of ovarian cancer is low and only 0.64% of the cohort eventually developed the disease.…”