Construction of a semi-automatic ICD-10 coding system

Abstract

Background: The International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Our aim is to construct a practical, automatic ICD-10 coding machine to improve coding efficiency and quality in daily work. Methods: In this study, we propose the use of regular expressions (regexps) to establish a correspondence between diagnosis codes and diagnosis descriptions in outpatient settings and at admission and discharge. The description models of the regexps were embedded in our upgraded coding system, which queries a diagnosis description and assigns a unique diagnosis code. Like most studies, the precision (P), recall (R), F-measure (F) and overall accuracy (A) were used to evaluate the system performance. Our study had two stages. The datasets were obtained from the diagnosis information on the homepage of the discharge medical record. The testing sets were from Results: The values of P were 89.27 and 88.38% in the first testing phase and the second testing phase, respectively, which demonstrate high precision. The automatic ICD-10 coding system completed more than 160,000 codes in 16 months, which reduced the workload of the coders. In addition, a comparison between the amount of time needed for manual coding and automatic coding indicated the effectiveness of the system-the time needed for automatic coding takes nearly 100 times less than manual coding. Conclusions: Our automatic coding system is well suited for the coding task. Further studies are warranted to perfect the description models of the regexps and to develop synthetic approaches to improve system performance.

Automated clinical coding: what, why, and where we are?

“…The issue with pure rule-based methods is that it is not straightforward and it can be time-consuming to extend rules to tens of thousands of codes and their varieties, and inter-relations among codes; this thus needs the support of machine learning with textual features for classification, and historically, some of the classifiers were Decision Trees, Support Vector Machine (SVM), etc 8 , 13 , 29 . Still, rule-based methods like using regular expressions to match various textual descriptions can result in high precision in coding (yet low recall), and have been used to support human coding to largely improve coding efficiency 30 .…”

Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities

“…61 Furthermore, data quality has plagued EHRs. Administrative coding systems like International Classification of Diseases version 10 have too many codes for providers and coders to document accurately without substantial computer support, [62][63][64] and these codes document billing, not patient care. Problem lists, ostensibly a clinical representation of a patient's conditions, are frequently inaccurate, incomplete, and out-of-date because of burdensome data entry.…”

Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing

“…Identifying and organizing the rich information on individual function currently locked away in the medical free text can unlock valuable details to enrich the understanding of researchers of rehabilitation outcomes, and highlight salient details of experiences of patients in clinical decision-making. Prior research on automated and semi-automated ICD coding systems using NLP methods provides an instructive example of how these approaches can streamline medical coding processes (36)(37)(38). The growing integration of the ICF into clinical and research settings, from primary care (39) and EHR implementation (40) to pediatric research (41), presents similar opportunities to smooth the adoption and practical use of ICF categories with NLPbased coding systems.…”