Conceptual-driven classification for coding advise in health insurance reimbursement

  • Authors:

  • Sheng-Tun Li;Chih-Chuan Chen;Fernando Huang

  • Affiliations:

  • Institute of Information Management, National Cheng Kung University, Department of Industrial and Information Management, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan 701, Taiwan, ...;Department of Leisure Information Management, Taiwan Shoufu University, No.168, Nanshih Li, Madou, Tainan 721, Taiwan, ROC;Institute of Information Management, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2011

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Abstract

Objective: With the non-stop increases in medical treatment fees, the economic survival of a hospital in Taiwan relies on the reimbursements received from the Bureau of National Health Insurance, which in turn depend on the accuracy and completeness of the content of the discharge summaries as well as the correctness of their International Classification of Diseases (ICD) codes. The purpose of this research is to enforce the entire disease classification framework by supporting disease classification specialists in the coding process. Methodology: This study developed an ICD code advisory system (ICD-AS) that performed knowledge discovery from discharge summaries and suggested ICD codes. Natural language processing and information retrieval techniques based on Zipf's Law were applied to process the content of discharge summaries, and fuzzy formal concept analysis was used to analyze and represent the relationships between the medical terms identified by MeSH. In addition, a certainty factor used as reference during the coding process was calculated to account for uncertainty and strengthen the credibility of the outcome. Results: Two sets of 360 and 2579 textual discharge summaries of patients suffering from cerebrovascular disease was processed to build up ICD-AS and to evaluate the prediction performance. A number of experiments were conducted to investigate the impact of system parameters on accuracy and compare the proposed model to traditional classification techniques including linear-kernel support vector machines. The comparison results showed that the proposed system achieves the better overall performance in terms of several measures. In addition, some useful implication rules were obtained, which improve comprehension of the field of cerebrovascular disease and give insights to the relationships between relevant medical terms. Conclusion: Our system contributes valuable guidance to disease classification specialists in the process of coding discharge summaries, which consequently brings benefits in aspects of patient, hospital, and healthcare system.