Journal of the Japan Society for Healthcare Administration
Online ISSN : 2185-422X
Print ISSN : 1882-594X
ISSN-L : 1882-594X
Research notes
Selection of a method for detecting cases of severe pneumothorax associated with central venous catheterization using information on fee-for-medical-service
──Potential utilizability of information from Fee-for-medical-service for internal audit──
Mikayo TobaMutsuko MoriwakiYuko SaseSatoshi ObayashiKiyohide Fushimi
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JOURNAL FREE ACCESS

2016 Volume 53 Issue 4 Pages 217-225

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Abstract

  [Introduction] This study represents an attempt to elaborate a method for detecting cases of severe pneumothorax associated with placement of a central venous catheter using information on fee-for-medical-service records, to realize conformation of internal audits with actual medical service situations. [Methods] Cases with calculated medical fees for central venous catheterization and continuous thoracic cavity drainage were extracted from the EF file information. In this study population, the detection sensitivity of the following methods for the above-mentioned adverse event cases were compared:Method 1, detection by reference to the name of the disorder occurring after hospital admission;Method 2, detection by reference to the diagnosis stated on the medical bill;Method 3, detection based on a survey of the medical records;Method 4, early detection by reference to the diagnosis stated on the medical bill without prior extraction from the EF files. [Results] The detection sensitivities of the four methods were found to be 15.4%, 42.3%, 69.2% and 53.8%, respectively, the incidence of central venous catheterization-associated severe pneumothorax was 0.8%, and the incident report submission rate was 61.5%. [Conclusion] Detection of adverse events based on Fee-for-medical-service information involves problems that need to be resolved, such as the detection sensitivity being dependent on the data input accuracy and the subject events being limited. Concurrent use of survey of the medical records, however, enabled even detection of events that could not be identified from incident reports.

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© 2016 Japan Society for Healthcare Administration
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