PubMed · 12455446
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Lee J Johnson. 2002-11-08. Application mistake? Better correct it.. https://pubmed.ncbi.nlm.nih.gov/12455446/
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On August 29, 2003, we published a final rule with comment period in the Federal Register that finalized two specific provisions: it established new 3-year recordkeeping requirements for drug manufacturers under the Medicaid drug rebate program and set a 3-year time limitation during which manufacturers must report changes to average manufacturer price and best price for purposes of reporting data to us. In addition, it announced the pressing need for codification of fundamental recordkeeping requirements. On September 26, 2003, we issued a correction notice to change the effective date of the August 29, 2003 rule from October 1, 2003 to January 1, 2004. In this interim final rule with comment period, we are removing the 3-year recordkeeping requirements, replacing them with 10-year recordkeeping requirements on a temporary basis, and soliciting comments on the 10-year requirements. Manufacturers must retain records beyond the 10-year period if the records are the subject of an audit or a government investigation of which the manufacturer is aware. These provisions contain a sunset date with respect to the record retention requirements to ensure that we reexamine whether the retention rule remain necessary and effective. This interim final rule with comment period also responds to public comments on the August 29, 2003 final rule with comment period that pertain to the 3-year recordkeeping requirement at Sec. 447.534(h).
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OBJECTIVE: To investigate the accuracy of a computerized method for classifying injury narratives into external-cause-of-injury and poisoning (E-code) categories. METHODS: This study used injury narratives and corresponding E-codes assigned by experts from the 1997 and 1998 US National Health Interview Survey (NHIS). A Fuzzy Bayesian model was used to assign injury descriptions to 13 E-code categories. Sensitivity, specificity and positive predictive value were measured by comparing the computer generated codes with E-code categories assigned by experts. RESULTS: The computer program correctly classified 4695 (82.7%) of the 5677 injury narratives when multiple words were included as keywords in the model. The use of multiple-word predictors compared with using single words alone improved both the sensitivity and specificity of the computer generated codes. The program is capable of identifying and filtering out cases that would benefit most from manual coding. For example, the program could be used to code the narrative if the maximum probability of a category given the keywords in the narrative was at least 0.9. If the maximum probability was lower than 0.9 (which will be the case for approximately 33% of the narratives) the case would be filtered out for manual review. CONCLUSIONS: A computer program based on Fuzzy Bayes logic is capable of accurately categorizing cause-of-injury codes from injury narratives. The capacity to filter out certain cases for manual coding improves the utility of this process.