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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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.
BACKGROUND: Administrative data are an attractive data source for the construction of quality indicators to assess and monitor quality of nursing care in hospitals. Current approaches to constructing measures from discharge abstracts apply substantial restrictions to exclude patients at high risk or with preexisting conditions. This study evaluates whether broader sample definitions combined with risk adjustment would allow for larger samples and increase analytic power. METHODS: Eight indicators were constructed from discharge abstracts of major surgical and medical patients from 799 hospitals in 11 states using existing definitions: pneumonia, urinary tract infection, decubitus ulcers, central nervous system complications, shock, sepsis, pulmonary failure, and upper gastrointestinal bleeding. We tested the effect of broadening the samples in 4 ways: comparing indicator rates in the broader and restrictive samples; assessing correlations of hospital ranks in the broader and restrictive samples; performing clinical reviews of cases in the added samples; and using different samples in regressions of indicators on nurse staffing variables, adjusting for patient risk. RESULTS: Indicator rates in the broader samples tended to be higher but did not change hospital rankings significantly. Clinical review suggested that many sample restrictions could be dropped. Using indicators based on broader definitions, coefficients on staffing variables increased in magnitude. CONCLUSION: Less restrictive sample definitions were shown to be feasible and increased the sensitivity of the indicators and thus the power of the analysis. Particularly in surgical patients, the samples could be broadened, although more conservative definitions appeared appropriate for medical patients.