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PubMed · 7731600

A case-mix classification system for long-term care facilities.

S Zbylot·C Job·E Mccormick·C Boulter·A Moore
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Abstract

The resident assessment and associated classification model inherent in any case-mix classification system are particularly germane to nursing. Resource Utilization Groups-Version III (RUG-III) illustrates how a case-mix classification system for long-term care nursing facilities is designed and constructed. Understanding the RUG-III classification system can assist nurses in evaluating other classification systems that may be adopted by Medicaid programs. Nurses provide a crucial perspective in forming and informing healthcare policy associated with nursing home payment and quality.

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Diagnosis-Related GroupsHumansMedicaidModels, NursingSkilled Nursing FacilitiesUnited StatesJournal Article

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S Zbylot, C Job, E Mccormick, C Boulter, A Moore. 1995. A case-mix classification system for long-term care facilities.. https://pubmed.ncbi.nlm.nih.gov/7731600/

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Reliability of diagnoses coding with ICD-10.

OBJECTIVE: Reliability of diagnoses coding is essential for the use of routine data in a national health care system. The present investigation compares reliability of diagnoses coding with ICD-10 between three groups of coding subjects. METHOD: One hundred and eighteen students coded 15 diagnoses lists, 27 medical managers from hospitals 34 discharge letters, and 13 coding specialists 12 discharge letters. Agreement in principal diagnosis was assessed using Cohen's Kappa and the fraction of coincidences over the number of pairs, agreement for the full set of diagnoses with a previously developed measure p(om). RESULTS: Kappa values were fair (managers) or moderate (coders) for terminal codes with 0.27 and 0.42 (agreement 29.2% versus 46.8%), substantial for the chapter level with 0.71 and 0.72 (agreement 78.3% versus 80.8%). p(om) was lower for the full set of diagnoses than for principal diagnoses, for example in case of managers with 0.21 versus 0.29 for terminal codes. Best results were achieved by students coding diagnoses lists. In summary, the results are remarkably lower than in earlier publications. CONCLUSION: The refinement of the ICD-10 accompanied by innumerous coding rules has established a complex environment that leads to significant uncertainties even for experts. Use of coded data for quality management, health care financing, and health care policy requires a remarkable simplification of ICD-10 to receive a valid image of health care reality.

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