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Building on the first part of the article, which described aims, forms and a new developed typology of Patient Classification Systems (PCS), the second part discusses three wrong assumptions that are often behind the application of common PCS. These assumptions deal mainly with indices of nursing care intensity for measuring of staff requirements and with patient characteristics (deficiencies and problems) in regard to the expenditure of time. Additional examinations show whether or not it is scientifically possible to express staff requirements on the basis of added up individual nursing activities. Data collected in several intensive care units by diagnosis related analysis of work activities (DTA) show that the distribution of expenditure of time for singular nursing activities does not result in meaningful arithmetic means usable for assessment instruments scheduling of personnel. Given these methodical constraints it should be examined with minuteness before the application of a PCS exactly which question should be answered by an instrument or method, and it should be examined whether the method is suitable for the question asked.

This article describes in two parts the basic principles of categorising nursing data into Patient Classification Systems (PCS) and related findings of nursing research. PCS are used to group patients into classes by using some specified criteria or indicators, e.g. needs of a patient, functional status or different nursing activities. They are often applied to constitute staff requirements. Since the large number of available classification systems differs in terms of development procedures and use of resulting data, it was rarely possible to compare different PCS with each other until now. Therefore a typology of PCS was developed, which is presented in the first part of this article. While assigning current PCS to adequate classes of the typology, the development and complexity of different PCS can be evaluated and discussed. Thus using the typology allows to identify advantages and limitations of individual classification systems. Conclusions can be drawn about requirements for further PCS development.