Knowledge discovery in nursing minimum data set using data mining.

PURPOSE The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. METHODS Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1,000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. RESULTS Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relieved pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. CONCLUSIONS This study demonstrated the utilization of data mining method through a large data set with standardized language format to identify the contribution of nursing care to patient's health.