A Nursing Care Plan Recommender System Using a Data Mining Approach

Recommender systems have been extensively studied to present items such as movies, music, and books that are likely of interest to the user. We propose to use correlations among nursing diagnoses, outcomes, and interventions to create a recommender system for constructing nursing care plans. Nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilizes a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multistep nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.