Development and Evaluation of Evidence-Based Nursing (EBN) Filters and Related Databases*

Article excerpt

Objectives: Difficulties encountered in the retrieval of evidence-based nursing (EBN) literature and recognition of terminology, research focus, and design differences between evidence-based medicine and nursing led to the realization that nursing needs its own filter strategies for evidence-based practice. This article describes the development and evaluation of filters that facilitate evidence-based nursing searches.

Methods: An inductive, multistep methodology was employed. A sleep search strategy was developed for uniform application to all filters for filter development and evaluation purposes. An EBN matrix was next developed as a framework to illustrate conceptually the placement of nursing-sensitive filters along two axes: horizontally, an adapted nursing process, and vertically, levels of evidence. Nursing diagnosis, patient outcomes, and primary data filters were developed recursively. Through an interface with the PubMed search engine, the EBN matrix filters were inserted into a database that executes filter searches, retrieves citations, and stores and updates retrieved citations sets hourly. For evaluation purposes, the filters were subjected to sensitivity and specificity analyses and retrieval set comparisons. Once the evaluation was complete, hyperlinks providing access to any one or a combination of completed filters to the EBN matrix were created. Subject searches on any topic may be applied to the filters, which interface with PubMed.

Results: Sensitivity and specificity for the combined nursing diagnosis and primary data filter were 64% and 99%, respectively; for the patient outcomes filter, the results were 75% and 71%, respectively. Comparisons were made between the EBN matrix filters (nursing diagnosis and primary data) and PubMed's Clinical Queries (diagnosis and sensitivity) filters. Additional comparisons examined publication types and indexing differences. Review articles accounted for the majority of the publication type differences, because "review" was accepted by the CQ but was "NOT'd" by the EBN filter. Indexing comparisons revealed that although the term "nursing diagnosis" is in Medical Subject Headings (MeSH), the nursing diagnoses themselves (e.g., sleep deprivation, disturbed sleep pattern) are not indexed as nursing diagnoses. As a result, abstracts deemed to be appropriate nursing diagnosis by the EBN filter were not accepted by the CQ diagnosis filter.

Conclusions: The EBN filter capture of desired articles may be enhanced by further refinement to achieve a greater degree of filter sensitivity. Retrieval set comparisons revealed publication type differences and indexing issues. The EBN matrix filter "NOT'd" out "review," while the CQ filter did not. Indexing issues were identified that explained the retrieval of articles deemed appropriate by the EBN filter matrix but not included in the CQ retrieval. These results have MeSH definition and indexing implications as well as implications for clinical decision support in nursing practice.

INTRODUCTION

This article describes the development of filters that facilitate evidence-based nursing (EBN) searches. Evidence-based practice, regardless of the discipline, consists of discrete steps that vary in number from five to eight [1-3]. Although the number of steps varies, the second step, conducting an evidence-based search, is common to all. A successful and efficient evidence-based search depends on more than the search terms or search string entered. It also depends on the sophistication of the search logic employed and on the searcher's ability to leverage key database features, including terminological control. One tool used to search databases for the best available evidence is a carefully constructed filter.

Numerous examples of filters have been developed to facilitate the retrieval of research or evidence-based health care citations [4-15]. While a review of the history of search filter development is beyond the scope of this paper, the work of Wilczynski, Haynes, and the Hedges Team at McMaster University is clearly foundational. …