Challenges in defining and categorizing falls on diverse unit types: lessons from expansion of the NDNQI Falls Indicator.

In 2012, the National Database of Nursing Quality Indicators launched a project to expand its falls indicator for use on pediatric, neonatal, and psychiatric units. We discuss challenges encountered, argue that schemes for categorizing falls by cause or supposed preventability are not suitable for large-scale efforts to track and prevent falls, express concern about the growing burden of collecting increasingly granular quality data, and discuss limitations of total and injurious fall rates as quality measures.

Input data quality control for NDNQI national comparative statistics and quarterly reports: a contrast of three robust scale estimators for multiple outlier detection.

Background: To evaluate institutional nursing care performance in the context of national comparative statistics (benchmarks), approximately one in every three major healthcare institutions (over 1,800 hospitals) across the United States, have joined the National Database for Nursing Quality Indicators (NDNQI). With over 18,000 hospital units contributing data for nearly 200 quantitative measures at present, a reliable and efficient input data screening for all quantitative measures for data quality control is critical to the integrity, validity, and on-time delivery of NDNQI reports.

Methods: With Monte Carlo simulation and quantitative NDNQI indicator examples, we compared two ad-hoc methods using robust scale estimators, Inter Quartile Range (IQR) and Median Absolute Deviation from the Median (MAD), to the classic, theoretically-based Minimum Covariance Determinant (FAST-MCD) approach, for initial univariate outlier detection.