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Creating and Analyzing a Statewide Nursing Quality Measurement Database

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Purpose: To explicate a replicable methodology for designing and analyzing a large ongoing reliable and valid quality database to examine nurse staffing and patient care outcomes in acute care hospitals.

Design: Prospective nurse staffing, process of care, and patient outcomes data based on the American Nurses Association's (ANA) nursing quality indicators collected from a voluntary convenience sample at acute care hospitals in California with rolling-site accrual.

Methods: The ongoing CalNOC database development and repository project, the largest statewide effort of its kind in the United States (US), currently includes data on hospital nurse staffing, patient days, patient falls, pressure ulcer and restraint prevalence, registered nurse (RN) education, and patients' perceptions of satisfaction with care.

Findings: As of May 2003, the CalNOC database contained staffing data from 842 units in 134 acute care hospitals over 20 quarters from April 1998 to March 2003. The repository also included clinical outcome information on 34,262 reported patient falls, pressure ulcer prevalence data on 41,982 patient observations, and service outcome data on patient satisfaction from 26,461 patients. Participating hospitals receive quarterly reports allowing them to benchmark their own performance against other participating hospitals. CalNOC methods have been adapted and replicated by both the Military Nursing Outcomes Database and VA Nursing Outcomes Database projects, and CalNOC nursing-sensitive measures have been endorsed by the National Quality Forum.

Conclusions: This working model for collecting reliable and valid data was derived from multiple hospitals across California. The data are the basis for studies to contribute to the development of evidence-based public policy, and for ongoing study of the effects of nurse staffing on clinical and service outcomes.

Journal of Nursing Scholarship, 2004; 36:4, 371-378. © 2004Sigma Theta Tau International.
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Keywords: data analysis; data collection methods; nursing workload; quality indicators

Document Type: Research Article

Publication date: 2004-12-01

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