AIM: This paper is a report of a study to develop a methodology that corrects nurse staffing for nursing care intensity in a way that allows nationwide benchmarking of nurse staffing data. BACKGROUND: Although nurse workload measurement systems are recognized to be informative in nurse staffing decisions, they are rarely used. When these systems are used, however, it is only possible to compare units within hospitals, because currently available instruments are not standardized for comparisons beyond hospital boundaries. The Belgian Nursing Minimum Dataset (B-NMDS) contains uniformly measured data about the intensity of nursing care and nurse staffing levels for all hospitals in Belgium. METHOD: We conducted a retrospective multilevel analysis of the B-NMDS for the year 2003. The sample included 690,258 inpatient days for 298,691 patients, recorded from 1637 acute care nursing units in 115 hospitals. We corrected the number of nursing staff by using different covariates available in the B-NMDS: intensity of nursing care, type of day (week vs. weekend), service type (general vs. intensive) and hospital type (academic vs. general). FINDINGS: The multilevel approach allowed us to explain about 70% of the variability in the number of nursing staff per nursing unit using hospital type (P = 0.0053); intensity of nursing care (P < 0.0001) and service type (P < 0.0001) as the only covariates. CONCLUSION: The feedback tool we developed can inform nurse managers and policymakers about nursing intensity-adjusted nurse staffing levels according to different benchmarks. Our study demonstrates that investing in large nursing datasets is appropriate for the international nursing community.
AIM: This paper is a report of a study to develop a methodology that corrects nurse staffing for nursing care intensity in a way that allows nationwide benchmarking of nurse staffing data. BACKGROUND: Although nurse workload measurement systems are recognized to be informative in nurse staffing decisions, they are rarely used. When these systems are used, however, it is only possible to compare units within hospitals, because currently available instruments are not standardized for comparisons beyond hospital boundaries. The Belgian Nursing Minimum Dataset (B-NMDS) contains uniformly measured data about the intensity of nursing care and nurse staffing levels for all hospitals in Belgium. METHOD: We conducted a retrospective multilevel analysis of the B-NMDS for the year 2003. The sample included 690,258 inpatient days for 298,691 patients, recorded from 1637 acute care nursing units in 115 hospitals. We corrected the number of nursing staff by using different covariates available in the B-NMDS: intensity of nursing care, type of day (week vs. weekend), service type (general vs. intensive) and hospital type (academic vs. general). FINDINGS: The multilevel approach allowed us to explain about 70% of the variability in the number of nursing staff per nursing unit using hospital type (P = 0.0053); intensity of nursing care (P < 0.0001) and service type (P < 0.0001) as the only covariates. CONCLUSION: The feedback tool we developed can inform nurse managers and policymakers about nursing intensity-adjusted nurse staffing levels according to different benchmarks. Our study demonstrates that investing in large nursing datasets is appropriate for the international nursing community.
Authors: Koen Van den Heede; Emmanuel Lesaffre; Luwis Diya; Arthur Vleugels; Sean P Clarke; Linda H Aiken; Walter Sermeus Journal: Int J Nurs Stud Date: 2009-02-07 Impact factor: 5.837
Authors: Koen Van den Heede; Walter Sermeus; Luwis Diya; Sean P Clarke; Emmanuel Lesaffre; Arthur Vleugels; Linda H Aiken Journal: Int J Nurs Stud Date: 2008-07-25 Impact factor: 5.837
Authors: Jane E Ball; Trevor Murrells; Anne Marie Rafferty; Elizabeth Morrow; Peter Griffiths Journal: BMJ Qual Saf Date: 2013-07-29 Impact factor: 7.035