2.50
Hdl Handle:
http://hdl.handle.net/10755/157787
Type:
Presentation
Title:
Moving Toward an Evidence-Based Nurse Staffing Model
Abstract:
Moving Toward an Evidence-Based Nurse Staffing Model
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Loan, Lori A., PhD, RNC
P.I. Institution Name:Madigan Army Medical Center, Nursing Research Service
Title:Chief, Nursing Research Service
Contact Address:ATTN: MCHJ-CN-NR, Tacoma, WA, 98431-1100, USA
Contact Telephone:253-968-2289
Co-Authors:Sandra Rolph, MAJ, AN, Head Nurse
Rationale/Background: Several studies looking at the impact of nurse staffing on patient outcomes have been conducted. Unfortunately, barriers between nursing leaders and the literature, such as the overwhelming number of studies, competing priorities, and resource limitations have made it difficult to interpret and use staffing study findings. Consequently, translating research results into the real-world environment has been difficult. Project Aim & Process: The project used Stetler's Model of Research Utilization to Facilitate Evidence-Based Practice to advance understanding of staffing issues and change hospital policy to improve unit specific nurse staffing models. Work for one step-down unit is described in this abstract. Preparatory Phase: Stakeholders that might impact the project or the application of project findings were identified. Sources of relevant information that would provide convincing evidence for stakeholders were selected. Validation Phase: A literature search was conducted. Review tables were constructed. Relevant staffing data were gathered and benchmarked against national comparison data. Decision Making Phase: A critical appraisal of the unit was conducted. Attributes that most accurately described the unit were selected from a list of universal attributes known to drive Chief Nursing Officer (CNO) staffing priorities. Challenges to improving patient care quality were described for each attribute. Two attributes that represented the greatest impediment to optimizing care quality where chosen and vetted by the CNO. A crosswalk between collected evidence and unit attributes was conducted to elicit specific staffing priorities. Translation Phase: A decision was made to recommend increasing the number of RNs in order to achieve a 1:3 nurse-to-patient ratio. Collected data plus salary costs were used to justify a staffing model change which was subsequently approved by the hospital CEO. Excerpts from the Policy Change Request: Two patient attributes represent the greatest impediment to optimizing nursing care on the stepdown unit - patients are at risk for deteriorating rapidly; and there are wide disparities in patient types and treatments. Evidence supports enhancing the RN to patient ratio as a lower ratio would provide more opportunities to perform advanced clinical observations and ensure an adequate number of RNs who can capably integrate new and seemingly disparate clinical data. Several studies report fewer adverse outcomes when the proportion of RNs is raised - medication errors (Blegen, 2001), mortality (Aiken, 2003), failure to rescue (Aiken, 2004), and UTI and length of stay (Needleman, 2002). Decreasing the RN to patient ratio without increasing total nursing care hours is associated with a net reduction in costs. This policy change will improve nursing care quality and patient health outcomes, and bring the unit's staffing model in line with national standards resulting in a $16,170 per year cost savings. Outcomes Achieved: Evaluation Phase: Metrics from the preparatory phase were used to determine if the project aim was achieved: 1) Staffing issues were defined, evidence-based, consensus-based, and prioritized; 2) Staffing variable benchmarks for future follow-up were defined; 3) The policy was changed in order to improve the nurse staffing model; and 4) A plan to follow clinical outcomes to determine new model effectiveness was initiated.  Conclusions & Implications - At the heart of this staffing model challenge is the imperative to maximize effective and efficient nursing care delivery, while maintaining the highest standards for quality and patient safety. Further evaluation to determine if policy change actually resulted in decreased nurse-to-patient ratios and improved clinical outcomes is needed. If this evidence-based staffing model evaluation methodology is proven to be successful, replication at other hospitals should be considered.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleMoving Toward an Evidence-Based Nurse Staffing Modelen_GB
dc.identifier.urihttp://hdl.handle.net/10755/157787-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Moving Toward an Evidence-Based Nurse Staffing Model</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Loan, Lori A., PhD, RNC</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Madigan Army Medical Center, Nursing Research Service</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Chief, Nursing Research Service</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">ATTN: MCHJ-CN-NR, Tacoma, WA, 98431-1100, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">253-968-2289</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">lori.loan@amedd.army.mil</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Sandra Rolph, MAJ, AN, Head Nurse</td></tr><tr><td colspan="2" class="item-abstract">Rationale/Background: Several studies looking at the impact of nurse staffing on patient outcomes have been conducted. Unfortunately, barriers between nursing leaders and the literature, such as the overwhelming number of studies, competing priorities, and resource limitations have made it difficult to interpret and use staffing study findings. Consequently, translating research results into the real-world environment has been difficult. Project Aim &amp; Process: The project used Stetler's Model of Research Utilization to Facilitate Evidence-Based Practice to advance understanding of staffing issues and change hospital policy to improve unit specific nurse staffing models. Work for one step-down unit is described in this abstract. Preparatory Phase: Stakeholders that might impact the project or the application of project findings were identified. Sources of relevant information that would provide convincing evidence for stakeholders were selected. Validation Phase: A literature search was conducted. Review tables were constructed. Relevant staffing data were gathered and benchmarked against national comparison data. Decision Making Phase: A critical appraisal of the unit was conducted. Attributes that most accurately described the unit were selected from a list of universal attributes known to drive Chief Nursing Officer (CNO) staffing priorities. Challenges to improving patient care quality were described for each attribute. Two attributes that represented the greatest impediment to optimizing care quality where chosen and vetted by the CNO. A crosswalk between collected evidence and unit attributes was conducted to elicit specific staffing priorities. Translation Phase: A decision was made to recommend increasing the number of RNs in order to achieve a 1:3 nurse-to-patient ratio. Collected data plus salary costs were used to justify a staffing model change which was subsequently approved by the hospital CEO. Excerpts from the Policy Change Request: Two patient attributes represent the greatest impediment to optimizing nursing care on the stepdown unit - patients are at risk for deteriorating rapidly; and there are wide disparities in patient types and treatments. Evidence supports enhancing the RN to patient ratio as a lower ratio would provide more opportunities to perform advanced clinical observations and ensure an adequate number of RNs who can capably integrate new and seemingly disparate clinical data. Several studies report fewer adverse outcomes when the proportion of RNs is raised - medication errors (Blegen, 2001), mortality (Aiken, 2003), failure to rescue (Aiken, 2004), and UTI and length of stay (Needleman, 2002). Decreasing the RN to patient ratio without increasing total nursing care hours is associated with a net reduction in costs. This policy change will improve nursing care quality and patient health outcomes, and bring the unit's staffing model in line with national standards resulting in a $16,170 per year cost savings. Outcomes Achieved: Evaluation Phase: Metrics from the preparatory phase were used to determine if the project aim was achieved: 1) Staffing issues were defined, evidence-based, consensus-based, and prioritized; 2) Staffing variable benchmarks for future follow-up were defined; 3) The policy was changed in order to improve the nurse staffing model; and 4) A plan to follow clinical outcomes to determine new model effectiveness was initiated.&nbsp; Conclusions &amp; Implications - At the heart of this staffing model challenge is the imperative to maximize effective and efficient nursing care delivery, while maintaining the highest standards for quality and patient safety. Further evaluation to determine if policy change actually resulted in decreased nurse-to-patient ratios and improved clinical outcomes is needed. If this evidence-based staffing model evaluation methodology is proven to be successful, replication at other hospitals should be considered.</td></tr></table>en_GB
dc.date.available2011-10-26T20:12:16Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:12:16Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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