The Use of Simulation Technology in Needs-Based Health Human Resource Planning: A Canadian Nursing Experience

2.50
Hdl Handle:
http://hdl.handle.net/10755/154131
Type:
Presentation
Title:
The Use of Simulation Technology in Needs-Based Health Human Resource Planning: A Canadian Nursing Experience
Abstract:
The Use of Simulation Technology in Needs-Based Health Human Resource Planning: A Canadian Nursing Experience
Conference Sponsor:Sigma Theta Tau International
Conference Year:2008
Author:Tomblin Murphy, Gail, RN, PhD
P.I. Institution Name:Dalhousie University
Title:Associate Professor
Co-Authors:Robert Alder, MMedSc, PhD; Cindy Pelletier, MSc; Adrian MacKenzie, BSc(H)
[Research Paper or Poster Presentation] Policy related to Health Human Resources (HHR) planning usually proceeds in the absence of evidence on the relative effectiveness of the policy options. In this study, based on a needs-based HHR planning framework, the authors employed system dynamic simulation technology to evaluate the relative effectiveness of policy options and provide a graphic display of the most effective combination of options. The requirements for Registered Nurses (RN) in Nova Scotia, Canada, were estimated based on projections in population size, health status (as a measure of health care need), estimated levels of service, and provider productivity. The supply of RNs in Nova Scotia was estimated based on the current stock and inputs to it from education programs and in-migration as well as outputs from it due to retirements, deaths, and out-migration. The difference between the two, supply and requirement, was the gap, where a negative reflects a shortage and positive reflects a surplus of RNs. It was found that at baseline, without any policy intervention and assuming that the trend in health status of the past 10 years continues, the gap over the next 15 years went from a shortage of 164 activity-adjusted RNs (similar to full-time equivalents) to and shortage of 1994. Addressing the downward trend in health status to improve it to match the Canadian national levels had a marked effect on the shortage, reducing it from a shortage of 1994 to a shortage of 854. An important finding is that a combination of policies directed at retention, productivity and activity levels for RNs were most effective compared to policies focused on training seat increases, student attrition and graduate out-migration. Hence, needs-based simulation models can provide a graphic presentation of relative effectiveness such that the most effective portfolio of options can be readily identified and discussed.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleThe Use of Simulation Technology in Needs-Based Health Human Resource Planning: A Canadian Nursing Experienceen_GB
dc.identifier.urihttp://hdl.handle.net/10755/154131-
dc.description.abstract<table><tr><td colspan="2" class="item-title">The Use of Simulation Technology in Needs-Based Health Human Resource Planning: A Canadian Nursing Experience</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2008</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Tomblin Murphy, Gail, RN, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Dalhousie University</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Associate Professor</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">gail.tomblin.murphy@dal.ca</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Robert Alder, MMedSc, PhD; Cindy Pelletier, MSc; Adrian MacKenzie, BSc(H)</td></tr><tr><td colspan="2" class="item-abstract">[Research Paper or Poster Presentation] Policy related to Health Human Resources (HHR) planning usually proceeds in the absence of evidence on the relative effectiveness of the policy options. In this study, based on a needs-based HHR planning framework, the authors employed system dynamic simulation technology to evaluate the relative effectiveness of policy options and provide a graphic display of the most effective combination of options. The requirements for Registered Nurses (RN) in Nova Scotia, Canada, were estimated based on projections in population size, health status (as a measure of health care need), estimated levels of service, and provider productivity. The supply of RNs in Nova Scotia was estimated based on the current stock and inputs to it from education programs and in-migration as well as outputs from it due to retirements, deaths, and out-migration. The difference between the two, supply and requirement, was the gap, where a negative reflects a shortage and positive reflects a surplus of RNs. It was found that at baseline, without any policy intervention and assuming that the trend in health status of the past 10 years continues, the gap over the next 15 years went from a shortage of 164 activity-adjusted RNs (similar to full-time equivalents) to and shortage of 1994. Addressing the downward trend in health status to improve it to match the Canadian national levels had a marked effect on the shortage, reducing it from a shortage of 1994 to a shortage of 854. An important finding is that a combination of policies directed at retention, productivity and activity levels for RNs were most effective compared to policies focused on training seat increases, student attrition and graduate out-migration. Hence, needs-based simulation models can provide a graphic presentation of relative effectiveness such that the most effective portfolio of options can be readily identified and discussed.</td></tr></table>en_GB
dc.date.available2011-10-26T12:46:02Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T12:46:02Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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