Innovative forecasting models for nurse demand in modern healthcare systems

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<img style="border:0;" alt="" id="cimg5" src="https://www.f6publishing.com/contents/images/collapsed_yes.gif" /> </div> <div class="sectiontext fl"> <span> Citation of this article </span> </div> </div> <div class="sectioncontent cl" id="cimg5div" style="display:none;"> <span id="citation_html">Singh K, Nashwan AJ. Innovative forecasting models for nurse demand in modern healthcare systems. <em>World J Methodol</em> 2025; 15(3): 99162 [DOI: <a href='https://dx.doi.org/10.5662/wjm.v15.i3.99162' target='_blank'>10.5662/wjm.v15.i3.99162</a>]</span> </div> </div> <div class="left-collapse cl"> <div class="sectiontitle cl" onclick="ShowDetail('cimg6');"> <div class="sectionimg fl"> <img style="border:0;" alt="" id="cimg6" src="https://www.f6publishing.com/contents/images/collapsed_yes.gif" /> </div> <div class="sectiontext fl"> <span> Corresponding Author of This Article </span> </div> </div> <div class="sectioncontent cl" id="cimg6div" style="display:none;"> <span>Abdulqadir J Nashwan, PhD, Department of Nursing and Midwifery Research, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. <email>anashwan@hamad.qa</email></span> </div> </div> <div class="left-collapse cl"> <div class="sectiontitle cl" onclick="ShowDetail('cimg10');"> <div class="sectionimg fl"> <img style="border:0;" alt="" id="cimg10" src="https://www.f6publishing.com/contents/images/collapsed_yes.gif" /> </div> <div class="sectiontext fl"> <span> Research Domain of This Article </span> </div> </div> <div class="sectioncontent cl" id="cimg10div" style="display:none;"> <span>Nursing</span> </div> </div> <div class="left-collapse cl"> <div class="sectiontitle cl" onclick="ShowDetail('cimg11');"> <div class="sectionimg fl"> <img style="border:0;" alt="" id="cimg11" src="https://www.f6publishing.com/contents/images/collapsed_yes.gif" /> </div> <div class="sectiontext fl"> <span> Article-Type of This Article </span> </div> </div> <div class="sectioncontent cl" id="cimg11div" style="display:none;"> <span>Editorial</span> </div> </div> <div class="left-collapse cl"> <div class="sectiontitle cl" onclick="ShowDetail('cimg12');"> <div class="sectionimg fl"> <img style="border:0;" alt="" id="cimg12" src="https://www.f6publishing.com/contents/images/collapsed_yes.gif" /> </div> <div class="sectiontext fl"> <span> Open-Access Policy of This Article </span> </div> </div> <div class="sectioncontent cl" id="cimg12div" style="display:none;"> <span>This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: <a href="http://creativecommons.org/licenses/by-nc/4.0/" target="_blank">http://creativecommons.org/licenses/by-nc/4.0/</a></span> </div> </div> <div class="left-collapse cl"> <div class="sectiontitle cl" onclick="ShowDetail('cimg15');"> <div class="sectionimg fl"> <img style="border:0;" alt="" id="cimg15" src="https://www.f6publishing.com/contents/images/collapsed_yes.gif" /> </div> <div class="sectiontext fl"> <span> Times Cited Counts in Google of This Article </span> </div> </div> <div class="sectioncontent cl" id="cimg15div" style="display:none;"> <ul> <li><a href="http://www.google.com/search?as_q=Innovative forecasting models for nurse demand in modern healthcare systems" target="_blank">Innovative forecasting models for nurse demand in modern healthcare systems</a></li> </ul> </div> </div> <div class="left-collapse cl"> <div class="sectiontitle cl"> <div class="sectiontext fl"> <span> Number of Hits and Downloads for This Article </span> </div> </div> <div class="sectioncontent cl"> <ul> <li><span>Total Article Views (221)</span></li> </ul> <div> <div class="infotitle"> All Articles published online </div> <div class="articlestatis"> <link rel="stylesheet" href="/DXR.axd?r=1_74,1_68,1_69,1_70,10_9-2NNPn" /> <script id="dxis_571550021" src="/DXR.axd?r=1_11,1_252,1_12,1_23,1_64,1_13,1_14,1_15,1_16,1_20,10_0,10_1,10_2,10_3,10_4,1_21,1_22,17_0,17_22-8NNPn"></script> <div style="padding:0;margin:0;margin-bottom:5px;"> <input type="hidden"/><script id="dxss_1978049490">  </script><div id="chart13c6658f-1cd8-4e0d-a8c2-02daa3731705" onmousemove="ASPx.chartMouseMove(event, 'chart13c6658f-1cd8-4e0d-a8c2-02daa3731705')" onclick="ASPx.chartClick(event, 'chart13c6658f-1cd8-4e0d-a8c2-02daa3731705')" style="height:300px;width:215px;"> <img id="chart13c6658f-1cd8-4e0d-a8c2-02daa3731705_IMG" src="/DXB.axd?DXCache=af097b44-e0c7-7a13-a302-7f6dafe894c6" alt="The chart showing PDF series, HTML series." style="display:block;" /> </div><script id="dxss_1945558502">  </script> </div> <div class="articlestatis"> <script id="dxis_215128587" src="/DXR.axd?r=1_19,1_213,1_224,1_225,1_211,1_227,1_236,1_238,1_239,1_230,1_234,1_240,17_48,17_2-8NNPn"></script><table class="dxgvControl dxgv" id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e" style="width:215px;border-collapse:separate;"> <tr> <td style="padding-left:0px;padding-right:0px;padding-top:0px;padding-bottom:0px;"><table id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_DXMainTable" class="dxgvTable dxgvRBB" oncontextmenu="return ASPx.GVContextMenu('gvStatise479740e-f641-4687-ba8e-eb05e23cb06e',event);" style="width:100%;empty-cells:show;"> <tr id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_DXHeadersRow0"> <td id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_col0" class="dxgvHeader" style="width:150px;border-top-width:0px;border-left-width:0px;"><table style="width:100%;"> <tr> <td>Item</td><td style="width:1px;text-align:right;"><span class="dx-vam"> </span></td> </tr> </table></td><td id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_col1" class="dxgvHeader" style="border-top-width:0px;border-left-width:0px;border-right-width:0px;"><table style="width:100%;"> <tr> <td>Count</td><td style="width:1px;text-align:right;"><span class="dx-vam"> </span></td> </tr> </table></td> </tr><tr id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_DXDataRow0" class="dxgvDataRow"> <td class="dxgv">PDF</td><td class="dxgv dx-ar" style="border-right-width:0px;">10</td> </tr><tr id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_DXDataRow1" class="dxgvDataRow"> <td class="dxgv">HTML</td><td class="dxgv dx-ar" style="border-right-width:0px;">158</td> </tr><tr id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_DXFooterRow" class="dxgvFooter"> <td class="dxgv" style="border-bottom-width:0px;"> </td><td class="dxgv" style="text-align:Right;border-right-width:0px;border-bottom-width:0px;">Sum=168</td> </tr> </table><table id="gvStatise479740e-f641-4687-ba8e-eb05e23cb06e_LP" 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class="dxgv dx-ar" style="border-right-width:0px;">14</td> </tr><tr id="gvStatisb14dc24c-8807-4738-9e66-cdab75752b01_DXDataRow1" class="dxgvDataRow"> <td class="dxgv">Download</td><td class="dxgv dx-ar" style="border-right-width:0px;">27</td> </tr><tr id="gvStatisb14dc24c-8807-4738-9e66-cdab75752b01_DXFooterRow" class="dxgvFooter"> <td class="dxgv" style="border-bottom-width:0px;"> </td><td class="dxgv" style="text-align:Right;border-right-width:0px;border-bottom-width:0px;">Sum=41</td> </tr> </table><table id="gvStatisb14dc24c-8807-4738-9e66-cdab75752b01_LP" class="dxgvLoadingPanel" style="left:0px;top:0px;z-index:30000;display:none;"> <tr> <td class="dx" style="padding-right:0px;"><img class="dxlp-loadingImage dxlp-imgPosLeft" src="/DXR.axd?r=1_89-2NNPn" alt="" style="vertical-align:middle;" /></td><td class="dx" style="padding-left:0px;"><span id="gvStatisb14dc24c-8807-4738-9e66-cdab75752b01_TL">Loading…</span></td> </tr> </table><div 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class="infotitle"> Publisher of This Article </div> <div class="infocontent"> Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA </div> <br /> </div> </div> </div> <div class="infopart fr"> <link rel="stylesheet" href="/DXR.axd?r=1_74,1_68,1_69,1_70,1_73,1_210,1_207-2NNPn" /><link rel="stylesheet" href="/DXR.axd?r=1_209,1_206,1_254,1_253,1_84-2NNPn" /> <script id="dxis_1324860310" 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2025.</b><span> Published by Baishideng Publishing Group Inc. All rights reserved.</span> </div> <div id="dpjournal"> <span>World J Methodol. </span><span>Sep 20, 2025; </span><span>15(3): 99162</span><br /> <span>Published online Sep 20, 2025. doi: <a href="http://dx.doi.org/10.5662/wjm.v15.i3.99162" target="_self">10.5662/wjm.v15.i3.99162</a></span> </div> <div id="dptitle"> Innovative forecasting models for nurse demand in modern healthcare systems </div> <div id="dpauthors"> <a href="javascript:void(0);" id="thisauthor729524" class="author-name-link" data-f-name="Kalpana" data-l-name="Singh" data-name="Kalpana Singh" target="_self">Kalpana Singh</a>, <a href="javascript:void(0);" id="thisauthor729525" class="author-name-link" data-f-name="Abdulqadir J" data-l-name="Nashwan" data-name="Abdulqadir J Nashwan" target="_self">Abdulqadir J Nashwan</a> </div> <div id="dpaff"> <div><b>Kalpana Singh, Abdulqadir J Nashwan, </b>Department of Nursing and Midwifery Research, Hamad Medical Corporation, Doha 3050, Qatar</div> </div> <div id="dporcid" style="display:;"> <b>ORCID number: </b><span>Abdulqadir J Nashwan (<a href='http://orcid.org/0000-0003-4845-4119' target='_blank'>0000-0003-4845-4119</a>).</span> </div> <div id="dpfn"> <div><b>Co-first authors</b>: Kalpana Singh and Abdulqadir J Nashwan.</div> <div><b>Author contributions</b>: Singh K wrote the draft and critically reviewing the literature; Singh K and Nashwan AJ revised the draft for important intellectual content, they contributed equally to this article, they are the co-first authors of this manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.</div> <div><b>Conflict-of-interest statement:</b> All the authors report no relevant conflicts of interest for this article.</div><div><b>Open Access:</b> This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: <a href="https://creativecommons.org/Licenses/by-nc/4.0/" xmlns:xlink="http://www.w3.org/1999/xlink">https://creativecommons.org/Licenses/by-nc/4.0/</a></div><div><b>Corresponding author</b>: Abdulqadir J Nashwan, PhD, Department of Nursing and Midwifery Research, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. <a href="mailto:anashwan@hamad.qa">anashwan@hamad.qa</a></div> <div><b>Received:</b> July 15, 2024<br /><b>Revised:</b> December 17, 2024<br /><b>Accepted:</b> December 23, 2024<br /><b>Published online:</b> September 20, 2025<br /><b>Processing time:</b> 234 Days and 1.6 Hours<br /></div> </div> <br /> </div>  <div id="article-abstract-container"> <div class="article-section-title-0"> <span>Abstract</span> </div> <div id="dpabstract"> <div><p>Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce, ensuring appropriate staffing levels, and providing high-quality care to patients. The intricacy and variety of contemporary heal<Hypen />thcare systems and a growing patient populace call for advanced forecasting models. Factors like technological advancements, novel treatment protocols, and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches. Novel forecasting methodologies, including time-series analysis, machine learning, and simulation-based techniques, have been developed to tackle these challenges. Time-series analysis recognizes patterns from past data, whereas machine learning uses extensive datasets to uncover concealed trends. Simulation models are employed to assess diverse scenarios, assisting in proactive adjustments to staffing. These techniques offer distinct advantages, such as the identification of seasonal patterns, the mana<Hypen />gement of large datasets, and the ability to test various assumptions. By inte<Hypen />grating these sophisticated models into workforce planning, organizations can optimize staffing, reduce financial waste, and elevate the standard of patient care. As the healthcare field progresses, the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.</p> </div> <div class="article-abstract-item" style="display:;"> <b>Key Words: </b><span><a href='https://www.f6publishing.com/ArticlesByKeywords?type=2&pageNumber=1&keyword=Nurse+demand+prediction' target='blank'>Nurse demand prediction</a>; <a href='https://www.f6publishing.com/ArticlesByKeywords?type=2&pageNumber=1&keyword=Time-series+analysis' target='blank'>Time-series analysis</a>; <a href='https://www.f6publishing.com/ArticlesByKeywords?type=2&pageNumber=1&keyword=Machine+learning' target='blank'>Machine learning</a>; <a href='https://www.f6publishing.com/ArticlesByKeywords?type=2&pageNumber=1&keyword=Simulation-based+methods' target='blank'>Simulation-based methods</a>; <a href='https://www.f6publishing.com/ArticlesByKeywords?type=2&pageNumber=1&keyword=Predictive+models' target='blank'>Predictive models</a></span> </div> <br /> <div class="article-abstract-item"> <span><p><b>Core Tip:</b> Accurate forecasting of nurse demand is critical for efficient workforce planning in healthcare. Leveraging advanced methods like time-series analysis, machine learning, and simulation models enables precise staffing predictions. These models address challenges posed by healthcare system complexities, seasonal fluctuations, and policy changes. By integrating these techniques, healthcare organizations can optimize resource allocation, reduce inefficiencies, and enhance patient care quality, ensuring adaptability in an evolving healthcare landscape.</p></span> </div> <div id="dpcitation" class="article-abstract-item rs_skip"> <hr /> <ul> <li> <b>Citation: </b>Singh K, Nashwan AJ. Innovative forecasting models for nurse demand in modern healthcare systems. <i>World J Methodol</i> 2025; 15(3): 99162 </li> <li> <b>URL: </b><a href="https://www.wjgnet.com/2222-0682/full/v15/i3/99162.htm" target="_self">https://www.wjgnet.com/2222-0682/full/v15/i3/99162.htm</a> </li> <li> <b>DOI: </b><a href="https://dx.doi.org/10.5662/wjm.v15.i3.99162" target="_self">https://dx.doi.org/10.5662/wjm.v15.i3.99162</a> </li> </ul> <hr/> </div> </div> </div>  <div id="article-main-container"> <div> <div id="sec11"><div id="sec11title" class="article-section-title-0">INTRODUCTION</div><p class="article-section-content" id="p11">Accurate prediction of the demand for nurses plays a crucial role in the strategic planning of the healthcare workforce, enabling healthcare managers to ensure appropriate staffing levels for providing high-quality care to patients[<a class="refcontent" href="#B1" data-jats-ref-type="bibr" data-jats-rid="B1">1</a>]. The intricate and diverse nature of contemporary healthcare systems and the continuously expanding patient population have significantly heightened the need for precise and sophisticated forecasting models to anticipate staffing needs effectively. The complexity of healthcare delivery has recently escalated owing to advancements in medical technology, the im<Hypen />plementation of novel treatment protocols, and the increasing prevalence of chronic illnesses[<a class="refcontent" href="#B2" data-jats-ref-type="bibr" data-jats-rid="B2">2</a>]. These elements have rendered the accurate estimation of nurse staffing requirements more challenging through conventional means. Consequently, there is an urgent need for innovative and resilient forecasting methodologies capable of considering these variables and delivering dependable predictions.</p><p class="article-section-content" id="p12">Numerous studies have investigated forecasting techniques, such as time-series analysis, machine learning models, and simulation-based methods[<a class="refcontent" href="#B3" data-jats-ref-type="bibr" data-jats-rid="B3">3</a>]. Time-series analysis involves using historical data to detect patterns and trends that can guide future staffing requirements. Conversely, machine learning models employ intricate algorithms to examine extensive datasets, reveal concealed patterns, and generate precise forecasts. Simulation-based approaches enable heal<Hypen />thcare administrators to simulate diverse scenarios and evaluate the potential impact of different factors on nurse demand[<a class="refcontent" href="#B3" data-jats-ref-type="bibr" data-jats-rid="B3">3</a>,<a class="refcontent" href="#B4" data-jats-ref-type="bibr" data-jats-rid="B4">4</a>]. Each of these methodologies presents distinct benefits and offers valuable insights into nurse workforce planning. Time-series analysis, for example, proves highly effective in detecting seasonal variations and recurring patterns, while machine learning models excel in managing large data sets and providing real-time forecasts. Simulation-based strategies offer a versatile framework for testing various assumptions and assessing their influence on staffing needs. Moreover, incorporating these forecasting techniques into healthcare workforce planning procedures has exhibited encouraging outcomes. By utilizing sophisticated predictive models, healthcare administrators can optimize nurse staffing levels, mitigate the risk of inadequate or excessive staffing, and ultimately enhance patient care quality. Additionally, precise forecasting can help alleviate the financial burden associated with staffing inefficiencies and ensure efficient resource allocation.</p></div><div id="sec12"><div id="sec12title" class="article-section-title-0">ADVANCED TECHNIQUES IN FORECASTING FOR NURSE WORKFORCE PLANNING</div><p class="article-section-content" id="p11">One of the primary challenges in nurse workforce planning is the variability in demand, driven by factors such as seasonal illnesses, demographic changes, and policy shifts[<a class="refcontent" href="#B5" data-jats-ref-type="bibr" data-jats-rid="B5">5</a>]. Traditional methods, such as time series analysis, have been widely used to predict future nurse demand based on historical staffing data and patient admission rates[<a class="refcontent" href="#B1" data-jats-ref-type="bibr" data-jats-rid="B1">1</a>,<a class="refcontent" href="#B6" data-jats-ref-type="bibr" data-jats-rid="B6">6</a>]. For instance, Pfeifer <i>et al</i>[<a class="refcontent" href="#B1" data-jats-ref-type="bibr" data-jats-rid="B1">1</a>] in 2024 developed a robust time series model that accurately projected nurse staffing needs up to five years into the future by analyzing these variables. This approach helps hospital administrators make informed staffing decisions, potentially reducing overtime costs and improving patient care[<a class="refcontent" href="#B1" data-jats-ref-type="bibr" data-jats-rid="B1">1</a>].</p><p class="article-section-content" id="p12">Machine learning and artificial intelligence have emerged as a powerful tool in forecasting nurse demand, offering more precise and adaptable predictions than traditional statistical methods[<a class="refcontent" href="#B2" data-jats-ref-type="bibr" data-jats-rid="B2">2</a>,<a class="refcontent" href="#B3" data-jats-ref-type="bibr" data-jats-rid="B3">3</a>]. Lin <i>et al</i>[<a class="refcontent" href="#B2" data-jats-ref-type="bibr" data-jats-rid="B2">2</a>] in 2024 demonstrated that machine learning algorithms, trained on diverse datasets from multiple healthcare facilities, could outperform traditional models in predicting nursing workforce requirements. Dynamic simulation models provide another innovative approach to forecasting nurse demand, continuously integrating real-time data to update staffing predictions[<a class="refcontent" href="#B3" data-jats-ref-type="bibr" data-jats-rid="B3">3</a>,<a class="refcontent" href="#B4" data-jats-ref-type="bibr" data-jats-rid="B4">4</a>]. MacKenzie <i>et al</i>[<a class="refcontent" href="#B3" data-jats-ref-type="bibr" data-jats-rid="B3">3</a>] in 2019 introduced a dynamic simulation model incorporating patient acuity levels and staff turnover rates, offering a more responsive and flexible workforce planning solution. This method allows healthcare managers to adjust staffing levels proactively, addressing changes in patient care needs.</p><p class="article-section-content" id="p13">Policy changes can significantly impact nurse staffing projections, as demonstrated by Yi and Kim[<a class="refcontent" href="#B4" data-jats-ref-type="bibr" data-jats-rid="B4">4</a>] in 2022. Their research utilized econometric modeling and scenario analysis to predict the long-term effects of policy adjustments, such as changes to nurse-to-patient ratio laws and funding for nursing education programs[<a class="refcontent" href="#B4" data-jats-ref-type="bibr" data-jats-rid="B4">4</a>]. The study concluded that supportive policies could mitigate projected nursing shortages, emphasizing the importance of strategic interventions in workforce planning[<a class="refcontent" href="#B4" data-jats-ref-type="bibr" data-jats-rid="B4">4</a>].</p><p class="article-section-content" id="p14">Regression analysis has also been employed to forecast nurse demand, particularly in rural healthcare settings where staffing needs differ significantly from urban areas[<a class="refcontent" href="#B5" data-jats-ref-type="bibr" data-jats-rid="B5">5</a>,<a class="refcontent" href="#B6" data-jats-ref-type="bibr" data-jats-rid="B6">6</a>]. Squires <i>et al</i>[<a class="refcontent" href="#B5" data-jats-ref-type="bibr" data-jats-rid="B5">5</a>] in 2017 developed a regression model that accounted for population growth and healthcare accessibility, providing accurate staffing predictions over ten years[<a class="refcontent" href="#B5" data-jats-ref-type="bibr" data-jats-rid="B5">5</a>]. This approach underscores the importance of considering regional differences in nurse workforce planning[<a class="refcontent" href="#B5" data-jats-ref-type="bibr" data-jats-rid="B5">5</a>]. Agent-based modeling is another innovative method used to simulate the behavior of individual nurses and patients within a healthcare system[<a class="refcontent" href="#B6" data-jats-ref-type="bibr" data-jats-rid="B6">6</a>]. Lopes <i>et al</i>[<a class="refcontent" href="#B6" data-jats-ref-type="bibr" data-jats-rid="B6">6</a>] in 2018 demonstrated that this approach could provide detailed insights into workforce dynamics, helping planners anticipate and respond to complex changes in nurse staffing needs. Agent-based models can simulate various scenarios, offering a comprehensive tool for managing workforce fluctuations[<a class="refcontent" href="#B6" data-jats-ref-type="bibr" data-jats-rid="B6">6</a>].</p><p class="article-section-content" id="p15">Technological advancements like telemedicine and automation have also influenced nurse demand forecasting[<a class="refcontent" href="#B7" data-jats-ref-type="bibr" data-jats-rid="B7">7</a>]. Ramsey <i>et al</i>[<a class="refcontent" href="#B7" data-jats-ref-type="bibr" data-jats-rid="B7">7</a>] in 2014 investigated the impact of these technologies on staffing needs, finding that while some technologies can reduce the need for specific nursing tasks, the overall demand for nurses remains strong due to the increasing complexity of patient care. Their study highlighted the need for adaptive workforce planning to address the evolving technological landscape in healthcare[<a class="refcontent" href="#B7" data-jats-ref-type="bibr" data-jats-rid="B7">7</a>]. Seasonal variations and pandemics pose additional challenges to nurse staffing, as seen during influenza seasons and the corona virus infectious disease-2019 pandemic[<a class="refcontent" href="#B8" data-jats-ref-type="bibr" data-jats-rid="B8">8</a>]. Dempsey and Batten[<a class="refcontent" href="#B8" data-jats-ref-type="bibr" data-jats-rid="B8">8</a>] in 2022 developed models to predict spikes in nurse demand during such events, using historical data to inform flexible staffing strategies and emergency preparedness. Their findings underscore the importance of quickly adapting staffing levels to meet sudden increases in patient care needs[<a class="refcontent" href="#B8" data-jats-ref-type="bibr" data-jats-rid="B8">8</a>].</p></div><div id="sec13"><div id="sec13title" class="article-section-title-0">CONCLUSION</div><p class="article-section-content" id="p11">Accurate forecasting of nurse demand plays a vital role in efficiently planning the healthcare workforce. Given the intricate nature of contemporary healthcare systems and the expanding patient populace, advanced prognostication models are indispensable for achieving optimal staffing levels. Techniques like time-series analysis, machine learning, and simulation methodologies provide distinct advantages and valuable insights. Integrating these methodologies enables healthcare administrators to optimize nurse staffing, minimizing understaffing and overstaffing occurrences. This, in turn, elevates the quality of patient care and ensures the efficient allocation of resources, thereby alleviating financial strains stemming from staffing inefficiencies. As the healthcare landscape progresses, integrating sophisticated predictive models into workforce planning will grow in significance. These models empower healthcare institutions to adjust to shifting demands, uphold superior standards of patient care, and cultivate a more resilient workforce equipped to confront forthcoming challenges.</p></div> </div> <div class="article-section-title-0" style="display:block"> <span>Footnotes</span> </div> <div style="display:block;" class="article-section-content"> <p><bold>Provenance and peer review:</bold> Invited article; Externally peer reviewed.</p><p><bold>Peer-review model:</bold> Single blind</p><p><bold>Specialty type:</bold> Nursing</p><p><bold>Country of origin:</bold> Qatar</p><p><bold>Peer-review report’s classification</bold></p><p><bold>Scientific Quality:</bold> Grade C</p><p><bold>Novelty:</bold> Grade B</p><p><bold>Creativity or Innovation:</bold> Grade B</p><p><bold>Scientific Significance:</bold> Grade B</p><p><bold><b>P-Reviewer:</b></bold> Itoh K <bold><b>S-Editor:</b></bold> Bai Y <bold><b>L-Editor:</b></bold> A <bold><b>P-Editor:</b></bold> Zhang XD</p> </div> <div class="article-section-title-0" style="display:block"> <span>References</span> </div> <div style="margin-top:10px;display:block"> <div class="article-ref" id="B1"> <table> <tr> <td style="vertical-align:top;width:30px;"><a class="article-ref-vol" title="View reference 1 in text" ref-list-rid="B1" target="_self">1. </a></td> <td> <span><b>Pfeifer A</b>, Knaup J, Holst C, Lohweg V. </span> <span>  Evaluation of Time Series Forecasting Strategies for Demand Management. 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