全球人工智慧市場規模、佔有率、趨勢和成長分析報告（2026-2034 年護理師就業市場）

全球護理人員配備人工智慧市場規模預計將從2025年的6.5326億美元成長至2034年的71.168億美元，預計在2026年至2034年間以30.39%的複合年成長率成長。隨著醫療服務提供者擴大利用人工智慧來改進人員排班、人員配備和需求預測，該市場正在不斷發展。醫院和醫療機構面臨複雜的人員配備需求，這些需求受到病患數量、技能水準、輪班模式、缺勤率和監管要求等因素的影響。人工智慧驅動的人員配備管理工具可以分析營運數據，並根據預測的需求提出人員提案。透過支援更系統化的排班，這些系統可以幫助機構管理可用人員，同時提高各部門和各班次人員配備需求的透明度。

機器學習可以識別患者需求模式、季節性波動、人員可用性和工作量需求。先進的平台可以整合排班、人才管理、病患數量和營運訊息，從而支援動態的人員配置決策。自動化還可以減輕與排班和調整相關的行政負擔。醫療機構越來越關注那些既能支持人才規劃，又能維持適當的人員配置政策和人力資源監管的技術。因此，與現有醫院人才管理系統的整合正成為實施過程中的關鍵考量。

未來前景得益於醫療工作者管理方面持續存在的挑戰以及預測分析技術的日益普及。隨著患者數量和人員配置的變化，人工智慧平台有望擴大提供即時建議。與電子健康記錄和醫院運營系統的整合，透過納入更多相關信息，有望提高預測的準確性。此外，醫療機構在實施這些系統時，可能會更加重視透明度、隱私保護、偏見監控和人工審核。持續開發可解釋且符合工作流程的解決方案，將有助於其在醫院和其他醫療機構中得到更廣泛的應用。

我們的報告經過精心撰寫，旨在提供涵蓋廣泛行業和市場的全面且切實可行的洞察。每份報告都包含幾個關鍵組成部分，旨在幫助您全面了解市場環境：

市場概覽：本節對市場進行了清晰的說明，包括關鍵定義、分類以及當前行業格局的概述。

市場動態：對影響市場成長的主要促進因素、限制因素、機會和挑戰進行詳細評估。這包括技術發展、法律規範和不斷變化的行業趨勢等因素。

市場區隔分析：本部分根據產品類型、應用、最終用戶和地區將市場系統性地分類為若干關鍵細分市場。本部分揭示了每個細分市場的表現、成長潛力和市場貢獻。

競爭格局：我們對主要市場參與企業進行了詳細評估，包括其市場定位、產品系列、策略舉措和財務表現。這有助於深入了解競爭趨勢和主要參與者所採取的策略。

市場預測：本部分提供基於數據的市場規模和成長模式預測，預測期為指定時期。它綜合考慮了歷史趨勢、當前市場狀況和定量分析，以識別預期的未來趨勢。

區域分析：這包括對主要地理區域的市場表現進行全面檢驗，確定高成長地區和區域趨勢，以更深入地了解區域市場機會。

新趨勢與新機會：識別關鍵市場趨勢、技術進步和新興投資機會。本部分重點在於潛在成長領域和未來產業趨勢。

客製化選項：我們提供靈活的報告客製化服務，以滿足您的特定需求。這包括額外的細分、國家/地區特定分析、競爭對手分析、客製化資料點或專注於特定細分市場的洞察，從而更有效地支援您的策略決策。

目錄

第1章：引言

第2章執行摘要

第3章 市場變數、趨勢與框架

• 市場譜系展望
• 滲透率和成長前景分析
• 價值鏈分析
• 法律規範
  • 標準與合規性
  • 監管影響分析
• 市場動態
  • 市場促進因素
  • 市場限制因素
  • 市場機遇
  • 市場挑戰
• 波特五力分析
• PESTLE分析

第4章：全球護理師招募中的人工智慧市場：按組件分類

• 市場分析、洞察與預測
• 軟體/平台
• 服務

第5章：全球護理人才招募中的人工智慧市場：按技術分類

• 市場分析、洞察與預測
• 機器學習和預測分析
• 最佳化演算法
• 自然語言處理/生成式人工智慧
• 其他

第6章：全球護理人才招募中的人工智慧市場：以部署方式分類

• 市場分析、洞察與預測
• 基於雲端的
• 現場
• 混合

第7章：全球人工智慧在護理崗位安置領域的市場：按應用分類

• 市場分析、洞察與預測
• 需求預測
• 輪班安排和最佳化
• 浮動池和資源分配
• 減少加班費和臨時工成本
• 預測缺勤和倦怠風險
• 合規性和資格驗證
• 其他

第8章：全球護理師招募中的人工智慧市場：按類型分類

• 市場分析、洞察與預測
• 獨立型
• 融合的

第9章：全球護理師招募中的人工智慧市場：按最終用戶分類

• 市場分析、洞察與預測
• 醫院和醫療保健系統
• 長期照護機構
• 人力資源公司
• 門診診所/門診中心
• 其他

第10章：全球護理招募領域的人工智慧市場：按地區分類

• 區域分析
• 北美市場分析、洞察與預測
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲市場分析、洞察與預測
  • 英國
  • 法國
  • 德國
  • 義大利
  • 西班牙
  • 俄羅斯
  • 其他歐洲國家
• 亞太市場分析、洞察與預測
  • 中國
  • 日本
  • 韓國
  • 印度
  • 澳洲
  • 東南亞
  • 其他亞太國家
• 拉丁美洲市場分析、洞察與預測
  • 巴西
  • 阿根廷
  • 秘魯
  • 智利
  • 其他拉丁美洲國家
• 中東和非洲市場分析、洞察與預測
  • 沙烏地阿拉伯
  • UAE
  • 以色列
  • 南非
  • 其他中東和非洲國家

第11章 競爭格局

• 最新趨勢
• 公司分類
• 供應鏈和銷售管道合作夥伴（根據現有資訊）
• 市場佔有率和市場定位分析（基於現有資訊）
• 供應商情況（基於現有資訊）
• 策略規劃

第12章：公司簡介

• 主要公司的市佔率分析
• 公司簡介
  • QGenda, LLC
  • Symplr
  • AMN Healthcare
  • Aya Healthcare
  • ShiftMed
  • UKG Inc
  • Oracle Corporation
  • Workday Inc
  • HealthStream Inc
  • RLDatix
  • IntelyCare
  • ShiftKey
  • LeanTaaS
  • Smartlinx
  • Lightning Bolt Solutions
  • NurseGrid
  • CareRev
  • Clipboard Health
  • Gale Healthcare Solutions
  • Trusted Health
  • Incredible Health
  • ConnectRN

The global AI in nurse staffing market size is expected to reach USD 7116.80 Million in 2034 from USD 653.26 Million in 2025, growing at a CAGR of 30.39% during 2026-2034.This market is developing as healthcare providers explore artificial intelligence to improve workforce scheduling, staffing allocation, and demand forecasting. Hospitals and healthcare facilities face complex staffing requirements influenced by patient volumes, skill levels, shift patterns, absenteeism, and regulatory requirements. AI-based workforce tools can analyze operational data and generate staffing recommendations based on anticipated demand. By supporting more systematic scheduling, these systems can help organizations manage available personnel while improving visibility into staffing requirements across departments and shifts.

Machine learning can identify patterns in patient demand, seasonal activity, staff availability, and workload requirements. Advanced platforms may integrate scheduling, workforce management, patient census, and operational information to support dynamic staffing decisions. Automation can also reduce administrative effort associated with creating and adjusting schedules. Healthcare organizations are increasingly interested in technologies that support workforce planning while maintaining appropriate staffing policies and human oversight. Integration with existing hospital workforce-management systems is therefore becoming an important consideration for adoption.

Future prospects are supported by ongoing healthcare workforce-management challenges and the growing use of predictive analytics. AI platforms may increasingly provide real-time recommendations as patient volumes and staffing conditions change. Integration with electronic health records and hospital operational systems could improve forecasting accuracy by incorporating more relevant information. Providers are also likely to focus on transparency, privacy, bias monitoring, and human review when deploying these systems. Continued development of explainable and workflow-friendly solutions can support broader adoption across hospitals and other healthcare facilities.

Our reports are carefully developed to deliver comprehensive and actionable insights across a wide range of industries and markets. Each report includes several essential components designed to provide a complete understanding of the market environment:

Market Overview: This section provides a clear introduction to the market, including key definitions, classifications, and an overview of the current industry landscape.

Market Dynamics: A detailed evaluation of the primary drivers, restraints, opportunities, and challenges shaping market growth. It covers factors such as technological developments, regulatory frameworks, and evolving industry trends.

Segmentation Analysis: A structured breakdown of the market into key segments based on product type, application, end-user, and geographic region. This section highlights the performance, growth potential, and contribution of each segment.

Competitive Landscape: An in-depth assessment of leading market participants, including their market positioning, product portfolios, strategic initiatives, and financial performance. It provides valuable insights into competitive dynamics and the strategies adopted by key players.

Market Forecast: Data-driven projections of market size and growth patterns over a defined forecast period. This section incorporates historical trends, current market conditions, and quantitative analysis to illustrate expected future developments.

Regional Analysis: A comprehensive review of market performance across major geographic regions, identifying high-growth areas and regional trends to better understand localized market opportunities.

Emerging Trends and Opportunities: Identification of significant market trends, technological advancements, and new investment opportunities. This section highlights potential growth areas and future industry developments.

Customization Options: We offer flexible customization services to tailor reports according to specific client requirements. This may include additional segmentation, country-level analysis, competitor profiling, customized data points, or focused insights on particular market segments to better support strategic decision-making.

MARKET SEGMENTATION

By Component

• Software/Platforms
• Services

By Technology

• Machine Learning & Predictive Analytics
• Optimization Algorithms
• NLP/Generative AI
• Others

By Deployment

• Cloud-Based
• On-Premises
• Hybrid

By Application

• Demand Forecasting
• Shift Scheduling & Optimization
• Float Pool & Resource Allocation
• Overtime & Agency Spend Reduction
• Absenteeism/Burnout Risk Prediction
• Compliance & Credential Matching
• Others

By Type

• Standalone
• Integrated

By End User

• Hospitals & Health Systems
• Long-Term Care Facilities
• Staffing Agencies
• Ambulatory/Outpatient Centers
• Others

COMPANIES PROFILED

• QGenda, LLC, symplr, AMN Healthcare, Aya Healthcare, ShiftMed, UKG Inc., Oracle Corporation, Workday Inc., HealthStream Inc., RLDatix, IntelyCare, ShiftKey, LeanTaaS, Smartlinx, Lightning Bolt Solutions, NurseGrid, CareRev, Clipboard Health, Gale Healthcare Solutions, Trusted Health, Incredible Health, ConnectRN

TABLE OF CONTENTS

Chapter 1. PREFACE

• 1.1. Market Segmentation & Scope
• 1.2. Market Definition
• 1.3. Information Procurement
  • 1.3.1 Information Analysis
  • 1.3.2 Market Formulation & Data Visualization
  • 1.3.3 Data Validation & Publishing
• 1.4. Research Scope and Assumptions
  • 1.4.1 List of Data Sources

Chapter 2. EXECUTIVE SUMMARY

• 2.1. Market Snapshot
• 2.2. Segmental Outlook
• 2.3. Competitive Outlook

Chapter 3. MARKET VARIABLES, TRENDS, FRAMEWORK

• 3.1. Market Lineage Outlook
• 3.2. Penetration & Growth Prospect Mapping
• 3.3. Value Chain Analysis
• 3.4. Regulatory Framework
  • 3.4.1 Standards & Compliance
  • 3.4.2 Regulatory Impact Analysis
• 3.5. Market Dynamics
  • 3.5.1 Market Drivers
  • 3.5.2 Market Restraints
  • 3.5.3 Market Opportunities
  • 3.5.4 Market Challenges
• 3.6. Porter's Five Forces Analysis
• 3.7. PESTLE Analysis

Chapter 4. GLOBAL AI IN NURSE STAFFING MARKET: BY COMPONENT 2022-2034 (USD MN)

• 4.1. Market Analysis, Insights and Forecast Component
• 4.2. Software/Platforms Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 4.3. Services Estimates and Forecasts By Regions 2022-2034 (USD MN)

Chapter 5. GLOBAL AI IN NURSE STAFFING MARKET: BY TECHNOLOGY 2022-2034 (USD MN)

• 5.1. Market Analysis, Insights and Forecast Technology
• 5.2. Machine Learning & Predictive Analytics Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 5.3. Optimization Algorithms Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 5.4. NLP/Generative AI Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 5.5. Others Estimates and Forecasts By Regions 2022-2034 (USD MN)

Chapter 6. GLOBAL AI IN NURSE STAFFING MARKET: BY DEPLOYMENT 2022-2034 (USD MN)

• 6.1. Market Analysis, Insights and Forecast Deployment
• 6.2. Cloud-Based Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 6.3. On-Premises Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 6.4. Hybrid Estimates and Forecasts By Regions 2022-2034 (USD MN)

Chapter 7. GLOBAL AI IN NURSE STAFFING MARKET: BY APPLICATION 2022-2034 (USD MN)

• 7.1. Market Analysis, Insights and Forecast Application
• 7.2. Demand Forecasting Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 7.3. Shift Scheduling & Optimization Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 7.4. Float Pool & Resource Allocation Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 7.5. Overtime & Agency Spend Reduction Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 7.6. Absenteeism/Burnout Risk Prediction Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 7.7. Compliance & Credential Matching Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 7.8. Others Estimates and Forecasts By Regions 2022-2034 (USD MN)

Chapter 8. GLOBAL AI IN NURSE STAFFING MARKET: BY TYPE 2022-2034 (USD MN)

• 8.1. Market Analysis, Insights and Forecast Type
• 8.2. Standalone Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 8.3. Integrated Estimates and Forecasts By Regions 2022-2034 (USD MN)

Chapter 9. GLOBAL AI IN NURSE STAFFING MARKET: BY END USER 2022-2034 (USD MN)

• 9.1. Market Analysis, Insights and Forecast End User
• 9.2. Hospitals & Health Systems Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 9.3. Long-Term Care Facilities Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 9.4. Staffing Agencies Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 9.5. Ambulatory/Outpatient Centers Estimates and Forecasts By Regions 2022-2034 (USD MN)
• 9.6. Others Estimates and Forecasts By Regions 2022-2034 (USD MN)

Chapter 10. GLOBAL AI IN NURSE STAFFING MARKET: BY REGION 2022-2034 (USD MN)

• 10.1. Regional Outlook
• 10.2. North America Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
  • 10.2.1 By Component
  • 10.2.2 By Technology
  • 10.2.3 By Deployment
  • 10.2.4 By Application
  • 10.2.5 By Type
  • 10.2.6 By End User
  • 10.2.7 United States
  • 10.2.8 Canada
  • 10.2.9 Mexico
• 10.3. Europe Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
  • 10.3.1 By Component
  • 10.3.2 By Technology
  • 10.3.3 By Deployment
  • 10.3.4 By Application
  • 10.3.5 By Type
  • 10.3.6 By End User
  • 10.3.7 United Kingdom
  • 10.3.8 France
  • 10.3.9 Germany
  • 10.3.10 Italy
  • 10.3.11 Spain
  • 10.3.12 Russia
  • 10.3.13 Rest Of Europe
• 10.4. Asia-Pacific Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
  • 10.4.1 By Component
  • 10.4.2 By Technology
  • 10.4.3 By Deployment
  • 10.4.4 By Application
  • 10.4.5 By Type
  • 10.4.6 By End User
  • 10.4.7 China
  • 10.4.8 Japan
  • 10.4.9 South Korea
  • 10.4.10 India
  • 10.4.11 Australia
  • 10.4.12 South East Asia
  • 10.4.13 Rest Of Asia Pacific
• 10.5. Latin America Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
  • 10.5.1 By Component
  • 10.5.2 By Technology
  • 10.5.3 By Deployment
  • 10.5.4 By Application
  • 10.5.5 By Type
  • 10.5.6 By End User
  • 10.5.7 Brazil
  • 10.5.8 Argentina
  • 10.5.9 Peru
  • 10.5.10 Chile
  • 10.5.11 Rest of Latin America
• 10.6. Middle East & Africa Market Analysis, Insights and Forecast, 2022-2034 (USD MN)
  • 10.6.1 By Component
  • 10.6.2 By Technology
  • 10.6.3 By Deployment
  • 10.6.4 By Application
  • 10.6.5 By Type
  • 10.6.6 By End User
  • 10.6.7 Saudi Arabia
  • 10.6.8 UAE
  • 10.6.9 Israel
  • 10.6.10 South Africa
  • 10.6.11 Rest of the Middle East And Africa

Chapter 11. COMPETITIVE LANDSCAPE

• 11.1. Recent Developments
• 11.2. Company Categorization
• 11.3. Supply Chain & Channel Partners (based on availability)
• 11.4. Market Share & Positioning Analysis (based on availability)
• 11.5. Vendor Landscape (based on availability)
• 11.6. Strategy Mapping

Chapter 12. COMPANY PROFILES OF GLOBAL AI IN NURSE STAFFING INDUSTRY

• 12.1. Top Companies Market Share Analysis
• 12.2. Company Profiles
  • 12.2.1 QGenda, LLC
  • 12.2.2 Symplr
  • 12.2.3 AMN Healthcare
  • 12.2.4 Aya Healthcare
  • 12.2.5 ShiftMed
  • 12.2.6 UKG Inc
  • 12.2.7 Oracle Corporation
  • 12.2.8 Workday Inc
  • 12.2.9 HealthStream Inc
  • 12.2.10 RLDatix
  • 12.2.11 IntelyCare
  • 12.2.12 ShiftKey
  • 12.2.13 LeanTaaS
  • 12.2.14 Smartlinx
  • 12.2.15 Lightning Bolt Solutions
  • 12.2.16 NurseGrid
  • 12.2.17 CareRev
  • 12.2.18 Clipboard Health
  • 12.2.19 Gale Healthcare Solutions
  • 12.2.20 Trusted Health
  • 12.2.21 Incredible Health
  • 12.2.22 ConnectRN