by The global nursing resource allocation market involves the planning and management of nursing staff, their schedules, and assignments across healthcare facilities and organizations. Nurse schedulers and staffing managers use resource allocation software to create work schedules, identify staffing needs, fill open shifts, and monitor nurse-to-patient ratios. The software handles fluctuating patient volumes and accounts for employees’ availability, skills, and certifications. Large hospitals deploy such solutions to efficiently plan staffing based on real-time patient census and acuity data.
The global nursing resource allocation market is estimated to be valued at US$ 1.98 billion in 2023 and is expected to exhibit a CAGR of 9.9% over the forecast period 2023 to 2030, as highlighted in a new report published by CoherentMI.
Market Dynamics:
The growing demand for telehealth services is expected to drive the nursing resource allocation market during the forecast period. Telehealth refers to the use of digital technologies and remote communication to access healthcare services across locations. With more patients opting for remote monitoring at home through connected devices, nurse schedulers can effectively rearrange staff assignments based on virtual caseloads.
Another driver for the market is the shortage of nurses globally. Statistics show that nursing staff numbers are declining in the face of rising patient volumes. Resource allocation tools help organizations optimize existing staff deployment and tackle shortages more efficiently. Automated scheduling reduces time spent on manual planning and allocation processes.
Segment Analysis:
The global nursing resource allocation market can be segmented based on deployment, solution, end user, and region. By deployment, the on premise segment dominates the market as it offers better security and control over data. By solution, the software segment holds the largest market share owing to its increasing usage for effective scheduling and management of nursing resources. By end user, hospitals segment generates maximum revenue due to high installation of software for streamlining patient care processes with optimum utilization of staff.
PEST Analysis:
Political: Rising healthcare expenditure by governments to improve quality of healthcare services is boosting market growth. Economic: Increasing cost of healthcare and shortage of nursing staff are key factors fueling demand for efficient resource allocation solutions.
Social: Growing geriatric population and associated rise in chronic diseases requiring long-term care is propelling the market.
Technological: Integration of advanced technologies like AI and predictive analytics into software is helping optimize allocation of resources based on real-time needs.
Key Takeaways:
The global Nursing Resource Allocation Market Size is expected to witness high growth. The global Nursing Resource Allocation Market is estimated to be valued at US$ 1.98 billion in 2023 and is expected to exhibit a CAGR of 9.9% over the forecast period 2023 to 2030.
North America dominates the market currently due to advanced healthcare infrastructure and aggressive adoption of automation technologies. Europe accounts for the second largest share owing to supportive government policies for quality healthcare.
Key players operating in the global nursing resource allocation market are Cerner Corporation, Allscripts, McKesson Corporation, Optum, Inc., IBM, Aptean, Health Systems Concepts, Advanced Software Products Group, Verge Solutions, QGenda, Infor, and F.A. Davis Company. Key players are focusing on new product innovations and mergers & acquisitions to strengthen their market position.
FAQ’s:
Q.1 What are the main factors influencing the Global Nursing Resource Allocation Market ?
Q.2 Which companies are the major sources in this industry?
Q.3 What are the market’s opportunities, risks, and general structure?
Q.4 Which of the top Global Nursing Resource Allocation Market Companies compare in terms of sales, revenue, and prices?
*Note:
1. Source: CoherentMI, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
