An aging population needs more healthcare while the healthcare workforce is shrinking and also getting older. By 2035, people aged 65 and older will grow by 32%. They will make up 23% of the total population and will account for 51% of adult inpatient discharges. This means hospitals and clinics will see more elderly patients who need complex care. At the same time, there are fewer workers because many nurses and doctors are close to retirement age. For example, 34% of nurses are over 55 years old, and 30% of doctors are older than 60. This means many staff may retire soon, leaving fewer people to care for patients. Hospital leaders and managers need to rethink how their workforce is organized to meet these changes well.
Flexpertise is a way to handle these challenges by making teams more flexible. It lets staff from different departments work together and learn new skills. Work is shared based on what the unit needs and expected patient numbers. Such models can make healthcare better and more efficient, especially for elderly patients who need ongoing and coordinated care.
Flexpertise is a strategy that combines flexibility and specialized skill. It does not tie workers to one fixed role or department. Instead, staff like nurses, physician assistants, social workers, and pharmacists can work in different units depending on where they are needed most.
This approach includes:
Tori Richie, a senior intelligence director, says workforce models need to be leaner and more team-focused. Because the elderly population is growing, flexibility helps healthcare groups adapt without needing more staff or bigger buildings.
The aging workforce brings staffing problems. Many nurses and doctors will retire soon. Hiring new workers cannot keep up with this loss. Flexpertise helps by using current staff better, not just by hiring more people. It shares tasks and helps different types of workers collaborate.
For example, nurses or other trained staff can handle administrative tasks usually done by doctors, like paperwork and approvals. Some tasks can be automated using AI tools. This allows doctors to spend more time with patients.
Flexpertise also lets workers move between units. Predictive data helps hospitals plan schedules based on patient numbers. This way, labor hours are used better and staff don’t have to work too much overtime.
Elderly patients with serious illnesses often need care from many types of specialists. These include doctors, advanced practice providers, pharmacists, social workers, and care navigators. Good teamwork between these roles leads to better care and outcomes.
The flexpertise model supports these integrated teams. It breaks down barriers between specialties and promotes shared responsibility for patient health. For example, after hospital discharge, a patient may need help managing medicines, nurse follow-up, and social services at home. These team efforts help prevent unnecessary hospital returns. About 28% of hospital readmissions happen at different hospitals, causing gaps in care.
AI can also be part of these teams by helping with workflow and decisions. This lets staff focus more on patients.
AI and automation are important to help flexpertise work well. AI tools can take over routine and paperwork tasks to improve how staff work.
Examples of how AI helps include:
Sushma Narra, a senior associate in health operations, says AI works best when focused on tasks with heavy workloads, like documentation and clinical tasks. This improves staff productivity and lets humans make the final judgments.
While AI brings benefits, healthcare must be careful. Research shows over 80% of AI projects in healthcare fail because of poor oversight, bias, or mismatched goals.
Hospitals using AI should have committees made up of many experts. These groups make sure AI is safe, ethical, fair, and clear. They check how AI affects patient care and watch for bias. Their job is to keep AI working well and not causing problems.
New workforce models and AI also improve how patients experience healthcare. This means not just the treatment during visits but the whole process before and after the visit. Patients want care to be easy to get, clear, and quick.
About 40% of healthcare users go to several health systems. Nearly two in five have changed their primary care doctor in the last three years. This shows patient loyalty is low. People choose health systems that offer smooth access and simple scheduling.
AI-driven tools can help with phone answering and appointment booking. This makes patients happier.
Better communication and care coordination help keep patients for a long time. This also prevents costly care problems for elderly patients with complex needs.
Using flexpertise and AI is part of a bigger plan health systems need to survive workforce and population changes. Leaders need plans that match service goals, capacity, and budgets over several years.
Such plans help hospitals use resources wisely and standardize care processes. This makes investments in workforce models and technology more effective. It also lets hospitals test new AI tools and use proven ones safely across the system.
This planning lowers the need to build new facilities or add more beds. Instead, hospitals use their current staff and assets better.
Some US health systems have started using these ideas with good results. For example, predictive analytics help hospitals change staffing quickly. This reduces ER boarding and shortens hospital stays. Some hospitals use hospitalist models that focus on moving patients out faster to free beds.
More than 80% of medical students who train locally stay in those communities after graduation. This shows local training partnerships help support flexible staffing. These partnerships build workers with skills needed for flexible care in their areas.
Flexpertise workforce models combined with AI tools provide a useful solution for rising care demands from the aging US population and shrinking workforce. By making teams flexible, sharing tasks, and using technology, healthcare can improve patient care and efficiency for elderly patients with serious needs. Using governance and focusing on patient experience helps healthcare organizations improve care in the coming years.
Healthcare leaders must evolve workforce models toward leaner, more flexible, and team-driven approaches like ‘flexpertise,’ enabling staff to work across departments and upskill. This approach addresses workforce shortages by redistributing tasks and increasing multidisciplinary engagement, improving cost, quality, and efficiency.
AI tools such as ambient scribing, scheduling bots, and symptom checkers automate administrative and clinical tasks, offloading burdens from physicians and staff, enabling right-task-right-person execution, and enhancing speed and accuracy in patient triage and documentation.
Multidisciplinary teams with shared metrics and accountability improve care for overlapping patient needs by combining physicians, advanced practitioners, social workers, pharmacists, and digital agents, delivering coordinated, holistic care early in the process.
Organizations should develop roadmaps prioritizing mature, high-impact AI applications for immediate rollout while maintaining controlled pilots for emerging tools, aligning deployment with organizational capacity and ensuring safety, ethics, and bias oversight.
Multidisciplinary AI governance committees with clear authority on safety, ethics, equity, and transparency guide evaluation, approval, and continuous monitoring of AI tools, ensuring alignment with clinical and operational goals.
Consumer experience encompasses the entire journey before, between, and after clinical visits focusing on ease, convenience, and transparency, unlike patient experience, which focuses on in-care clinical satisfaction. Addressing both builds loyalty and trust in healthcare systems.
Healthcare leaders should integrate early scenario modeling and adaptive strategic planning using data and predictive analytics to protect margins, prioritize value-based care, community partnerships, and leverage M&A or partnerships to strengthen market position.
These models allow dynamic staff redeployment based on predictive analytics, reduce bottlenecks through shared accountability, relieve physician administrative delays, and optimize care readiness, collectively decreasing ED boarding times and inpatient length of stay.
A multi-year roadmap linking market demand, service line priorities, capacity needs, and financial forecasts ensures aligned resource allocation, standardized processes, interoperability, and cultural alignment across entities, driving clinical standardization and operational efficiency.
Incorporate equity reviews into AI model development, use representative datasets reflecting community demographics, and establish continuous bias monitoring to prevent disparities, ensuring AI supports equitable care delivery across populations.
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