By 2035, the number of people aged 65 and older in the U.S. is expected to grow by 32%. This group will make up nearly 23% of the total population. They will also account for about 51% of adult hospital stays. At the same time, fewer people will be available to work in healthcare. For example, 34% of nurses are over 55, and 30% of doctors are older than 60. This change means there will be staff shortages and more risk of workers feeling very tired and stressed.
Many older adults have several long-term illnesses like heart disease, diabetes, high blood pressure, or breathing problems. Treating them needs more than just separate medical visits. It needs a complete and coordinated way that involves many types of doctors and health workers.
Healthcare leaders are working on new ways to organize their staff to be smaller, more flexible, and team-focused. For medical offices, this means using care systems that bring together main care doctors, specialists, nurses, pharmacists, social workers, and others to work as a team.
More and more, healthcare experts suggest using multidisciplinary teams to improve care quality and access. A review published in eClinicalMedicine looked at 39 studies about MDTs from 2014 to 2025. It found these teams can look very different. But most include many kinds of health workers working together to handle patients with complex needs.
Research shows MDTs can help manage chronic diseases better. They also help provide more complete and organized care. For example, patients with diabetes or high blood pressure often get better care when dietitians, endocrinologists, nurses, and pharmacists all work together. This lets the team create treatment plans that fit each patient and check in on them regularly.
Still, MDTs can sometimes harm the ongoing relationship between a patient and their main doctor. Because care is shared among many professionals, patients might feel less connected or have trouble communicating. To avoid this, careful planning is needed to keep teamwork strong and maintain trust with the main doctor.
When these factors come together, MDTs can provide care that meets complex medical and social needs.
Team-based care shows clear effects on health results, especially for diseases you live with a long time. A review and combined analysis of 54 studies done by the Royal Society for Public Health looked at how teamwork helps control high blood pressure and diabetes in primary care.
The study showed that care teams reduced blood pressure. The average drop in the top number (systolic) was 5.88 mmHg. The bottom number (diastolic) went down by 3.23 mmHg on average. Blood sugar control in diabetes, shown by HbA1C levels, improved by 0.38% on average.
Teams that had four or five well-organized parts, like shared decisions and strong leadership, helped more with lowering blood pressure than teams with fewer parts. Just one or two actions from the team did not significantly change blood sugar levels. This means a complete team effort is needed for big improvements.
Still, more work is needed to learn how teamwork affects cholesterol levels, hospital visits, emergencies, and breathing diseases like COPD. More research will help create better team models for these health issues.
Cross-specialty collaboration means health workers from different medical areas working together to care for patients with complex needs. This is useful for patients who have many long-term conditions and need help from heart doctors, hormone specialists, lung doctors, mental health experts, and others.
Healthcare teams using this approach have seen several benefits:
However, cross-specialty collaboration also faces problems like electronic record systems that don’t work well together, differing ways of working, and unclear team roles. Fixing these needs strong support from healthcare organizations and investment in technology that connects systems.
Technology, especially artificial intelligence (AI), can help support team-based and cross-specialty care. AI tools can reduce paperwork and improve how work flows. This lets healthcare workers spend more time caring for patients.
AI automation helps teamwork in several ways:
Experts say AI should focus on the busiest areas where it can save time and money, such as cutting doctor paperwork and improving operations. But AI must be used carefully. More than 80% of AI projects in healthcare fail, often because of bias, safety problems, or lack of teamwork in oversight.
To use AI well, healthcare groups should create AI committees. These groups include people from clinical, technical, and ethics backgrounds. They watch AI tools for safety, fairness, and clear use, making sure the technology matches patient care goals.
Medical practice leaders and IT managers in the U.S. should think about these points when adding AI to teamwork-based care. Using AI to help front-office tasks like answering phones and scheduling can improve patient access and let staff focus on care.
Because there will be fewer nurses and doctors soon, many healthcare groups are making new staffing plans that rely on teamwork and flexibility. The idea of “flexpertise” means staff are trained to work in different roles and places.
Predictive analytics help by guessing patient numbers and letting managers plan staff shifts better. This reduces crowding in emergency rooms and hospital wards. It also cuts down waiting times and shortens hospital stays.
Cross-specialty teamwork also helps share responsibility among doctors, nurse practitioners, pharmacists, social workers, and others. Together, they make care more efficient and ready for patients.
There is a difference between consumer experience and patient experience in healthcare. Patient experience is about how satisfied someone feels during doctor visits. Consumer experience is about the whole journey, including before and after those visits.
Healthcare leaders say that today’s consumers want care that is easy to access, clear, and personal. Digital tools and cross-specialty teams working together can offer better access, faster communication, and continuous care.
About 40% of U.S. consumers use more than one health system, and nearly two in five have changed their main care doctor in the last three years. This shows that patients are not always loyal to one provider. Healthcare offices need to give smooth and connected care to keep their patients.
Working together across specialties and using teams are key ways to provide coordinated and whole-person care for patients with complex needs in the U.S. Team-based care can help improve long-term disease results, increase access to care, and reduce wasted effort.
Investing in AI and automation tools can support these goals by lowering paperwork, helping flexible staffing, and guiding patients better. Using proper oversight for AI makes sure technology is safe and fair while supporting medical goals.
For medical practice managers, owners, and IT teams, knowing about these changes is important to improve how clinics run and deliver care in a healthcare system that is getting more complex.
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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