Healthcare organizations face a labor problem that recruitment alone cannot solve, with staff shortages extending beyond physicians and nurses to administrative and IT teams. A simultaneous demand for healthcare professionals — coupled with persistent and raised levels of burnout — are placing ever more pressure on the remaining employees.
AI Steps In: Freeing Up Time, Not Replacing Workers
Artificial intelligence offers a solution, not by creating new talent, but by boosting existing teams. AI tackles repetitive tasks, surfaces key information, and speeds up decision-making.
The goal isn’t to replace staff but to redirect their time towards work requiring expertise, judgment, and empathy.
Where AI Makes the Biggest Impact
Health systems see significant gains in clinical documentation. Ambient AI captures conversations and drafts notes, cutting down on after-hours paperwork.
AI also streamlines appointment scheduling, referral processing, patient communication, and revenue cycle operations.
“There are a lot of capacity gains being realized by automating those very manual and laborious processes end to end,” says Mutaz Shegewi, senior research director for worldwide healthcare provider AI, platforms and technologies at IDC.
The best opportunities often involve work surrounding care, rather than care itself. Mark E. Benden, department head of environmental and occupational health at the Texas A&M University School of Public Health, recommends identifying “shadow work” performed by clinicians that does not require a licensed professional.
“The goal for all of these systems should be more patient time and attention from the humans in the loop with even more confidence in the diagnosis and treatment plan,” Benden says.
Reducing burnout requires distinguishing between physical workload, administrative burden, and cognitive overload. Automating the wrong task might save little time or create new tool management burdens.
Administrative work is a relatively safe starting point. AI can handle patient messaging, claims, coding, denials, appeals, and prior authorization.
In clinical settings, AI can summarize records, surface relevant information, and identify care gaps, all without taking final decisions away from clinicians.
Shegewi emphasizes that AI is evolving into an intelligence layer complementing healthcare workers, allowing them to handle more complex tasks rather than being bogged down by repetitive workflows.
Employee Involvement and Workflow Design
AI’s potential extends beyond clinical and administrative teams. Administrative staff can use AI for referral processing, call summarization, and revenue cycle acceleration. IT teams can leverage AI-assisted development and natural-language coding tools to create internal solutions, troubleshoot systems, and automate routine support tasks.
Predictive scheduling and workforce planning can help optimize staffing levels, reduce overtime, and anticipate demand. However, implementing these solutions in healthcare settings is challenging due to specialized roles and long hours.
Technical considerations, such as data security, regulatory compliance, and legal review, must be addressed. A robust governance framework is essential but should not hinder beneficial AI solutions. IT leaders must balance enabling experimentation with maintaining oversight over data, models, and workflow decisions.
Shegewi recommends starting with narrow, measurable problems that can demonstrate whether AI is returning meaningful time to the workforce.
Successful AI adoption requires involving end-users in the design process, focusing on core friction points rather than adding features. Co-designing solutions with clinicians, administrators, and IT staff creates intuitive tools that integrate seamlessly into workflows.
“AI can do a lot, but it’s pointless having a very feature-rich AI tool that can’t address the core friction points,” Shegewi says. Employees should participate in designing the future workflow, testing the tool, and refining it after deployment.
Representatives from clinical, nonclinical, IT, and business teams should play a continuing role in governance. Their involvement allows leaders to monitor whether AI tools save time, shift work, or increase pace unsustainably. Useful measures include documentation time, after-hours work, task completion, overtime, employee satisfaction, and time returned to patient care.
Benden reinforces that workforce augmentation should enable people to focus on tasks only humans can perform. “When clinicians have more time for medicine and less time spent on shadow work, everyone wins,” Benden says.