AI is quietly reshaping health care decisions—influencing who gets care, and for how long. As insurers increasingly use algorithms to deny or limit treatment, staffing leaders are feeling the ripple effects through disrupted schedules, shortened patient stays, and new ethical challenges they can’t afford to ignore.
Artificial intelligence (AI) is reshaping industries across the board, and health care is no exception. From predictive analytics to workflow automation, AI tools promise greater efficiency, cost savings, and even better patient outcomes. But there’s a darker side to the story: The same algorithms streamlining administrative processes are also being used by major insurers to deny or limit care.
According to a 2025 Guardian investigation, UnitedHealth’s subsidiary naviHealth has used a predictive tool called nH Predict to recommend when patients should be discharged from post-acute care facilities such as rehab centers or nursing homes. While the software relies on historical data to project average recovery times, critics argue it often overrides treating physicians’ recommendations—sometimes leading to premature discharges and costly hospital readmissions.
For staffing agencies working in health care, this trend carries big implications: Sudden patient discharges disrupt scheduling, reduce demand for clinical staff, and put agencies in the middle of disputes between providers, payers, and families.
When Algorithms Outweigh Expertise
The legal and ethical concerns are significant. Health care laws generally require decisions about medical necessity to be made by licensed clinicians—not algorithms (UCLA Law Review, 2025). Yet in practice, predictive tools increasingly influence (if not dictate) these determinations.
From an ethical standpoint, patients and families may not even know that AI influenced their care decisions. This “black box” problem raises questions about autonomy, informed consent, and transparency. For clinicians, the frustration of having their professional judgment routinely second-guessed by a statistical model can be demoralizing. And for staffing agencies, the ripple effect can mean canceled shifts, rehospitalizations that require re-staffing, and added strain in already tight labor markets.
The Business Risk Beyond Health Care
For insurers, AI-driven denials may save money in the short term. But as law firms like Morgan Lewis (2025) note, these practices expose payers to serious legal risks, including class-action lawsuits and potential violations of the False Claims Act. The reputational fallout can be just as damaging—especially when media outlets highlight stories of elderly patients being discharged too soon.
Health care staffing companies aren’t immune from this fallout. Agencies may lose business when patient stays are cut short, or face operational headaches when clients push back against staffing plans that no longer match AI-driven discharge decisions. In a business built on trust and reliability, this kind of disruption erodes relationships with both clinicians and health care facilities.
The patients most vulnerable to automated denials are often elderly or recovering from serious illness. When care is cut off prematurely, families may shoulder unexpected caregiving duties, while providers are left scrambling to fill gaps. For staffing agencies, every premature discharge means sudden changes in scheduling and workforce allocation.
This cascade of challenges doesn’t just hit the bottom line: It compounds stress on caregivers and clinicians. Agencies that can anticipate and navigate these challenges will stand out as valuable partners to health care organizations.
Building Guardrails Around AI
So, what’s the path forward?
- Maintain human oversight. States like California are already taking action: Senate Bill 1120 (2025) requires that licensed professionals—not algorithms—make final determinations on medical necessity.
- Conduct audits and be transparent. Regular reviews for bias and accuracy are needed to ensure AI systems don’t disproportionately harm certain groups (Reuters, 2025).
- Ensure cross-disciplinary collaboration. Staffing leaders, clinicians, and compliance teams must work together with insurers and technology vendors to ensure AI tools support—not replace—human judgment.
- Educate staffing teams. Clinicians, recruiters, and managers should understand how AI-driven denials work so they can help patients and providers navigate appeals when necessary.
AI in health care isn’t going away. But as these technologies expand, staffing firms must be prepared for the ripple effects: shortened patient stays, unpredictable scheduling, and the ethical questions that come with algorithmic decision-making.
For staffing leaders, the takeaway is clear: Staying informed about AI-driven denials isn’t just about compliance; it’s about protecting client relationships, supporting clinicians, and ensuring that patient care remains at the center of health care delivery.
Matt Nedrow is vice president of operations at Plexsum Staffing Solutions Inc., where he leads strategic initiatives and daily operations across health care staffing functions. With more than 25 years of leadership experience, Nedrow brings a unique blend of military precision and business acumen to the staffing industry, and specializes in workforce optimization, compliance, and operational strategy. Send feedback on this article to m***@plexsum.com.
ASA does not necessarily endorse the content of this article—the statements, views, and opinions expressed are those of the author alone.