Optimizing Length of Stay Through AI-Enabled Care Coordination in Critical Access Hospitals

The Current State: Length of Stay as a Silent Margin Killer

Length of stay (LOS) has become one of the most influential, yet misunderstood, drivers of financial and operational performance in Critical Access Hospitals. While CAHs are reimbursed on a cost basis, inefficient LOS still erodes margins, limits access, and strains already stretched staff.

National data from CMS shows that rural hospitals experience higher variability in LOS, often driven by discharge delays rather than clinical necessity.

“Extended hospital stays increase costs, expose patients to harm, and reduce capacity, particularly in rural settings.” — AHRQ

Why LOS Is Harder to Control in CAHs

CAHs face systemic barriers including:

• Limited post-acute care availability, especially in rural areas

• Transportation challenges

• Fewer care managers and social workers

• Delays in diagnostics or specialty consults

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Traditional LOS improvement efforts often rely on retrospective reporting, leaving leaders explaining delays instead of preventing them. When CAHs are a part of a larger health system, CAHs are overlooked in LOS reduction efforts, reducing resources for improvement.

The Limits of Traditional LOS Management

While multidisciplinary rounds and discharge checklists are essential, they depend heavily on:

• Human vigilance

• Consistent communication

• Manual tracking

These approaches struggle to scale in environments where clinicians already juggle multiple roles.

How AI Enables Proactive LOS Management

AI transforms LOS management by identifying:

• Patterns and trends in discharge planning for patients at risk for extended stays

• Emerging and common discharge barriers

• Care process variation

• Documentation indicating delays in discharge (e.g. clinical indications appropriate for discharge present)

  
  

Predictive models can flag patients early in their admission, allowing teams to mobilize resources sooner.

Research published in BMJ Quality & Safety demonstrated that predictive analytics reduced LOS by up to 15% without increasing readmissions.

“Predictive analytics support earlier, more targeted interventions that reduce unnecessary hospital days.” — BMJ Quality & Safety

Strategic Impact for CAH Leadership

AI-enabled LOS optimization supports:

• Improved throughput

• Better bed utilization

• Lower cost per case

• Improved patient experience

Most importantly, it aligns operational efficiency with patient-centered care — a critical balance for rural hospitals.

CARE-AI is a CAH's Proactive Solution to LOS Reduction

EvaluCare’s CARE-AI solution is a quality assurance program built for Critical Access Hospitals and small hospitals that shifts LOS prevention upstream with continuous, AI-driven surveillance of drivers to discharge delays. It augments small case management teams with early risk stratification and actionable alerts, reducing LOS, improving staff efficiency, and strengthening quality of care. Purpose-built for rural hospitals, CARE-AI aligns quality with financial sustainability, and its costs can be funded as much as 75% by Medicare. CARE-AI offers project management resources for a full spectrum of support to augment any quality team or strategy. For more information visit https://www.EvaluCare.net/care-ai

References

• Centers for Disease Control and Prevention (CDC). Healthcare-Associated Infections (HAI) Progress Report

• Agency for Healthcare Research and Quality (AHRQ). HAI Prevention Toolkit

• Henry KE et al. A targeted real-time early warning score (TREWScore) for septic shock. Clinical Infectious Diseases

About the Author

Jason Minor is a healthcare quality and transformation leader with nearly 30 years of continuous improvement experience. A Certified Lean Six Sigma Black Belt, Certified Professional in Healthcare Quality, Certified Professional in Patient Safety, and Certified Utilization Review Professional, he has led thousands of end‑to‑end improvement projects, mentored dozens of quality professionals, and pioneered healthcare SaaS innovations.

As Board Chair of the Vermont Program for Quality in Health Care, Jason has partnered with hospitals, non‑profits, and state agencies to elevate patient safety and care quality statewide. Previously, as Network Vice President of Quality at the UVM Health Network and through the Jeffords Institute for Quality, he guided the redesign of a system‑wide quality framework and led initiatives that achieved a number‑one patient safety ranking among the nation’s top academic medical centers.

In 2020, Jason founded EvaluCare to help organizations shift from episodic improvement to a robust quality assurance approach.

EvaluCare’s Eva platform leverages AI‑powered natural language processing, machine learning, and agentic orchestration to analyze and improve inpatient care and support comprehensive quality, mortality, peer, and utilization reviews.

Jason Minor, EvaluCare Executive Director

Network Director Continuous Systems Improvement Jeffords Institute for Quality UVM Health

Board Chair Vermont Program for Quality in Health Care Inc.,

Vice Chair Northwestern Counseling & Support Services, Inc

Lecturer UVM College of Nursing & Health Sciences

Quality Peer Reviewer Vermont Care Partners: Centers of Excellence