Using AI to Predict and Reduce Hospital-Acquired Infections in Critical Access Hospitals

The Current State: Why HAIs Remain a Defining Risk for CAHs

Hospital-acquired infections (HAIs) continue to represent one of the most preventable , yet costly, threats to patient safety in the United States. For Critical Access Hospitals (CAHs), the stakes are even higher. With limited staffing, constrained budgets, and smaller patient volumes, even a single HAI event can disproportionately impact quality metrics, financial performance, and community trust.

The Centers for Disease Control and Prevention (CDC) estimates that approximately one in 31 hospitalized patients has at least one HAI on any given day, contributing to tens of thousands of deaths annually. While national attention often focuses on large health systems, rural hospitals face unique structural disadvantages that make proactive infection prevention especially challenging.

“HAIs increase hospital length of stay, costs, and mortality, and many are preventable with early detection and timely intervention.” — CDC

Why HAIs Are Especially Challenging in Rural Hospitals

Critical Access Hospitals frequently operate with:

• One or fewer dedicated infection preventionists

• Limited informatics support

• Manual surveillance processes

• Delayed access to specialty consultation

These constraints force infection prevention teams into retrospective review, identifying infections days or weeks after clinical deterioration has already occurred.

The Agency for Healthcare Research and Quality (AHRQ) has emphasized that early detection and risk stratification are essential to preventing HAIs, yet most CAHs lack the infrastructure to continuously monitor complex clinical data streams in real time.

The Cost of Reactive Infection Surveillance

Traditional HAI monitoring methods rely on:

• Manual chart abstraction

• Lagging indicators

• Retrospective reporting to NHSN

This approach:

• Consumes valuable staff time

• Fails to prevent harm in real time

• Limits actionable insights for frontline clinicians

“You cannot prevent what you only measure after the fact.”

Financially, HAIs drive:

• Increased length of stay

• Higher pharmacy and treatment costs

• Lost reimbursement under value-based programs

• Reputational damage in publicly reported quality data

How Quality and AI Shift Infection Prevention Upstream

Artificial intelligence introduces a fundamentally different model, predictive prevention with solutions like CARE-AI from EvaluCare, which is a quality assurance approach to continuous vigilance.

AI-enabled quality platforms can continuously analyze:

• Vital sign trends

• Laboratory results

• Device utilization

• Antibiotic exposure

• Nursing documentation patterns

By identifying subtle patterns associated with early infection risk, AI enables clinicians to intervene before diagnostic thresholds are crossed.

According to a study published in Clinical Infectious Diseases, machine learning models were able to predict sepsis and infection risk hours earlier than traditional methods, significantly improving outcomes.

“Predictive analytics can identify infection risk earlier than rule-based surveillance, allowing clinicians to act when prevention is still possible.” — Clinical Infectious Diseases

The Strategic Value for CAH Leaders

For executive and quality leaders, AI-enabled HAI prevention delivers:

• Measurable reductions in infection rates

• Improved staff efficiency

• Stronger compliance with regulatory expectations

• Better alignment between quality and financial sustainability

Importantly, AI does not replace infection preventionists — it augments their expertise, allowing small teams to function with enterprise-level insight.

CARE-AI is a CAH's Proactive Solution to HAIs

EvaluCare’s CARE-AI solution is a quality assurance program built for Critical Access Hospitals that shifts HAI prevention upstream with continuous, AI-driven surveillance of vital signs, labs, device utilization, antibiotic exposure, and nursing documentation. It augments small infection prevention teams with early risk stratification and actionable alerts, reducing infections, improving staff efficiency, and strengthening regulatory compliance. 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.

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