, a global digital intelligence and software solutions provider, where he oversees general, specialty, and remote health care portfolios.

Deepak Borole

That is how many US hospital patients have at least one health care-associated infection (HAI), according to the 

 (WHO) reports that the global rate is one in every 10 patients, causing more than 3 million deaths annually due to unsafe care.

These are not just statistics but reflect extended hospital stays, increased costs, greater patient suffering, and preventable deaths.

Traditionally, the health care industry uses a “reactive” infection prevention and control (IPC) model, which identifies infections after lab confirmation, tracks contacts manually, and executes containment measures only after transmission has begun.

Infection rates have declined significantly since 2011, with continued improvement in recent 

 data despite setbacks during the COVID-19 period. HAIs, however, are still a significant patient safety problem, especially since rising antibiotic-resistant infections make it more difficult for doctors to treat patients successfully.

IPC specialists face obstacles because analyzing large volumes of data takes time, making it difficult to stay ahead of health risks. These operational constraints demonstrate weaknesses of conventional IPC models. Other structural challenges include:

 Infection cluster detection often occurs only after the spread has begun.

Disconnected Data Systems: Electronic health records(

EHRs), laboratory systems, hygiene protocols, and IoT sensor data are often disconnected from each other. As a result, critical warning signals may be missed across fragmented systems.

Static rule-based approaches generate many low-context notifications that erode clinical trust.

Staff and resource shortages increase workload pressure, leaving less time for risk detection and prevention.

These gaps reflect limitations in current systems rather than a lack of skills or effort.

Modern health care requires a proactive approach powered by artificial intelligence (AI) and agentic AI solutions. Rather than reacting to outbreaks, AI identifies threats and prioritizes interventions, while agentic AI facilitates earlier clinical intervention with minimal human involvement.

AI in IPC is not just a single tool. It is a layered system of data analysis, risk identification, and faster response capabilities. These systems do not just alert; they assist timely clinical action.

How exactly do AI-driven systems transform IPC?

Real-Time Surveillance/Early Outbreak Detection: AI systems are capable of analyzing both structured and unstructured data in real time, including EHRs, microbiology results, transfer-discharge feeds, and device logs. Instead of waiting for cases to appear, these systems detect early deviations in patterns that may develop into problems. For IPC teams, this means earlier cluster identification and quicker isolation decisions, which directly reduces operational and financial impacts.

Predictive Risk Modeling: Predictive AI models aim to identify patients at higher risk for infections. This includes surgical site infections (SSIs), central line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), and sepsis before symptoms occur. Instead of applying the same precautions to every patient, it is now possible to target advanced preventive measures to those who are most at risk. Such a directed approach improves efficiency and strengthens patient safety.

Antimicrobial Resistance: AI and agentic systems address this threat by identifying unusual prescribing patterns and flagging when decisions deviate from recommended guidelines. The WHO has identified antimicrobial resistance as a top global public health threat. For IPC and pharmacy teams, this represents an early intervention opportunity, resulting in more effective antibiotic use and a slower spread of resistance.

Environmental and Sterile Processing Optimization: Surfaces and medical equipment can contribute to infection transmission when cleaning and sterilization processes fail. These systems continuously monitor, analyzing data from multiple sources, such as room cleaning records, ultraviolet disinfection logs, sterilization cycle documentation, and airflow. Rather than rely on periodic audits, hospitals gain consistent vigilance. When something falls outside of expectations, teams can quickly act before infections spread.

Intelligent Alert Reduction: AI systems enable alert prioritization based on patient conditions, infection severity, trends, and patient movement. Instead of multiple notifications, more relevant alerts improve clinical engagement and support faster containment decisions.

Applications already demonstrate AI’s value in infection prevention.

University of Pittsburgh School of Medicine and Carnegie Mellon University

’s editorial advisory board member, Alexander Sundermann, DrPH, AL-CIP, FAPIC, combined AI’s machine learning with whole-genome sequencing, which “greatly improved the quick detection of infectious disease outbreaks within a hospital setting over traditional methods for tracking outbreaks.”

Successful deployment requires more than IT expertise; it also involves organizational transformation, model transparency, governance, and clinical integration. Apart from this, organizations must evaluate strategic deployment models:

 A fully customized platform usually requires a higher initial cost and longer lead time, but it meets all the specific requirements of the health care organization and can be less expensive going forward.

 This approach usually has lower upfront costs and quicker deployment but offers less customization and usually includes monthly or annual fees.

 For some companies, this method combines the best of both worlds. It uses an industry-standard third-party system combined with added customization tailored to the health care enterprise’s unique requirements.

Using proven AI platforms with local model refinement is often the most practical approach but this option still requires highly skilled software developers. Many organizations don’t have the talent needed in-house. When that is the case, the organizations need to find a vetted software solutions provider with a reputation for excellence in AI and agentic AI, as well as experience in the health care industry.

It is important to note that AI cannot replace IPC professionals, but it can strengthen their ability to act sooner and with precision. By transforming fragmented hospital data into actionable intelligence, AI allows IPC teams to detect emerging risks earlier and intervene before outbreaks escalate.

The future of IPC will not depend on how quickly we respond to outbreaks; it will depend more on how effectively we prevent them. AI is a shift that will ultimately help save lives.

Articles

AI and agentic systems are transforming infection prevention by enabling real-time surveillance, predictive risk modeling, and earlier intervention, shifting health care from reactive response to proactive prevention and improving patient safety outcomes.

Deepak Borole

Articles

The Proactive Shift: How AI/Agentic AI Is Revolutionizing Infection Prevention