Potential Serious Injury or Fatality (pSIF)

Across high-risk industries—construction, oil & gas, mining, and manufacturing—Serious Injuries and Fatalities (SIFs) remain a sobering reality. Despite advancements in EHS programs, human error, system lapses, and unpredictable events continue to lead to preventable loss of life or life-altering injuries.

According to the latest U.S. Bureau of Labor Statistics, 5283 workplace fatalities were recorded in the United States alone, making the rate stand at 3.5 fatalities per 100,000 full-time equivalent workers.

This rounds up to one worker dying every 99 minutes due to an injury at work.

While many organizations have implemented incident reporting systems and safety audits, a critical question lingers:

Is identifying near misses enough to prevent the next serious injury or fatality?

Traditionally, near miss reporting has been considered a valuable tool for capturing safety gaps. But in today’s data-driven environment, relying solely on AI-based near miss detections and incident documentation is proving insufficient.

The event is flagged as a near miss. Supervisors document the incident thoroughly: time, location, task, team involved, environmental conditions, and operational lapse.

With advanced AI video analytics and predictive modelling, this incident would not just be an unexpected close call. The system would have already flagged the task as high-risk during scheduling, identifying potential conflicts in crane operation timing and pedestrian movements.

The worker passing below the suspended load wasn’t an isolated human error—it was a predictable outcome.

Based on AI-driven pSIF (Potential Serious Injury or Fatality), the area was already marked restricted for pedestrian workers, leaving no scope for a near miss.

That’s where the shift from near miss reporting to prediction begins.

Potential Serious Injury or Fatality (pSIF) refers to any unplanned event that, while not resulting in actual harm, has a credible potential to result in death or serious injury. The key difference is the level of predictive insight and preventative action tied to it.

Instead of simply reporting what “almost” happened, pSIF frameworks identify high-risk patterns and precursors to anticipate and mitigate incidents before they materialize.

Difference Between Near Miss and pSIF

The power of pSIF lies in proactive innovation. Instead of retroactively reviewing incidents, companies can re-engineer tasks, environments, and behaviors based on forecasted risk patterns.

Here’s a framework to understand its impact:

In high-risk crane operations, for instance, the predictive model analyzes live video, task schedules, and data to detect unsafe patterns, such as unsafe load swing, overloads, repeated proximity violations, and operator inattentiveness under crane activity. This triggers an automatic intervention like rerouting pedestrian paths or restricting zone access, preventing future pSIFs.

AI model input comes from a blend of live camera feeds, historical task data, and real-time environmental metrics. When integrated, it can identify conflicts like forklift paths overlapping with worker walkways, which would otherwise go unnoticed.

Interventions based on this predictive insight can range from automatic permit suspension and equipment shutdowns to wearable alerts such as helmet vibrations when a worker nears an unguarded edge.

As a result, organizations not only reduce potential SIFs but also improve site compliance, reduce downtime, and increase safety confidence.

The benefits driven by pSIF extend beyond making the safety compliance accurate, but it works towards the generation of an AI-driven safety mindset that alters the culture for good.

Here are some top benefits that pSIF provides:

Traditional near miss reporting might list hundreds of minor and major incidents with equal weight. But pSIF-based scoring brings clarity. It filters noise by highlighting high-potential events that deserve immediate action. This enables safety teams to channel their time and resources where it matters most.

Predictive alerts tied to real-time video analysis condition workers to modify actions over time. In a Malaysian manufacturing plant, pSIF-driven behavior monitoring revealed a trend of workers bypassing a machine guard during peak production hours. Within weeks of introducing automated feedback and supervisor prompts, violations dropped by 62%, and overall machine safety compliance improved dramatically.

The operational efficiency can be highly restructured using AI-based insights. As any incident in a worksite halts operations leading to project delays, predictive insights removes this condition.

In a UAE oil & gas project, repeated predictive alerts tied to vehicle-pedestrian conflicts led to a complete redesign of traffic flow and shift scheduling, resulting in a 40% drop in incident reports across three months.

Instead of generic site-wide programs, training can now be tailored to individual behavior profiles generated through AI. For example, a worker frequently involved in proximity alerts near heavy equipment receives refresher modules focused on situational awareness. This targeted learning approach increases retention and relevance, reducing future risk.

By scoring different subcontractors on predictive safety behavior (rather than just incident reports), organizations can reward best practices and phase out high-risk collaborators. One construction conglomerate in Vietnam implemented this scoring strategy and improved subcontractor compliance scores by 33% within the first year.

Reduced rework, fewer delays from incidents, less paperwork, and improved audit readiness translate into measurable financial savings. A mining firm in Australia calculated that its pSIF implementation helped avert an estimated $2.1 million in project delay losses in a single fiscal year.

In short, predictive safety frameworks don’t just avoid harm—they build high-trust, data-smart environments where workers feel seen, protected, and heard. That’s the culture shift the industry needs.

A predictive AI safety platform delivers pSIF visibility through an integrated dashboard that merges historical trends with real-time insights.

The dashboard draws from incident logs, video feeds, environmental sensors, task scheduling data, and behavioral patterns. It visually maps worker movements, identifies recurring high-risk behaviors, and uses AI algorithms to assign predictive scores to potential incidents.

Expanded Dashboard Snapshot – Real Case View

This level of integration transforms scattered data into actionable intelligence, enabling safety teams to go from reactive reporting to real-time prevention, powered by predictive analytics.

With industrial work environments becoming more dynamic and complex, the shift toward predictive safety frameworks is no longer optional. pSIF-based models allow organizations to move from reaction to prevention.

By identifying patterns before incidents occur, teams can redesign workflows, allocate resources more effectively, and reduce human dependency in hazard detection.

Compliance becomes proactive. Training becomes personalized. Risks become visible even before tasks begin.

The future of workplace safety isn’t just about avoiding accidents—it’s about eliminating the possibility of them altogether. And with AI-driven pSIF frameworks, that future is already here.

Yes — most AI models for pSIF operate with 95%+ accuracy in identifying patterns that precede high-risk incidents. These systems learn from thousands of hours of cross-industry footage and improve continuously with real-time feedback loops.

The deployment process is very easy and hassle-free, which does not impact any existing activities on site. The process includes the following steps-

• Assess existing CCTV and IP camera infrastructure

• Integrate with 100+ AI Modules built in a safety system like viAct

• Activate modules based on requirement, such as for behavior analysis, PPE compliance, or zone breach detection

• Begin receiving real-time alerts and predictive pSIF scores

As one EHS head at a manufacturing hub in Singapore shared:

“We had over 100 cameras onboarded with zero disruption. The AI went live in under 72 hours — and we saw 34% fewer near misses within weeks.”

Using an AI-driven pSIF system, an EHS leader can tailor:

In a Saudi oil firm, the use of adjusted and customised pSIF parameters for desert heat conditions led to a reduction of fatigue events by 41%.

Yes! AI-based monitoring across sites creates individual worker profiles based on their past behavior and near miss reporting. While keeping their privacy intact, platforms like viAct comply with GDPR regulations using the Face blur method and design safety training based on worker IDs. Those frequently involved in unsafe patterns receive tailored training modules, reducing recurrence and improving relevance.

Absolutely. Systems like viAct allow centralized command centres to:

• View pSIF heatmaps per site

• Benchmark contractor safety scores

• Analyze patterns across geographies

This makes it easier for safety heads to enforce consistency across global operations. viAct is already working across major industrial sites in Hong Kong, Singapore, Saudi Arabia, United Arab Emirates (UAE), Qatar, Oman, Bahrain, Kuwait, and other GCC and Middle East markets, as well as India, Southeast Asia, Australia, Europe, and North America, while expanding rapidly in other regions as well. 

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