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Fatigue Science’s Readi vs Predictive Safety's PRISM Fatigue Management System

Which is the right fatigue risk management system (FRMS) for your fleet?

While both Fatigue Science’s Readi platform and Predictive Safety's PRISM aim to address fatigue risks in transportation, discover how they differ in their approach, features, and technology.

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Readi Overview

Readi fatigue risk management software provides real-time predictive insights into driver fatigue, allowing you to mitigate fatigue risk before it becomes dangerous. Our system uses advanced machine learning to provide accurate predictions up to 18 hours in advance (no wearables or invasive sleep tracking required).

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Readi Key Differentiators

Predicting Fatigue Risk for the Entire Shift

Hour-by-hour, predictive personalized fatigue scores for every driver for up to 18 hours, allowing a manager to assess the risk across the entire driver’s shift

Real-time Data Sync through Dispatch Screens

Our API-enabled ReadiDispatch allows the critical data from the Readi FRMS to be displayed on dispatchers’ existing screens, allowing them to proactively mitigate driver fatigue risk to prevent fatigue-related nuclear events on the road

No Wearable or Hardware Required

No invasive sleep tracking or wearables required

88% Fatigue Accuracy

Best-in-class sleep tracking up to 88% validated accuracy compared to a wearable

Impact Analysis

The Readi system is the only FRMS that has the ability to connect a driver’s fatigue scores to safety events (e.g., harsh braking, speeding), allowing fleet managers to quantify the impact of mitigating operator fatigue across the organization.

Validated Technology

Validated by the US Department of Transportation, the Federal Aviation Administration, and the US Army

Predictive Safety: How PRISM Predicts Fatigue

PRISM from Predictive Safety relies on time and attendance and schedule analysis, sleep diaries and questionnaires, and the secondary AlertMeter® PVT test for real-time, point-in-time assessments of worker alertness.
While it provides immediate feedback on impairment, PRISM lacks the machine learning capabilities and historical sleep dataset necessary for long-term fatigue predictions, which limits its ability to forecast fatigue risks in advance. The technology is not predictive at an individual level for the shift ahead. The technology is likely to miss many cases caused by personal sleep health intersecting with schedule.

Because Predictive Safety relies solely on driver schedules, and does not use machine learning to predict fatigue, they do not offer the ability to cluster drivers against a large sleep database. This impacts accuracy and overall usefulness.

Readi Integrates with All Major ELDs for Personalized Driver Fatigue Prediction

Explore how our technology uses Hours of Service data from Electronic Logging Devices (ELDs) as one of several sources of data that inform personalized fatigue predictions for each driver, generated in advance of every shift.

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Validated Technology

The ReadiScore has been validated extensively by the US Dept. of Transportation, the Federal Aviation Administration, and many other public and private institutions.

The ReadiScore is the output of the SAFTE Biomathematical Fatigue Model, which was developed by researchers at the US Army’s Walter Reed Army Institute of Research. It has been studied and validated in 13 published independent papers. Our fatigue risk prediction has a validated 88% accuracy level.

Predictive Safety’s PRISM, on the other hand, has no accuracy figures quantified on schedule and time and attendance analysis.

Impact Analysis

The Readi system has the ability to connect a driver’s fatigue scores to safety events to quantify the impact of fatigue mitigation on the organization.

A recent meta-study found that Readi could reliably predict if and when a driver would experience a 12x higher incidence of microsleeps vs. their baseline rested state. Additionally, the US Dept. of Transportation published a study revealing a 7.3x higher accident cost in cases where the ReadiScore had predicted high fatigue.

Similarly, two telematics studies conducted by Fatigue Science clients showed 8.5x higher incidence of harsh braking and a 4x higher incidence of speeding in cases where Readi had predicted high fatigue.

Pulsar Informatics, on the other hand, primarily offers insights into immediate fatigue risk based on PVT results and schedule data, lacking detailed impact analysis on safety events over time.

Full Cycle FRMS: Predicting Fatigue Risk Is Only the Start

Readi provides an organization with a full Fatigue Risk Management System. This means that a user can start and finish the fatigue workflow within the tools:

-Identify fatigued drivers
-Conduct a fit for duty assessment
-Evaluate and assign mitigations actions
-Analyze historical data to assess effectiveness of the actions and adjust policy and procedure accordingly

Predictive Safety, on the other hand, focuses only on identifying fatigued drivers.

FAQ

How does Readi integrate with our existing fleet management systems?

Readi provides seamless integration with major ELD systems like Lytx and Omnitracs, enhancing the functionality of in-cab cameras and enabling more accurate fatigue monitoring and risk prediction.

How does Readi’s machine learning improve fatigue prediction?

Readi uses over 6 million sleep data points captured from our ReadiWatch users and AI-driven analytics to predict fatigue risk up to 18 hours in advance, offering more accurate and actionable insights compared to point-in-time tests.

Can we customize fatigue risk thresholds with Readi?

Yes, Readi allows fleet managers to customize fatigue risk thresholds based on their operational needs, ensuring that safety measures are tailored to specific fleet environments.