How Have Police Early Intervention Systems Evolved?

An early intervention system (EIS) is one piece of a larger officer management system (OMS), the platform an agency uses to turn personnel data into measurable agency performance. The real test of any such system is not what it records but whether anything actually changes: fewer officer injuries, fewer citizen complaints, fewer future investigations. Benchmark Analytics® calls this The Outcomes Standard for Agency Performance, and an early intervention system only earns its place by contributing to it. 

Most chiefs already know why an agency would implement an EIS: to help leadership identify officers who need additional support before a difficult situation becomes an adverse incident involving a citizen or fellow personnel. What is less widely understood is what actually triggers an alert, and how accurately those alerts identify the officers who genuinely need support. 

Traditionally, these systems use triggers tuned to flag an officer whose activity exceeds agency-decided thresholds. Awareness was the design goal of that generation of systems: give executives visibility into potential personnel issues. That awareness came with a caveat, and it was flagged early: The U.S. Department of Justice’s Office of Community Oriented Policing Services (COPS Office) published its planning guide in 2003, authored by police-accountability scholar Samuel Walker, framing early intervention as a non-punitive, data-driven management tool while cautioning that any such system is only as good as the data and thresholds behind it. 

Two decades of research have borne that caution out, and revealed two distinct problems with trigger-based systems. 

 

The first is false alarms. Studies found trigger-based solutions deliver only about 30% precision, meaning roughly seven of every ten officers they flag are doing their jobs as they should. The second is the officers they miss: while supervisors chase false positives, officers who are genuinely struggling go unflagged and unsupported. By contrast, Benchmark’s research-based model reaches 85% precision. And the stakes are not abstract. Research has found that 17% of officers will have an adverse incident within a year. Awareness is not an outcome, and an agency cannot afford decisions built on an inaccurate view of its officers. 

How Do Agencies Get Better at Early Intervention?

The case for getting this right is decades old. The Independent Commission on the Los Angeles Police Department, known as the Christopher Commission, reported in 1991 that of roughly 1,800 officers with an excessive-force or improper-tactics allegation between 1986 and 1990, just 44 had six or more, a small group responsible for a disproportionate share of incidents. That concentration pattern still holds today. Approximately 5% of officers identified at risk are responsible for 66% of injuries., When risk is that concentrated, precision in identifying the right officers is not a technical nicety. It is the difference between an intervention program that works and one that generates paperwork. 

Improving that precision starts with an honest assessment of where an agency stands. When Benchmark begins working with a partner agency, the first step is locating them on the path to digital transformation 

The path runs through five stages, beginning with Undefined and ending with Predictive. Traditional EIS can carry an agency as far as the Analytic stage. But the architecture of trigger-based software imposes a ceiling: thresholds can tell you what already happened, and they cannot proactively identify which officers need support before an incident occurs. Reaching the Predictive stage, where intervention happens early enough to change outcomes, requires a different foundation. 

 

 

What Changed When Early Intervention Became Predictive?

As policing moved toward data-centric operations, early intervention systems lagged behind, and independent research has now documented the cost. A 2025 evaluation of the Phoenix Police Department’s early intervention system, published in Police Quarterly by researchers at Arizona State University, examined more than 2,000 officers over five years and found the department’s threshold-based indicators had predictive validity no greater than 4%. The same study found that implementing the system produced no significant reduction in problematic officer behavior. In other words, the trigger-based model failed at both halves of its job: identifying the right officers and changing outcomes. 

Research-based prediction takes a fundamentally different approach. Rather than waiting for a fixed count of incidents to be crossed, First Sign® by Benchmark draws on multiple data sources, including arrest records and use-of-force reports, and weighs 91 model variables per officer to surface predictive patterns before an adverse incident occurs. 

That model is not built on assertion. Benchmark operates the largest officer performance database in policing, more than 80 million officer performance records, and validates its predictive analytics in partnership with the University of Chicago and the Joyce Foundation. Independent scrutiny is the difference between a research-based early intervention system and a threshold tool that simply counts. 

Watch Nick Montgomery, Chief Research Officer at Benchmark Analytics, share the studies behind Benchmark’s research-based approach, including how to identify your agency’s current transformational stage and what you gain by evolving to a predictive model. 

 

Frequently Asked Questions

What is an officer management system? An officer management system (OMS) is the platform an agency uses to manage the full officer lifecycle, from training and performance to early intervention and wellness, and to turn that personnel data into measurable agency performance. Benchmark is the officer management system for law enforcement, built on a research foundation rather than a workflow checklist. 

How have police early intervention systems evolved? Early intervention systems began in the 1970s as trigger-based tools that flagged officers after they crossed fixed thresholds. They have since evolved into research-based predictive models that weigh dozens of variables across years of data, identifying who may need support earlier and far more accurately than counting incidents alone. 

Why do threshold-based early intervention systems fail? Threshold-based systems flag officers only after they cross a fixed count of incidents, which produces roughly 30% precision and a high volume of false flags. First Sign reaches about 85% model precision by weighing predictive patterns against a national model built on more than 80 million officer performance records. 

Does early intervention replace supervisor judgment? No. Early intervention is non-punitive and supports supervisor judgment rather than replacing it. A flag is the start of a supportive conversation, not a verdict, and structured follow-up turns that insight into a documented, trackable plan for the officer. 

Sources

Samuel Walker, “Early Intervention Systems for Law Enforcement Agencies: A Planning and Management Guide,” U.S. Department of Justice, Office of Community Oriented Policing Services (COPS Office), 2003. 

Independent Commission on the Los Angeles Police Department (Christopher Commission), Report of the Independent Commission on the Los Angeles Police Department, 1991. 

Charles M. Katz, Hyunjung Cheon, Kayla Freemon, and Danielle Wallace, “Evaluating the Effectiveness of a Police Early Intervention System,” Police Quarterly, 2025. 

 

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