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Police Early Intervention System

Leading the Charge: Why Public Agencies Must Pioneer Technology Adoption

Posted on by Amy Vracar

Recently, Benchmark Analytics attended the AWS Summit in Washington, DC, where it was emphasized that government agencies, especially those in public safety, should lead in adopting technology. Recognizing and implementing technology-first strategies is crucial for agencies to maintain relevance, efficiency, and effectiveness in their operations. Here are several compelling reasons why prioritizing and being at the forefront of technology adoption is paramount:

  1. Security and Compliance

As cyber threats become more sophisticated and prevalent, it is crucial for government agencies to adopt advanced security technologies to protect sensitive data and ensure the integrity of their operations. This includes:

  • Implementing Modern Technologies: Modern security technologies, such as artificial intelligence and machine learning, can significantly enhance threat detection and response capabilities. These technologies can analyze vast amounts of data in real-time, identify unusual patterns, and detect potential security breaches before they can cause significant damage.
  • Using Automated Systems: Automated systems can then initiate immediate responses, such as isolating compromised networks or alerting security personnel, thereby reducing the window of vulnerability.

Additionally, a critical component of security and compliance is ensuring that all employees are knowledgeable about best practices and potential threats. Regular training programs can educate staff on recognizing phishing attempts, handling sensitive data securely, and following established security protocols.

  1. Enhanced Efficiency and Productivity

One of the most significant advantages of integrating technology into operations is the marked increase in efficiency and productivity. The adoption of digital tools and systems allows for the streamlining of processes, which reduces the reliance on manual paperwork, minimizes the time required to complete various tasks, and improves the handling and retrieval of information — making it faster and more reliable.

For example, in a law enforcement agency, this can include:

  • Use of Force Reporting Tools: The process of receiving, documenting, and managing use of force reports is crucial for law enforcement agencies for several reasons, including properly documenting incidents to ensure accountability to the public and stakeholders. Analysis of use-of-force data also allows agencies to identify trends, patterns, and areas for improvement. This information informs training programs and policy development aimed at minimizing the need for force and enhancing officer safety and community relations.
  • Internal Affairs Investigations: This involves the process of investigating complaints and allegations against officers. Effective handling of internal affairs (IA) investigations with IA software ensures that allegations of misconduct or wrongdoing by officers are thoroughly and impartially examined. This process upholds the agency’s commitment to integrity and accountability.
  • Training and Certification: This workflow manages the training schedules, certification processes, and continuing education requirements for officers. It includes tracking attendance, test scores, and certification renewals. Managing training and certification workflows is essential for law enforcement agencies to ensure operational readiness, uphold legal standards, foster public trust, mitigate risks, and support officer development and retention.
  1. Data-Driven Decision Making:

By harnessing the power of data, law enforcement agencies can make more informed and evidence-based decisions. Advanced data analytics tools can process and analyze large datasets to uncover insights and patterns that would be impossible to detect manually. This approach allows law enforcement agencies to base their decisions on concrete evidence, leading to more effective and strategic operations. Examples of this include:

  • Community Engagement: Data analytics can also improve community engagement and relations. By analyzing data on public concerns, complaints, and interactions with law enforcement, agencies can better understand community needs and address them effectively. This fosters trust and cooperation between the police and the community they serve.
  • Early Intervention Systems: By tracking and analyzing data on officer behavior and performance, law enforcement agencies can ensure accountability.

Research-based early intervention systems (EIS) help identify officers who may be at risk of behavioral issues or misconduct before serious problems occur. This allows agencies to take proactive measures such as additional training, counseling, or support, potentially preventing future incidents. Additionally, EIS supports officer well-being by identifying officers who may be experiencing stress, burnout, or other personal challenges early on allows agencies to provide necessary support and resources. This can improve overall officer well-being and job satisfaction.

For example, Benchmark Analytics’ First Sign® Early Intervention supports data-driven decision making. Through its advanced analytics, First Sign provides predictive insights into potential future incidents or behavioral patterns. This foresight allows agencies to intervene proactively, preventing problems rather than reacting to them.

Overall, embracing technology-first strategies is essential for government organizations, like law enforcement agencies, to uphold their commitment to public service, enhance security measures, and promote a forward-thinking and sustainable future. By leveraging tools like First Sign, agencies can proactively address challenges, improve decision-making processes, and ultimately, create safer and more resilient communities. This proactive approach not only meets the demands of today’s dynamic landscape but also lays the foundation for continued innovation and excellence in public service.

Benchmark’s Continued Collaboration with AWS

Posted on by Amy Vracar

Government organizations – including law enforcement agencies – often grapple with the responsibility of managing highly sensitive data digitally. A resilient, secure, and compliant cloud infrastructure is critical for data agility, efficiency, and effectiveness. AWS GovCloud, launched in 2011, was developed as a specialized system to meet these distinctive security and compliance requirements of the public sector.

Benchmark and AWS

AWS GovCloud is transforming local and state government operations and how they engage with data, by incorporating extra safeguards to protect sensitive information. AWS GovCloud adheres to rigorous compliance standards, including FedRAMP High, ITAR, DoD SRG, CJIS, and HIPAA, ensuring an unparalleled level of security and regulatory adherence.

Becoming an AWS Public Sector Partner

As a data and analytics company focused on personnel management within law enforcement, Benchmark is proud to host our suite of platforms on AWS GovCloud. The advantages of choosing a solution hosted on AWS GovCloud are numerous, and include:

  1. Enhanced Security: AWS GovCloud was built to manage sensitive data and regulated workloads with an additional layer of protection for heightened security assurance.
  2. Compliance: AWS GovCloud meticulously aligns with an extensive array of stringent compliance standards, including but not limited to FedRAMP High, ITAR, DoD SRG, CJIS, and HIPAA.
  3. Data Sovereignty: GovCloud provides an unyielding commitment to data sovereignty, alleviating concerns regarding the geographical location of data.
  4. Tailored Services: AWS GovCloud presents a suite of specialized services meticulously designed to cater to the unique demands of government agencies and organizations.
  5. Seamless Integration: AWS GovCloud seamlessly integrates with other AWS partners, ensuring a harmonious synergy. This strategic integration empowers organizations to harness the full spectrum of AWS capabilities while upholding compliance standards.
  6. Scalability and Flexibility: GovCloud seamlessly delivers unparalleled scalability and flexibility. This emboldens government agencies and organizations with the capacity to innovate, expand, and modernize their IT infrastructure with utmost efficiency.

Over the years, Benchmark and AWS have achieved remarkable milestones together — including in 2022 when Benchmark graduated from the AWS Accelerated Partner Program and gained acceptance into the AWS Public Sector Partner initiative. This accomplishment was the result of combined efforts from Benchmark’s sales, marketing, and technical teams, including completing the AWS Foundational Technical Review (FTR).

The AWS FTR enables AWS Partners to demonstrate that their software solutions meet industry best-practices based on the AWS Well-Architected Framework as well as standards for evaluating systems architecture and operational practices. The AWS Well-Architected Framework is built around six pillars:

  1. Operational excellence
  2. Security
  3. Reliability
  4. Performance efficiency
  5. Cost optimization
  6. Sustainability

These pillars focus on technical design principles and best practices, including running and monitoring systems and protecting data and information.

Acceptance in AWS ISV Accelerate and AWS Marketplace

In 2023, Benchmark continued its dedication to growing its collaboration with AWS, with acceptance into AWS ISV Accelerate and a position on the AWS Marketplace.

The AWS ISV Accelerate Program is a co-sell initiative designed for organizations offering software solutions that run on or integrate with AWS. This program not only accelerates the sales cycle but also fosters new business opportunities, demonstrating a mutual commitment between AWS and Benchmark.

In addition to joining ISV Accelerate, Benchmark’s inclusion in the AWS Marketplace represents another significant milestone. This digital catalog simplifies the discovery, testing, purchasing, and deployment of software on Amazon Web Services (AWS). AWS customers can now access and acquire Benchmark Management System® (BMS), First Sign® Early Intervention, and Case Action Response Engine® (C.A.R.E.) directly through their Marketplace accounts.

“We have entered a new era of policing as cities can take advantage of methodical, data-driven systems to systemize and engage officers who are engaged in problematic conduct, while simultaneously identifying and supporting officers who are successfully on track,” said Chris Casula, Chief Partnerships Officer, Benchmark Analytics. “By accessing BMS, First Sign and C.A.R.E. on AWS Marketplace, customers can now take the first step in capturing important people data all in one place.”

To learn more about Benchmark’s collaboration with AWS Marketplace, read our press release here.

Early Intervention Systems: The Impact of Establishing Best Practice Policies

Posted on by Nicholas Frankel

EIS PolicyLaw enforcement personnel have a unique position in our society. They are responsible for the safety and security of all those who reside in, work in, and visit their jurisdictions. As such, they have great responsibility to carry out their duties and exercise their authority within the bounds of established policies and procedures, which are an essential component of any law enforcement agency. Policies address pertinent matters, such as what entails acceptable behavior by employees. Procedures within a policy define a sequence of steps to be followed in a consistent manner — for example, the actions that need to be taken for an out-of-policy event.

As a general roadmap, policies ensure:

  • Strategic Alignment: Policies safeguard that law enforcement agencies’ actions are aligned with their mission, goals, and objectives.
  • Integrity: Clearly written policies help law enforcement personnel understand agency values and be more responsible for their actions.
  • Fairness and Consistency: Policies ensure all law enforcement personnel are treated impartially and in a reasonable manner.
  • Efficiency: Setting expectations and rules ahead of time saves time and any costs associated with inefficiencies.
  • Safety and Risk Management: Policies can prevent some problems from occurring or getting worse.

Policies and Procedures are Key to your EIS Success

Early Intervention Systems (EIS) are designed to identify officers who may be at risk of incidents, enabling targeted education and support to prevent potential issues. These systems are integral to modern policing strategies, focusing on officer wellness and performance improvement.

Having well-defined policies and procedures around an agency’s Early Intervention System has many important benefits, including:

  • Facilitating the prompt review of work-related occurrences involving their personnel that is non-disciplinary in nature.
  • Helping supervisory personnel make informed, fair, reasonable, and consistent decisions regarding the behavior and/or performance of their personnel.
  • Assisting agencies in exercising their responsibility to identify and support their personnel whose actions or inactions indicate possible stressors and/or need for intervention.

For agencies looking to enlist an Early Intervention System, establishing and maintaining specific EIS policies will improve the likelihood of getting off-track officers back on track; aid supervisors in their planning and support of officers; and build trust that the EIS at its core is a support tool. In the end, it will impact the overall integrity and success of your EIS.

Exploring EIS Policy Best Practices

In Benchmark’s recent installment of our Data Dialogue webinar series, Chief Partnerships Officer Chris Casula led a best-practice discussion on EIS polices. Titled “Early Intervention Systems: Exploring Best Practices for Establishing Your Agency’s Policies”, the webinar included an esteemed panel of agency leaders who have implemented – or currently are implementing – and EIS for their agencies, including Major Mike Harris and Captain Stephen Flatt at Charlotte-Mecklenburg PD; Sergeant Darwin Naval from San Francisco PD; and Senior Corporal Jared Nielsen with Dallas PD.

Each panelist highlighted their department’s unique approach to early intervention, discussing key issues surrounding policy, including:

  • Establishing an overall process for prompt review of flagged officers
  • Determining key stakeholders
  • Categorizing levels of at-risk behaviors
  • Developing non-punitive interventions
  • And developing training and mentoring resources

Policy in Action — Responding to Alerts

The discussion commenced with a summary of the four prevalent policy and procedural strategies agencies employ when responding to EIS alerts — with panelists weighing in on which one their agency uses and why. The four approaches – which were discussed in-depth in a previous session – include:

  1. Centralized: A chosen group of department leaders addresses each alert. Although efficient, this method can seem detached in larger agencies.
  2. Centralized Review: Here, a dedicated risk management team supervises all alerts. While this approach leverages specialized knowledge, it can inadvertently create a divide between the unit and the broader group of officers, possibly engendering an “us vs. them” attitude.
  3. Decentralized: Here, front-line supervisors are responsible for responding to their officers’ alerts. While fostering close bonds, this might result in inconsistencies due to diverse supervisory styles.
  4. Capacity Building: This is a combined effort where the risk management team collaborates with supervisors to provide training and expertise in deciphering alerts. It promises consistency and local responsibility, connecting various stakeholders and fostering trust.

Charlotte Mecklenburg PD’s decentralized model empowers the immediate chain of command to address alerts, believing supervisors are best positioned to understand and support their officers. According to Major Mike Harris, Charlotte Mecklenburg PD: “Ours is decentralized, pushing everything to the chain of command because with 1800 officers a centralized EIS couldn’t get to the granular issues like direct supervisors can. It’s meant as an intervention and employee wellness program.”

In contrast, San Francisco PD adopts a hybrid model, combining centralized review with decentralized action, depending on the alert’s severity. Senior Corporal Jared Nielsen, Dallas PD: “The officer’s chain of command reviews the alert and meets with them to create an action plan if needed. The goal is helping officers perform better.” Dallas PD is overhauling its program to transition from a threshold-based model, focusing on supporting officers through various non-punitive measures.

Across the board, panelists emphasized the importance of differentiating EIS from disciplinary measures. The systems are designed to support officers through voluntary training or incremental guidance and assistance, depending on the situation’s specifics. The goal is to help officers perform better and achieve more favorable career outcomes.

Building Trust through Policy Communication

A recurring theme was the need to build trust and ensure communication about the intent of an EIS — before, during and after the implementation process. Educating officers about the non-punitive nature of these systems is crucial. Many officers are unaware of the EIS in their departments, so informing them about its supportive purpose is key. This approach involves offering various options, like training or counseling, to assist officers effectively.

Sergeant Darwin Naval, San Francisco PD: “The biggest obstacle is communicating to officers that the system is non-punitive and supportive. Many don’t know we have an EIS. Educating all levels of the department has been crucial.”

According to Major Harris, “I think the most important thing I’ve learned in developing this program with Benchmark and First Sign is first clearly defining your ultimate goal for your EIS. For us, although the public likes terms like ‘early intervention,’ the backdrop is officer wellness.”

The panelists agreed it’s important that agencies clearly communicate an EIS as a supportive employee wellness initiative rather than a punitive tool. Conveying positive intent behind intervention policies and procedures proves vital for trust, participation, and outcomes. A cohesive internal team for continuity and momentum is recommended for achieving an agency’s communication goals.

EIS Customization and Flexibility is Key

The panelists agreed on the importance of customization and flexibility with an EIS. The systems should be tailored to individual departments and officers’ needs. Rigidity in such programs can be counterproductive. Continuous collaboration between law enforcement agencies and their EIS partner is vital to ensure the systems meet specific requirements and effectively support officers. According to Captain Flatt, “We held classes on the policy and system for all supervisors, stressing its supportive, non-punitive purpose as an early warning tool to make officers better. The flexibility to customize officer action plans has brought surprisingly good ideas from sergeants and lieutenants.”

Customization to each department’s policies, data sources, and localized needs makes platforms markedly more effective than inflexible one-size-fits-all systems. Frequent in-person communication from leadership around the progress and impact of their EIS can also be invaluable.

Addressing Performance and Wellness with Confidence

This final webinar of the year underscored the importance of Early Intervention Systems as a personnel management tool — and how having thoughtful EIS policies and procedures in place will enhance and impact results for your agency. The insights shared by the panelists provide valuable guidance for agencies looking to implement a new EIS.

Enlisting an advanced, data-driven solution like First Sign Early Intervention is crucial for adopting a proactive approach to officer wellness and performance. It goes beyond standard systems by establishing benchmarks that more accurately identify levels of behavior in need of support.

Adopting an Early Intervention System: A Blueprint for Success

Posted on by Nicholas Frankel

EIS Blueprint for Success

An Early Intervention System (EIS) can be a crucial asset for law enforcement agencies interested in managing their risk, in part by identifying officers who need assistance or support. The right system should monitor officer behavior and performance data to identify potential issues early, enabling focused interventions to minimize misconduct. However, the successful adoption of an EIS involves nuanced considerations in change management, data utilization, stakeholder engagement, implementation, and outcome measurement. This blueprint outlines essential factors in each area and serves as a roadmap for those agencies considering an EIS for optimizing officer performance.

Managing Change with Data

Introducing an EIS to an agency constitutes a significant cultural and technological shift that requires meticulous planning. According to an IACP policy document published in May 2020, agencies should consider several essential factors before moving forward with an EIS, such as:

  • The time commitment to administer the program
  • Deciding which agency-specific data points are critical for tracking and identifying performance trends
  • Establishing how that data will be collected, tracked, and used
  • Establishing policy for mapping potential actionable next steps once that data is extrapolated
  • Having alignment on who will be managing the execution and oversight of those next steps

Change management within any organization is never a light undertaking; it requires a strong commitment to achieve the objective at hand. For law enforcement agencies adopting an EIS it can mean the difference between helping struggling officers get back on track to become more productive in a non-punitive way — versus missing the opportunity to give them the incremental attention they need.

Using Data Effectively

The effectiveness of an EIS hinges on the quality of its data. Best practices for data application are:

  • Indicator selection: Prioritize in-depth data points that correlate closely with risk, such as arrest history, use of force incidents, internal affairs complaints, and missed court appearances. As stated in PERF’s 2015 article, Managing the Risks of Officer Misconduct and Failure through Early Intervention Systems: “Careful selection of data indicators based on those most predictive of risk is crucial for an EIS to flag situations accurately.”
  • Context analysis: Understanding the situations surrounding data points is critical for distinguishing meaningful trends — driven by complex, nuanced factors, such as adverse incidents, sequence of events, patterns of behavior and peer comparisons.
  • Ongoing indicator updates: Regular evaluations can guide adjustments for iterative learning, so that your EIS gets smarter and more efficient over time.
  • Data system integration: an EIS should be built on a modern suite of software with structured and accessible data — so that it’s easily integrated with incident data-capture systems, including computer-aided dispatch (CAD) systems and record management systems (RMS) — as well as any existing personnel management systems in place, for a holistic ‘data in’ view that connects disparate information.

Measuring Outcomes

Quantifiable metrics are vital for realizing the impact of an EIS. Best-practice performance indicators include:

  • A predictive model that identifies patterns of problematic behavior and patterns of exceptional conduct
  • Understanding context of activity to distinguish between Quality and Quantity of activity to eliminate excessive flags and investigations
  • Account for detailed officer activity relative to immediate peer groups to determine risk levels
  • Provide explainable, actionable alerts with non-punitive, non-disciplinary interventions
  • Transform risk management by significantly reducing exposure to rising liability costs

By consistently tracking such metrics, police departments can validate the advantages of an EIS for officers, departments as a whole, as well as the communities they serve.

Grounded in Research

It is critical that any data analysis is informed by research focused on utilizing performance data of officers so that the EIS can identify officers needing incremental support. First Sign® Early Intervention is the only EIS that uses national research combined with the patterns of data generated within an individual agency over several years to identify those law enforcement personnel with the greatest need for intervention.

Data scientists, who are experts in the field, developed First Sign based on a holistic view of available information that is indicative of risk. Drawing from multiple indicator categories, the First Sign system calculates overall activity and behavior, as well as trends compared to peer groups based on rank, nature of assignment, geography, and deployment time.

Because of this expertise, First Sign is a proven, predictive, and preventative system unlike any other to identify officers at risk for problematic behavior:

  • First Sign has seen an average model precision of 85%. For comparison, traditional early warning systems have a model precision of roughly 30%.
  • With a great degree of confidence, First Sign can identify an average of 5% of officers at risk within an agency.
  • Additionally, that 5% is responsible for 66% of injuries (both officer and citizens) and disproportionate use of force incidents.

Assessing Levels of Risk and Courses of Action

The effectiveness of any EIS largely depends on a department’s ability to manage a systematic set of actions to assist officers displaying at-risk behaviors. Upon identifying such behavior, it is advisable for agencies to have a process for assessing the officer’s level of risk. Subsequently, a specific, monitored plan that is non-punitive and non-disciplinary should be developed and implemented to provide the officer with the necessary support.

To facilitate this crucial phase, Benchmark offers a platform known as C.A.R.E. (Case Action Response Engine®). This course-of-action platform aids law enforcement agencies in managing officers identified as at-risk with First Sign, by featuring research-based case management modules. These modules are tailored for officer-specific interventions and include benchmarks for best practices at various levels of intervention. The goal of C.A.R.E. is to assist departments in ensuring that no officers displaying at-risk behavior go unattended.

A Skilled Implementation Team is Key

Getting to go-live in order to harness the full power of an EIS requires a seasoned implementation team — preferably one comprised of people who have either served in government roles or have substantial work experience serving complex municipal and government customers specifically. Certainly, all team members should have deep experience deploying configurable off-the-shelf software to customers.

Ideally, you should anticipate ongoing investment and research that constantly increases functionality, provides guidance on best practices, and allows access to research on personnel development.

Finally, the team should consist of a strategic mix of implementers, data scientists, and engineers to ensure an effective and efficient implementation.

The Path Forward: Navigating the Road to Early Intervention Success

Adopting an effective early intervention system requires a collective dedication to change, while the rewards to agencies can be substantial — from improved officer performance to enhanced community relations.

If your department is considering implementing an EIS — or you believe you can do better than your current system, contact Benchmark Analytics to speak with a solutions expert about First Sign® Early Intervention System. As the only data-driven, research-based EIS available today, First Sign empowers law enforcement agencies to harness their data for exceptional personnel management.

 

Early Alerts, Timely Action: The Impact of a Research-Based EIS

Posted on by Nicholas Frankel

In an ever-evolving society, the roles and expectations placed on law enforcement officers – including how they engage and interact with the communities they serve – are continually changing. The same is true for how they are managed and supported for optimal on-the-job performance. As part of that infrastructure, the right early intervention system can become an indispensable tool for agency leaders aiming to discern and act on any potentially problematic patterns in officer behavior.

Such a system would be designed to identify and address these patterns before they develop into major incidents, ensuring the public’s safety and the officer’s well-being. Yet, the thoughtful implementation of a successful EIS requires careful consideration, adept change management, and a comprehensive understanding of an agency’s culture.

This summer, Benchmark Analytics presented the second installation of their Data Dialogue webinar series, led by CEO Ron Huberman, titled “Navigating EIS Alerts: Mastering the Right Approach for Your Agency.” Among the panel of participants were Benchmark’s Chief Research Officer, Nick Montgomery, Vice President of Data Science, Dr. Ugochi Jones, and Director of Data and Enterprise Analytics, Riley Maloney.

Four Approaches to EIS Alerts

Riley Maloney kicked off the discussion by outlining the four prevalent strategies that agencies employ in response to EIS alerts, signaling that an officer might need intervention. These strategies included:

  1. Centralized: A chosen group of department leaders addresses each alert. Although efficient, this method can seem detached in larger agencies.
  2. Centralized Review: Here, a dedicated risk management team supervises all alerts. While this approach leverages specialized knowledge, it can inadvertently create a divide between the unit and the broader group of officers, possibly engendering an “us vs. them” attitude.
  3. Decentralized: Here, front-line supervisors are responsible for responding to their officers’ alerts. While fostering close bonds, this might result in inconsistencies due to diverse supervisory styles.
  4. Capacity Building: This is a combined effort where the risk management team collaborates with supervisors to provide training and expertise in deciphering alerts. It promises consistency and local responsibility, connecting various stakeholders and fostering trust.

Riley observed that the efficacy of each model is contingent on the agency’s internal intervention mechanisms. More and more agencies are turning to Benchmark – with its First Sign® Early Intervention and C.A.R.E. platforms – and as a result, these agencies can discern the most fitting approach for their organizational design.

“In each of these systems, it’s vital to remember that an early intervention system’s strength lies in the interventions allocated in response to an alert. If an alert arises but is not acted upon, it’s futile. Benchmark dedicates significant time collaborating with agencies during the rollout phase to identify which of the four methods, or perhaps a new one, is most suited for their specific context. The aim is to determine how an agency can respond most effectively to an EIS alert.”

The Importance of Documentation

Often, in decentralized models, sergeants – due to their close ties with officers – are the first responders to EIS alerts. Yet, some supervisors might hesitate in documenting interventions, choosing instead to address matters informally. Despite good intentions, more documentation is needed to ascertain the efficacy of these interventions.

The panel acknowledged the importance of maintaining productive relationships with officers. However, they also emphasized that thorough documentation is indispensable for gauging success.

Challenges and Solutions in EIS Implementation

The discussion evolved toward potential obstacles in implementing an early intervention system. For instance, in larger agencies, supervising officers may become disconnected from frontline officers, complicating meaningful interventions. Additionally, some supervisors might need more training to formally document interventions due to existing cultural norms within the agency or lack of training.

Benchmark’s Chief Research Officer Nick Montgomery championed the capacity-building model, underscoring its balance between immediate alert responses and aiding supervisors in interpreting data and devising meaningful interventions.

“In any department, promotions are inevitable. Officers ascend to the rank of sergeants, and sergeants get promoted to lieutenants, among other shifts. There will be departures and new inductions, signifying change. This capacity-building method isn’t just about managing this flux. It’s centered on empowering individuals with the requisite skills to flourish in this dynamic setting. This isn’t confined to logistical details but extends to enhancing communication with officers, interpreting data accurately, and formulating robust strategies. Ultimately, it prepares the department for sustained improvement.”

Dr. Ugochi Jones delved into the shortcomings of casual interventions and emphasized the need for careful documentation. “In my discussions with supervisors, many who aren’t deeply engaged with the system (First Sign) still favor informal intervention. While they value effective communication when addressing potential issues among officers, they feel documentation makes it overly formal. We must consider this sentiment in our approach.”

Dr. Jones stressed the importance of constructive, data-driven exchanges with officers and the imperative to shift the perception and reality of interventions as a punitive measure to a supportive tool for officers. Meanwhile, Riley Maloney advocated for the inclusion of diverse stakeholders when shaping EIS policies, positing that this broad-based approach bolsters system trust.

The Path Forward

Effective communication and supervisory advancements are crucial. Benchmark’s First Sign is the only peer-to-peer, research-backed early intervention system — and has the potential to become a force multiplier for positive organizational transformation, with its implementation varying from agency to agency. The consensus among the panel was clear: for agencies with over a hundred officers, the capacity-building approach appears to be the most fitting. Meanwhile, smaller agencies benefit from a centralized or centralized review approach. However, the panel emphasized that agencies should not choose an approach based solely on size.

No single method can achieve widespread organizational change. Successful implementation requires a comprehensive strategy, strong stakeholder engagement, and the careful integration of technology.

 

Officer Management in the Digital Age: The Impact of Predictive Early Intervention Systems

Posted on by Nicholas Frankel

predictive early intervention systems

An early intervention system (EIS) can be an instrumental tool for law enforcement agencies looking to track and address problematic patterns in officer behavior. The ideal EIS should be proactive in nature by enabling leadership to identify off-track behavior before it becomes a real problem — and should work seamlessly within the agency’s unique organizational needs and culture. Conversely, an EIS should also be able to identify positive patterns of behavior in officers exhibiting exemplary performance.

This spring, Benchmark Analytics conducted a webinar delving deep into this subject as part of our ongoing Data Dialogue series. Panelists included Ron Huberman, CEO of Benchmark Analytics and Nick Montgomery, Chief Research Officer at Benchmark. The dialogue centered around four principal areas concerning EIS: its evolution, data significance, its departmental adaptation, and perceptions towards its daily use by agencies and officers.

Evolution of Early Intervention Systems

Huberman, who rose through the ranks of the Chicago Police Department to serve as Assistant Deputy Superintendent, shed light on the progression of EIS within police departments — tracing its origins from the 1970s up to the recent advancements of today. Initially, departments utilized rudimentary “trigger-based” systems. However, these systems often produced “false positives” and “false negatives.”

As Huberman explained, “The University of Chicago published a lot of compelling research that showed trigger-based systems typically had a 70% false positive problem, which means 70% of the time it was flagging officers that were doing their job as they should. Furthermore, they had about a 40% false negative problem, meaning they were actually missing officers who were really struggling out there.”

While policing continued to evolve around more data-centric solutions, early intervention systems failed to keep up — until the introduction of First Sign® by Benchmark. First Sign offers a research-driven early intervention system that utilizes various data sources, including arrest records and use-of-force reports, with machine learning to identify predictive patterns. The digital transformation of departmental records and advanced algorithms provide a level of accuracy that trigger-based systems lacked at the time and still do to this day.

The Power of Integrated Datasets

Benchmark’s Chief Research Officer Nick Montgomery emphasized the power of converging various departmental datasets. An amalgamation of data, including over twenty event markers, can lead to over ninety predictive variables for each officer. This holistic approach greatly enhances the understanding of officer behavior compared to analyzing singular incidents. As he so powerfully stated, “…a research-based early warning system takes all of the data inside a police department looking back over five, ten, fifteen years and uses those various patterns of behavior to create a system that is far more predictive and can accurately identify officers likely to have an adverse event based on past events.”

Proactive Prevention of Harm

Early intervention systems can offer valuable insights by identifying officers exhibiting at-risk behavior, allowing for prompt intervention through training or counseling before problems escalate. Research conducted with the University of Chicago demonstrates that traditional EIS platforms using threshold-based triggers have a 70% fallacy rate for flagging at-risk officers. In contrast, First Sign boasts an 85% efficacy rate. Furthermore, our findings suggest that no more than 5% of officers contribute to over 60% of excessive force incidents.

Addressing the challenges with this specific group can significantly enhance community trust. Constructive, non-punitive supervision remains crucial for the efficacy of early intervention systems. In Huberman’s words, a modern research-based system like First Sign enables supervisors to say, “‘Hey, Officer Smith – I know you’re a good guy – and a good officer – but you were flagged in the system, so it’s important I intervene. Let’s talk about next steps to correct your at-risk behavior and how you’re engaging the public.'”

Signs of Impact

Preliminary data suggests that the implementation of an EIS can yield tangible results. Huberman shared data indicating a 50% reduction in the severity of force used by flagged officers post-supervisory intervention. Complaint rates against these officers also saw a significant decrease. Importantly, essential enforcement activities remain largely unaffected. And how do we know this? Benchmark has the world’s largest database on officer performance — and we’ve validated all predictive analytics through a standardized national model developed in partnership with the University of Chicago.

Embracing Modernization: The Future of Policing

In today’s rapidly evolving world, modernization touches every corner of our lives, from how we communicate to how we conduct business. Similarly, the realm of law enforcement isn’t exempt from this wave of change. The adoption of research-based methodologies combined with technological innovation reflects the evolution of policing in line with modern trends.

Historically, police reform efforts have been broad and overarching, often applying wide-reaching solutions like general de-escalation training for entire forces. But as we’ve seen, these blanket approaches might not always address the nuances and individual needs of officers. In many cases, the emphasis is shifting to more tailored tactics and tools, most likely powered by data science and analytics.

By harnessing the capabilities of modern data science, First Sign Early Intervention allows for a more focused course of action, directly targeting officers who genuinely need support. As a result, law enforcement stands at a pivotal moment where trust can be fostered within the communities they serve, internal personnel practices improved, and genuine change affected.

As Ron Huberman eloquently summarized, “There’s a tremendous opportunity before all of us in this profession – who view it as a noble calling and care deeply about policing – to say, ‘Let’s make a difference. Let’s turn this corner now because we have the tools to do so.’ I truly believe this is the moment we’re at.”

Mapping the Effectiveness of EIS

Posted on by Nicholas Frankel

At their core, early intervention systems have similarities with the philosophies that guide personnel development and human resources best practices commonly seen in the private sector. Their goal is to aid in evaluating an officer’s (employee) performance, identifying areas of improvement, and opportunities for support. However, in the case of law enforcement, the stakes are much higher as these performance issues relate directly to critical matters like agency readiness and maintaining the public’s trust.

Given that early intervention systems can significantly impact public trust and perception of policing, it stands that they should be held to a more rigorous standard than performance review technology used in much of the private sector. False negatives derived from faulty analysis are potentially costly, contributing to an agency’s exposure to the risk of a lawsuit or civil rights claim. What’s more, failure to provide adequate support in the case of a true positive can have a similar effect. This article explores the intersections of technology and human performance and how their relationship can assist, or hinder, a high-performing early intervention program.

Building the Tech

True/False PositivesThe first iterations of early intervention systems were relatively primitive compared to some of the more sophisticated systems in use today. Most notably, limitations in tech – processors, and data storage capacity posed challenges to complex and resource-intensive data analysis. These early systems also lacked the benefits of decades of research and on-the-ground experience, which are critical elements of an effective EIS. Given these limitations, most of those systems (and almost all today) relied on rules-based thresholds:

  • Department-level thresholds: There are standards set at the departmental level—for example, a certain number of complaints in a specified period.
  • Performance indicator ratios: As the name implies, these are a ratio between two different performance variables. Shifts worked versus complaints, for example.
  • Peer-officer average thresholds: a “like to like” comparison of officers in similar assignments or positions.

In the early 1990s, early intervention systems became increasingly common in best practice recommendations from the major professional and certification organizations. Then starting in 1997, federal consent decrees frequently mandated the use of EIS as a part of broader reform-minded measures. As EIS were more widely deployed, this added a more comprehensive sample size of users that researchers could use to study such a system’s efficacy and identify where they could be improved.

Those studying early intervention systems began to realize that the predictive ability of threshold-based systems was poor. One study found that a threshold-based system deployed in a major metro police department with more than 1,800 sworn officers incorrectly flagged hundreds of officers and failed to identify many others requiring intervention support. Even when additional variables were added to more rudimentary threshold-based systems, evidence showed they generally lacked the contextual analysis needed to perform as well as systems that incorporated predictive modeling. The same study found that when the department deployed a new, modeling-based EIS developed at the University of Chicago and now offered by Benchmark Analytics as First Sign® Early Intervention, there was a 20% reduction in false positives and a 75% increase in true positives, demonstrating the advantages of a more sophisticated, predictive EIS.

The Human Element

Early intervention systems are an excellent tool for supervisors and departmental leaders, but they are still a tool. They can be used to inform personnel decisions but are not intended to be the sole arbiter of these decisions. A well-constructed EIS can point to appropriate and evidence-based interventions, but ultimately that follow-through requires human interaction and support to be effective and lasting. These are a few practical and human factors that can contribute to or detract from an early intervention system’s success:

Accountability: Just as officers are held accountable for their actions on duty, supervisors are ultimately responsible for the quality of support they give to their reports. Interventions triggered by an EIS are almost always confidential as they can involve human resources issues, sometimes concerning mental health or sensitive matters. The private nature of these discussions risks inconsistency in intervention tactics, a major hurdle for effective intervention as it can lead to a perception of favoritism.

Budget and capital constraints: An EIS requires people to perform the intervention whether that is a referral, conversation, or other ongoing support. All of these require time, which has a cost. Agencies with access to more resources typically have more robust mental health and officer support systems. In contrast, agencies with more limited funding need to balance immediate and long-term funding needs. Officer mental health and wellness are essential facets of early intervention, and having the resources to maintain these support programs is a significant advantage in fielding an adequate EIS.

EIS design: Just as personal bias can affect intervention decisions, bias in design can lessen the effectiveness of an EIS. Technology is not inherently neutral and, without careful design, can consciously or unconsciously reflect the biases of its creators. While it is important to note there has been no research to suggest any evidence of this in early intervention systems, basing design decisions on evidence generated from peer-reviewed research can serve as a safeguard against unconscious bias in tech design.

Making the Technology Work

A new era of police reform has increased public interest in early intervention and made it a priority for professional organizations supporting the research and the academics involved in it. Data and experience are driving the newest iterations of these systems, and it is clear from these insights that incorporating research-based design into a predictive rather than threshold-based EIS is the most promising path for effective intervention. In partnership with a diversified research consortium, Benchmark Analytics uses peer-reviewed research-based design to build its EIS, First Sign®. Benchmark’s data scientists and engineers leverage the power of the world’s largest multi-jurisdictional officer performance database while incorporating iterative learning that uses cumulative analytics to get “smarter” and more efficient over time. This technology gives supervisors a more holistic picture of an officer’s performance, especially relative to others, and enables them to engage in more targeted and meaningful intervention.

The Logic Behind Data-Driven Early Intervention Systems

Posted on by Nicholas Frankel

Accountability and transparency are the “name of the game” in police reform. As much as that’s a contemporary topic of the reform dialog, it has its roots in the evolution of modern policing. As Sir Robert Peel put it, “The ability of the police to perform their duties is dependent upon public approval of police existence, actions, behavior and the ability of the police to secure and maintain public respect.”

EISProviding support to officers so that they may live up to those high ideals described by Peel is a crucial component of maintaining the public trust in policing. In an era of smartphone cameras and social media, agency leaders must utilize the most promising, evidence-based tools available to prevent misconduct and excessive use of force before it occurs and becomes an issue of public concern. Early intervention systems (EIS) emerged some 50 years ago and are an essential and effective means of demonstrating accountability by addressing performance issues. This article looks at how these systems have risen to prominence and how they’ve evolved to meet the needs of 21st century policing.

Early Intervention as Accountability

Thought leaders in law enforcement began to recognize the importance of early invention as far back as the early 1980s. The United States Commission on Civil Rights (USCCR) published a report in 1981 (republished in 2000) entitled Who is Guarding the Guardians? which took an in-depth look at official misconduct, community trust, and accountability in American law enforcement. Notably, the report detailed nascent efforts at systematically analyzing officer performance as a part of a performance-improving early intervention philosophy.

Building on the first early intervention recommendation from the USCCR, The International Association of Chiefs of Police (IACP) issued guidance for early intervention in 1989, citing them to control corruption and build integrity in policing. Later in 2001, both the Department of Justice (DOJ) and the Commission on Accreditation for Law Enforcement Agencies (CALEA) recommended them as a best practice, with CALEA stating, “[a] comprehensive Personnel Early Warning System is an essential component of good discipline in a well-managed law enforcement agency. The early identification of potential problem employees and a menu of remedial actions can increase agency accountability and offer employees a better opportunity to meet the agency’s values and mission statement.”

In addition to the recommendations from scholars, government entities, and accreditation organizations, early intervention systems are present in consent decrees. Since 1997, most consent decrees have required adopting an early intervention system to improve accountability and reduce citizen complaints.

Diving Deeper

Broadly speaking, an EIS seeks to identify potentially problematic patterns of officer behavior and offer them support before an adverse event occurs. Typically, these systems track disciplinary action rising to the level of suspension, excessive or repeated use of force, citizen complaints, and incidents that may be associated with drug or alcohol abuse. The underlying philosophy is to envision these factors as opportunities for support – additional training, coaching, mentorship, or other forms of professional and personal support. Furthermore, early intervention systems conclude that many if not most of these data points suggest knowable risk factors can be mitigated outside of the disciplinary process, leading to better outcomes for the officer, the agency, and ultimately the community.

There are different methodologies early intervention systems use to achieve these goals. At its most basic, early intervention can be rooted in the social interactions within a department, with supervisors recognizing changes in an officer’s pattern of behavior that could indicate an underlying issue. This methodology is evident in agencies with fewer sworn officers, where a smaller and consistent headcount can lead to close relationships. However, this approach is by its nature anecdotal and does readily lend itself towards data analysis or easy reproduction in other agencies.

More formal systems rely on analyzing a set of data points to determine when prescribed action is necessary. The systems analyze a limited set of indicators, usually expressed in the form of a threshold. Once that established threshold is reached, an officer is “flagged” and typically referred to a supervisor or internal affairs for additional support. Systems vary widely, but common thresholds include the number of use of force incidents in a given period, a pre-determined number of citizen complaints, or other data points.

A drawback of these systems is that they are limited by the number of data points they utilize in the analytical phase. Limited datasets can offer a somewhat narrow assessment of an officer’s activities while on duty, often not fully considering the nuance and context involved in an officer’s patrol. Once a threshold is reached, prescribed actions can lead to instances in which a supervisor’s best judgment or knowledge of circumstances is not incorporated into the analysis – leading to a false positive (or false negative) created by the system. This can lead to the perception among officers that it is “only a matter of time” before they are flagged and prescribed corrective actions that may not be useful or appropriate.

Reform-Minded Early Intervention

Benchmark Analytics’ First Sign® Early Intervention takes a different approach to early intervention compared to informal or threshold-based systems. Building on decades of knowledge and experience derived from generational iterations of early intervention systems and a research consortium dedicated to studying policing transparency and accountability, First Sign is the only EIS that uses a research-based statistical model that is preventative by design.

This means that First Sign does not simply analyze an officer’s performance in a vacuum but instead contextualizes it by using peer analysis, which leverages multiple data sources to present a more holistic picture of the officer and pinpoint where they can best be supported. These data points can be what time of day their shifts are, what areas they patrol, and their assigned unit. An example of this could be the difference between an officer assigned to a drug task force in a higher crime neighborhood at night is likely to have different performance data points than one patrolling a relatively quiet business district on a daytime shift. A threshold-based system doesn’t take this into account. It would instead assign the same thresholds to both officers which, lacking context, could present an inaccurate portrait of an officer’s behavior and potential for risky behavior.

First Sign may be predictive, but it is not prescriptive by nature. Instead, using the Case Action Response Engine (C.A.R.E.), it gives departmental leaders and supervisors a suite of tools and data analysis dashboards to understand an officer’s performance to facilitate thoughtful and effective intervention. It is non-disciplinary and non-punitive by design, leading to better alignment among all stakeholders – departmental leaders, supervisors, civic leaders and advocates, and officers – in building a more meaningful and lasting sense of buy-in.

Advancing reform

Though there are distinct differences in early intervention systems, they all seek to advance the reform dialog by providing law enforcement with a valuable tool to address off-track officer behavior before it manifests as misconduct, excessive use-of-force, or other actions that have a detrimental affect on community trust of police. First Sign distinguishes itself in the field by offering a reform-minded EIS that supports forward-thinking policy decisions designed to build and strengthen that community trust that is fundamental to 21st-century policing.

Officer Wellness in Practice

Posted on by Nicholas Frankel

In our previous article, we explored how researchers studying officer wellness are responding to a more urgent need to understand the psychological and physiological factors that impact an officer’s health and job performance. As research expands and the body of knowledge concerning officer wellness evolves, this newly gathered data is being put into practice. Departmental leaders are basing their decisions on this research when crafting policies and creating programs designed to support and enhance officer wellness.

The Law Enforcement Mental Health and Wellness Act of 2017 was passed to give policymakers and police leaders more information to make these decisions. A significant component of the act was the mandate that the U.S. Department of Justice (DOJ) make recommendations to Congress for ways to boost officer wellness. This multifaceted mandate focused on providing recommendations in three areas to improve officer wellness: how to best support agencies, their officers, and mental health providers working with law enforcement agencies.

Officer Wellness in PracticeTo understand the broader picture of best practices in officer wellness, the DOJ produced case studies of several departments throughout the country. The resulting report, Law Enforcement Mental Health and Wellness Programs: Eleven Case Studies, utilized field interviews and site visits to document programs and support services currently in use. It emphasized a “continuum of mental health and wellness strategies, programs, and methodologies” that begin with the recruit and last through retirement. The strategies that the researchers investigated suggested significant efficacy in the departments that implemented them and have been held up as positive examples meriting further research.

The report looked at both common elements of these departments’ approaches to wellness and unique aspects of their efforts. Below are some notable examples from the case studies.

Indianapolis, IN

Leaders at the Indianapolis Metropolitan Police Department created what is now known as the Office of Professional Development and Wellness (OPDW) in 2010. The program emphasizes intensive peer-mentoring — provided by more than 100 experienced officers trained in peer support — that begins before an officer is sworn and lasts up to two years into their service. OPDW programs are specifically designed to help officers adapt to both the physical and mental demands of the job as well as provide ongoing support, connecting officers with mental health and counseling services. Leaders in the department credit the program with helping to change the agency’s culture by making it acceptable to talk about one’s personal and professional life and giving the mentor officers a sense of ownership of the department and in shaping its culture.

Bend, OR

The Bend Police Department began focusing resources on officer health and wellness in the early 2000s. The first major initiative was altering the shift schedule with sufficient overlap in shifts to give officers one hour a week of on-duty physical fitness programs. This early wellness effort eventually expanded into more comprehensive physical wellness programs for officers that, over time, correlated with a 40% decrease in on-the-job injuries. The department has employed an on-site psychologist since 2015 who is “embedded” with officers and engages in ride-alongs to build officer rapport and trust in an effort to “change the culture” around mental health in the department.

Dallas, TX

After a 2016 mass-casualty event affecting 14 officers, the Dallas Police Department greatly expanded its mental health services. It created the DPD Employee Support Program (ESP), which uses officer self-referrals and leaders’ referrals based on early intervention tactics. Referrals are confidential, regardless of source, and do not appear on an officer’s record with the goal of reducing the stigma of seeking support. Three staff psychologists provide services to support the research-based wellness needs of police officers and departments. These include pre-employment screening, family and marriage counseling, debriefing after critical incidents, and fostering peer-support networks. In 2018 DPD partnered with the Brain Health Brain Performance Institute at the University of Texas in Dallas to create a data-driven 12-hour mindfulness course to reduce stress, promote cognitive resilience, and improve focus.

Milwaukee, WI

In 2014 the Milwaukee Police Department began steps towards a substantial shift in the way they approached early intervention. Conducting focus groups with officers and drawing on research from the International Association of Chiefs of Police (IACP), they moved their early intervention program out of Internal Affairs to its Training Division and changed its focus to officer wellness. This was complemented by hiring a full-time psychologist to assist officers in general referrals and work-related trauma cases. Along with the shift in early intervention strategy and a staff psychologist, the department’s non-denominational chaplains are a key element of a three-pronged approach to officer wellness. The lead chaplain is a retired MPD officer, sits on the POST board, and has a stellar reputation among the officers. The chaplains are covered by laws similar to attorney-client privilege, therefore providing a highly trusted source of peer and trauma support that is, in many ways, unique to the MPD.

Tucson, AZ

The Tucson Police Department was one of the first departments in the country to have a unit devoted to officer wellness in mental health when it created the Behavioral Sciences Unit (BSU) in the early 1980s. It presently consists of a psychologist and two sergeants acting as peer support supervisors. Owing to the unit’s importance in the department, its funding has been protected even during major budget cutbacks. The BSU provides a very comprehensive line-up of services to officers that are proactive, focusing on resilience and coping strategies to mitigate stress. The BSU hosts “Family Day” as part of a recruit’s training, taking this focus a step forward by recognizing the demonstrated importance that family support plays. In these sessions, BSU staff members help families understand the potential effects of a career in law enforcement and, most importantly, the confidential support services available to them.

Officer Wellness and Early Intervention

All of these departments are united in emphasizing a proactive approach to monitoring and improving officer wellness. Early intervention, especially in the case of trauma and work-related stress, shows up in many examples as a critical strategy in this approach. Benchmark Analytics’ First Sign® stands alone in its capacity to comprehensively analyze officer performance data for off-track behavior, which can be an important indicator of mental or physical health challenges, family issues, or workplace stress.

These distinct examples show that by following the research, encouraging top-down buy-in, and a willingness to try new methods, departmental leaders have the potential to make a real impact on their officers’ wellness.

Our next article will focus on some of the more innovative ways departments are promoting officer wellness.

Police Reform and Escalating Misconduct Settlements

Posted on by Nicholas Frankel

A crucial element of the police reform discourse is the rising cost of police misconduct settlements and the impact they have on municipal budgets. Elected and agency officials must contend with these costs though taxpayers in most instances are subsidizing the funding used in settlements. There is ample reason for law enforcement personnel, lawmakers, and taxpayers to all be keenly interested in the cost of these settlements. What do misconduct settlements look like? What is being done to mitigate the costs of misconduct settlements? These are questions this article endeavors to answer.

Misconduct Settlements: A Nationwide Concern

Even tracking the overall costs of police misconduct settlements has proven a significant challenge for researchers. There is currently no national reporting database and municipalities’ approach to record-keeping can vary widely. On a national level, the data just doesn’t exist to present a broader picture of how much misconduct settlements cost taxpayers. In an effort to address this lack of data, in March of 2021, the Cost of Police Misconduct Act was introduced into Congress, which seeks to compel reporting to federal authorities. Whether or not this bill will be passed into law is presently unclear, but it does represent one of the most high-profile efforts to explicitly tie the costs of settlements with the pushes for reform.

Despite the lack of specific data, it is well-understood that the costs of misconduct settlements are quite substantial and create strain on city budgets. In a recent survey of 31 of the 50 cities with the highest police-to-civilian ratios in the country, available data shows settlements cost these municipalities more than $3 billion over the last decade. Though the dataset is incomplete, it illustrates the substantial figures municipalities are contending with in settling misconduct allegations. Until recently, these costs were not typically figured into municipal budgets for policing and related costs.

City Budgets Misconduct SettlementsFurther complicating matters is how municipalities pay for misconduct settlements. In general, many small to medium-sized cities carry some form of liability insurance or risk-pool while the largest cities are either self-insured or will issue bonds to cover settlements and their related costs. Both approaches have their drawbacks. Bonds accumulate interest and servicing fees while insurance typically is funded via property taxes or other public user fees. In either model, it is once again taxpayers that ultimately bear the cost.

These settlement costs appear to be trending upwards too. In a survey of ten cities, the Wall Street Journal found misconduct settlement amounts rose from $1 billion between 2010 and 2014 to $1.6 billion from 2015 to 2019. There is no one reason for this rise but it is generally thought that things like increasing public pressure in favor of reform efforts and the widespread use of smart phone cameras have contributed to this rise. Additionally, some agencies and municipal governments look towards a settlement as a way to avoid a drawn out and expensive court battle.

Agencies Respond

Law enforcement leaders, elected officials, and taxpayers all have an obvious interest in controlling misconduct settlement costs. These unplanned expenses can significantly impact municipal budgets and can force unexpected reallocation of funds. Agencies and municipal governments are employing a number of different methods to control these costs. Here are some of the most common and effective examples:

  • Additional training is a strategy agencies and municipalities are using to reduce the likelihood of an adverse event leading to a settlement. Training requirements are frequently a component of new reform legislation being passed at the state level. De-escalation training and coursework related to identifying and responding to mental health crises are becoming more prevalent as are new standards in use-of-force training. Effectively managing and tracking officer training is seen as a proactive tool aimed at preventing law enforcement encounters that end up in settlements.
  • In recent years, the companies that insure small to medium municipalities are responding to the growing cost of settlements by exerting more control over agencies’ operations. This insurance risk management oversight takes on many forms such as policy audits, use-of-force simulators, and even ride-alongs to observe officer behavior. In some cases, insurers can even influence staffing decisions. Other municipalities participate in risk pools in which they “share” risk. The Association of Government Risk Pools connects member cities and facilitates collaboration while providing best standards and education.
  • Improvements to data collection are furthering policymakers’ and agency leadership’s ability to base decisions on rigorous analysis of data. This encompasses everything from body-worn cameras and audio recording devices to Internal Affairs and Use-of-Force metrics that are tracked and monitored via software. With more comprehensive and smarter data collection, policymakers and agency heads can be more confident in their decision-making, knowing it is based on a holistic view of performance and personnel data.
  • Early Intervention Systems (EIS), are software suites designed to help agency leaders monitor officer behavior and, ideally, intervene before any issues arise. Benchmark Analytics’ First Sign® Early Intervention System is preventative by design and more sophisticated than other, trigger or threshold-based systems, allowing leaders to identify off-track officer behavior before it rises to a level of seriousness that could involve an out-of-policy incident.
  • Once an EIS has alerted an agency’s leadership to the potential for performance issues, it is up to them to implement corrective measures to get that officer back on track. This often involves additional support like further training and mentorship. Benchmark’s Case Action Response Engine® (C.A.R.E.) helps track not only that assigned interventions are being completed but that officer performance is in fact on a path to improvement.

Elected officials, taxpayers, and agency leaders all have a vested interest in seeing the costs of misconduct settlements minimized. Data collection and analysis pertaining to officer performance are vital parts of the conversation around reducing the overall costs of misconduct settlements. By using new research-based software tools to better understand this wealth of data, agency leaders are empowering themselves to make the decisions necessary to ensure police funding is wisely spent on things like agency growth and training — and ultimately reducing the likelihood of problematic behavior that can potentially contribute to the rising cost of misconduct.