Part 2 of this piece on Optimizing EMS systems by reducing Ambulance Patient Offload Times (APOT) focuses primarily on PerformanceStat – a data-driven management framework that supports continuous performance improvement through data, collection, analysis and accountability. The author lays out its four core principles, a detailed description of its process implementation, and a lengthy, detailed description of ‘the seven big errors of Performance Stat.”
Aside though from nominally mentioning that PerformanceStat has been implemented in three systems – CitiStat in Baltimore, StateStat in Maryland, and FEMAStat by FEMA – no details are provided about what the data showed and what these systems found, what effect it had on ambulance patient offload times or other related performance metrics and measures, and what kind of resources – financial and otherwise – it may have taken to accomplish any of this. It thus appears very abstract and process oriented, and it’s unclear if this is something that can be effectively operationalized or has demonstrated any measurable system improvement.
In Part 1 of this article, we defined the problems associated with EMS, hospital emergency departments and extended ambulance patient offload times. Let’s look at some strategies that may point toward solving this issue.
EMS is a cornerstone of public health and safety, responsible for responding to 911 calls, administering prehospital care, and ensuring timely transport of patients to healthcare facilities. However, EMS has been challenged by APOT, where EMS providers experience prolonged delays at EDs before transferring patient care. These delays are primarily due to ED overcrowding, limited inpatient capacity, and resource shortages.
APOT not only hampers EMS’s ability to provide timely care for new calls but also has financial implications, given the operational costs associated with prolonged waiting times and reduced unit availability. As APOT grows, EMS agencies face workforce burnout, heightened response times, and increased risks to community health, necessitating a robust, coordinated response that engages multiple facets of the healthcare system.1-2
A potential solution lies in the integration of Medical Operations Coordination Centers (MOCCs) and healthcare coalitions as part of a comprehensive strategy to optimize patient flow, reduce APOT, and improve EMS operations. Central to this approach is the use of PerformanceStat,3 a data-driven management framework developed by Robert Behn, PhD, at Harvard University, which supports continuous performance improvement and accountability.
Medical Operations Coordination Centers (MOCCs) and Healthcare Coalitions
While the term “Medical Operations Coordination Center” (MOCC) was coined in 2020 during the COVID pandemic, the concept of regional coordination and transfer management centers existed long before the COVID-19 pandemic. These centers have been utilized in various regions to route trauma patients to the nearest appropriate facility, and healthcare systems frequently use similar centers to manage transfers and optimize capacity across multiple hospitals.
However, the COVID-19 pandemic exposed significant gaps in efficiently utilizing hospital resources without centralized, jurisdiction-wide coordination in order to achieve load balancing and transfer management for an overburdened healthcare and EMS system.4-5-6
MOCCs play a vital role in ensuring that healthcare resources are consistently managed across a region, often spanning a state, sometimes extending across a Healthcare Coalition (HCC), and occasionally even crossing state boundaries.4-5
By fostering a uniform standard of care throughout a region, MOCCs not only enhance access to critical and specialized care but also optimize the use of available resources. Additionally, they support equity by helping to balance the load on hospitals that often serve larger populations of at-risk individuals and face greater levels of operational strain.
MOCCs are centralized, regionally-focused hubs designed to manage and allocate medical resources effectively, particularly during crises or high-demand periods. MOCCs bring together healthcare providers, EMS agencies, public health authorities, and emergency management teams to coordinate patient flow, hospital resources and emergency response.
During the COVID-19 pandemic, MOCCs played a vital role in distributing patient loads across hospitals, sharing real-time data on bed availability, and ensuring that EMS transports were directed to the most appropriate facilities based on current capacity.6
Healthcare coalitions further enhance this approach by fostering collaboration across hospitals, public health entities, EMS, and other community resources. These coalitions allow healthcare systems to respond more cohesively to surges in demand by sharing resources, managing mutual aid, and standardizing protocols for APOT reduction. Coalitions also promote continuous communication, enabling EMS to make informed transport decisions and minimize delays upon arrival at hospitals.
The integration of MOCCs and healthcare coalitions creates an interconnected network that enables real-time visibility into healthcare system capacity and streamlines patient handoffs, reducing APOT and optimizing EMS resource utilization.6
Healthcare coalitions can also play a vital role in reducing ambulance patient offload times by fostering collaboration among hospitals, insurance carriers, and EMS agencies to address system-level barriers that impact patient flow and timely care. During COVID-19, healthcare coalitions demonstrated the power of collaborative frameworks that allowed patients to be directed to facilities with available resources, regardless of insurance or healthcare network affiliation.
This approach helped streamline patient offloading by prioritizing patient needs over network restrictions, significantly enhancing patient outcomes and system efficiency during a crisis. However, with the return to network-based exclusion practices, offload times have increased again, as patients are often required to wait for a bed in their designated in-network hospitals, often on an ambulance stretcher, in the hall of their emergency department, even when other facilities may have immediate availability.
Minnesota’s Statewide MOCC the center coordinated the placement of patients requiring intensive care and medical-surgical beds, particularly during the fall 2020 surge.7 Maryland’s Critical Care Coordination Center (C4) was established to facilitate the transfer of critically ill patients, ensuring they received care at facilities equipped to meet their needs. C4 functioned as a one-call center for rapid patient placement, particularly for those requiring specialized critical care services.
This center is an excellent example of how to effectively manage patient distribution, which resulted in alleviating pressure on individual hospitals and improving patient outcomes.8 Finally Nebraska’s Medical Emergency Operations Center (MEOC) to oversee patient movement and resource allocation during the pandemic. The center collaborated with hospitals statewide, regardless of network or insurance affiliation, to monitor capacities and facilitate patient transfers, ensuring balanced utilization of healthcare resource.8
To reduce these bottlenecks, healthcare coalitions can advocate for policies that allow temporary or crisis-mode flexibility in insurance and network guidelines, ensuring patients can be redirected to the nearest appropriate facility when system strain is high.
This would involve creating inter-facility agreements that promote patient-centered transfers, as well as establishing guidelines that allow EMS providers to bypass in-network restrictions when necessary for patient safety. By working closely with insurers, healthcare coalitions can push for emergency transport protocols that override exclusivity agreements during times of high demand, facilitating a more resilient and equitable system that reduces offload times and prioritizes timely patient care.
PerformanceStat: A Framework for Data-Driven Improvement
PerformanceStat,3 a more encompassing term, was coined by Dr. Behn at Harvard University. A potential tool for utilization that may more comprehensively address APOT, PerformanceStat has not been used to date to apply toward EMS or hospital emergency departments. Originally deriving from CompStat developed by NYPD Commissioner Willaim Bratton and Deputy Commissioner Jack Maple, PerformanceStat has been leveraged well beyond the policing realm.
Dr. Behn describes PerformanceStat as a performance management strategy that utilizes regular, data-driven meetings to monitor performance metrics, set goals, and hold participants accountable for achieving results. Originally used to improve government operations, PerformanceStat is centered around four core principles: targeted performance measurement, regular reviews, rigorous analysis, and accountability.3
Dr. Behn’s work has been instrumental in analyzing and promoting the PerformanceStat model as a tool for improving public sector performance, and his efforts have helped describe this broader application of data-driven performance management across different government functions.
1. Targeted Performance Measurement: PerformanceStat requires precise, targeted metrics to capture key aspects of the problem being addressed, in this case, APOT metrics. EMS and ED performance data, such as average APOT, patient volume, staffing levels (emergency department and EMS), and ambulance availability, can provide a comprehensive view of the bottlenecks and facilitate evidence-based decisions.
2. Regular Reviews: PerformanceStat advocates for structured, recurring meetings where data is reviewed, and strategies are adjusted as needed. In the context of EMS and APOT, these meetings can involve representatives from MOCCs, healthcare coalitions, EMS agencies, and EDs, enabling continuous monitoring and collaborative problem-solving. Regular reviews ensure all stakeholders are updated on the latest performance data and progress toward APOT reduction goals.
3. Rigorous Analysis: PerformanceStat emphasizes the importance of analyzing data trends to identify root causes and areas for improvement. For APOT, analysis can uncover correlations between peak APOT hours, patient surges, and ED staffing, helping stakeholders identify targeted interventions, such as increased staffing during high-demand periods or alternative transport destinations for non-critical patients.
4. Accountability: PerformanceStat establishes a culture of accountability by assigning clear responsibilities to each stakeholder. EMS, ED personnel, and MOCCs are all responsible for implementing and adhering to protocols designed to reduce APOT, fostering a sense of ownership and commitment to shared goals.
The PerformanceStat model aligns with the objectives of MOCCs and healthcare coalitions, creating a structured framework for collaborative, data-driven management and continuous improvement in APOT.
Implementing PerformanceStat within MOCCs and Healthcare Coalitions
PerformanceStat has been implemented in a variety of different ways beyond what was originally accomplished by CompStat by the NYPD. CitiStat in Baltimore, StateStat in Maryland, and FEMAStat by FEMA are just a few examples of governmental agencies and departments using data-driven approaches to solve problems.
PerformanceStat has not been used directly by EMS but FEMA Stat’s approach to using enterprise data level analytics to respond to disaster driven events is a close example, as is the progenitor to PerformanceStat, CompStat utilized by the NYPD. Boston 911, while not necessarily referred to as a CompStat model uses regular data collection, performance evaluation, and strategy adjustment, all essential principles aligned with the PerformanceStat model.
While some will argue that CompStat was not the sole source of the rational for the drop in crime in NYC, the key elements of: data-driven insights; accountability through regular meetings; strategic and tactical resource re-deployment, are all part and parcel of CompStat’s success. Think about this, that NYC’s crime rate fell dramatically in the years following the implementation of CompStat in 1994.
The system was integral to crime reduction strategies during this period. Murder rates dropped by 67% from 1993 to 1998,3 and other serious crimes saw comparable declines. CompStat’s ability to enhance focus, coordination, and accountability made it a cornerstone of the NYPD’s approach to crime reduction.
Why hasn’t PerformanceStat been utilized in EMS or for hospitals is an interesting question. In Atul Gawande’s book, ‘Being Mortal: Medicine and What Matters in the End’ he summed it up perfectly: “Culture has tremendous inertia, that’s why it’s culture. It works because it lasts. Culture strangles innovation in the crib.”
If one were brave enough integrating PerformanceStat within MOCCs and healthcare coalitions involves several strategic steps:
1. Data Collection and Metric Standardization: Accurate data collection on APOT and EMS performance is essential. Establishing standardized metrics across EMS agencies, EDs, and MOCCs will facilitate consistent data comparisons and improve the reliability of APOT tracking. Metrics should include patient offload times, EMS unit availability, and ED occupancy rates, among others.
2. Establishing Performance Targets: Using baseline APOT data, stakeholders can set achievable targets for APOT reduction. Targets should be specific, time-bound, and reflective of each hospital’s unique patient flow dynamics and regional healthcare capacity.
3. Collaborative Performance Reviews: Routine performance meetings bring together EMS, MOCCs, EDs, and coalition representatives to review APOT metrics, analyze data trends, and develop joint action plans. These reviews encourage transparency, foster interagency communication, and facilitate real-time problem-solving.
4. Implementing Targeted Interventions: Based on performance review insights, targeted interventions may include increasing ED staff during peak hours, enhancing community-based care options to reduce ED demand, or redirecting non-emergency patients to alternative facilities. These interventions can be piloted, reviewed, and refined based on PerformanceStat’s continuous feedback loop.
5. Accountability and Adaptation: PerformanceStat’s accountability framework ensures all stakeholders are invested in the APOT reduction strategy, with each agency committed to monitoring and adapting their practices. Regular feedback cycles allow for adjustments in response to changing conditions, such as seasonal demand fluctuations or staffing shortages.
Ensuring PerformanceStat Success
Behn, in “The Seven Big Errors of PerformanceStat,”10 outlined the errors associated with organizations and communities who have had challenges with PerformanceStat. To improve success, keep these points in mind:
Error #1: In EMS, 911 communications, and hospital emergency departments, any initiative must begin with a clearly defined objective. For example, an EMS director might set a specific goal to reduce ambulance patient offload times, or a 911 communications manager might aim to improve call processing speeds for quicker emergency response.
Nietzsche’s observation that “forgetting our objectives is the most frequent act of stupidity” serves as a reminder of the importance of purpose. Often, PerformanceStat can risk becoming a passing trend if leaders focus more on the tool itself than on the results it should achieve. Effective managers in EMS, 911, and hospital departments should ask, “What are the specific outcomes we want?” “What would improved performance look like for us?” and “How will we track and measure our progress?”
By developing a shared understanding of these goals, leaders can customize the PerformanceStat process to drive real improvements in their unique settings.10
If an organization were to implement PerformanceStat to reduce APOT, the broad goals should focus on improving patient flow, optimizing resource availability, and ensuring timely patient care transitions. Three essential goals that should guide this initiative:
1. Reduce APOT Across All Emergency Departments in the System: Establish a target reduction in APOT, perhaps based on national benchmarks or an agreed-upon percentage decrease, to ensure timely patient handoffs from EMS to ED staff. This goal would encourage a system-wide focus on reducing bottlenecks and streamlining processes in emergency departments.
2. Increase EMS Unit Availability and Response Readiness: By decreasing APOT, the goal should be to increase the time EMS units are available to respond to new calls, thereby improving response times and overall EMS system readiness. Performance metrics might include ambulance availability rates and average EMS response times, aiming to demonstrate how APOT reductions benefit community response capacity.
3. Enhance Communication and Collaboration Among EMS, ED Staff, and Healthcare Coalitions: Establish regular performance review meetings among EMS leaders, ED managers, and healthcare coalition representatives. The goal is to create an integrated response to APOT by fostering communication and accountability across departments.
PerformanceStat sessions should focus on collaborative problem-solving, data-sharing, and transparent discussions to identify ongoing challenges and implement real-time solutions. Utilize MOCCs and Healthcare Coalitions, preferably standing them up in EMS communication centers to operate 24/7 real time.
These broad goals can be tailored to include specific performance metrics, like average APOT in minutes, ED occupancy rates, and EMS unit availability, which would provide clear, measurable indicators of progress.
Error #2: In EMS, 911 communications, and hospital emergency departments, defining specific responsibilities is essential for achieving results. Who will carry out these tasks? Who is accountable for each part of the process? For any organization to succeed, certain individuals must be responsible for producing results. However, no one can effectively answer the “Who?” question without first addressing “What?”
Clearly defined goals must be translated into specific responsibilities. In these settings, responsibilities might be structured around measurable output targets: for example, the EMS director and each EMS unit leader could be responsible for achieving a specified average offload time, or the 911 communications manager could be tasked with ensuring that call-processing times are minimized to expedite EMS response.
These kinds of output-based responsibilities are manageable because they align with each organization’s capacity to produce measurable results. If EMS, 911, or hospital departments have the necessary resources—trained personnel, adequate equipment, and relevant knowledge—they can effectively achieve the outcomes for which they are accountable.
Error #3: In EMS, 911 communications, and hospital emergency departments, a critical part of a successful PerformanceStat strategy is holding consistent, regular, and well-coordinated meetings. These recurring sessions provide crucial feedback on what’s working and what isn’t, highlighting both successes and areas needing improvement.
They offer a chance to draw lessons that can help enhance performance over time. By meeting frequently, leaders in each area, EMS, 911, and hospital departments, stay current with the challenges and progress within each unit. Additionally, these regular meetings ensure that the leadership teams in each sector remain focused on hitting their specific performance targets, reinforcing a culture of accountability and continuous improvement.
Error #4: For PerformanceStat meetings to be effective in EMS, 911 communications, and hospital emergency departments, a consistent leader must conduct each session. The success of this strategy relies on regular discussions about performance, but rotating facilitators or random personnel leading these meetings diminishes their impact.
To maintain continuity in performance analysis and ensure follow-through on improvements, the same individual should lead each meeting, month to month. This designated person should also have clear authority to run the meetings, ensuring focus, accountability, and sustained progress across EMS, 911, and hospital units.8
Error #5: For PerformanceStat to be effective in EMS, 911 communications, and hospital emergency departments, dedicated analytical staff are essential. PerformanceStat relies on data to reveal how well the system is functioning. Who examines this data to determine if performance is improving or declining? Who analyzes trends and identifies potential new strategies? While managers within each department play a role in this, the agency’s leadership team also requires dedicated analysts whose primary focus is this task.
These analysts can’t be distracted by competing responsibilities; they must be able to concentrate fully on assessing the data to understand actual results. For PerformanceStat to drive meaningful improvements, it requires a small team of analysts dedicated solely to analyzing performance data and uncovering actionable insights.
Error #6: In EMS, 911 communications, and hospital emergency departments, effective PerformanceStat meetings must have strong follow-up to ensure real improvements. Each meeting should build on the issues, solutions, and commitments from previous sessions rather than starting anew each time.
For the PerformanceStat process to drive meaningful change, it must focus on critical performance goals, revisiting them consistently in every meeting. However, without clear goals, designated responsibilities, and dedicated analysts to prepare follow-up materials, these meetings lose direction. Additionally, if there is no consistent leader to oversee and track progress, there will be no one accountable for follow-up.
Without thorough follow-up, the PerformanceStat strategy risks becoming a “PerformanceSham,” failing to deliver the improvements it promises.
Error #7: In EMS, 911 communications, and hospital emergency departments, effective PerformanceStat meetings require a balance between accountability and support. High-performing models like NYPD’s CompStat and Baltimore’s CitiStat are known for being uncompromising and sometimes even harsh with those not meeting performance standards.
However, this strict approach can lead to an environment seen as punitive rather than collaborative. In response, some agencies attempt to keep meetings harmonious and positive, which often results in overly polished presentations that lack critical analysis.8
To avoid both extremes, leadership teams in EMS, 911, and hospital departments should hold operational officers and supervisors accountable by requiring data-backed evidence of progress, but they should also avoid excessive criticism without providing constructive support.
If the team fails to define clear objectives, assign a consistent meeting leader, establish analytical support, or follow up on previous discussions, then meetings risk becoming superficial presentations with little impact on real performance.
Achieving improvement requires a balanced approach where leaders challenge each unit to meet measurable goals while also offering guidance and resources to help them succeed. This mix of constructive pressure and supportive oversight enables EMS, 911, and hospitals to continuously elevate their performance.10
(Note: The author of this article has included and applied to Dr. Behn’s precepts regarding errors to EMS, 911, and hospitals for the sake of discussion in the above descriptions.)
Ambulance Patient Offload Time represents a significant barrier to EMS effectiveness and patient care continuity. Addressing this issue requires a coordinated response that integrates the strengths of Medical Operations Coordination Centers and healthcare coalitions.
By leveraging the data-driven, collaborative, and accountable approach of PerformanceStat, EMS agencies and healthcare partners can reduce APOT, enhance patient flow, and improve the overall responsiveness of the healthcare system.
The application of PerformanceStat within this framework underscores the importance of continuous performance improvement and the role of shared accountability in achieving measurable results.
Through targeted data analysis, routine performance reviews, and collaborative action, the integration of PerformanceStat offers EMS systems an effective strategy for overcoming APOT challenges and fostering a resilient, responsive, and efficient emergency care network.
Reducing APOT is essential for maintaining the effectiveness of EMS and ensuring timely care for all patients. By implementing PerformanceStat, EMS systems, 911 communications, and hospital emergency departments can engage in structured, data-driven approaches to enhance performance across their operations.
The combination of Medical Operations Coordination Centers (MOCCs) and healthcare coalitions offers a powerful framework for regional coordination and efficient resource allocation, fostering consistency, accountability, and improved patient outcomes.
The integration of PerformanceStat within MOCCs and healthcare coalitions emphasizes the need for ongoing performance monitoring, collaborative decision-making, and accountability to achieve measurable improvements in APOT. This strategy not only enhances operational efficiency but also builds a resilient emergency care system that can adapt to rising demands and emerging healthcare challenges.
By addressing APOT through these comprehensive measures, EMS and healthcare partners can strengthen the entire continuum of emergency care, creating a system that is responsive, sustainable, and capable of delivering high-quality care to the communities it serves.
References
- Richardson, L. D., Hwang, U., Asplin, B. R., & Lowe, R. A. (2002). Emergency department crowding as a health policy issue: Past development, future directions. Annals of Emergency Medicine, 40(4), 388–393.
- Sartini, M., Carbone, A., Demartini, A., Giribone, L., Oliva, M., Spagnolo, A. M., Cremonesi, P., Canale, F., & Cristina, M. L. (2022). Overcrowding in emergency department: Causes, consequences, and solutions—a narrative review. Healthcare, 10(9), 1625. https://doi.org/10.3390/healthcare10091625
- Behn, R. D. (2014). The PerformanceStat Potential: A Leadership Strategy for Producing Results. Brookings Institution Press.
- Franklin, B., Yenduri, R., Parekh, V., et al. (2023). Hospital Capacity Command Centers: A Benchmarking Survey on an Emerging Mechanism to Manage Patient Flow. The Joint Commission Journal on Quality and Patient Safety. 49(4):189-198.
- Franklin, B., Mitchell, S., Villarroel, L, et al. (2023). State Capacity Coordination Centers to Facilitate Access to Acute Care: A National Survey and Analysis. NEJM Catalyst Innovations in Care Delivery. 5(1).
- Mitchell, S., Taylor, M., Paulsen, M., and Morris, S. (2023). The Statewide Patient Load Balancing Work of Washington State’s Medical Operations Coordination Center. Disaster Medicine and Public Health Preparedness. 17:e556.
- Biddinger, P. D., Hanfling, D., & Jaffe, E. (2020). “System-Level Planning for Emergency Care in the COVID-19 Pandemic.” New England Journal of Medicine, 382(12), 1956-1958.
- The Minnesota Medical Operations Coordination Center Baum, Karyn D. et al. CHEST, Volume 165, Issue 1, 95 – 109
- ASPR TRACIE Medical Operations Coordination Centers Toolkit Third Edition Originally Produced in 2020 by the NRCC Healthcare Resilience Task Force Updated by ASPR TRACIE in November 2021 (Second Edition), April 2024 (Third Edition), May 2024 (Appendix C), and October 2024 (Appendix D)
- Behn, R. D. (2008). The seven big errors of PerformanceStat. Harvard Kennedy School of Government. https://www.hks.harvard.edu/sites/default/files/centers/rappaport/files/performancestat.pdf
