Computer-aided dispatch (CAD), electronic patient care records (ePCR), patient satisfaction surveys, billing systems, medical devices, etc as data collection have become more prevalent in EMS agencies, so does the opportunity to use that data to more effectively manage the organization. Understanding the types of data available, and how that data are best used to measure performance, is essential to the success of an EMS agency.
The majority of efforts in measuring EMS performance fall into three categories: clinical, operational, and financial. EMS leaders find it increasingly necessary to balance these categories in order to maintain a healthy organization. In addition to maintaining equilibrium among these areas, many of the measures described in this chapter have some level of correlation to other measures. One can easily see that poor financial performance might lead to an inability to fund clinical advancements, or that longer response times might impact the survival rate of out-of-hospital cardiac arrest (OHCA) patients. The more we study EMS performance measures, the more we see new examples of not only correlation, but in some cases direct causal relationships. Although clinical measures may be the area that the medical director is called upon for more guidance, it is important for anyone in that position to understand all measures used to manage an EMS agency and how they relate to the overall success of the organization.
Define key terminology used in EMS performance measures.
Describe the value of monitoring EMS performance measures as related to the strength of an EMS agency.
Outline the most commonly used clinical performance measures in EMS.
Describe the components of response time compliance monitoring.
Describe the unit hour utilization formula, and its use.
Describe the demand analysis process used to match staffing levels with EMS call volume.
Describe the concept of developing and utilizing a vehicle deployment plan.
Outline common safety related performance measures.
Terminology used in EMS performance measurement can vary from one source to another. For the purposes of understanding the contents of this chapter, as well as understanding performance measures shared among EMS agencies, the following is offered as a clarification.
Key performance indicators (KPIs) are specific areas of measurement determined to be valuable to monitor. This is the “what” in terms of measuring EMS Performance Measurement. Medication errors, response time reporting, and cash collections are examples of clinical, operational, and financial components of KPIs. Specific examples of the most commonly used clinical and operational KPIs in EMS will be the focus of this chapter.
A benchmark may be used to describe one of two things. More commonly, the term benchmark is used to define a goal to aim for, or a level to try to achieve. This use of the term is often derived from identifying best practices, or a measure that has been recognized as being done well in another similar circumstance. The second use of the term benchmark would be to simply set a point of measurement that can be used for future comparison. This is often the current state, documented prior to efforts undertaken to improve measured results of a specific item.
Where monitoring ambulance response times for certain types of calls may be the KPI, a benchmark may be to achieve 90% compliance within a certain time standard defined for that type of call.
Worth mentioning, but outside the scope of this chapter are two other areas of using EMS data to measure performance and ultimately benefit the EMS agency and or/system.
First is the use of data to answer individual questions that arise outside of regular KPI measures. The more the EMS agency uses and understands their data, the more opportunities they will discover to use it for nontraditional benefit. An example would be a police agency looking to determine if there is value to adding naloxone to their patrol cars. Analyzing patient care report and CAD data to determine the amount of times an EMS agency utilizes Naloxone on calls where police arrived first on scene would be a valuable piece of information to provide the decision makers.
Second is the practice of data mining, or predictive analytics. This practice is gaining attention in the EMS industry. Essentially, data mining is the process of analyzing large quantities of data to look for patterns or relationships that might otherwise go unnoticed. Being able to discover and predict these patterns and relationships is the reward. As large data collections begin to emerge, such as the National EMS Information System, the idea of data mining in EMS becomes much more feasible.
One of the more common performance measures involves the monitoring and tallying of clinical skills. Endotracheal intubation, IV, intra osseous, and other common skills are measured by displaying successful attempts versus total attempts. Although often an indicator for areas where training may need to be enhanced, these measures are falling out of favor in many areas as EMS leaders gain a better understanding of both the number of variables that go into the success of a particular skill attempt and the acceptable alternatives for many skills, such as alternative airway devices available for patients who may be difficult to intubate. Large quantities of aggregate data can provide a picture of system performance, while reviewing data for individual providers needs to be done with caution and full consideration of the number of things that may impact those numbers.
For many reasons, not the least of which involves the delegated medical practice within EMS systems, adherence to specific medical protocols is of value to monitor and review for the EMS medical director and all EMS leaders. With the growing number of EMS agencies using electronic patient care reports, automated reporting of variances from medical protocol is becoming the norm. Specific examples include:
Percentage of chest pain patients of a particular age group that received NTG and or ASA within a certain time of arrival at the scene.
Percentage of chest pain patients of a particular age group that received a 12-lead ECG.
Medication administration matching all protocol components.
Time spent on scene for major trauma
Transport to designated specialty hospitals. What percentage of the time are patients delivered to designated specialty hospitals when indicated? STEMI, stroke, trauma, and burn designations are common examples.
Additionally, reporting on recently updated medical protocols can also be set up to provide an opportunity for the medical director to view all cases in a particular area, looking to see if the changes made to a protocol had the desired impact.
In out-of-hospital cardiac arrest, where defibrillation is performed on the presenting rhythm, many agencies measure the time from activation of the EMS system (caller contacting PSAP) until the first defibrillation. This is used to determine the overall effectiveness of the EMS system response to cardiac arrest.
This measure is used to display how pain has increased, decreased, or remains the same from first recorded EMS measurement to final recorded EMS measurement on an event. This is viewed by many agencies as an area that EMS is able to make a measurable difference to patients.
Monitoring all reported medication errors, flagged either through the protocol compliance review process or otherwise, is a critical component of measuring EMS performance. As with most EMS performance measures, this is one is of particular importance to monitor for provider and/or system-wide trends. Timely reporting of issues, with a historical review of any similar issues, will help the agency quickly devise a plan to prevent future recurrences. Medication errors and unrecognized esophageal intubations are the most common to be reviewed.
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