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Performance Measures for (50,000 call per year) EMS
EMS ORGANIZATIONAL PERFORMANCE MEASUREMENT
That the organization implements additional clinical performance measures, including those to evaluate the quality of the EMS.
That the organization uses survey data to evaluate and analyze customer and employee satisfaction and that a proper feedback and control mechanism is in place to use this data to implement required changes.
This report starts from the premise that Emergency Medical Services will be treated as any other service. As a consequence, this type of service reflects the relationship between the service recipient (in this case the patients) and the service provider (in this case the medium-sized organization being analyzed in this report).
This means that this report will use many of the existing research and business literature and apply business principles such as customer and employee satisfaction in presenting and analyzing a comprehensive set of recommended performance measures for a medium-sized (50,000 call per year) emergency medical service organization.
Operational performance measures
The Littleton Fire Rescue service has been providing citizens emergency medical services for decades. However, the performance of their EMS was not measures and its influence on patient outcome could not be determined. The Littleton Fire Rescue department then developed an applied research process intended to identify key performance indicators that could be used in measuring the department's performance (Zygowicz, 2010).
The operational segment was of particular interest to researchers in this case, as operational performance measures have been identified in order to improve EMS system efficiency and to improve customer service. These measures can be successfully benchmarked by our EMS organization.
One of the measures proposed by the study refers to monitoring individual paramedic performance and giving feedback to crew members. This measure is intended to personalize recommendations for each crew members, which increases their efficiency.
Another important measure in the operational segment is to determine the number of tracheal intubations performed by the staff. If the reported number is lower in comparison with the state average number of tracheal intubations, training programs must be acquired for the organization's personnel (Zygowitz, 2010).
Measures that should be benchmarked by the organization refer to its communication center or dispatch, light and siren response, and primary EMS complaints. Collecting and analyzing data on patient satisfaction is probably the areas that can provide important information to be used in developing measures that improve satisfaction. However, it is worth mentioning that patent satisfaction surveys have their level of subjectivity. Patients' responses in such surveys should be measured against more objective indicators in order to build an accurate image of the service's performance. In order to facilitate the implementation of these surveys, they can be sent to patients together with their transportation bills.
It is recommended that the organization posts on its websites information about performance measures it takes and their indicators. By publishing such results, the organization ensures the transparency of its activity (Zygowitz, 2010).
Clinical performance measures
Myers, Slovis et al. (2007) have conducted a significant amount of research regarding the measures of clinical performance. It is useful to start this subchapter with a brief analysis of some of the measures they have identified and employed in their research. Their study starts with some of the traditional performance measures. One of the most important in this category is the response time intervals.
There are several advantages with regards to the response time intervals. First of all, they are easy to quantify and this is usually very helpful in the dialogue that medical centers have with external shareholders, such as the public and the central administration. Being able to quantify a clinical performance measure will objectively reflect how well such a center is doing.
Benchmarking response times remains a challenge because, as the medical science evolved, more individuals could perform some of the basic resuscitation measures such as advanced life support (ALS) or cardiopulmonary resuscitation (CPR). The latter is now receiving less attention as part of the measuring process and the ALS response-time interval tends to be the norm in clinical performance measurement. It shows how soon it takes the responders to arrive and perform ALS.
The average response time is already in use in this organization. The only problem is that this is the only performance measure that the organization uses. However, it is a useful measure in regulating the organization's human resources, particularly in terms of the number of paramedics it uses and how they should be allocated to the different cases.
Among other traditional performance measurements that are mentioned in this study are the out of hospital cardiac arrest survival rates. The complaints that Myers et al. have is that this type of measurement approach it is only focused on a particular area of EMS action, namely cardiac arrest. According to their work, this only covers about 2% of the total number of EMS responses. Other approaches, such as the ST-Segment Elevation Myocardial Infarction (STEMI) Performance Measures or Respiratory Distress Performance Measures appear to have a similar problem.
The model proposed by Myers et al. For clinical performance benchmarking is applied, as the case study mentioned, to large urban or suburban EMS Systems. The organization we are discussing here is a medium-sized EMS provider. As such, the best approach in terms of clinical performance measures should be to focus on the general measures and implement a series of quality performance measurement systems, such as the ones described further below.
As El Sayed (2011) pointed out, more and more emergency medical services have started to be evaluated in a similar manner with healthcare services, where an important aspect is the quality of these services. In the same manner as the healthcare industry, measurements of quality in EMS have looked to quality management systems used in the business industry (El Sayed, 2011), since, after all, the objective of this process is to improve the quality of service for the end consumer (or the quality of the product).
An important category of measures used in this area is process data. Process data looks at the interaction between the patient and the prehospital or EMS provider and analyzes the different steps that are part of this interaction. This is a very useful approach, because it involves a very direct evaluation of the quality of EMS. However, the more complex the interaction (and, at the same time, the more complex the level of services that the organization provides to its patients), the more difficult this analysis process is likely to become.
In the case of this particular organization, it seems that its medium size (50,000 calls a year) encourages such an approach, since it is sufficiently small to ensure a proper implementation, without too many resources. Likely, the best way forward would be to implement a separate structure that would monitor, record and correct the quality of services towards patients.
Another type of quality performance measurement that El Sayed discussed is outcome data. This type of quality measurement looks at the change in the state of the patient following EMS. El Sayed points out to several important advantages and disadvantages for this type of approach. First of all, it is very easy to implement and understand by evaluators. After all, it involves a transparent analysis of whether the condition of the patient has improved during EMS or not.
Second, it is a comprehensive approach to evaluating the quality of EMS. In order to evaluate the outcome, it looks at all different aspects about EMS and it will judge the effectiveness of an intervention from a wider perspective than the process data approach. Rather than sequential, such as the process data approach, outcome research is more holistic and, as a consequence, the results will probably better reflect the situation on the ground.
There are also a few disadvantages with this approach to quality measurement. El Sayed points out that theory is yet underdeveloped when it comes to standardizing norms related to outcome. For some medical facilities, improvement in a condition may be a show of quality care, while for others, this may just be the regular standard or may just be a consequence of other factors than medical care.
However, this is an area that is gradually improving and concerted efforts are undertaken in this sense. For example, the U.S. National Highway Traffic Safety Administration (NHTSA) has launched the EMS Outcomes Project. The concrete result of this project was to define six categories of potential outcomes, ranging from survival to limited disability and to cost-effectiveness.
In the particular case of this organization, the outcome model is an excellent, low-cost solution, but it implies a customized research in order to determine how the model could be best matched on the existing structure and on the organization's characteristics. Some of the categories of potential outcomes that the NHTSA has put forward can be retained as such.
Finally, another category of measures for the quality of services is structural data. This approach looks at the different components in the EMS system and evaluates each of…[continue]
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