Stroke Recognizing by Emergency Medical Dispatchers

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Table of Contents

Situation
Questions
Hypothesis
Proof and Action
Alternatives
Works Cited

Situation

Stroke is a serious condition that is associated with a high mortality rate. The main determinant of a patient’s successful recovery from stroke is a timely response to the condition, where the early referral to a specialist significantly reduces mortality and dependence (Watkins et al. 1). Currently, the majority of stroke patients enter the system through Emergency Medical Services (EMS), with Emergency Medical Dispatchers (EMDs) evaluating the condition of patients and deciding on the level of health risks involved using an Advanced Medical Priority Dispatch System (AMPDS) (Watkins et al. 1).

Thus, the patients’ outcomes ultimately depend on the judgment made by EMDs. Unfortunately, the dispatchers in the UK do not receive specialist medical training, which makes stroke identification a much less reliable process. The situation is further aggravated by the fact that correct identification of stroke is a complicated procedure that ends in misclassification in more than 50% of instances (Watkins et al. 1).

As a result, a scenario is possible where a high-priority ambulance is not dispatched on time, decreasing the patient’s chances of receiving timely professional care and leading to adverse health consequences. It is thus crucial to explore possible solutions for the issue and assess their validity prior to their practical implementation. One such approach is the introduction of a training program that would be focused on stroke identification and would have a low entry barrier for the employees with no prior special training. Therefore, the case is an evaluation with a well-defined decision requiring a thorough assessment (Ellet 11).

Questions

The issue requiring evaluation is a solution to the insufficient stroke identification rate in the form of the online stroke-specific training package. The team responsible for the evaluation is a group of researchers on behalf of the ESCORTT group – an organization responsible for the effectiveness of telephone triage in emergency stroke cases (Watkins et al. 1). The evaluation is important for two reasons.

From the patient’s perspective, the effectiveness of the proposed solution would decrease the risks of high mortality and disabilities associated with stroke. From the healthcare provider’s perspective, the benefits are twofold: the evaluation would ensure that the resources for intervention’s implementation are allocated properly, and outline the scope of the probable improvement. In simple terms, the efficiency of the department is at stake in the evaluation. The most evident possible criteria are the increased rate of appropriate stroke identification and, by extension, improved patient outcomes. While these criteria are not explicitly identified in the title, they can be derived from it and are mentioned in the abstract of the article.

Hypothesis

The identified criteria are utilized by the article’s authors and provide the most confidence due to their direct relationship to the issue at hand. Of the two, the former (the stroke identification rate) has a closer causal relationship to the problem and has an advantage in terms of measurement, as the results can be obtained sooner and properly quantified.

Therefore, the hypothesis can be formulated as follows: In emergency medical dispatchers exposed to the training program, the rate of correctly identified cases of stroke will be higher in the post-implementation period than in the pre-implementation period. It is also reasonable to expect related improvements, such as the decreased time of identification, after the program implementation, but the data available in the case is insufficient to categorize it as a reliable criterion.

Proof and Action

The strongest evidence to confirm the formulated hypothesis is quantitative data. Such evidence can eliminate most of the possible biases and ensure the highest relevance of the results (Perrin 86). In the case at hand, the data was collected from a single hospital and an emergency service in England in the course of eighteen months period. The reliability of evidence depends strongly on the data collection process. In this case, the sample was restricted by the set of the well-defined inclusion and exclusion criteria, in order to eliminate the situation where the judgment of the EMDs was assisted by the General Practitioner or a previous medical record (Watkins et al. 2).

A set of criteria was also devised to establish the instance of stroke diagnosing. Data collection was performed in three sessions: prior to training package implementation, during implementation, and after the completion of the program. This gave the researchers the possibility to compare the results to the baseline and increased the precision of measurement by adding a middle point (Guyatt et al. 421). All of the mentioned components contribute to the reliability of evidence.

The collected data was analyzed using a segmented regression model to determine an overall linear trend, with the expected gradual improvement derived from three measurement points. Numerous corrections were introduced during the data analysis, such as sensitivity analysis, investigation of the potential autocorrelation, and the adjustment due to standard errors. As a result of the described measures, the findings of the research team can be considered sufficiently reliable to be qualified as evidence to confirm the hypothesis.

The results indicate that in the pre-implementation period, 63% of the patients diagnosed with stroke by the EMDs were confirmed to have the condition after their admission to emergency care. This result increased to 80% after the implementation of the training program. The difference was a statistically significant result (p=0.003) (Watkins et al. 5). Such a result is also significantly higher than the average identification rate suggested in the literature (approx. 50%).

In addition, a small decrease in time was detected from the call to the arrival of the ambulance on the scene, which was not statistically significant. Therefore, the change in the most relevant criterion is consistent with the hypothesis, as is the change in non-significant criterion excluded from the evaluation. In other words, the evidence suggests that the chosen approach is viable and offers much-needed improvement. The developed training intervention is thus valid for implementation and is recommended for implementation in the healthcare setting. Since the training package is freely available online, there is also no significant financial risk associated with its implementation.

Alternatives

It is important to note that while the utilized criterion is clearly directly related to the studied issue, the connection between in and the decreased mortality and disability rate is implied. In other words, the current evaluation does not provide conclusive evidence that the introduction of a training package would decrease deaths resulting from incorrectly recognized stroke – only that such instances can be reduced. Therefore, an alternative evaluation can be suggested that would directly assess the change in patient outcomes associated with training. Such an alternative would be more valuable and would address the core question rather than the nearest important factor.

However, it would also be much more time- and resource-consuming, so it would be reasonable to reserve it until the preliminary confirmation of its viability is obtained. Thus, it can be conducted in the future, both as a follow-up for the study at hand and in the new setting.

Works Cited

Ellet, William. The Case Study Handbook: How to Read, Discuss, and Write Persuasively about Cases. Harvard Business Press, 2007.

Guyatt, Gordon, et al., editors. Users’ Guides to the Medical Literature: A Manual for Evidence-Based Clinical Practice. 3rd ed., AMA press, 2014.

Perrin, Karen M. Essentials of Planning and Evaluation for Public Health. Jones & Bartlett Publishers, 2014.

Watkins, Caroline L., et al. “Training Emergency Services’ Dispatchers To Recognise Stroke: An Interrupted Time-Series Analysis.” BMC Health Services Research, vol. 13, no. 318, 2013, pp. 1-9.

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