I’m trying to study for my Health & Medical course and I need some help to understand this question.
It may come as no surprise that advances in technology have had a dramatic impact on healthcare delivery. Advances in health information technology, such as patient portals; electronic health records (EHRs) or electronic medical records (EMRs); and real-time coordination of patient care, etc., all have greatly contributed to enhancements in healthcare delivery. However, they too presented several challenges to healthcare administration leaders and clinical staff in how to best orient and implement such technology to enhance healthcare delivery.
One such advancement in healthcare technology concerns the use of telemedicine to provide patient care and treatment. While delivery of patient care is usually a direct transaction, interfacing with patients and physicians virtually, or at a distance, could greatly enhance how healthcare services are delivered for certain situations, such as disaster events or in rural locales.
For this Discussion, review the resources for this week. Reflect on the Torabi et al. (2016) article in the resources for this week, and consider the distributions the authors selected for the given simulation.
Post a description of the distributions selected by the authors in the Torabi et al. (2016) article, and then explain whether the distributions selected are appropriate for practice, and why. Explain what was done well in the study, as well as areas of weakness for the considerations described by the authors. Be specific, and provide examples.
Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.
- Chapter 16, “Simulation Models”
Torabi, E., Froehle, C. M., Lindsell, C. J., Moomaw, C. J., Kanter, D., Kleindorfer, D., & Adeoye, O. (2016). Monte Carlo simulation modeling of a regional stroke team’s use of telemedicine. Academic Emergency Medicine, 23(1), 55–62.
Expert Solution Preview
Introduction:
The question pertains to the appropriate selection of distributions for a Monte Carlo simulation modeling of a regional stroke team’s use of telemedicine. The Torabi et al. (2016) article from the resources of the week is the focus of this post, and the objective is to determine whether the distributions selected by the authors are appropriate for practice and highlight the strengths and weaknesses of the study.
Answer:
The authors of the Torabi et al. (2016) article selected two distributions for their Monte Carlo simulation modeling study of a regional stroke team’s use of telemedicine. The first was the triangular distribution, which was used for the time taken by the virtual physician to assess the patient, and the second was the normal distribution, which was used for the time taken by the EMS team to reach the patient. In my opinion, the distributions selected by the authors are appropriate for practice, as both distributions account for the uncertainty involved in the assessment and delivery of care in a telemedicine setting.
The strength of the study lies in the fact that it provides a comprehensive overview of the application of Monte Carlo simulation models for the implementation of telemedicine in healthcare delivery. The authors utilized the distributions that best fit their study, and the simulation results provided a clear insight into the efficiency and effectiveness of the proposed telemedicine system. However, a weakness of the study could be the limited scope of the simulation modeling, as there was no analysis of the impact of distribution parameters on the results obtained. Nonetheless, the study provides a starting point for future research in the field of telemedicine and healthcare technology implementation.
In summary, the distributions selected by the authors in the Torabi et al. (2016) study are appropriate for practice, as they are suited to account for the uncertainty involved in the assessment and delivery of care in a telemedicine setting. The study highlights the strengths of using Monte Carlo simulation modeling in telemedicine and provides a foundation for future research in the field.
