06 Dec Discussion: Simulation of Telemedicine
Discussion: Simulation of Telemedicine
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.
Discussion
Continue the Discussion and respond to your colleagues in one or more of the following ways:
Each Colleagues 250 words or more (Colleague 1 250 words, Colleague 2 250 words, Total 500 words):
· Ask a probing question, substantiated with additional background information, evidence, or research.
· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
· Offer and support an alternative perspective, using readings from the classroom or from your own research in the Walden Library.
· Validate an idea with your own experience and additional research.
· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.
Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.
Colleague 1
Analytical tools are so useful for healthcare leaders as we have learned within the past few weeks. With simulation distributions, there are some strengths and weaknesses. Telemedicine has advanced by providing patients with treatment and care within the past few years. Healthcare services have drastically improved with these enhancements for leaders within the field for their patients.
In the article of Torabi et al. (2016), the distribution included a Monte Carlo simulation that was using 121 ischemic stroke patients that were eligible for rt-PA (Torabi et al., 2016). The IRB (Institutional Review Board) determined that the subjects were “non-human” because data that was identified before was previously used (Torabi et al., 2016). One strength I did notice was how some of the healthcare service organizations that participated with timelines of patient treatment improvements. Additionally, how each deployment of policies was compared. In essence, the location of physicians in comparison to each facility. Each scenario was utilized by the Monte Carlo simulation method. Ultimately, the leaders in healthcare facilities were able to determine if the telemedicine procedures were an asset including reliable analysis. What was also done quite well in this article was the depiction of the graph that indicated 17 hospital locations with 9,200 patient location in the Cincinnati area. By using this graph, it was helpful to see the locations of all hospitals and outer ring hospitals used in the data analysis. I also think that the addition of the sensitivity analysis was an important tool because it gave information as to how the results would look like if the assumptions were inaccurate (Torabi et al., 2016). One last positive aspect I noticed in the article is the overall elimination of travel as a result of utilization of telemedicine services (Torabi et al., 2016). After reviewing Table 3 sensitivity of percent treated, the percentage of treated within 3 hours was at a decent rate with an average of 80% (Torabi et al., 2016).
Perhaps one of the weaknesses was the limitations on the study in one geographical area within Kentucky. Maybe if there were other locations to conduct the analysis, the patients arrival would be compared with other variables at other locations.
References
Albright, S.C., & Winston, W.L. (2017). Business analytics: Data analysis and decision making
(6th ed). Stamford, CT.
Torabi, E., Froehle, C.M., Lindsell, C.J., Moomaw, C.J., Kanter, D., Kleindorfer, D., & Adeoye,
O., (2016). Academic Emergency Medicine, 23 (1), 55-62.
Colleague 2
A Telemedicine allows the sharing of medical information and the remote administration of thrombolysis to a person who has suffered a stroke, regardless of their location. The objective of the present study is to provide a tool to model different care scenarios of a stroke patient and to measure results in terms of efficiency and cost.
A decision tree-based analytical model representing some scenarios. The baseline scenario represented the current management of patients in the emergency departments via a telemedicine tool. Efficiency criteria were thrombolysis rate, rate of return home, and quality-adjusted life-years. Costs were based on actual patient resources used.
Once the model parameters were identified, the results were used to compare the scenarios in terms of cost and efficiency. The different assumptions, transition probabilities, data source, and efficiency and modeling costs are discussed.
The model provides a medico-economic evaluation of the telemedicine system. It is an adjustable and scalable model that can be applied in other regions and adapted to other medical fields.
Telemedicine appears to be a cost-effective modality for the management of stroke. The modeling makes it possible to take into account the regional specificities and the organization.
Reference:
Albright, S.C. & Winston, W. L. (2017). Business Analytics. Data Analysis and Decision Making. (6th ed) CENGAGE Learning.
Torabi, E., Frohle, C.M., Lindell, C. J., Moomaw, C.J., Kanter, D., Kleindorfer, D., & Adeaoye, O. (2015). Monte Carlo Simulation Modeling of a Regional Stroke Team’s Use of Telemedicine. Academic Emergency Medicine. DOI: 10.111/acem12839
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