Response 1- Simulation of Telemedicine
Description of the Distributions Selected by the Authors
Health care simulation is a “technique that produces a situation or environment to appropriate persons to experience a representation of the actual health care case for practice, learning, evaluation, testing, or to gain an understanding of systems and human actions” (Lopreiato, 2017). The article summarized distributions of variables, treatment of care for stroke patients, and how telemedicine can play a part in the care of a person. The study mapped out strategies for stroke symptoms patients on transportation and how they will receive care from the stroke care team. Some patients drove themselves to the hospital, and others received vehicles for emergency medical services. When the stroke team physician is located randomly in the region, the proportion of the simulated stroke population is treated within 3 hours of symptom. Onset increases when comparing no telemedicine to partial telemedicine deployment. The total telemedicine deployment of 70% versus 80%, respectively. Telemedicine is partially deployed in the region, locating the stroke team physician within the zip code of the hospital near the center of the area resulting in a reduction in onset-to-treatment time compared to when the physician’s location was 157.7 versus 164.4 minutes (Torabi et al., n.d.).
The Distributions Selected are Appropriate for the Practice
Telemedicine will increase care and satisfaction rates by utilizing these methods. Health care providers are turning to progress telemedicine technologies to provide treatment more rapidly. Telestroke provides stroke team physicians with heightened communication with remote patients by providing a two-way, audio-visual associate with desegregating electronic medical information, scans, test results, and clinical assessment tools (Torabi et al., n.d.).
What Was Done Well as well as Weakness for Considerations
The performance outcomes of the simulated scenarios, the layout of the locations of the physician, the telemedicine deployment, and the triage timeline identify a picture. And how the results showed the data of the stroke team physician and the telemedicine deployment. Partial deployment improved door-to-needle time to a mean of 61.4 minutes, compared to 72.7 minutes with no telemedicine. Telemedicine at all hospitals improved the door-to-needle time to mean 45 minutes, well below the 60 minutes American Stroke Association recommendation (Torabi et al., n.d.).
The only downfall would be the funding. Potential societal benefits continued efforts to deploy telemedicine appear warranted. Aligning the incentives between those who would fund the upfront technology investments and those who will benefit over time from reduced ongoing health care expenses will be necessary to fully realize the benefits of telemedicine for stroke care patients (Torabi et al., n.d.).
Lopreiato, J. (2017, August 1). How Does Health Care Simulation Affect Patient Care? Agency for Healthcare Research and Quality. http://psnet.ahrq.gov/perspective/how-does-health-care-simulation-affect-patient-care
Torabi, E., Froehle, C. M., Lindsell, C. J., Moomaw, C. J., Kanter, D., Kleindorfer, D., & Adeoye, O. (n.d.). Monte Carlo Simulation Modeling of Regional Stroke Team’s Use of Telemedicine. Wiley Online Library. http://onlinelibrary.wiley.com/doi/10.1111/acem.12839
RESPONSE 2- Simulation of Telemedicine
The use of analytical tools is an important consideration in the field of healthcare. With simulation distributions, some strengths and weaknesses can be experienced. Research shows that telemedicine has continued to advance over the years, thus providing patients with care and treatment at their convenience. The use of telemedicine reinforces the fact that healthcare services have experienced a drastic improvement with further enhancements for the leaders in healthcare organizations and also for patients. This week’s discussion explores the work of Torabi et al. (2016) to unveil different distributions which the authors within the article have identified for the Monte Carlo simulation.
The Selected Distribution
Torabi et al. (2016) selected the use of Monte Carlo simulation that included 121 patients with ischemic stroke who were also considered as eligible for the rt-PA process (Torabi et al., 2016). The Institutional Review Board (IRB) established that the identified subjects for the study as “non-human”. This is driven by the fact that the identified data on the subjects had already been utilized previously (Torabi et al., 2016).
The chosen Monte Carlo simulation distribution was appropriate for practice. Notably, the Monte Carlo simulation distribution enables a person to visualize all the possible outcomes of a decision while also providing an assessment of the risk impact when considering the use of the previous data on the telemedicine innovation, thus facilitating a better process of decision making even when featured with uncertainty (Kim, 2017).
What was done Well
The authors relied on the use of a graph that effectively provided an illustration of the location of 17 hospitals alongside 9,200 patient location within the Cincinnati region (Torabi et al., 2016). The use of the graph was desirable for the study since it made it simpler to point out different hospitals, including the outer ring ones which relied on the use of the data analysis process to its advantage. Moreover, the use of sensitivity analysis is also an area of strength within the study (Torabi et al., 2016). Arguably, the sensitivity analysis was effective in providing a general outlook of how the generated findings would appear in case the assumptions implemented in the research were inaccurate. Furthermore, another area of strength outlined in the article includes doing away with travel as an outcome of the use of the telemedicine innovation (Torabi et al., 2016).
Besides the identified areas of strengths, the work of Torabi et al. (2016) was also featured by several weaknesses. For instance, the fact that the researchers relied on conducting the study in a single geographical location in Kentucky limited the desirability of the findings collected from the investigation. The investigators should have considered conducting the study in more than a single destination to create an environment whereby they could draw comparisons of the study findings to generate more conclusive results. For instance, conducting the research in two areas would make it simpler to compare the level of patient arrival to other variables available in other locations to draw results on the effectiveness of telemedicine usage based on destinations.
Conclusively, the use of telemedicine is one of the technological innovations that is increasingly finding its way into the healthcare sector. Monte Carlo simulation was utilized in the article proving to be effective to be implemented in the healthcare practice.
Kim, Y. (2017). Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis. Sustainability, 9(4), 539. https://doi.org/10.3390/su9040539
Torabi, E., Froehle, C., Lindsell, C., Moomaw, C., Kanter, D., Kleindorfer, D., & Adeoye, O. (2015). Monte Carlo Simulation Modeling of a Regional Stroke Team’s Use of Telemedicine. Academic Emergency Medicine, 23(1), 55-62. https://doi.org/10.1111/acem.12839