MTHD 8003 Assignment Synthesis of Preliminary Findings

MTHD 8003 Assignment Synthesis of Preliminary Findings Student Name Franklin University MTHD 8003 Professor Name Date Synthesis of Preliminary Findings The analysis is based on the data that was procured from a survey conducted a few days ago about telehealth services. The survey sought to capture the perceptions of the community regarding telehealth, the advantages, the setbacks, and what is deemed to be lacking in this area. Thus, by understanding the responses, we can get information like how it is possible to improve in terms of telehealth care services, particularly concerning the elderly population. Observations from Data Key observations Observations from Data Telehealth utilization When analyzing the survey outcomes, specific information about the use of telehealth services can be identified among the respondents at different levels. Participants usage levels The participants attested to a variety of telehealth utilization with some expressing that they used it often while others almost never or never at all. Patient comment One of the most common comments that patients make regarding the use of telehealth is time saved, as well as being able to consult with their healthcare provider while at home (Batko et al., 2022). Accessibility issue Nevertheless, some issues appear with accessibility, particularly for elderly patients, which indicates further opportunities for development. Key Impressions Based on the material, one can identify two key sentiments concerning telemedicine: the positive attitude towards its availability, while some doubts remain regarding its efficiency and accessibility, especially for elderly individuals (Kraus et al., 2021). This dichotomy highlights a critical area for further exploration: though telehealth is appreciated for convenience, there are essential problems of equity and access for all population categories. These impressions indicate that telehealth services require further flexibility depending on the users.  Type of Analysis For this kind of survey, it was possible to apply a mixed-methods approach where qualitative and quantitative data both have been used. Therefore, the use and recommendation of telehealth can be measured in terms of quantity. In qualitative settings, the free-text responses addressing user experiences and areas of change can be categorized based on common patterns (Batko et al., 2022). Taken together, these two approaches should assist in offering a more complete picture of the users’ satisfaction and where improvements can be made.  Visualization Ideas Bar graphs could illustrate the nature of the usage of telehealth among adults; on the other hand, pie graphs could indicate what percentage of the respondents would recommend the use of telehealth (Vellido, 2019). This is a categorical approach that could be adopted to emphasize the frequently mentioned benefits and challenges in the open-ended responses. Judging by the results of the survey, this variety of visual aids will assist in the qualitative and quantitative communication of the results.  Conclusion The survey data reveals that while people find the telehealth services useful and convenient, they are concerned with their accessibility and inclusiveness, especially for the elderly. These insights bring out the need to make telehealth accessible to everyone through enhancing convenience. Both qualitative and quantitative insights, as well as different types of presenting data, can provide a better understanding of how telehealth is perceived at the moment and how it needs to be further developed to enhance user satisfaction. References Batko, K., & Ślęzak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-021-00553-4 Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. (2021). Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research, 123(123), 557–567. sciencedirect. https://doi.org/10.1016/j.jbusres.2020.10.030 Vellido, A. (2019). The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications, 32. https://doi.org/10.1007/s00521-019-04051-w