|
Analytical Case Study
Table of Contents
Introduction
Outlining the problems
Discussion on management process
Implementation challenges and mitigation tools
Model to reflect innovations
Conclusion
References
Rapid Deployment of Telehealth Services for Rural Hospitals Fighting COVID-19
Introduction
This study is going to highlight different steps of leading and managing changes in the healthcare domain. A part of key concerns is associated with ever changing facilities and service management. Rapid Deployment of Telehealth Services for Rural Hospitals Fighting COVID-19 is an innovation that can be taken into consideration. At this stage, it is quite possible to keep health records and monitor their presence. This study is supposed to evaluate a problem of the healthcare sector and use tools for mitigation purposes. There are different types of management and problem solving skills, which will be discussed in the course of time. A specified model is being implemented through this study to access the decision making process.
This discussion mostly concentrates on the improvements of the healthcare industry by applying technology. Rapid Deployment of Telehealth Services for Rural Hospitals Fighting COVID-19 is a topic that is being focused in this case. According to Alagoz et al. (2018), virtual monitoring has had its records in healthcare since for some time now. It is observed that all sorts of patient monitoring has been digitalized within a few years. Health care is being strategically improved with the components of big data and block chain health record monitoring and other technical statements. One such idea is of wearable health monitoring devices. People are having issues with diabetes along with cholesterols.
It needs to be monitored on a regular basis and their heart rates are supposed to be monitored by these wearable technical devices. As stated by Westra et al. (2017), a dozen companies are working in the context of delivering such devices to a number of people. Wearable are not merely a device, it engages into a complete service of getting into the management. Moreover, it needs to have a more precise version of tele monitoring over patients who are suffering from heart related issues. As developed by Wang, Kung and Byrd (2018), senior people along with people of middle aged are at a risk of heart failure diseases. However, a regular monitoring via the wearable devices has definitely improved the health records. Some people that are residing in remote areas should get faster protection through tele monitoring of cardiac patients. Wearable devices are one of their kinds and this is helping people to keep an eye over their cardiac needs (Moreira, Gherman and Sousa, 2017). Keeping regular check of BP and heart checks are reducing the chances of heart failure to an extent. In this case, of Covid-19, there is a particular model of care accumulated by the staff training.
Virtual monitoring of patients' needs has already been discussed and it is the same thing in this case as well (Gilligan and Lowe, 2018). A success factor of this particular model is underlying with the process of medical innovation and procurements.
The management process is about divide and conquers rules. As commented by Al Khamisi, Khan and Hernandez (2017), different teams of doctors, nurses and IT personnel are created to provide relief to this global pandemic in remote areas. Management process is endured within the monitoring of patients by means of artificial intelligence. A quick transferring of records and their specific management is done via this process. Video conferencing and managing to connect with families of doctors have been quiet comfort when these people were unable to meet their families due to the spread. Virtual appointments to patients ranging from 1 year to 96 years have been addressed (Bhatt, Dey and Ashour, 2017). This is one of the finest parts of the decision making process and it addresses the particular needs.
Digital innovation services and health care departments are jointly spreading this success factor. As explained by Dixon and Knapp (2018), this entire may lack some components regarding AI facilities integration in remote locations. Although platforms like Zoom are usually used in business platforms, it requires checking on systems that are providing advanced healthcare through constant monitoring at the patients. There are structured and specific hurdles provided for problem solving via a response of engagements. It is quite motivating as it builds up emotional intelligence through the proper use of AI. It is one of the core concerns that can be indulged into a competency level (Bokhour et al. 2018). These concerns are not only being beneficial to humankind, it brings out all consequences that are missed over health care foundations. AI is one of the core technical alignments that can be done with the management process. A consideration of specified models and innovation can be made amidst a global crisis like this. There are millions of people suffering right now, they need to be healed using remote monitoring, and AI based systems.
Governments of different countries worldwide are funding chatbots that can help solve people's queries. As mentioned by Kitson et al. (2018), this will ensure that people are not being panicked at this situation and they receive proper medical help as well. Artificial intelligence is one of the most important features to engage into an error free method in the life saving process of humans. Using technical aspects like deep learning can help give solutions to the distance based challenges and improve health conditions. It has undergone challenges like implementing a service within a short span of time. There are different types of algorithms, which engage into tests and match with specific health conditions. One such example of application includes Buoy health, which is an AI based system to identify symptoms.
Now, it can be taken under consideration of identifying COVID-19 cases and monitoring them remotely. In this way, there will be a less chance of contact spread and both doctors and patients will be saved. As described by Negash et al. (2018), rapid monitoring over the context of tele-health management is possible with this context. However, this idea needs a huge number of investments at the initial stage. This is indeed becoming an issue for government sponsored as well as private healthcare sectors in rural areas. At a first stage, distribution of tools like the wearable health monitoring devices can be used to keep track of patients BP even without being physically present there.
This change management approach can take over the consequences and there is use of critical analysis. In case a patient is stuck at a remote place, there is a particular engagement filled with managing the role and engaging AI based emotional intelligence (Healthaffairs.org, 2020). A robot based system that will answer queries and specific steps taken in health care will be used to mitigate the challenges.
An AI based health care model itself reflects with innovations and new approaches towards healing patients. According to Palanisamy and Thirunavukarasu (2019), expert systems can be taken under consideration to ensure that patients are being treated properly. There is a proper use of a hybrid artificial intelligence system, which cooperates with remote monitoring over the multi agents and fuzzy logic based systems. This particular model intends to patient observation and measurement along with data interpretation as a second stage. There exists data categorization that is helpful in the planning phase of health care models. All these segments can be followed within patient data, information and therapy management. This innovation is just helping millions of people and getting the courage to battle in these global crisis moments (Aha.org, 2020). There are specific models like data mining to accompany the innovation. Initially collected data from patients will go to a data-warehousing database along with a data selection model.
Then the process is followed by cleaning and processing. It will further accompany the pattern identification process along with interpretation of evaluative studies. Finally knowledge will be occupied to complete the model integration. As commented by Ristevski and Chen (2018), the out of pocket model can be engaged in this case. Rural areas and remote locations are considerably taking into treatment of this global issue. Regardless of prices a tightly controlled infrastructure domain can help with this mechanism for health modifications. This innovation is surely reflecting the physical presence of doctors along with managing their issues by rapid intelligence.
There are significant challenges that need to be met by health care people. Health care facilities are supposed to be having a rigorous impact over the scenarios of developing technical modifications. It is observed that an entire set of data that focuses on a health care model of artificial intelligence and remote health monitoring. There is maintenance of information model that will help in analyzing health records among patients. This situation requires a proper solution and it can be a man-to-man one interaction. Hence, health monitoring will be an innovation that can help save both the lives of patients and doctors itself. An artificial intelligence can cause a quick identification of this disease. There will be use of a matchmaking algorithm that will understand whether the collected samples are infected or not, then the patient's will be admitted to facilities and they will gain medical help remotely by experts. There will be wearable’s provided to monitor heart health.
Book
Gilligan, C. and Lowe, R., 2018. Marketing and healthcare organizations. Boca Raton: CRC Press.
Journals
Al Khamisi, Y.N., Khan, M.K. and Hernandez, E.M., 2017. A conceptual model for a hybrid knowledge-based system for quality management at healthcare environment. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 24-32).
Moreira, M.R., Gherman, M. and Sousa, P.S., 2017. Does innovation influence the performance of healthcare organizations?. Innovation, 19(3), pp.335-352.
Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, pp.3-13.
Westra, D., Angeli, F., Carree, M. and Ruwaard, D., 2017. Understanding competition between healthcare providers: Introducing an intermediary inter-organizational perspective. Health Policy, 121(2), pp.149-157.
Online Articles
Alagoz, E., Chih, M.Y., Hitchcock, M., Brown, R. and Quanbeck, A., 2018. The use of external change agents to promote quality improvement and organizational change in healthcare organizations: a systematic review. BMC health services research, 18(1), p.42. [Online], Available at: <https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-018-2856-9> [Accessed on 01/04/2020]
Bhatt, C., Dey, N. and Ashour, A.S. eds., 2017. Internet of things and big data technologies for next generation healthcare. [Online], Available at: <https://link.springer.com/book/10.1007%2F978-3-319-49736-5> [Accessed on 01/04/2020]
Bokhour, B.G., Fix, G.M., Mueller, N.M., Barker, A.M., Lavela, S.L., Hill, J.N., Solomon, J.L. and Lukas, C.V., 2018. How can healthcare organizations implement patient-centered care? Examining a large-scale cultural transformation. BMC health services research, 18(1), p.168. [Online], Available at: <https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-018-2949-5> [Accessed on 01/04/2020]
Dixon, J. and Knapp, M., 2018. Whose job? The staffing of advance care planning support in twelve international healthcare organizations: a qualitative interview study. BMC palliative care, 17(1), p.78. [Online], Available at: <https://bmcpalliatcare.biomedcentral.com/articles/10.1186/s12904-018-0333-1> [Accessed on 01/04/2020]
Kitson, A., Brook, A., Harvey, G., Jordan, Z., Marshall, R., O’Shea, R. and Wilson, D., 2018. Using complexity and network concepts to inform healthcare knowledge translation. International journal of health policy and management, 7(3), p.231. [Online], Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890068/> [Accessed on 01/04/2020]
Negash, S., Musa, P., Vogel, D. and Sahay, S., 2018. Healthcare information technology for development: improvements in people’s lives through innovations in the uses of technologies. [Online], Available at: <https://www.tandfonline.com/doi/full/10.1080/02681102.2018.1422477> [Accessed on 01/04/2020]
Palanisamy, V. and Thirunavukarasu, R., 2019. Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences, 31(4), pp.415-425. [Online], Available at: <https://www.sciencedirect.com/science/article/pii/S1319157817302938> [Accessed on 01/04/2020]
Ristevski, B. and Chen, M., 2018. Big data analytics in medicine and healthcare. Journal of integrative bioinformatics, 15(3). [Online], Available at: <https://www.degruyter.com/view/journals/jib/15/3/article-20170030.xml> [Accessed on 01/04/2020]
Websites
Aha.org, 2020. Members In Action Case Study: Rapid Deployment of Telehealth Services for Rural Hospitals Fighting COVID-19, Available at: <https://www.aha.org/case-studies/2020-04-08-case-study-rapid-deployment-telehealth-services-rural-hospitals-fighting> [Accessed on 01/04/2020]
Healthaffairs.org, 2020. The Changing Health Care World, Available at: <https://www.healthaffairs.org/do/10.1377/hblog20140210.036868/full/> [Accessed on 01/04/2020]
|
