301029 | Healthcare | Current Assessment Answers
Answer:
Introduction
In the recent past, the world has experienced significant growth and development in the use of technology. Importantly most of the institutions have widely adopted the technology in the day to day operations. The health sector has also not been left behind, and there have been numerous technological advances in the health industry. One of the most noticeable technological development in the health sector is the electronic health record system which has been widely adopted by numerous health facilities. Despite the demanding need for health facilities to adopt technology in the daily operations of health facilities it is essential to indicate that the processes of changing from the traditional mode of recordingkeeping to the electronic processes and technologies have significantly been faced with change challenges which in turn hinder effective implementation of the change. As a result, strategic planning is key to bringing about change in the health sector.
Role of a nurse Leader
The leaders of the health institution play an important role in shaping the success or failure of a change process.For example, as a chief nursing officer entrusted with the duty of purchasing and implementing the electronic health record system in a health facility one should lead by example. The change process is complex, and people lack adequate motivation to pursue the change process, therefore, as a leader one should lead by example to be emulated by the subordinate and other staff members.The nurse leaders are called to be proactive in the process in matters relating to the reception, designing, development, and utilization of the electronic health record in an attempt to develop an organizational culture that allows swift movement of data through the electronic health record system.
Source: Deloitte Center for Health Solutions. © 2010 Deloitte Development LLC. All rights reserved.
Current environment of clinical decision support, considering the existing gaps
IBM Watson and DeepMind technology are attempting to bring in artificial technology into the world of medicine to help in diagnosis and treatment of various medical conditions. Despite the existing gaps such as the inaccuracy to process data perfectly for perfect decisions, the health sector is embracing these technologies (Bauer et al 2017:6). For instance, in the U.K, DeepMind has revolutionized the eye care health sector. The IBM Watson is used in the early detection of cancer.
Comparison of IBM Watson and DeepMind Technologies
IBM Watson Technology
When it comes to the development of AIs, IBM has made a lot of strides that are aimed at advancing the field of medicine. Their system of AI is significant in data interpretation including texts that are not structured, images, video as well as audio (Coccoli, Maresca and Stanganelli, 2016: 56). The AI systems of IBM continue to impact the health sector through ways that follow. First, the IBM Watson is good in better imaging analysis. Secondly, it can detect various disorders in early stages and finally it can enable hyper imaging which is important in disease tracing at a nanoscale.
In ensuring that its performance is high, IBM purchased Merge healthcare, a move that improved their access to 30billion images of medical from more than 7,500 healthcare facilities thus scaling up their base of knowledge. This enhances the systems through their interpretation of images; consequently interpreting what a doctor may miss (Gupta, 2017:89). This coupled with the capability of the Watson to learn deeply , it enables the software in spotting patterns in amounts of data that are large which helps in the prediction of for instance rare forms of diseases that affected patients in their childhood and also give a path that cancer may take in spreading before it does(Zang et al 2015: 142).IBM is also working on cognitive systems that will impact in analyzing speeches and finding signs related to disorders during the development stages, mental illness and diseases of neurology. This will equip doctors in predicting such disorders before them happening.
DeepMind Technology
Google’s DeepMind is majoring in the exploration of every aspect concerning machine learning. This ranges from recognition of speech as well as translation that is driven by a machine to a language that can be recognized in a natural way and how to understand the speech of human (Kelders and Howard, 2018: 69). This is helpful in ensuring that a patient is understood by a doctor speaking a different language. DeepMind technology also helps in improving the capability of visual processing. The technology has the ability of processing large data involving queries in a short time (Wiens and Shenoy, 2017: 150). Accessing data from Gmail, Android as well as YouTube on top of their search engine helps in teaching as well as A.I training and finding solutions for a range of a problems.
Clinical usage scenarios for these technologies
IBM Watson can be used in a scenario where cost efficiency is paramount as it improves the cost efficiency. It can be used to match an individual with a provider of healthcare that matches his or her needs within the healthcare budget that has been allocated to them (Machado et al 2010: 438). DeepMind can be used in a scenario where the patient and the healthcare provider are not speaking the same language as it does the translation.
Analysis
IBM Watson technology is efficient in hyper imaging. This is significant in disease tracing at a nano-scale (Popenici and Kerr, 2017:22).The ability of interpreting large amounts of data including images as well as audio and texts helps and spotting patterns helps in interpreting what healthcare doctors had missed. This is valuable in ensuring quality healthcare. Its detection of rare forms of disorders of a patient helps further in ensuring better diagnosis as the history of a patient is known (Sharon, 2016: 564). Also, its ability of cancer at its early stages helps in treating it. This meets the challenge of detecting cancer at its early stages which is critical to its efficient treatment.
DeepMind technology’s speech recognition helps in the identification of patients including the varying moods which is used in determining the changes in the patients’ health. This improves the care provided (Ren et al 2010: 87). It also helps in visual processing. This has bridged the gap that existed in the eye care sector. It also helps in translation thus bridging the problem of language understanding between a patient and a physician. It also helps in faster processing of data thus helping in solving problems and improving technology.
Personal reflection and knowledge gained
AIs are very significant towards ensuring quality healthcare. This is because of the impact they are making towards the detection of diseases such as cancer for instance the IBM Watson. The AIs have the ability of taking in data from a patient and processing it. DeepMind also helps in human speech and language translation thus bridging the problem of language barrier between a patient and a doctor.
For instance, the IBM Watson can interpret data of a patient. This is helpful as it helps beyond where the human resources cannot help. This is because the human resource cannot store as much data as possible concerning a patient and this may be detrimental to the provision of quality health. These technologies are helpful since a lot of data is needed concerning a patient for better diagnosis. They are also significant in giving better imaging which enables better detection of diseases. Therefore, it is important that these technologies be adopted in the healthcare system for quality healthcare.
The effectiveness of Electronic Health Record
Electronic health record (EHR) is one of the modern technologies in my area of specialty. As a nurse, it is very important to ensure the safety of the patient records. In the past, most of the medical records were stored manually.For example, the nurses were supposed to store such data in files which could be early lost or stolen. However, with the introduction of Electronic Health records, it has helped to transform the health care system.Nurses can record any information regarding their patient on the EHR. In most of the healthcare facility, limited numbers of people can access the EHR device. This is because it was noted that some of the people could take advantage by stealing any value information recorded in that devices (Jamoom, 2013). By limiting the number of people, it will guarantee effectiveness regarding record keeping. Another benefit of using the Electronic Health record is the fact that the same information concerning a patient could be shared with other medical practitioners in other parts of the country of the world. For example, in the past, patients were supposed to carry their files every time they visit a new medical institution. This is for the medical practitioners to identify their previous history with a certain illness. However, by using the Electronic health record, such information could be shared online with other nurses. As such, once the patient information is stored in that device, it cannot be altered in any way.
One several occasion, Nurses, tend to face new challenges on how to handle a certain type of diseases of patients. With the use of Electronic Health Record, the patient could seek help from other nurses in other parts of the world. The technology allows medical practitioners to communicate and share any information that might help a patient. In that light, it has ensured effectiveness in that work of the nurses.It is also important to note that the Electronic Health records can be used to help patients identify a new type of medical treatment that could be useful in the case. The law permits the patient to decide matters about their health. With such aspects, the patients could find more information on the type of available treatments through Electronic Health records.
Source: Deloitte Center for Health Solutions. © 2010 Deloitte Development LLC. All rights reserved.
Barriers to implementing Electronic Health Record
Cost is one of the major barriers of implementing the use of Electronic Health record. It is established that only 44% of the institutions in the country could afford such device (Walker, 2016). The government, on the other hand, has not been effective in providing the available resources for a medical institution to purchase such type of technology. As such, it limits its usage in a different hospital. In fact, some of the hospitals tend to share such devices which make it less effective. Another barrier of implementing such device is that fact that it will initiate change regarding operation in the healthcare. It is certain that it is the nature of every human to try and resist change. In that case, it might take a much longer time for the change process to be administered in that health care and for the employee to accept such type of new device.
Challenges and Opportunities for implementing HER
The adaptation of the electronic health record system is likely to face numerous challenges which will, in turn, interfere with the effective implementation of the system. Firstly, it is essential to indicate that the process of implementing the HER is costly regarding time and resources.In this case, it is costly to purchase all the necessary equipment such as the Epic and cables.In addition to the cost of purchasing the EHR systems, the health facility will also ensure cost associated with the repair and maintenance of the systems which further makes the healthcare systems system complex and expensive. Additional cost also arises from employing technicians as well as training the staff on appropriate use of the system.
Another challenge of the implementation of the healthcare systems also arises from the question of safety and liability while using healthcare systems system.Existing statistics indicate that the system might fail which will, in turn, hinder the users from accessing the information, hence posing a threat to the safety of the patients. Additionally, the system is also vulnerable to the increasing rates of cyber attacks.
Additionally, the healthcare systems have high chances of being opposed by the physicians because it has been proven to reduce the quality of interaction between the patient and the physician as well as between physicians. As a result, the need for marinating quality health services achieved through interaction might significantly hinder the implementation of the HER system.
Opportunities
On the contrary, the HER provides more opportunities that outweigh the challenges. For example, the system has been proved to improve the quality of health care services in the health sector. Firstly, it allows easy and quick sharing of information between departments which in turn helps to enhance the quality of health services. Secondly, the system has significantly reduced errors that arise from human mistakes which further leads to quality care.
Timeline
The process of implementing the HER varies from one health facility to the other.In this case, the whole process of developing to the implementation of the HER will take approximately three years. This is because the processes will involve multiple departments and different stakeholders.The planning process will take about one year, designing for about six months, implementation one year and finally, the evaluation will take six months. However, the process of evaluation will be continuous to ensure the effectiveness of the system in meeting the desired goals.
Conclusion
Firms using AI technology expects a lot. For example, hospitals will have robots to help in operational functions; financial sectors will also use them for investment decision purposes while in the agricultural sector there are expectations of driverless tractors, and milk bots being used in farms. Likewise, markets are expecting more sales of automated vehicles. In an attempt to acquire funds to facilitate the implementation of better healthcare systems, one can acquire funds from long-term loans and government support. Additionally, healthcare systems system can be funded by donors and well-wishers after presenting a convincing budget that demonstrates the health facility financial inabilities to meet the high cost of implementing the healthcare systems.
Source AI in Health Care Startups. From CB Insights (2016) [9].
References
Bauer, M., Glenn, T., Monteith, S., Bauer, R., Whybrow, P.C. and Geddes, J., 2017.Ethical
perspectives on recommending digital technology for patients with mental illness. International journal of bipolar disorders, 5(1), p.6.
Coccoli, M., Maresca, P. and Stanganelli, L., 2016.Cognitive computing in education. Journal of E-learning and Knowledge Society, 12(2).
Gupta, P., 2017. Machine Learning: The Future of Healthcare. Harvard Sci. Rev.
Jamoom, E., & National Center for Health Statistics (U.S.),. (2013). Physician adoption of electronic health record systems: United States, 2011.
Kelders, S.M. and Howard, M., 2018.Opportunities of technology to promote health and well-being.In eHealth Research, Theory and Development (pp. 68-88).Routledge.
Machado, J., Abelha, A., Novais, P., Neves, J. and Neves, J., 2010.Quality of service in healthcare units. International Journal of Computer Aided Engineering and Technology, 2(4), pp.436-449.
Popenici, S.A. and Kerr, S., 2017.Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), p.22.
Ren, Y., Werner, R., Pazzi, N. and Boukerche, A., 2010. Monitoring patients via a secure and mobile healthcare system. IEEE Wireless Communications, 17(1).
Sharon, T., 2016. The Googlization of health research: from disruptive innovation to disruptive ethics. Personalized medicine, 13(6), pp.563-574.
Walker, J. M., Bieber, E. J., & Richards, F. (2016).Implementing an electronic health record system. New York: Springer.
Wiens, J. and Shenoy, E.S., 2017. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), pp.149-153.
Zang, Y., Zhang, F., Di, C.A. and Zhu, D., 2015.Advances of flexible pressure sensors toward artificial intelligence and health care applications. Materials Horizons, 2(2), pp.140-156.
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