Research Proposal on AI in the Healthcare System
Introduction
Artificial intelligence is being applied in healthcare due to the complexity and rise of data in healthcare systems. Artificial intelligence is commonly applied in diagnosis, patient adherence and engagement, administrative activities, and treatment recommendations. There are many instances where artificial intelligence and its aspects in healthcare can be applied. Still, implementation elements prevent the larger-scale automation for professional jobs for a considerable period. Literature and research suggest that artificial intelligence can perform better than human beings in healthcare delivery (Jain et al., 2020). For instance, the application of algorithms outperforms the role of the radiologist. Implementing artificial intelligence in healthcare has prevented it from being successful and beneficial, as cited by various research. The review gives a purpose of the research and proposal for the need for artificial intelligence in healthcare. the research acknowledges the transformative and disruptive influence that accompanies the adoption of artificial intelligence systems and calculations in healthcare and how air calculations are shaping the sustainable future of healthcare delivery
Research purpose
The proposed research does not explain the ethical issue, artificial intelligence-related risk, and complex issues accompanying the implementation of artificial intelligence in healthcare delivery. The proposed research aims to identify the transformative impact of artificial intelligence calculations in healthcare delivery (Shinners et al., 2019). The research aims to acknowledge the transformative and disruptive influence that accompanies the adoption of artificial intelligence systems and calculations in healthcare and how air calculations are shaping the sustainable future of healthcare delivery (Jain et al., 2020). Artificial intelligence is at its infancy stage in healthcare, and its impacts cannot be considered uncertain. The research, therefore, explains the future implications of artificial intelligence calculations in healthcare delivery and how it shapes how individuals think about their health to be transformative. The research creates a perception for the audience that population data obtained through artificial intelligence calculations change individuals’ perception about biology and how medications work, thus understanding their healthcare needs and how they work further increasing patient satisfaction and management (Shinners et al., 2019). The research focuses on how the implementation of air will enable innovation rather than exploring the aspect of individualized treatments and medicine (Lee & Yoon, 2021). Although the implementation of air calculation in healthcare delivery, the short-term opportunities are clear, which enable healthcare givers to population and bring benefits of satisfaction.
The discussion and aims of the research are to propose the 3 phase implementation of artificial intelligence calculations in healthcare delivery. The implementation process explains the importance of full potential in healthcare delivery and considers the influence of artificial intelligence calculations in individualized patient treatment and management (Jain et al., 2020). The proposal to implement artificial intelligence calculations is the solution to address the continuous routines and recurring administrative task that absorbs significant time from doctors, nurses, and stakeholder caregivers, which emphasize increasing the adoption of artificial intelligence calculations to optimize healthcare delivery operations (Lee & Yoon, 2021). The phase identifies how artificial intelligence technologies have augmented areas in healthcare such as radiology and ophthalmology.
In the second phase of the implementation of artificial intelligence calculations, the process will improve the radical shift from hospital base-treatment to remote caring of patients. The artificial intelligence systems allow patients to have individualized care, which virtual assistance promotes (Rahmatizadeh et al., 2020). The phrase highlights the importance of utilizing artificial intelligence calculations because of the broad application of its elements in nutrition, ecology, and neurology. The phase identifies why artificial intelligent calculations need to be applied, which needs cooperation from artificial intelligence providers and healthcare professionals (Lee & Yoon, 2021). The developing population and the complex nature of ailments need well-developed artificial intelligence technologies and the utilization of existing technologies in a transformative approach (LaRosa & Danks, 2018). The last phase would see more artificial intelligence calculations and applications informing clinical decision-making based on evidence-based practice (Jain et al., 2020). The common tool for implementation in this phase is a clinical decision support system from the basis of earlier trials of implementing the technology in healthcare delivery.
Research context
Artificial intelligence is vital for the transformation of healthcare. The research will explore how incorporating artificial intelligence calculations in healthcare could improve health outcomes, access to healthcare services, and patient experience. Artificial intelligence can improve efficiency and augment productivity for better healthcare for people (Olczak et al., 2021). Artificial intelligence also can help improve the practitioner’s experience, thus reducing work-related stress, and spent more time direct patient engagement, and reducing burnout. Healthcare is the major success in the history of humanity (Lee & Yoon, 2021). Medical science is improving, life expectancy is developing throughout the world, healthcare systems are faced with the increasing demand for services workforces that are rising, and the cost of running healthcare facilities is improving (Rahmatizadeh et al., 2020). The increasing demand for healthcare services is driven by increasing patient demand, a growing, and aging population, and shifting lifestyle choices (Jain et al., 2020). The research focuses on how patients’ rising context increases compressibility in delivery to a diverse population. Managing the needs of diverse patients is costly and demands healthcare systems to shift from the episodic care approach and move to a more proactive one, which is dynamic and focused on patient management.
The governments are burdened with the rising cost of healthcare. Healthcare delivery systems will not become sustainable if they do not shift and develop transformational structures. The healthcare systems do not need to hire more healthcare workers, nurses, and even midwives; rather, the healthcare systems should ascertain that time used for healthcare delivery is sustainable (Rahmatizadeh et al., 2020). The research emphasizes that investing in artificial intelligence calculation in healthcare will transform the system and solve the problems above (LaRosa & Danks, 2018). The research will explain how artificial intelligence improves the efficiency and effectiveness of care delivery (Lee & Yoon, 2021). Artificial intelligence leads to increased life savings treatments because of less time consumed (Olczak et al., 2021). The research thus highlights the parts of healthcare that can be improved and the positive impact it would create on healthcare delivery.
Through the emphasis and proposal, the report enters into the discourse of the influence of artificial intelligence in healthcare. In a few ways, the discussion on the impact of artificial intelligence in healthcare is the considerate efficacy of the system implementation brings to healthcare delivery (Olczak et al., 2021). The research develops new mythology for evaluating the importance of artificial intelligence calculations on the activities and skills of auctioneers (Lee & Yoon, 2021). The research is placed on the view that artificial intellect calculations and systems deliver benefits through healthcare delivery and should be implemented (LaRosa & Danks, 2018). Through analysis of various literature, the research identifies the perspective of frontline workers, startup executives, communities, and healthcare professionals, on the impacts and opportunities artificial intelligence brings to the healthcare delivery system.
Literature review
Healthcare involves the intersection of scientific data and human justifications. Usage of artificial intelligence calculations in healthcare brings technology and human judgment together and has a greater impact on healthcare delivery. Computers will perform tasks that would require human intelligence (Lee & Yoon, 2021). The aspect of artificial intelligence allows the canalization of data to work as human beings without programming (Jain et al., 2020). The capability of artificial intelligence in bringing change to diagnosis proves an important component for care delivery, diagnosis, cost, and outcomes of the care (Olczak et al., 2021). Artificial intelligence will transform healthcare delivery through big data policy and clinical decision support (Olczak et al., 2021).
Artificial Intelligence will shift the way clinical providers make decisions. Compared to computerized program systems, the systems play a vital role in health decision-making as it delivers data that assist in diagnosis, population health management, and treatment planning (Rahmatizadeh et al., 2020). The proposition for AI application in the healthcare industry is due to the amount of data that machine learning can harness, ranging from biomarker, pin type, genomic, delivery systems, and health records (LaRosa & Danks, 2018). The capability of the artificial intelligence systems in analyzing and harnessing data could help make decisions in the data-intensive process such as ophthalmology, pathology, and radiology; the later in the future will allow the performance of certain tasks autonomously (Jain et al., 2020). The advantage of the system is the ability to self-update, which allows enabled decision-making that can be applied practically in healthcare delivery.
Artificial intelligence reduces the administrative burdens laced on clinicians through informed and improved decision-making. With the advanced natural language processing capabilities, the system can help translate clinical notes, which implies that the data is entered once into the system (Naveen Ananda Kumar & Suresh, 2019). An artificial intelligence system in healthcare can allow the outsourcing of data from diverse sources, including her data, medical images, and consumer materials such as smartphones and other assorted medical devices (Olczak et al., 2021). The capability of artificial intelligence systems to pool data expands the treatment and diagnostic options available for clinicians, which would later transform the health outcomes and patient satisfaction while creating more specialized individual care (LaRosa & Danks, 2018). There is a rise in amounts of big data, which comes in large portions which prove difficult to analyze; the aptitude of artificial intelligence in analysis and predictive analytical features plays an important role in healthcare delivery (Naveen Ananda Kumar & Suresh, 2019). For instance, through comparative analysis, the artificial intelligence systems can assist nurses in determining the appropriate days an individual can spend in a hospital for complete recovery, which assists in planning and managing patients to prevent complications and reduce the impact of costly readmissions.
The studies on how artificial intelligence is likely to impact future work conclude that automation will influence the labor markets to different degrees. But the healthcare system has a probability of low automation at 35 percent. It is noted that the potential for automation is far from the perspective of adoption (Olczak et al., 2021). The healthcare workforce gap is widening, and the overall need for workers is rising gradually (Rahmatizadeh et al., 2020). The analysis clearly shows that automation would mitigate the risk of worker crisis in healthcare delivery (Lee & Yoon, 2021). The impact of artificial intelligence will not instead focus on automation to reduce the workload of professions, but it would lessen the redundant work (Naveen Ananda Kumar & Suresh, 2019). The transformative change produced by implementing artificial intelligence is an improved focus on individualized patient care (Jain et al., 2020). The main objective is to minimize recurring administrative tasks. Artificial intelligence improves healthcare activities and gives caregivers access to information that improves the quality of care and improves patient outcomes (LaRosa & Danks, 2018). The implication is that the artificial intelligence calculations improve the accuracy and speed of diagnosis, giving practitioners access to further knowledge and encouraging removing monitoring for advanced care (Naveen Ananda Kumar & Suresh, 2019). In healthcare delivery, learning will not be based on griming facts. Still, it will be based on the capitalization of innovation so that auctioneers have embedded artificial intelligence skills so that skills are well-shaped and sustainable.
Clinical engagement with patients will be more accessible through the decisions based on artificial intelligence healthcare systems. The rising question is how artificial intelligence would play a role in improving healthcare delivery in society (LaRosa & Danks, 2018). The range of artificial intelligence areas is the apps that asset patients in managing themselves and others, like online symptom checkers (Naveen Ananda Kumar & Suresh, 2019). Some applications and virtual assistance help improve healthcare delivery that, optimizes patient management that, improves population health, and enable the detection of disease at the early stages
Methodology
What is the implication of healthcare delivery? The research will include artificial intelligence calculations which influence healthcare. The research analyzes both the existing healthcare delivery approaches and how the healthcare needs of populations disrupt, and the need for informative strategies to address them. The analysis will also include applications utilizing artificial intelligence calculations that improve and enhance healthcare delivery, from daily operations to innovations that help manage diverse populations in the world. Through analysis, the research will measure the difference between stand-in healthcare that utilizes artificial intelligence and the others that give services without the involvement of artificial intelligence systems. The research goal is to gather 20 clinical staff and split them into two groups. The first group, as mentioned earlier, will give artificial intelligence-supported services, while the other group will give services without artificial intelligence assistance. After the experiment, the research will gather data from two groups and analyze them (LaRosa & Danks, 2018). Both groups will be interchanged and asked how artificial intelligence helped them in service delivery (Naveen Ananda Kumar & Suresh, 2019). The goal is to get measurable terms on how the clinicians in the two groups managed their patients, the difference in time usage amongst the different groups, and lastly, the level of patient satisfaction achieved through the different processes.
Conclusion
The research acknowledges the transformative and disruptive influence that accompanies the adoption of artificial intelligence systems and calculations in healthcare and how air calculations are shaping the sustainable future of healthcare delivery. Implementing artificial intelligence in healthcare has prevented it from being successful and beneficial, as cited by various research. The research analyzes both the existing healthcare delivery approaches and how the healthcare needs of populations disrupt, and the need for an informative strategy to address them. Artificial bits of intelligence will shift the way clinical providers make decisions. Compared to computerized program systems, the systems play a vital role in health decision-making as it delivers data that assist in diagnosis, population health management, and treatment planning. The proposition for artificial intelligence application in the healthcare industry is due to the amount of data that machine learning can harness. The transformative change produced by implementing artificial intelligence is an improved focus on individualized patient care.
Artificial intelligence improves healthcare activities and gives caregivers access to information that improves the quality of care and improves patient outcomes. Usage of artificial intelligence calculations in healthcare brings technology and human judgment together and has a greater impact on healthcare delivery. Computers will perform the task that would require human intelligence. The capability of artificial intelligence in bringing change to diagnosis proves an important component for care delivery, diagnosis, cost, and outcomes of the care. Artificial intelligence will transform healthcare delivery through aspects of big data policy and clinical decision support.
References
Jain, P., Anand, A., Saria, M., Kumari, R., Bothra, P., & Sultana, M. (2020, June 1). A Prototype Proposal for AI-based Smart Integrated Platform for Doctors and Patients. IEEE Xplore. https://doi.org/10.1109/ICRITO48877.2020.9197871
LaRosa, E., & Danks, D. (2018). Impacts on Trust of Healthcare AI. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society – AIES ’18. https://doi.org/10.1145/3278721.3278771
Lee, D., & Yoon, S. N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271
Naveen Ananda Kumar, J., & Suresh, S. (2019). A Proposal of smart hospital management using hybrid Cloud, IoT, ML, and AI. 2019 International Conference on Communication and Electronics Systems (ICCES). https://doi.org/10.1109/icces45898.2019.9002098
Olczak, J., Pavlopoulos, J., Prijs, J., Ijpma, F. F. A., Doornberg, J. N., Lundström, C., Hedlund, J., & Gordon, M. (2021). Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthopaedica, 1–13. https://doi.org/10.1080/17453674.2021.1918389
Rahmatizadeh, S., Valizadeh-Haghi, S., & Dabbagh, A. (2020). The role of Artificial Intelligence in Management of Critical COVID-19 patients. Journal of Cellular & Molecular Anesthesia, 5(1), 16–22. https://doi.org/10.22037/jcma.v5i1.29752
Shinners, L., Aggar, C., Grace, S., & Smith, S. (2019). Exploring healthcare professionals’ understanding and experiences of artificial intelligence technology used in the delivery of healthcare: An integrative review. Health Informatics Journal, 146045821987464. https://doi.org/10.1177/1460458219874641
Väänänen, A., Haataja, K., Vehviläinen-Julkunen, K., & Toivanen, P. (2021). Proposal of a novel Artificial Intelligence Distribution Service platform for healthcare. F1000Research, 10, 245. https://doi.org/10.12688/f1000research.36775.1