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Artificial intelligence (AI) technology penetrates different aspects of people’s business and everyday life. It can also be applied to such aspects of healthcare as caring for patients and managing administrative tasks. This innovative technology can enhance the continuous improvement of services and working conditions of healthcare providers and realize a new opportunity to predict and identify diseases and abnormalities. AI is a significant contribution to the development of healthcare because it provides the chance to surpass the abilities of humans and reduce the number of errors caused by human failures. The drivers for the innovation are the increase in the aging population, the National Health Service’s strategies to enhance the well-being of citizens and healthcare services’ quality, and the expansion of modern technologies in other domains (Al-Amoudi & Latsis, 2019).
The Institute of Cancer Research requires introducing this innovation to cope with the challenges of diagnosing and treating patients. The institute is located in London and specializes in all kinds of cancers and tumors. My role in the service would be to prepare the project management plan to implement the innovation. The agile approach helps introduce AI innovations because it guarantees the project’s adaptability to changes, following five stages of change, and the ability of communication, collaboration, and trust to influence the product.
Project management plays an essential role in the process of planning the implementation of innovation. It is the combination of techniques, methods, knowledge, and expertise used by the manager to organize the realization of a specific project (Demirkesen & Ozorhon, 2017). The most significant characteristics of project management are the particular timescale and budget limits, which frame the time dedicated to the project and the financing of its implementation.
The project is a type of endeavor with specific purposes and benefits for the organization (Tereso et al., 2018). One of the essential components of project management is identifying the project’s purpose, which helps to understand what peculiar outcomes are anticipated. Then, it includes leading and motivating the team organized to implement the plan (Lock, 2017). Next, a manager has to manage conflicts, risks, and issues developed in the process of realization. Finally, a manager has to monitor the progress of the team and compare it with the plan.
The most common approaches to project management include traditional and agile methods of implementation, which differ in their structure. The traditional approach presupposes the application of linear strategy when all the processes occur in sequence. According to this method, all the projects evolve according to the predetermined lifecycle, including plan, design, testing, production, and support (Musawir et al., 2017). The agile strategy, on the other hand, is known for its flexibility. According to this approach, such elements as teamwork, collaboration with the customer, and project adaptability are essential (Stoddard, Gillis, and Cohn, 2019).
These characteristics signify that achieving the necessary result is the central focus of the managers choosing this method. They comprehend that the requirements and situations may change during the process of implementation. Consequently, they may bring changes to guarantee that the predetermined outcome is achieved.
The agile approaches are more popular among managers because these methods make the projects more flexible. In particular, a flexible strategy allows managers to make changes when other interconnected components of the project change their course or do not correspond to the predictions (Mergel, Ganapati, and Whitford, 2020). This ability to make changes without interfering with other aspects of work provides the chance to continue the project.
The next advantage concerns the complex plans that comprise multiple stages of implementation and complex interactions between team members. When a client introduces new requirements, the plan that follows the traditional plan should be changed completely, and all the processes should start from the beginning because the project is linear (Kaim, Härting, and Reichstein, 2019). On the other hand, agile methods allow managers to adapt to changes and proceed. Additionally, these methods enable the clients to validate the plan’s steps (Loiro et al., 2019). It allows them to observe whether these elements of the project correspond to the requirements and expectations.
The application of agile strategies in introducing AI in healthcare settings provides the opportunity to test the changes to bring the most appropriate solutions. Since the traditional method is inflexible, it does not suit the case of introducing innovations because it fails to manage the changes. Consequently, the flexible model is suitable because it allows the manager to make necessary changes during the project’s implementation (Holden, Boustani, and Azar, 2021).
In particular, the introduction of machine learning might require healthcare professionals’ supervision and inspection of the functionality of the technique. The complex form of machine learning, deep learning, helps to identify cancer using radiology images. The supervisors have to check the application to determine whether the diagnosis is relevant (Verma et al., 2021). In addition, they have to understand whether the recommended treatments are sufficient and the drug development is timely. If some errors are detected, the flexible strategies allow the managers to make necessary changes that prove beneficial for the clinic.
The introduction of agile techniques enhances communication and cooperation between the team members. The project requires the organization of a specific team, which should include healthcare workers, managers, data scientists, and engineers (Glazkova, Fortin, and Podladchikova, 2019). The application of agile methods allows the creation of several teams to work on different AI innovations. For instance, the introduction of diagnosis and treatment applications might help apply machine learning to identify the types of cancer and the methods of treating a disease (Davenport & Kalakota, 2019).
The development of these teams provides the opportunity to make the implementation process divided into several iterative cycles. Completing these cycles requires cooperation and interaction between team members and different groups (Wiencierz, 2021). These groups can be self-organized; they can diagram workflows and create tests for the software and implementation process. The weekly meetings provide the chance to review progress and analyze the completed stages discussing the achievements and necessary corrections. Consequently, the cooperation and communication between the team members promoted by agile methods help shorten the time needed to exchange essential data, improve understanding, and enhance collaboration.
The implementation of AI in the institute project consists of several stages. The business case and project charter would be developed during the first stage, the project initiation phase (Altunel, 2017). The project charter would include implementing AI in the cancer institute, project constraints, including risks and customer satisfaction, timeline, and budget. During the next stage, project planning, the team should work together to identify the client’s characteristics, what the customers expect from the AI’s use, and prioritize the plan’s elements based on their value. The development phase comprises working on the product’s first iteration (Bergmann & Karwowski, 2018).
In particular, the deep learning techniques and diagnosis and treatment applications might evolve into a usable product, revised during successive iterations. During this period, the teams would cooperate, test the product, and follow the guidelines to prepare the solution, which is ready to be released into production (Gren, Goldman, and Jacobsson, 2019). In the fourth phase, production, the patients might use the applications, and the teams should monitor their functioning. The final stage, project closing, comprises the completion of the plan and analysis of its results.
Communication, collaboration, and trust are the most significant human elements in the agile project of implementing AI in healthcare settings. According to Malik, Sarwar, and Orr (2021), face-to-face conversations and the exchange of information are crucial for project performance. Healthcare professionals, engineers, and managers have to interact during the plan’s implementation to guarantee that they share their ideas and observations and follow the same purpose.
Noguera, Guerrero-Roldán, and Masó (2018) explain that collaboration and the agile approach are inseparable in this type of project. In particular, collaboration provides the opportunity to enhance productivity and ensure that healthcare providers and engineers work together to find the most appropriate solution in applying AI in healthcare settings. Trust influences how the team members communicate, share thoughts, and comprehend each other (Imam & Zaheer, 2021). The development of trust might help the healthcare workers be ready to share their findings, observations, and examinations during all the stages of project implementation. Consequently, combining these human factors might bring the project to success and guarantee the appropriate introduction of AI in the institute.
Thus, agile strategies would make the project successful because they make the plan adaptable, divided into five specific stages, and effective due to communication, collaboration, and trust. The Institute of Cancer Research requires the introduction of AI innovations to improve the process of diagnosing and treating patients. The agile approach is the most suitable method of implementing new applications because it allows making the necessary changes and enhances cooperation between team members.
The plan’s implementation would be divided into five stages, project initiation, planning, development, production, and project closing. Such human elements as communication, collaboration, and trust might be considered to guarantee that the project achieves its goal. Implementing these aspects might promote healthcare services’ quality because it would ensure that patients experience better quality care and that hospitals benefit from innovative technologies.
Reference List
Al-Amoudi, I., and Latsis, J., 2019. Anormative black boxes: Artificial intelligence and health policy. In Post-Human Institutions and Organizations (pp. 119-142). Routledge.
Altunel, H., 2017. Agile project management in product life cycle. International Journal of Information Technology Project Management, 8(2), pp.50-63.
Bergmann, T. and Karwowski, W., 2018. Agile project management and project success: A Literature Review. Advances in Intelligent Systems and Computing, pp. 405-414.
Demirkesen, S. and Ozorhon, B., 2017. Impact of integration management on construction project management performance. International Journal of Project Management, 35(8), pp.1639-1654.
Davenport, T. and Kalakota, R., 2019. The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), pp. 94-98.
Glazkova, N., Fortin, C. and Podladchikova, T., 2019. Application of lean-agile approach for medical wearable device development. 2019 14th Annual Conference System of Systems Engineering (SoSE).
Gren, L., Goldman, A. and Jacobsson, C., 2019. Agile ways of working: A team maturity perspective. Journal of Software: Evolution and Process, 32(6).
Holden, R., Boustani, M. and Azar, J., 2021. Agile Innovation to transform healthcare: innovating in complex adaptive systems is an everyday process, not a light bulb event. BMJ Innovations, 7(2), pp. 499-505.
Imam, H. and Zaheer, M., 2021. Shared leadership and project success: The roles of knowledge sharing, cohesion and trust in the team. International Journal of Project Management.
Kaim, R., Härting, R. and Reichstein, C., 2019. Benefits of Agile Project Management in an Environment of Increasing Complexity—A Transaction Cost Analysis. Intelligent Decision Technologies 2019, pp.195-204.
Lock, D., 2017. The essentials of project management. Routledge.
Loiro, C., Castro, H., Ávila, P., Cruz-Cunha, M., Putnik, G. and Ferreira, L., 2019. Agile project management: A communicational workflow proposal. Procedia Computer Science, 164, pp.485-490.
Malik, M., Sarwar, S. and Orr, S., 2021. Agile practices and performance: Examining the role of psychological empowerment. International Journal of Project Management, 39(1), pp.10-20.
Mergel, I., Ganapati, S. and Whitford, A., 2020. Agile: A New Way of Governing. Public Administration Review, 81(1), pp.161-165.
Musawir, A., Serra, C., Zwikael, O. and Ali, I., 2017. Project governance, benefit management, and project success: Towards a framework for supporting organizational strategy implementation. International Journal of Project Management, 35(8), pp.1658-1672.
Noguera, I., Guerrero-Roldán, A. and Masó, R., 2018. Collaborative agile learning in online environments: Strategies for improving team regulation and project management. Computers & Education, 116, pp.110-129.
Stoddard, M., Gillis, B. and Cohn, P., 2019. Agile project management in libraries: Creating collaborative, resilient, responsive organizations. Journal of Library Administration, 59(5), pp.492-511.
Tereso, A., Ribeiro, P., Fernandes, G., Loureiro, I. and Ferreira, M., 2018. Project management practices in private organizations. Project Management Journal, 50(1), pp.6-22.
Verma, S., Popli, R. and Kumar, H., 2021. The Agile Deployment Using Machine Learning in Healthcare Service. Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences, pp.879-890.
Wiencierz, C., Röttger, U. and Fuhrmann, C., 2021. Agile Cooperation between Communication Agencies and Companies. International Journal of Strategic Communication, 15(2), pp.144-158.
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