The decision-making process in the healthcare sector is complex, multi-component and largely dependent on the qualifications of the healthcare professional. The complexity of the existing factors is in many ways an obstacle to the implementation of any automated decision-making system. Until recently, technical solutions were unable to provide something qualitatively new. Therefore, the few scientific articles available on this topic focus mainly on the negative aspects (Holmes et al., 2016). However, artificial intelligence technologies are becoming more popular in recent years, bringing significant changes to the clinical process (Nath, 2019). The purpose of this paper is to study one such project, the AI-Pathway Companion, to analyze the impact of this information system on the decision-making process and assess the role of nurses in the application of this system.
AI-Pathway Companion is a product of Siemens’ healthcare solutions division. As the name of the product itself implies, its distinguishing feature is the use of artificial intelligence technologies (“AI-Pathway companion,” n.d.). The product’s primary purpose is to help establish a diagnosis and make therapeutic decisions following a specific disease. According to the official website and available scientific data, this system has already been successfully tested in oncology, cardiology, and infectious diseases. First of all, simplification and assistance to employees are made by compiling all patient data in a convenient form. In addition, the AI component analyzes the current state of patients, comparing them with existing guidelines and, through a diagram, demonstrates the following possible stages of the development of therapy (“AI-Pathway companion,” n.d.). Thus, the system acts as a supporting source of information.
AI-Pathway Companion is a clinical decision support system (CDSS), i.e., software suggesting treatment options based on patient characteristics. In addition, this CDSS is non-knowledge based, working based not on data programmed into it but on the principles of artificial intelligence (Ghosh & Aslam, 2021). This CDSS compiles all information related to this disease and patient, thus saving the time usually spent manually entering patient information into the MDT system (“AI-Pathway companion,” n.d.). Thus, the efficiency of working time is significantly increased, which is demonstrated by the example of the implementation of this system in the treatment of lung cancer. As practice shows, 43% of patients have to come to several appointments due to the lack of specific data (“AI-Pathway companion lung cancer,” n.d.). Having a digital assistant can reduce the number of visits, increasing the efficiency of each one.
In addition to the AI-Pathway Companion, there are other systems, including those that involve the AI component. At the moment, learning health systems are widespread, and many resources have been devoted to their study and development (Ghosh & Aslam, 2021). Since the project is under development and applications are being added gradually, this application is not suitable for all scenarios. However, the AI-Pathway Companion can be used effectively in the context of prostate cancer and lung cancer. For the successful implementation of this system, cooperation with Siemens Healthineers is required, which ensures the adjustment of the system to the needs of a particular institution. The AI-Pathway Companion is provided with specialized software targeting a wide variety of devices, including mobile devices (“AI-Pathway companion,” n.d.). The implementation of the system includes, among other things, the processes of docking with various databases. After carrying out this process, the artificial intelligence component can start processing information for each case.
However, despite all the positive aspects, there is a question of the cost of introducing such services into operation. First, systems based on artificial intelligence require a complex infrastructure, which makes the task of implementing such products extremely difficult for small institutions (Ghosh & Aslam, 2021). Secondly, there are regulatory barriers: the need to obtain permission to install such a system. Unfortunately, the approximate costs of implementing the system are difficult to estimate as they vary from institution to institution. However, it should be noted that IT technologies in recent years have become more widespread and, as a result, cheap (Nath, 2019). Therefore, although most executives are still wary of such projects, their availability is increasing significantly, and their usefulness to health care far exceeds the possible costs.
The higher ranks often do not want to implement such programs precisely because of the high cost. Therefore, the effectiveness of using such methods should be assessed by those who will directly work with these systems to show the positive effects of these projects. In this context, this role can be assigned to nurses as people directly working with patients and many documentations and databases. For them, it is easiest to assess the benefits of transferring, for example, working with databases from manual to automatic mode. An obstacle to such an assessment is the lack of technical qualifications of the staff. However, in this case, innovative collaborations such as nurse-engineer are the solution (Glasgow et al., 2018). Since implementing such CDSSs is creative, the medical institution staff must match the solutions used. Nurses with a technical background can most accurately assess both the positive and negative costs of implementing the system, which is why their role and opinion in this matter is crucial. When combined with executives’ views, the most accurate decision can be made.
Thus, the AI-Pathway Companion is an effective system that can assist in clinical decision-making with the help of artificial intelligence. This project is ultimately aimed at simplifying and reducing staff paperwork so that specialists can pay more attention directly to patients. However, the use and implementation of such a system are associated with the difficulties of technical performance, which can only be qualified professionally by nurses. Moreover, their role and contribution to implementing these programs are fundamental since no such system can be effectively and efficiently implemented without qualified input from the application level.
AI-Pathway companion. (n.d.). Siemens Healthineers. Web.
AI-Pathway companion lung cancer. (n.d.). Siemens Healthineers. Web.
Ghosh, A., & Aslam, M. (2020). A learning health system for a clinical problem -Lay summary. International Journal of Science and Innovative Research, 2(1). 4-10.
Glasgow, M. E. S., Colbert, A., Viator, J., & Cavanagh, S. (2018). The nurse‐engineer: a new role to improve nurse technology interface and patient care device innovations. Journal of Nursing Scholarship, 50(6), 601-611. Web.
Holmes, E.S., dos Santos, S.R., Almeida, A.F., de Oliveira, J.H.D., de Carvalho, G.D.A., da Fonsêca, L.D.C.T., de Sousa Costa, M.B., Medeiros, J.B., & Neto, E.D.A.L. (2016). Health information systems in the decision-making process in primary care. International Archives of Medicine, 9. Web.
Nath, D. (2019). AI’s impact on in vitro diagnostics. Clinical Lab Products. Web.