Artificial Intelligence Tools in Healthcare

Introduction

Artificial intelligence or AI is the use of algorithms and software solutions to enable computers to analyze data and make conclusions without people’s help. Nowadays, AI is increasingly used in healthcare, and the purpose of such technologies is to improve clinical decision-making and reduce the impact of the human factor on patient outcomes. Nursing robots, decision support systems, and other examples indicate that AI is changing nursing and healthcare by removing unnecessary workload and increasing accuracy in medical treatment and care.

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Example of Artificial Intelligence

AI is among the current technological advancements that have significant potential in many fields of activity, including healthcare delivery and nursing care. The reasons to use AI technologies and software applications are strictly interconnected with some economic considerations, such as the problem of increasing healthcare costs (Jiang et al., 2017). AI in the form of up-to-date software solutions for specialists in the field can reduce the amount of time needed to perform some routine tasks, which has implications for costs.

As for particular examples, the development of robots that can take on some duties of nurses is among the most promising applications of AI in healthcare. Nowadays, the majority of robots that learn from previous experiences and are capable of performing some physical tasks typically associated with nursing duties are developed and used in China and Japan (Harrington, 2018). The creation of robotic nurses is an important scientific achievement because programming and helping machines to learn is not as difficult as making them act in physical space based on this knowledge (Harrington, 2018). This fact explains why it is still preferable to use medical and nurse robots under human supervision.

Interestingly, modern nurse robots vary in terms of their physical opportunities. For instance, Actroid-F, a realistic robot presented nine years ago, can observe patients, support them, and even display emotions when interacting with people (Harington, 2018). In general, modern machines do not resemble humans to the same extent and are designed to perform more specific tasks. For example, service robots can help nurses to lift and move patients or facilitate the delivery of necessary documents and the results of medical tests (Harrington, 2018; Mettler, Sprenger, & Winter, 2017). Personally, I have not utilized the assistance of service robots to perform my duties, but it is clear that they can benefit nursing and clinical practice in many ways. Even though modern AI robots are imperfect in their recognition of emotions, they perform basic tasks (transportation of patients, drug administration, laundry and cleaning services, etc.) with accuracy (Mettler et al., 2017). Taking that into consideration, such devices can facilitate patient care by encouraging workload optimization.

Apart from economic benefits, the reasons why the topic is gaining more and more attention include such applications’ convenience for all people involved in the process of care delivery. By implementing high-quality solutions that decrease the amount of work to be performed manually, it is possible to improve the situation with staff burnout rates (Jiang et al., 2017). The fruits of technological progress used in healthcare organizations can help reduce the workload on doctors and nurses and make sure that each patient receives enough attention.

Continuing on the advantages of AI linked with the quality of medical and nursing care, the role of robots and other devices in reducing human errors cannot be overstated. To make the best out of this advantage, hospitals implement computer systems that prevent nurses from making drug administration mistakes (Jiang et al., 2017; Lee et al., 2018). In many cases, the analysis of a patient’s health data and medical test results is a complicated task that requires maximum concentration, and people cannot compete with AI in this regard. Thus, the use of modern technology allows minimizing the impact that the imperfection of the human mind can have on patient outcomes.

Personal Experience

AI in healthcare is the topic that impacts many people in countries with developed economies since its real-life applications can be found in many hospitals. As for my personal experience with AI, I can evaluate modern technology from the perspective of a patient. A few years ago, I had a minor health problem and decided to consult a doctor who was my parents’ friend. Many tools were used to manage my case and make a correct diagnosis. Among them was DXplain, one of the first decision support systems facilitating clinical decision-making (Martinez-Franco et al., 2018). Since I knew this doctor personally and my problem was not life-threatening, he was ready to discuss the system with me. With the help of the mentioned tool, the doctor managed to clarify the diagnosis and provide the necessary education.

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The positive aspect of this experience is strictly interconnected with the system’s accuracy in diagnostic decision-making and ability to analyze data systematically. From my experience and the doctor’s impressions, the system could draw conclusions from large amounts of patient data, and this process did not require a lot of time. In addition to that, special attention should be paid to such tools’ diagnostic accuracy in real-life cases. In relation to my problem, the clinical decision support system proved to be effective, as it was clear from my health outcomes. Moreover, it is important that such conclusions align with modern experimental findings. For instance, according to Martinez-Franco et al. (2018), the diagnostic accuracy rate of DXplain exceeds 80%, which makes such tools extremely helpful to doctors who lack clinical experience with certain health concerns. At the same time, to improve this experience, even more, the hospital management could have upgraded its IT assets. Overall, taking such systems’ benefits into account, they can improve patient outcomes and save healthcare specialists’ time.

Due to their advantages, AI-based systems that facilitate clinical decision-making can help to increase future doctors’ knowledge in specific areas. Thus, explain and similar tools are widely used in medical education and staff training (Martinez-Franco et al., 2018). As a patient, I noticed many advantages of AI applications in diagnostic processes. However, in real life, qualified professionals do not use them on a regular basis, partially due to the complexity of everyday clinical practice (Martinez-Franco et al., 2018). Instead, such systems are often regarded as a perfect tool helping students to deal with problems related to the results of diagnostic tests and disease classification (Martinez-Franco et al., 2018). Thus, it is not valid to make generalizations and claim that this way of using AI is extremely popular among qualified and experienced healthcare professionals.

Conclusion

To sum it up, the uses of AI have a significant impact on healthcare and remove the need to perform some energy-consuming tasks. AI serves an important purpose and allows machines and online applications to help specialists even when it comes to unpredictable situations. The analysis of helpful inventions based on AI and my personal experience can positively influence my approach to work. For instance, to improve patients’ experiences, I will try to be more open to innovation.

References

Harrington, L. (2018). Nurse robots. AACN Advanced Critical Care, 29(2), 107-110.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S.,… Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243.

Lee, J. Y., Song, Y. A., Jung, J. Y., Kim, H. J., Kim, B. R., Do, H. K., & Lim, J. Y. (2018). Nurses’ needs for care robots in integrated nursing care services. Journal of Advanced Nursing, 74(9), 2094-2105.

Martinez-Franco, A. I., Sanchez-Mendiola, M., Mazon-Ramirez, J. J., Hernandez-Torres, I., Rivero-Lopez, C., Spicer, T., & Martinez-Gonzalez, A. (2018). Diagnostic accuracy in Family Medicine residents using a clinical decision support system (DXplain): A randomized-controlled trial. Diagnosis, 5(2), 71-76.

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Mettler, T., Sprenger, M., & Winter, R. (2017). Service robots in hospitals: New perspectives on niche evolution and technology affordances. European Journal of Information Systems, 26(5), 451-468.

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