Technology and Healthcare Ethics: Machine Learning

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Table of Contents

Introduction
Machine Learning and Healthcare Ethics
Patient Autonomy
Patient Confidentiality
Conclusion
References

Introduction

Machine learning is a technology that has attracted the attention of many researchers and theorists in medical practice. Engineers have been focusing on artificial intelligence to produce machines that can perform repeated tasks based on acquired experience. Although the technology is promising especially in healthcare, numerous concerns related to healthcare ethics have been raised by many theorists. This paper examines the impact of machine learning on these two ethical aspects: patient autonomy and confidentiality.

Machine Learning and Healthcare Ethics

Modern technologies in healthcare such as machine learning present numerous opportunities that can transform the sector. The process entails the use of computers and machines that can learn from collected data, diagnose medical conditions, and provide adequate treatment to patients (Mandal, 2017). For instance, technology has made it possible for companies such as Google to develop devices that can identify tumors. Using appropriate databases and algorithms, machine learning can be developed to analyze medical images and locate abnormalities (Byrne, 2017). Experts in the field argue that practitioners and physicians will be able to use the technology to gather opinions and eventually improve reliability and efficiency. This is the reason why the technology has found its way in medical applications.

Although the expertise is capable of replacing certain jobs or roles, the undeniable fact is that caregivers must be present physically to offer compassionate care and human touch. This means that patients will always be in need of medical practitioners undivided attention. Machine learning is, therefore, capable of improving the speed, quality, and efficiency of healthcare delivery (Wang & Alexander, 2015). Despite the promising nature of machine learning, some skeptics have indicated clearly that the development presents numerous ethical issues that must be taken seriously.

Patient Autonomy

The first ethical implication of technology is patient autonomy. Medical ethics is founded on basic principles that dictate the practice of nurses and physicians (Wang & Alexander, 2015). These principles include beneficence, non-maleficence, justice, and autonomy. Patient autonomy is an ethical concept whereby individuals have the right to make desirable decisions in accordance with their health demands. Ethicists in healthcare believe strongly that patients should be allowed to come up with their personal insights and viewpoints without any form of coercion (Denecke et al., 2015). The patient must outline the best procedures for care delivery after examining the benefits and risks involved.

The implementation of machine learning can ensure patients receive quality and timely medical services. This aim can be realized since computers are capable of using an optimized algorithm. However, machine learning can be employed to complete various medical activities without focusing on the issue of patient autonomy. Throughout the care delivery process, such techniques cannot make sure patients insights, needs, and decisions are taken into consideration (Malin, El Emam, & OKeefe, 2013). This issue raises numerous concerns because the autonomy of the targeted patient is ignored. This fact indicates that the application of machine learning can threaten the ethical principle.

The use of machine learning might not allow patients to offer their insights or ideas throughout the care delivery process. This development explains why the concept of patient autonomy might be disregarded when technology is applied in different healthcare settings. Some programmers have acknowledged that future machine learning processes will be advanced in such a way that they can address this ethical concern (Byrne, 2017). The expectation is that technology can be designed in a professional manner to ensure patients can make appropriate decisions that inform every process.

Robotic technologies can deviate from their assigned roles due to specific factors such as corruption of programmed information or hacking. This challenge explains why machine learning is a technology that might be unable to support or improve patient autonomy (Denecke et al., 2015). Programmers and pioneers of machine learning must, therefore, be on the frontline to consider emerging ethical issues that can affect a patients autonomy throughout the medical care delivery process.

Patient Confidentiality

Patient confidentiality is a critical area that governs the practice of nurse practitioners, physicians, and caregivers. Hospitals and clinics have vast stores of confidential patient information that must be protected from unauthorized access. The recorded information captures patients conditions, treatment regimes, care delivery models, and desired health outcomes (Gill, 2017). Patient confidentiality is an ethical concept aimed at protecting private data or information. Machine learning is a technology that can be targeted by hackers to monitor or access various health records. Crucial files such as scans, medical images, and X-rays should be protected to ensure they are unavailable to unauthorized persons.

Autonomous machines are capable of undertaking numerous roles or functions without any form of human intervention. Unfortunately, any form of error can result in unexpected data sharing. Machines would not seek consent to share patients data or information with other users. The issue of privacy explains why companies and health institutions might be unable to develop powerful life-saving machines that can offer quality interventions (Mandal, 2017). Any unauthorized access will be against this ethical standard. Hospitals with shared systems might be forced to come up with new regulations to ensure the needs of every patient are met.

Jordan and Mitchell (2015) indicate that developers should consider the issue of patient confidentiality to ensure machine learning is capable of promoting privacy. This approach will ensure data access, interpretation, and implementation are controlled to minimize the chances of patient identification. Homomorphic encryption has also been suggested whenever embracing the use of machine learning. The devices should be programmed in such a way that they can use data effectively and take the issue of informed consent seriously. The ultimate goal is to safeguard confidential data. The move will support the ethical needs of every patient. If this ethical concern is not addressed, then it might take some time before patients and healthcare workers can appreciate the importance of machine learning.

Despite the above hurdles, many people believe strongly that innovation is a new opportunity that can be tapped by medical specialists to maximize the privacy of health information and deliver high-quality medical support to patients in need of high-quality services (Kuo, Kim, & Ohno-Machado, 2017). The technology can speed up the rate at which conditions are diagnosed and treated. The important objective should be to identify the unique opportunities of machine learning and use them to address emerging risks that can make it unethical.

Conclusion

Machine learning is a powerful technology that can improve the quality of medical services available to the greatest number of patients. However, this development posses numerous ethical concerns such as patient confidentiality and autonomy. Since these are moral principles of medical practice, developers and users of the technology must begin by addressing these issues in order to protect the rights of every person. This move will ensure the technology supports peoples health needs while at the same time protecting their ethical rights.

References

Byrne, M. D. (2017). Machine learning in health care. Journal of PeriAnesthesia Nursing, 32(5), 494-496. Web.

Denecke, K., Bamidis, P., Bond, C., Gabarron, E., Househ, M., Lau, A. Y., & Hansen, M. (2015). Ethical issues of social media usage in healthcare. Yearbook of Medical Informatics, 10(1), 137-147. Web.

Gill, K. S. (2017). Uncommon voices of AI. AI & Society, 32(4), 475-482. Web.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. Web.

Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211-1220. Web.

Malin, B. A., El Emam, K., & OKeefe, C. M. (2013). Biomedical data privacy: Problems, perspectives, and recent advances. Journal of the American Medical Informatics Association, 20(1), 2-6. Web.

Mandal, I. (2017). Machine learning algorithms for the creation of clinical healthcare enterprise systems. Enterprise Information Systems, 11(9), 1374-1400. Web.

Wang, L., & Alexander, C. A. (2015). Big data in medical applications and health care. Current Research in Medicine, 6(1), 1-8. Web.

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