Ethics at the Intersection of Healthcare and Technology: A Call for Transdisciplinary Wisdom

Introduction

The section highlights the extensive evidence supporting the positive impact of technology in healthcare.

It identifies several categories where technology has proven beneficial, including integrated management of information technology, medical images, electronic medical records, portable and mobile devices, access to e-health, electronic telemedicine, and privacy of medical data.

These advancements have significant implications for health care providers.

Technology has had a significant impact on healthcare, improving diagnosis, treatment, and cost-effectiveness. However, it has also raised ethical concerns. The use of technologies like CT scans, ultrasound, MRI, and PET scans has become commonplace in healthcare. Moreover, the integration of big data, artificial intelligence, and machine learning has transformed healthcare delivery, data collection, diagnosis, treatment, and disease prediction. This rapid technological advancement brings forth challenges that require a multidisciplinary approach to address, involving technology developers, healthcare professionals, bioethicists, and the population. Neglecting these challenges could jeopardize the safety and security of healthcare. As technology in healthcare expands beyond treating illnesses and injuries to include genomics, diagnostics, surveillance, and big data, ethical issues shift towards integrity and equity for individuals, communities, and healthcare systems. However, there is a lack of research on the impact of technology on individual and community health, with most studies focusing on the intent of technology use rather than its consequences. To address this gap, a broader approach to ethical issues related to health technology is needed, encompassing training, research design, funding, presentation, and publication. Furthermore, it is insufficient to solely examine the ethics of technology functions; a comprehensive ethical analysis of digital health technologies' impact on individuals, communities, and healthcare systems is necessary. This requires a sociotechnical system for the adoption of digital health technologies in healthcare ecosystems, with a focus on social justice within the ethical framework.

Impact of technology on healthcare

This section discusses the impact of technology on the patient journey in healthcare. It highlights the various areas where technology can improve healthcare, such as workflow, teamwork, and family engagement. The integration of technology is crucial in designing healthcare environments. The use of big data and artificial intelligence (AI) can aid in decision-making, disease surveillance, and planning. Technology adoption in healthcare delivery is focused on information platforms like data collection technology, market intermediaries, remote and on-demand healthcare services, augmented and virtual reality, blockchain-based Electronic Health Records, cloud services, and intelligent data analysis. These advancements have transformed communication between stakeholders and enabled remote consultations. Wearable monitoring devices also provide connectivity opportunities but raise concerns regarding data privacy and data breaches. Addressing challenges like data interoperability, standardization, bias in algorithms, reimbursement, quality improvement, and ethical issues is essential. Data protection is a major ethical concern due to the potential misuse and theft of healthcare data. Biased AI algorithms can lead to unfair outcomes, highlighting the need to reduce biases for more equitable health outcomes.

The bioethics framework and healthcare professionals

The main points of this section are as follows:. 1. The principal goal of healthcare is to relieve pain and suffering, improve functional status, and provide care and support to patients. 2. Transparent and sensitive end-of-life conversations are crucial to preserve autonomy and dignity while ensuring quality of life. 3. Predictive, Preventive and Personalised Medicine (PPPM) is driving efforts to prevent and mitigate diseases through a multidisciplinary approach and innovative technology. 4. Secondary goals of medicine include medical research, education, clinical governance, and professional governance, which must align with the primary goals. 5. Healthcare professionals must prioritize patient welfare and avoid conflicts of interest, particularly regarding commercialization and data exploitation. 6. Upholding scientific standards and promoting evidence-based practice is essential in healthcare. 7. Professional organizations and regulators have provided guidelines and codes of conduct to ensure standardized practice. 8. The biopsychosocial model of medicine emphasizes the importance of considering psychological, social, economic, and political factors in addition to the physical impact of illness. Overall, this section highlights the diverse goals and responsibilities of healthcare professionals, the need for ethical and evidence-based practice, and the importance of considering the holistic impact of illness on individuals and communities.

Figure 2: Biopsychosocial and economic impact of illness Adapted from [22]

This section emphasizes the importance of bioethics in addressing ethical, social, and legal issues within civil society. It highlights the need for a comprehensive framework to understand and address the impact of technology on individuals in healthcare. The suggested bioethics framework should encompass the social, economic, political, technological, and environmental domains.

Beneficence -Medical beneficence and good quality healthcare

This section emphasizes the responsibility of healthcare professionals to use various interventions and technologies to improve the lives of individuals while prioritizing their well-being and avoiding harm. The adoption of healthcare technology should focus on providing good quality healthcare and should involve continuous learning and improvement in areas such as patient safety, effectiveness of care, patient-centered care, timeliness of care, efficiency of care, and equitable nature of care. The language used in this context emphasizes the proactive effort of professionals to do good rather than simply avoiding harm.

Beneficence Nonmaleficence

This section emphasizes the importance of ensuring that technology in healthcare aligns with the goals of providing high-quality medical care. Before implementing new technologies, it is crucial to have strong evidence supporting their beneficial, effective, and safe outcomes. Healthcare professionals also have a responsibility to collaborate with other professionals and disciplines to reduce medical errors, enhance patient safety, minimize excessive use of healthcare resources, and optimize care outcomes.

Nonmaleficence

This section discusses two important principles in healthcare ethics: nonmaleficence and veracity/fidelity. Nonmaleficence refers to the duty of healthcare professionals to avoid harm to patients and the community, encompassing not only physical but also mental, social, and economic harm. Healthcare professionals should carefully consider the benefits and risks of interventions to ensure they do not cause harm. Veracity and fidelity involve the honest and complete communication of information to patients. Healthcare professionals should provide sufficient, clear, and contextualized information before and after treatment, ensuring patients are well-informed and have given informed consent. In cases of medical errors that harm patients, prompt disclosure is crucial to maintain trust. Reporting and analyzing mistakes help prevent future errors, provide appropriate compensation and remediation, and minimize ethical and legal consequences. Additionally, in the context of technology in healthcare, timely investigations and truthful information sharing are essential when errors or injuries occur.

Autonomy

This section emphasizes the importance of patient autonomy and informed consent in healthcare. It highlights the need for healthcare professionals to respect individuals' rights to make decisions about their own body, health, and lifestyle. The section acknowledges that certain societies may have a paternalistic approach to healthcare, which can pose challenges to autonomy. It stresses the requirement for healthcare professionals to provide full disclosure of information to enable patients and their families to make informed decisions. The section also discusses the impact of electronic medical records on patient confidentiality and the need to protect personal information. It concludes by outlining guiding principles for respecting patient autonomy and obtaining informed consent, as well as guidelines for preserving patient privacy and confidentiality in the digital era.

Justice

This section discusses the concepts of social justice and distributive justice in bioethics, particularly in relation to the fair distribution of healthcare resources. It highlights the general principles that guide the equitable distribution of these resources, such as equal share, need, effort, contribution, merit, and freemarket exchanges. However, adhering to these principles can be challenging and requires guidelines and structures to ensure social justice in healthcare. One way to address conflicts of interest is through the incorporation of unbiased third-party opinions, often facilitated by local ethical committees or boards. The section also emphasizes the commitment of healthcare professionals to promote justice in the healthcare system, including the elimination of discrimination based on various social categories. It underscores the importance of health equity and access, particularly for vulnerable populations who may face challenges in using healthcare technologies.

Healthcare Technology and Ethics

This section discusses the role of big data, cloud computing, artificial intelligence (A.I.), and machine learning in healthcare. It emphasizes the need for specialized analytical software to handle and analyze the large and complex data sets generated in healthcare. The integration of data science, A.I., and machine learning can enhance the healthcare system by providing comprehensive understanding of diseases and enabling data-driven decision-making. Cloud computing offers a platform for efficient access to healthcare data. A.I. involves developing algorithms to replicate tasks that require human intelligence, while machine learning allows for pattern recognition and prediction based on prior behavior. These technologies have applications in diagnosis, treatment, research, drug development, and administrative tasks. However, the adoption of these technologies faces challenges due to resistance from patients and healthcare professionals who prefer traditional models of interaction. Trust and transparency are crucial for the successful implementation of A.I. systems in healthcare. Ethical considerations, such as patient autonomy, beneficence, confidentiality, and shared decision-making, must be taken into account. The design of A.I. for healthcare should involve collaboration between bioethicists and A.I. experts to ensure ethical and patient-centered outcomes. The use of data, information sharing, and explanation should be prioritized to prevent a return to a paternalistic healthcare model.

Evidence-based medicine, Technology and Ethics

The shift in modern healthcare emphasizes the use of evidence-based practice rather than relying solely on anecdotal experiences. Evidence-based practice involves making healthcare decisions based on the best available, current, valid, and relevant evidence. These decisions should be made by the patients themselves, taking into account the knowledge and experience of healthcare providers and the available resources. It combines the expert clinician's opinion with evidence from quality data, including literature and patient-specific information. Informed and shared decision-making between the clinician, patient, and caregivers is crucial, considering the preferences and context of the patients and caregivers.

Figure 1: Components of Evidence-based practice.[39]

Implementing evidence-based medicine involves developing a clinical question and searching for relevant information in the literature. Once the evidence is located, it is critically appraised for validity and applicability. After assessing the quality of the evidence, it can be incorporated into clinical decision-making to improve the quality of care. An audit process is necessary to ensure the desired objectives of evidence-based changes have been achieved.

EVIDENCE BASED PRACTICE

This section discusses the importance of evidence data in evidence-based medicine (EBM) for making informed decisions. It mentions that evidence data is collected from the body of knowledge. When the components of EBM align, clinicians, patients, families, and caregivers can make evidence-based decisions that incorporate individuals' interests, values, needs, and choices.

Figure 2: Process of Evidence-Based Medicine Adapted from

The exponential growth of information and data, along with contradictory recommendations, has posed challenges for clinicians seeking evidence-based medicine. The Cochrane database was established in the 1990s to address this issue, but it also faced challenges. A review in 2004 revealed unethical practices in drug trials, where negative results were suppressed and biased industry-written studies were published. Technology, specifically the use of tamperproof and auditable data, can help overcome these issues. The blockchain approach has been recommended as a strategy to ensure data integrity and prevent fraudulent analysis.

Blockchain and Ethics

Blockchain technology is a decentralized database system that can securely store and manage medical records. It uses a ledger technology to create a chronological sequence of time-stamped data blocks that cannot be retroactively corrupted. The technology ensures data integrity and prevents fraud, making it ideal for research replication and transparent peer review processes. By integrating blockchain into healthcare systems, patient data can be securely shared among stakeholders, promoting ethical practice, patient autonomy, and shared decision-making. The technology also contributes to social justice by reducing delivery costs and preventing fraudulent claims. Patients can access and contribute to their own data, including data from wearables, in real-time. Ethical decision-making in healthcare requires comprehensive evaluation of patient needs, contextualized data, and meaningful communication between clinicians and patients. The implementation of AI systems in healthcare must consider these ethical processes to ensure trust and shared decision-making.

(b) Trust in evidence

This section highlights several key points regarding the ethical challenges of implementing AI in healthcare. Firstly, doctors must rely on trustworthy sources of evidence to make informed decisions that are relevant and adequate. If they base their recommendations on AI sources without fully understanding them, it can lead to blind trust and paternalism. Secondly, doctors need to have a comprehensive understanding of the benefits, risks, and trade-offs associated with AI outcomes. If they cannot comprehend how and why AI systems reach their conclusions, it undermines patients' confidence in their judgments and their ability to facilitate patients' understanding. Furthermore, doctors must effectively communicate assessments of risks and benefits to patients, ensuring clarity and accessibility. If AI systems make it difficult for doctors to understand their outcomes, they may resort to paternalism by requiring patients to accept the AI's recommendations without question. The authors argue that applying a bioethics model alone may not adequately address the challenges of AI implementation in healthcare. Instead, a transdisciplinary and participative approach involving healthcare professionals, bioethicists, and AI designers is recommended. This collaboration should incorporate ethical guidelines, minimize algorithmic bias, and promote shared decision-making. Extended stakeholders like healthcare financiers and regulatory bodies should also have a voice in the ethical implementation and evaluation of AI in healthcare. Reddy and coworkers propose a governance framework based on fairness, transparency, trustworthiness, and accountability, with ongoing monitoring and evaluation of AI applications. They emphasize the importance of awareness, education, and partnership with academic institutions and healthcare providers. Overall, this section highlights the need for doctors to trust reliable sources of evidence, understand the benefits and risks of AI, effectively communicate with patients, and engage in transdisciplinary collaboration to address ethical challenges in AI implementation in healthcare.

Sociotechnical Ethics of Digital Health

De Cremer and Kasparov argue that the effectiveness of healthcare technology is influenced by its availability, which can lead to inequality and social harm. While technology can enhance healthcare, it can also create divisions and discrimination, particularly among vulnerable individuals. The authors suggest that technology innovators and industry should collaborate with regulatory and government bodies to establish an ethical framework aligned with societal and regulatory frameworks.

? Responsible leadership

This section emphasizes the need for ethical leadership in the healthcare industry's adoption of AI technology. It recommends incorporating ethical considerations into the organization's structure and systems, including leadership and boards. The authors highlight the importance of social justice and early access to AI in healthcare, stressing the need to prevent inequalities and exclusion. They propose a sociotechnical framework that examines the impact of technology on social arrangements within healthcare, introducing potential ethical harms. The framework considers various domains, such as mental health, autonomy, ecological impact, labor exploitation, and the digital divide. The authors caution against the negative consequences of wearable devices, misinformation, and biased data availability. They also raise concerns about the power and control of commercial players in digital health and advocate for anticipatory governance that involves diverse stakeholders to ensure social justice in healthcare.

Conclusion

The advancement of technology in healthcare has raised ethical challenges that are not fully understood by healthcare professionals and technology developers. Regulators and policymakers need to raise awareness about the importance of ethical considerations in adopting healthcare technology and engage in public conversations. A transdisciplinary approach should be taken to understand and address concerns and challenges. Including biosocial and technological ethics in the education and competencies of health professionals, technology developers, engineers, regulators, and policymakers is necessary. It is crucial to recognize that healthcare technology should not be solely entrusted to healthcare professionals and technology developers.