Trends in Informatics
This is the week to really see where technology and healthcare are connecting. Select ONE of the following trends and discuss your understanding of this trend in healthcare and its potential impact on your practice as a nurse. What are the legal, privacy, and ethical considerations of this trend? (Everyone attempt to choose a different topic so that we will learn about the many advancements in technology).
Nanotechnology
Consumer health informatics (CHI)
Social media healthcare applications
Health-focused wearable technology
eHealth
Artificial Intelligence
Simulation
Computerized provider order entry (CPOE)
Bar Code Medication Administration (BCMA)
Creative measures in healthcare for use with 3D printers
Smart Pumps
Smart Rooms
Robotics in healthcare
Mobile technology in outpatient care
Web-based tools and software technology
Risk Management tools
Chatbots or Bots in healthcare
Telenursing
Telemedicine
Telepharmacy
Telerehabilitation
Teleconsultation
Telehospice
Technology advancement from your clinical practice
Palm Vein Technology
Microchip use in healthcare
Optical head-mounted computer glasses (Google Glass)
Smart hospital beds
References
American Medical Informatics Association. (2015). Consumer health informatics. https://www.amia.org/
Belmonte-Fernández, Ó., Puertas-Cabedo, A., Torres-Sospedra, J., Montoliu-Colás, R., & Trilles-Oliver, S. (2017). An indoor positioning system based on wearables for ambient-assisted living. Sensors, 17(1), 1-22. https://doi.org/10.3390/s17010036
Denecke, K. (2014). Ethical aspects of using medical social media in healthcare applications. Studies in Health Technology and Informatics, 198, 55-62.
Hebda, T., Hunter, K., & Czar, P. (2019). Handbook of informatics for nurses & healthcare professionals (6th ed.). Pearson.
Minnesota Board of Nursing. (2017). Telenursing across state boarders. https://mn.gov/boards/nursing/practice/nursing-pra…
Ross, J., Stevenson, F., Lau, R., & Murray, E. (2016). Factors that influence the implementation of eHealth: A systematic review of systematic reviews (an update). Implementation Science, 11, 1-12. https://doi.org/10.1186/s13012-016-0510-7
United States National Nanotechnology Initiative. (n.d.). What is nanotechnology? https://www.nano.gov/nanotech-101/what/definition
Artificial Intelligence in Healthcare: Opportunities and Challenges
Introduction
Artificial intelligence (AI) is transforming many industries and healthcare is no exception. AI refers to machines that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision-making. In healthcare, AI is being applied to improve diagnosis, treatment, drug discovery, and more. However, the use of AI also raises important legal, ethical, and privacy concerns that must be addressed. This article will explore the opportunities and challenges of AI in healthcare.
Applications of AI in Healthcare
AI is being used in healthcare in many ways. Some key applications include:
Medical Imaging Analysis. AI can analyze medical images like X-rays and CT/MRI scans to detect abnormalities. Deep learning algorithms have shown accuracy comparable to human experts in detecting cancers, fractures, and other conditions (Esteva et al., 2017). This can help improve diagnosis.
Personalized Treatment Recommendations. By analyzing huge datasets of patient medical records and outcomes, AI systems can recommend the most effective treatments tailored to an individual patient’s characteristics. This “precision medicine” approach has the potential to improve outcomes and reduce unnecessary treatments (Topol, 2019).
Drug Discovery. Pharmaceutical companies are using AI to speed up drug discovery by analyzing molecular structures to predict how well a compound may bind to its target and whether it will be safe and effective as a drug (Lunder et al., 2021). This has the potential to accelerate development of new treatments.
Virtual Assistants. Chatbots and virtual assistants powered by AI are being developed to provide patients with health information, schedule appointments, answer basic questions, and perform administrative tasks. This could help improve access to care and reduce burdens on clinicians (Baumel et al., 2019).
Predictive Analytics. By analyzing large healthcare datasets, AI systems can predict future medical events like disease outbreaks, patient readmissions, and high-risk patients needing interventions. This enables more proactive, preventative care (Topol, 2019).
However, while promising, the use of AI in healthcare also raises important challenges that must be addressed regarding privacy, ethics, and regulation.
Privacy and Security Concerns
One major concern around AI in healthcare is privacy and security of patients’ sensitive medical data. As AI systems require huge amounts of patient data to train algorithms and make predictions, there are risks of:
Data Breaches. With more data centralized and flowing through networks, there is increased risk of hackers accessing systems and stealing records. Strong security practices are needed (Topol, 2019).
Re-identification. Even if identifying information like names are removed, there is a risk AI could re-identify individuals by linking datasets based on other details like zip codes, dates of birth, etc. (Crawford & Calo, 2016).
Bias and Discrimination. If datasets used to train AI reflect societal biases, systems may discriminate against or disadvantage certain groups in treatment recommendations, access to care, and other ways (Obermeyer et al., 2019).
Lack of Transparency. The complex algorithms behind many AI systems are often opaque “black boxes”, making it difficult to audit how systems reach conclusions or detect errors or biases (Topol, 2019).
Addressing these privacy and security risks will require strict regulation, oversight, and responsible practices around data use and management by healthcare organizations and technology companies. Patient consent and control over personal data is also important.
Ethical Challenges
In addition to privacy issues, the use of AI in healthcare raises important ethical challenges:
Accountability and Responsibility. It may be unclear who is responsible when an AI system makes an error or harmful recommendation – the developers, clinicians, or others (Topol, 2019). Proper governance models are needed.
Fairness and Justice. As noted, there are risks AI could disadvantage or harm certain groups if not developed and tested carefully to avoid biases (Crawford & Calo, 2016).
Autonomy, Transparency and Explainability. Patients value understanding the basis for medical decisions affecting them. But “black box” AI may not provide explanations, undermining trust and autonomy (Topol, 2019).
Job Disruption. Widespread use of AI assistants could significantly disrupt some healthcare jobs like administrative roles. Its impact on the workforce needs consideration (Topol, 2019).
Addressing these ethical challenges will require multidisciplinary collaboration between technologists, clinicians, ethicists, and patients. Guidelines are also needed around transparency, oversight, and accountability when AI is used in ways that could impact individuals.
Regulatory Considerations
Finally, the use of AI in healthcare raises regulatory issues that must be addressed:
Approval Pathways. New regulatory frameworks and approval pathways may be needed by the FDA and other agencies to evaluate AI/machine learning technologies as “medical devices” enter clinical use (Topol, 2019).
Quality Standards. With AI increasingly supporting clinical decision-making, performance and quality standards will be needed to ensure safe, effective use (Topol, 2019).
Interoperability & Data Standards. For AI to realize its full benefits, medical data from different systems and organizations need common standards and frameworks for interoperability (Topol, 2019).
Oversight. Mechanisms are required for ongoing monitoring, evaluation and oversight of AI technologies post-market to ensure safety, effectiveness and address issues or biases that emerge over time (Topol, 2019).
Transnational Considerations. As data and technologies cross borders, international cooperation on harmonizing regulations and oversight will also be important (Topol, 2019).
Conclusion
In conclusion, AI has enormous potential to transform and improve many aspects of healthcare. However, realizing this potential will require addressing significant challenges regarding privacy, ethics, regulation and oversight. With responsible development and use, as well as guidelines and safeguards to ensure transparency, fairness and accountability, AI can help advance medicine while protecting patients. Ongoing multidisciplinary collaboration will be key to maximizing AI’s benefits and minimizing risks as these technologies become more widely adopted in clinical care.
