NURS FPX 6616 Assessment 1 Community Resources and Best Practices

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Capella university

NURS-FPX 6616 Ethical and Legal Considerations in Care Coordination

Prof. Name

Date

Community Resources and Best Practices

Hello healthcare leaders, my name is ________, and today I will be discussing a vital topic concerning community resources and effective practices for a well-coordinated healthcare system and care management.

Purpose

This presentation focuses on the growing challenges of integrating artificial intelligence (AI) in healthcare, despite its potential to revolutionize patient care and operational efficiency. While AI technologies have demonstrated effectiveness in diagnosing diseases, personalizing treatment plans, and reducing administrative burdens, concerns around algorithmic bias, transparency, and patient safety have emerged. Studies show that biased algorithms in AI systems can lead to misdiagnosis, particularly in underrepresented populations, further exacerbating healthcare disparities (Evans & Snead, 2023).

Additionally, the lack of transparency in AI decision-making poses ethical challenges for healthcare providers in ensuring accountability. The financial impact of AI implementation also cannot be overlooked, as it requires substantial investment in infrastructure, staff training, and continuous system updates. This presentation will explore the drawbacks of incorporating AI in healthcare, focusing on the ethical, social, and economic implications, while highlighting evidence-based strategies to mitigate these challenges and improve outcomes.

A Particular Case Concerning the Provision of Care and the Existing Organizational Resources

OakBend Medical Center in Texas implemented an AI-based diagnostic tool to assist physicians in identifying early-stage cancers. The aim was to improve diagnostic accuracy and expedite treatment decisions. However, the AI system failed to account for racial and genetic variations in the patient population, particularly among African American and Hispanic individuals. This resulted in several cases of misdiagnosis, where critical cancer diagnoses were either delayed or missed, leading to poor patient outcomes.

The hospital initially relied heavily on the AI tool, but it soon became evident that the training data used for the system lacked diversity, causing biased and inaccurate results. OakBend Medical Center had insufficient resources to monitor the AI’s performance and ensure its decisions were safe. The situation necessitated a comprehensive review of the hospital’s AI usage, leading to efforts to integrate more diverse datasets and establish a human oversight mechanism to safeguard patient safety. This incident emphasizes the need for vigilance and proper resource allocation when adopting AI in healthcare.

Ethical Issues Related to Use of Healthcare Information Systems

One key ethical issue in using AI within healthcare information systems for care coordination is the potential for algorithmic bias, which can lead to unequal care outcomes. AI systems rely on large datasets to make decisions, and if these datasets are not diverse or representative of all patient populations, the system may provide biased recommendations.

For instance, AI algorithms trained predominantly on data from specific racial or socioeconomic groups may perform poorly when applied to underrepresented populations, exacerbating healthcare disparities (Moore, 2022). This raises ethical concerns about equity in care and the obligation of healthcare providers to ensure that AI tools do not inadvertently harm vulnerable groups.

NURS FPX 6616 Assessment 1 Community Resources and Best Practices

Furthermore, accountability and openness in AI decision-making processes are critical ethical concerns. Many AI systems operate as “black boxes,” where the rationale behind a decision or recommendation is not fully explainable to clinicians or patients (Felder, 2021). This lack of transparency can undermine trust in the healthcare system and make it difficult to address errors when they occur. Ensuring that AI tools used in care coordination are transparent, accountable, and subject to rigorous oversight is vital to maintaining ethical standards in healthcare.

Scholarly resources emphasize the importance of human oversight in AI deployment to mitigate risks and ensure that AI enhances rather than compromises patient care (Curtis et al., 2022). These concerns highlight the need for continuous evaluation and improvement of AI systems to ensure they support ethical care coordination and do not contribute to disparities or unintended patient harm.

Legal Issues of Current Practices and Potential Changes

At Oakbend Medical Center, the use of AI in healthcare introduces specific legal challenges, particularly concerning data privacy and accountability. One significant issue is the risk of data breaches, as AI systems require access to extensive patient health information (PHI) (Murdoch, 2021). Inadequate data security measures can lead to violations of the Health Insurance Portability and Accountability Act (HIPAA), resulting in severe financial penalties and reputational damage (Hlávka, 2020). Another pressing issue is determining liability when AI systems cause harm. The lack of clear liability frameworks complicates accountability, potentially leading to disputes over whether responsibility lies with the healthcare provider and the AI developers, or both (Schneeberger et al., 2020).

To address these concerns, Oakbend Medical Center should implement stronger data security protocols, like strict access limits and multi-factor authentication. By lowering the chance of data breaches and preventing unwanted access, these improvements can help ensure compliance with HIPAA regulations and protecting against legal repercussions (Suleski et al., 2023).

Comparison of Current Outcomes with Best Practices

At Oakbend Medical Center, the implementation of the AI-based diagnostic tool revealed several critical issues related to its efficacy and the associated risks. The AI system, which was intended to improve diagnostic accuracy and expedite treatment decisions, failed to account for racial and genetic variations in the patient population. This oversight led to several cases of misdiagnosis, particularly affecting African American and Hispanic patients, resulting in delayed or missed cancer diagnoses. The lack of diverse training data, coupled with insufficient monitoring resources, further exacerbated these issues, leading to poor patient outcomes and erosion of trust in the healthcare system.

To address these problems and improve AI outcomes, best practices should be implemented. First, integrating diverse and representative datasets into the AI training process is essential to ensure that the system accurately reflects the patient population and reduces biases ((Park et al., 2021). Additionally, establishing robust human oversight mechanisms is crucial for continuous evaluation of the AI tool’s performance to safeguard patient safety and ensure the accuracy of diagnoses.

Finally, promoting transparent communication with patients about the limitations of AI systems and the steps being taken to address these limitations can help rebuild trust and demonstrate the organization’s commitment to improving patient care (Kiseleva et al., 2022). By adopting these best practices, Oakbend Medical Center can enhance the effectiveness of its AI tools and better align with the principles of equitable and accurate healthcare delivery.

An Evidence-Based Intervention

Integrating diverse data sets into the training processes of AI systems is a robust evidence-based intervention to improve the accuracy and fairness of AI in healthcare. This approach involves incorporating a wide range of patient demographics, including various racial, ethnic, and genetic backgrounds, into the AI training data. By doing so, the AI system becomes better equipped to handle the variability in patient populations, reducing the risk of biased outcomes and misdiagnoses.

Diverse data sets are crucial for ensuring that AI models provide equitable and accurate diagnostic results across different demographic groups. Research by Chen et al. (2023) demonstrate that AI systems trained on diverse data sets show improved performance and reduced biases, leading to more accurate and fair healthcare outcomes. This is particularly important in addressing issues where current AI tools, such as the one at Oakbend Medical Center, have failed to account for racial and genetic variations, resulting in misdiagnoses and poor patient outcomes.

Additionally, a study by Zha et al. (2023) highlights the effectiveness of using varied data in AI training to mitigate biases and enhance the reliability of diagnostic tools. By integrating a broad spectrum of patient data, healthcare organizations can improve the performance of AI systems and ensure that they serve all patients equitably. This intervention not only addresses the current shortcomings of AI tools but also aligns with best practices in AI development, promoting more accurate and inclusive healthcare solutions (Suleski et al., 2023).

Role of Stakeholders and Interprofessional Team

The role of stakeholders in implementing diverse data sets into AI training processes is crucial for ensuring the intervention’s success. Key stakeholders, including hospital leadership, data scientists, and AI developers, are responsible for overseeing the integration of representative patient data. Hospital leadership must allocate resources for data collection, while Data scientists and AI developers make sure that the data used is inclusive and unbiased. Collaboration between these stakeholders promotes accountability and helps create more accurate AI systems that reduce disparities in healthcare outcomes (Hofmann et al., 2024).

The interprofessional team, including clinicians, IT specialists, and ethicists, plays an essential role in overseeing the ethical implementation and continuous evaluation of the AI system. Clinicians provide insights into patient populations, helping ensure the data reflects clinical realities. IT specialists facilitate the technical aspects of integrating diverse data into AI systems, while ethicists ensure patient privacy and equity are maintained throughout the process (Khanna et al., 2020). Together, these professionals contribute to creating AI tools that are both clinically relevant and ethically sound, improving patient care 

Explanation of Data-Driven Outcomes

The use of data-driven outcomes in the intervention of integrating diverse data sets into AI training processes is essential for evaluating its effectiveness. Key data measures include the accuracy of AI diagnostic tools across different racial and ethnic groups, the rate of misdiagnoses, and patient outcomes post-intervention. These measures can be tracked through AI system performance reports, clinical audits, and patient feedback, ensuring the AI system improves diagnostic accuracy across diverse populations. Regular evaluation periods, such as quarterly or bi-annual reviews, allow for ongoing monitoring and timely adjustments to the AI model based on emerging data trends (Feng et al., 2022).

Additionally, health disparities metrics, such as the reduction of diagnostic bias across demographic groups, should be included in data-driven assessments. These evaluations can be enhanced by using real-time analytics to identify patterns in AI performance and patient outcomes (Akter et al., 2021). By using data-driven outcomes, healthcare organizations can enhance the reliability and fairness of AI systems, ultimately improving patient care.

Practices to Sustain Outcomes

To sustain the outcomes of integrating diverse data sets into AI training processes, healthcare organizations should implement ongoing practices such as continuous data collection and periodic AI model retraining. Regularly updating AI systems with current, diverse patient data ensures that the tools remain accurate and relevant as patient demographics evolve. This practice minimizes biases and enhances diagnostic precision across different populations (Feng et al., 2022). 

Another key practice is fostering collaboration between clinical staff, data scientists, and AI developers. Establishing interdisciplinary review committees ensures ongoing evaluation of the AI system’s impact on patient care, promoting accountability and transparency. Furthermore, implementing feedback loops from clinicians and patients can offer insights into real-world AI performance, leading to continuous improvements (Feng et al., 2022). By maintaining these practices, healthcare organizations can ensure long-term success in using AI to deliver equitable, data-driven care.

Conclusion

In conclusion, integrating diverse data sets into AI training processes is essential for improving the accuracy, fairness, and effectiveness of AI systems in healthcare. By addressing the challenges of algorithmic bias, healthcare disparities, and transparency, this intervention promotes equitable patient care and more reliable diagnostic tools. Stakeholders and interprofessional teams play critical roles in overseeing data integration, ensuring that AI models reflect the diversity of patient populations.

Continuous data-driven evaluation, combined with regular audits and collaboration among healthcare professionals, ensures that AI systems remain adaptable and beneficial in improving patient outcomes. By implementing these strategies, healthcare organizations can achieve sustainable and ethical AI integration that enhances care quality for all populations.

References

Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60(60), 102387. https://doi.org/10.1016/j.ijinfomgt.2021.102387 

Chen, P., Wu, L., & Wang, L. (2023). AI fairness in data management and analytics: A review on challenges, methodologies and applications. Applied Sciences, 13(18), 10258–10258. https://doi.org/10.3390/app131810258 

Curtis, C., Gillespie, N., & Lockey, S. (2022). AI-deploying organizations are key to addressing “perfect storm” of AI risks. AI and Ethics, 3. https://doi.org/10.1007/s43681-022-00163-7 

Evans, H., & Snead, D. (2023). Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them? Histopathology. https://doi.org/10.1111/his.15071 

Felder, R. M. (2021). Coming to terms with the black box problem: How to justify AI systems in health care. Hastings Center Report, 51(4). https://doi.org/10.1002/hast.1248 

Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022). Clinical artificial intelligence quality improvement: Towards continual monitoring and updating of AI algorithms in healthcare. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00611-y 

NURS FPX 6616 Assessment 1 Community Resources and Best Practices

Hlávka, J. P. (2020). Security, privacy, and information-sharing aspects of healthcare artificial intelligence. Artificial Intelligence in Healthcare, 235–270. https://doi.org/10.1016/b978-0-12-818438-7.00010-1 

Hofmann, P., Lämmermann, L., & Urbach, N. (2024). Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders. International Journal of Information Management, 75, 102728. https://doi.org/10.1016/j.ijinfomgt.2023.102728 

Khanna, S., Srivastava, S., Khanna, I., & Pandey, V. (2020). Current challenges and opportunities in implementing AI/ML in cancer imaging: Integration, development, and adoption perspectives. Journal of Advanced Analytics in Healthcare Management, 4(10), 1–25. https://research.tensorgate.org/index.php/JAAHM/article/view/104 

Kiseleva, A., Kotzinos, D., & De Hert, P. (2022). Transparency of AI in healthcare as a multilayered system of accountabilities: Between legal requirements and technical limitations. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.879603 

Moore, C. M. (2022). The challenges of health inequities and AI. Intelligence-Based Medicine, 6, 100067. https://doi.org/10.1016/j.ibmed.2022.100067 

Murdoch, B. (2021). Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Medical Ethics, 22(1). https://doi.org/10.1186/s12910-021-00687-3 

NURS FPX 6616 Assessment 1 Community Resources and Best Practices

Park, J. S., Bernstein, M. S., Brewer, R., Kamar, E., & Morris, M. R. (2021). Understanding the representation and representativeness of age in AI data sets. ArXiv (Cornell University). https://doi.org/10.1145/3461702.3462590 

Schneeberger, D. F., Stöger, K., & Holzinger, A. (2020). The european legal framework for medical AI. Lecture Notes in Computer Science, 209–226. https://doi.org/10.1007/978-3-030-57321-8_12 

Suleski, T., Ahmed, M., Yang, W., & Wang, E. (2023). A review of multi-factor authentication in the internet of healthcare things. Digital Health, 9(1), 205520762311771-205520762311771. https://doi.org/10.1177/20552076231177144 

Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2023, June 11). Data-centric Artificial Intelligence: A Survey. ArXiv.org. https://doi.org/10.48550/arXiv.2303.10158