Optimizing AI-Powered Teletriage

Using artificial intelligence algorithms to assess and prioritize patient symptoms remotely

Stressed Teletriage Nurses

In the rapidly advancing landscape of healthcare, AI-powered teletriage stands out as a transformative tool with the potential to revolutionize patient care. Teletriage involves the use of artificial intelligence algorithms to assess and prioritize patient symptoms remotely, enabling quicker and more efficient healthcare decision-making. To maximize the benefits and minimize potential harms of this innovative approach, a systems engineering perspective is crucial.

Stephen Deans Solution for Teletriage

Stephen Dean, one of the founders of Keona Health, highlights the transformative potential of AI-powered teletriage in addressing various challenges encountered in traditional teletriage services . This advanced technology empowers nurses to enhance the efficiency of their clinical assessments by aggregating personalized real-time health data, enabling them to navigate calls more strategically. Moreover, the incorporation of decision support capabilities aids in informed clinical decision-making by presenting pertinent evidence-based guidelines at the crucial moments. Despite initial concerns among healthcare providers about the integration of AI, Dean emphasizes that it actually eases the technological burden. Specifically, AI automation streamlines tasks such as navigating through multiple software applications and adhering to providers’ preferences for on-call escalation and medication directives. In essence, AI emerges not as a source of intimidation but as a valuable ally in optimizing teletriage processes.

The Human Component

Stephen Dean underscores a holistic understanding of AI-powered teletriage, outlining a systematic four-step approach for pinpointing and refining workflow inefficiencies . His methodology involves establishing a straightforward standardized workflow, implementing modular templates for seamless workflow management, eliminating redundancies, and leveraging AI automation to facilitate patient self-service and reduce the burden on nurses. Notably, Dean’s strategy implicitly recognizes the interconnected systems, processes, and key stakeholders — primarily nurses, patients, and providers — that play pivotal roles in the success of AI-powered triage.

In alignment with Dean’s practical approach, Section 10.13 of the INCOSE Systems Engineering Handbook delves into the concept of human systems integration (HSI). HSI is defined as an interdisciplinary technical and management process aimed at seamlessly incorporating human considerations throughout all elements of a system . This is particularly relevant for a system like AI-powered teletriage, which seeks to enhance human performance through AI capabilities. The handbook emphasizes the critical role of HSI in the systems engineering process, stating that HSI analyses allocate human-centered functions within the system and identify potential gaps in human or system capabilities . This underscores the importance of a robust stakeholder needs and systems requirements definition process, which proves instrumental in designing and implementing an effective AI-powered teletriage system.

A Question Of Safety

Stephen Dean emphasizes the liberating potential of AI in easing the workload for nurses and empowering patients, a critical concern that resonates across stakeholders and the public is the safety implications tied to the intimate integration of AI influencing clinical judgment. The incorporation of AI in teletriage brings forth apprehensions about its accuracy and the potential impact on patient outcomes. To address these concerns, a systems engineer would employ a comprehensive risk management process, encompassing activities such as planning risk management, managing the risk profile, risk analysis, risk treatment, and ongoing risk monitoring . Regular communication of identified risks with relevant stakeholders is a crucial aspect of managing risk profiles, fostering appreciation from healthcare providers and enhancing their involvement and commitment.

System safety engineering, a subset of systems engineering, involves scrutinizing each requirement, system element, and macro-to-micro behavior within the system’s context to identify and mitigate potential safety risks . Considering the potential consequences on human life associated with errors in the shift to AI-powered teletriage, the application of system safety engineering becomes particularly pertinent in ensuring the utmost safety and reliability in the development, operation, and sustenance of such systems.

Conclusion

Undoubtedly, AI holds the promise of relieving teletriage nurses of mundane tasks, enabling them to optimize their time spent with patients and offering a more personalized and accessible healthcare service. However, introducing such technology into critical and stressful environments could amplify existing challenges and potentially give rise to new issues. To navigate these complexities, a systems engineering approach proves essential. This method is adept at not only maximizing the benefits of AI but also mitigating potential harms, ensuring a balanced and effective integration of technology in healthcare services.

References

1. Siwicki, B. How AI can Ease the Burden of Stressed-Out Teletriage Nurses. March 22, 2023. https://www.healthcareitnews.com/news/how-ai-can-ease-burden-stressed-out-teletriage-nurse
2. Walden, D., et al. (2015). INCOSE Systems Engineering Handbook (4th ed.). Page 237. John Wiley & Sons.
3. Ibid., Page 238.
4. Ibid., Page 114.
5. Ibid., Page 231.