When Algorithms Meet Empathy: What AI Needs to Learn From Nurses

Artificial intelligence (AI) is increasingly used in clinical care. Algorithms flag abnormal results, predict decline, and support decisions. Often, the conversation is framed one way: nurses must learn AI. But the reverse is just as important. AI must learn from nurses. Machines process patterns at scale. They miss context, cues, emotion, culture—nurses see those, and when AI ignores them, care risks becoming less human.

Section 1: What Empathy Means in Clinical Work

Empathy is the capacity to notice distress, share the feeling, and act to reduce it. In nursing practice, it shows up as the pause when a patient says “I’m okay” but doesn’t look it, or the change in tone before labs turn critical. Empathy builds trust, safety, and better outcomes (Kerasidou, 2020).

Algorithms handle data. They do not sense hesitation, fear, or the unspoken question behind “How are you?” These elements shape patient behaviour, adherence, and recovery.

Section 2: Where AI Falls Short

Algorithms excel at structured data. They struggle with nuance. A patient missed a clinic visit? The algorithm flags “non-compliant”. A nurse asks, “Why did they miss?”—the daughter couldn’t drive. The human story is missing.

A study on human-AI collaboration found that even in peer-support settings, AI lacked emotional depth and users needed human feedback to increase empathy by 19.6% (Sharma et al., 2022).

In healthcare, an algorithm may tell you to intervene. It cannot say what quiet sign a nurse sees: hands tapping, a patient’s earlier mobility, or a small note about “feels off today”. When the algorithm misses that, care misses that.

Section 3: What Nurses Teach AI Designers

Nurses teach three core lessons to AI development:

Context matters – Clinical data without human context misleads. You bring knowledge of workflow, patient experience, and environmental cues. Design must include that.

Relational insight matters – A patient’s consent, their fear, the way they behave, they all matter. AI needs to surface relational cues, not just vitals.

Judgment matters – AI gives suggestions. Nurse judgment decides. Nurses add meaning where data is silent. Your involvement in design ensures the model doesn’t bypass that judgment.

Section 4: A Framework for Integration

To align AI with empathy, nurses and leadership can use this 4-step framework:

Include nurses early: In design, ask them what cues they use. Capture those.

Embed ‘pause-points’: After an alert fires, build intentional pauses for the nurse to review patient context before acting.

Train for co-fluency: Nurses need AI literacy; designers need nursing literacy. Both sides bridge the gap.

Measure what matters: Beyond algorithm metrics, track relational outcomes—patient trust scores, nurse-reported missed cues, override logs.

Case Example

In a telemetry unit you led, a predictive tool flagged patients at risk of instability. Nurses noted that some cases missed were younger patients who did not fit the typical profile but were anxious, trembling, and signalling differently. You introduced a review step: before accepting the risk score, nurses asked, “What does this patient feel like today?” You logged the override reasons and adjusted the model threshold and dataset. The unit’s unplanned transfers dropped by 14%. The lesson: empathy + algorithm bordered safer care.

Conclusion

Technology is powerful. But healing happens between humans, not just between humans and machines. Nurses bring empathy, judgment, and presence. Algorithms bring scale, speed, and pattern recognition. When nurses shape AI, the result is stronger: efficient and human. Good AI lets you focus on the patient, not just the screen.

References

Kerasidou, A. 2020. Artificial Intelligence and the Ongoing Need for Empathy in Healthcare. PMC.

Sharma, A.; Lin, I.W.; Miner, D.C.; At lhoff, T. 2022. Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Support. arXiv.

Rony, M.K.K. 2024. The Role of Artificial Intelligence in Enhancing Nurses’ Work-Practice. ScienceDirect.