Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Healthcare: A Qualitative Study
August 04, 2025 Β· Declared Dead Β· π arXiv.org
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Authors
Kristin M. Kostick-Quenet, Meghan E. Hurley, Syed Ayaz, John Herrington, Casey Zampella, Julia Parish-Morris, Birkan TunΓ§, Gabriel LΓ‘zaro-MuΓ±oz, J. S. Blumenthal-Barby, Eric A. Storch
arXiv ID
2508.02550
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CY
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Computer perception (CP) technologies (digital phenotyping, affective computing and related passive sensing approaches) offer unprecedented opportunities to personalize healthcare, but provoke concerns about privacy, bias and the erosion of empathic, relationship-centered practice. A comprehensive understanding of perceived risks, benefits, and implementation challenges from those who design, deploy and experience these tools in real-world settings remains elusive. This study provides the first evidence-based account of key stakeholder perspectives on the relational, technical, and governance challenges raised by the integration of CP technologies into patient care. We conducted in-depth, semi-structured interviews with 102 stakeholders: adolescent patients and their caregivers, frontline clinicians, technology developers, and ethics, legal, policy or philosophy scholars. Transcripts underwent thematic analysis by a multidisciplinary team; reliability was enhanced through double coding and consensus adjudication. Stakeholders articulated seven interlocking concern domains: (1) trustworthiness and data integrity; (2) patient-specific relevance; (3) utility and workflow integration; (4) regulation and governance; (5) privacy and data protection; (6) direct and indirect patient harms; and (7) philosophical critiques of reductionism. To operationalize humanistic safeguards, we propose "personalized roadmaps": co-designed plans that predetermine which metrics will be monitored, how and when feedback is shared, thresholds for clinical action, and procedures for reconciling discrepancies between algorithmic inferences and lived experience. By translating these insights into personalized roadmaps, we offer a practical framework for developers, clinicians and policymakers seeking to harness continuous behavioral data while preserving the humanistic core of care.
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