# Ethical and Human Factors Impacting Successful Translation of Perceptual Computing to Improve Clinical Care

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $519,164

## Abstract

PROJECT SUMMARY
Perceptual computing (PC), in combination with artificial intelligence and machine learning (AI/ML), is poised to
revolutionize clinical approaches to diagnosis, personalized treatment (precision medicine), symptom and out-
come monitoring, telemedicine/mobile health, and primary prevention across a wide range of disorders. PC tools
are rapidly expanding but unresolved ethical and practical challenges stand in the way of responsible translation
into clinical care. These challenges stem from the specific, novel features of PC metrics, which differ from tradi-
tional measures of emotional and social behavior in that they 1) represent objectively observed rather than sub-
jectively elicited states and may involve collection of digital data that patients may not be aware of or wish to
share with their clinicians; 2) collect data passively using digital devices that observe and register moment-to-
moment emotional and behavioral information; 3) yield voluminous material (i.e. “big data”) that is difficult to
scale into actionable information at the individual level; and 4) rest on data easy to collect and make inferences
from, inviting engagement from commercial and other entities whose goals may be profit-driven rather than fidu-
ciary, as in healthcare. To help realize the potential of this technology with widespread clinical impacts, the
objective of this research is to identify and anticipate benefits and concerns (Aim 1); prioritize these concerns
and assess risk/benefit tradeoffs (Aim 2); and evaluate impacts of integrating PC into clinical care (Aim 3). In
Aim 1, we will conduct in-depth interviews with diverse stakeholders (researcher/developers of PC tools intended
to improve healthcare; clinicians across medical specialties; patients; and caregivers) to identify high priority
concerns and information needs for interpreting and integrating PC findings into clinical care. Interview findings
will form the content to be evaluated by expert stakeholders in Aim 2 using a 3-phase modified Delphi. In the
first round, we will conduct a survey entailing Multi-criteria Decision Analysis to confirm and prioritize salient
benefits and potential harms among expert stakeholders. A subset of representative participants (statistically
defined) will be invited to convene in two subsequent rounds, each involving a Decision Conference to review
MCDA results and generate actionable solutions and policy guidelines. In Aim 3, we will collaborate with re-
searchers developing a multimodal PC tool (NIH R01MH125958) to present mental health clinicians with video
and audio recordings of patient intakes involving PC observations in addition to standard intake measures. Cli-
nicians across multiple sites (unaffiliated with NIH R01MH125958) will be asked to provide their best clinical
estimates before and after being presented with PC metrics derived from the audio/video data, and to evaluate
their interpretability, relevance, appropriateness, and accepta...

## Key facts

- **NIH application ID:** 10502082
- **Project number:** 1R01TR004243-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** JOHN David HERRINGTON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $519,164
- **Award type:** 1
- **Project period:** 2022-08-10 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10502082

## Citation

> US National Institutes of Health, RePORTER application 10502082, Ethical and Human Factors Impacting Successful Translation of Perceptual Computing to Improve Clinical Care (1R01TR004243-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10502082. Licensed CC0.

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