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

Baylor College Of Medicine, Houston TX

Investigators

Linked publications & trials

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 acceptability in the context of a cognitive interview. Trian- gulated results from these aims will contribute concrete insights into what diverse stakeholders need to know in order to understand and translate PC metrics into actionable clinical knowledge and will contribute to NCAT’s aims by ensuring the success and predictable impacts of translating PC metrics into clinical care.