Using computational ethnography to enhance the curation of real-world data (RWD) for chronic pain and invisible disability use cases

Abstract

Chronic pain is a significant source of suffering, disability and societal cost in the US. However, while the ability to detect a person's risk for developing persistent pain is desirable for timely assessment, management, treatment, and reduced health care costs---no objective measure to detect clinical pain intensity exist. Recent Artificial Intelligence (AI) methods have deployed clinical decision- making and assessment tools to enhance pain risk detection across core social and clinical domains. Yet, risk assessment models are only as "good" as the data they are based on. Thus, ensuring fairness is also a critical component of equitable care in both the short and long term. This paper takes an intersectional and public health approach to AI fairness in the context of pain and invisible disability, suggesting that computational ethnography is a multimodal and participatory real-world data (RWD) methodology that can be used to enhance the curation of intersectional knowledge bases, thereby expanding existing boundaries of AI fairness in terms of inclusiveness and transparency for pain and invisible disability use cases.

Conceptualising fairness: three pillars for medical algorithms and health equity

“…Domain experts, such as clinicians, social scientists or patient advocacy groups, have enhanced understandings of context situated bias,114 116 118 support the curation of salient axes of difference,119 and improve topic modelling and natural language processing models by aiding social bias detection 120–122. For example, ‘computational ethnography’ is an approach to fairness-aware ML that emphasises the importance of a holistic understanding of any given dataset 123 124. In sum, provenance requires more than a bias assessment that measures predictive accuracy across protected groups.…”

Predictive care: a protocol for a computational ethnographic approach to building fair models of inpatient violence in emergency psychiatry

“…Computational ethnography is an approach that extends ethnography’s toolkit to include computational methods, such ML. 50 It continues to leverage the strength of ethnography in terms of understanding the messy realities that surround the collection, use and interpretation of datasets, while also documenting the connections from everyday work practices to broader organisational and political processes (eg, the impact COVID-19 might have on risk management). 51 In particular, drawing on the insights from institutional ethnography, 52 we will observe how texts, such as textual data contained in EHRs, are ‘activated’ when they are read, completed or filled in by staff as they go about their work.…”