Human-Centered Design to Address Biases in Artificial Intelligence

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care

Information Needs and Requirements for Decision Support in Primary Care: An Analysis of Chronic Pain Care

Decision support system designs often do not align with the information environments in which clinicians work. These work environments may increase Clinicians’ cognitive workload and harm their decision making. The objective of this study was to identify information needs and decision support requirements for assessing, diagnosing, and treating chronic noncancer pain in primary care. We conducted a qualitative study involving 30 interviews with 10 primary care clinicians and a subsequent multidisciplinary systems design workshop. Our analysis identified four key decision requirements, eight clinical information needs, and four decision support design seeds. Our findings indicate that clinicians caring for chronic pain need decision support that aggregates many disparate information elements and helps them navigate and contextualize that information. By attending to the needs identified in this study, decision support designers may improve Clinicians’ efficiency, reduce mental workload, and positively affect patient care quality and outcomes

Understanding how primary care clinicians make sense of chronic pain

Chronic pain leads to reduced quality of life for patients, and strains health systems worldwide. In the US and some other countries, the complexities of caring for chronic pain are exacerbated by individual and public health risks associated with commonly used opioid analgesics. To help understand and improve pain care, this article uses the data frame theory of sensemaking to explore how primary care clinicians in the US manage their patients with chronic noncancer pain. We conducted Critical Decision Method interviews with ten primary care clinicians about 30 individual patients with chronic pain. In these interviews, we identified several patients, social/environmental, and clinician factors that influence the frames clinicians use to assess their patients and determine a pain management plan. Findings suggest significant ambiguity and uncertainty in clinical pain management decision making. Therefore, interventions to improve pain care might focus on supporting sensemaking in the context of clinical evidence rather than attempting to provide clinicians with decontextualized and/or algorithm-based decision rules. Interventions might focus on delivering convenient and easily interpreted patient and social/environmental information in the context of clinical practice guidelines