Conversational avoidance during existential interviews with patients with progressive illness.

We examined patterns of avoidance when existential emotional topics were raised during conversations with patients with pulmonary arterial hypertension (PAH), an incurable life-limiting disease. 30 adult outpatients with PAH were recruited for a 20 to 60-minute interview about their illness experience. Qualitative content analysis was used to identify avoidance strategies that patients employed. Participants averaged 58 years in age (SD = 18), 77% were female, and mean length of illness was 6.3 years (SD = 5.3). We found four avoidance strategies: (1) Reversal, when individuals would begin discussing a negative concern and then backtrack to more positive sentiments; (2) Diversion for when patients would sidetrack the conversation to a different and less uncomfortable topic; (3) Diminishment for when a concern is raised and then made to seem unimportant; and (4) Obstruction, when patients refuse to discuss a concern further. Exploration of existential concerns can elicit distress but may be necessary to promote adaptation to progressive illness and to the foreseeable challenges that may affect the sense of life meaning and value. By recognizing when existential concerns may be present but not adequately discussed, clinicians may be better able to assist patients to cope and prepare for the future.

Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research.

OBJECTIVES: Given widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research. DESIGN: A qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI research scenarios. Data were analysed using qualitative description thematic analysis. SETTINGS: Two cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga (part of the Greater Toronto Area). PARTICIPANTS: Forty-one purposively sampled members of the public (21M:20F, 25-65 years, median age 40). RESULTS: Participants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed using data for health AI research when there is strong potential for public benefit, providing that concerns about privacy, commercial motives and other risks were addressed. Inductive thematic analysis identified AI-specific hopes (eg, potential for faster and more accurate analyses, ability to use more data), fears (eg, loss of human touch, skill depreciation from over-reliance on machines) and conditions (eg, human verification of computer-aided decisions, transparency). There were mixed views about whether data subject consent is required for health AI research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect. CONCLUSIONS: Notwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research.