ABSTRACT
OBJECTIVE: Meningiomas can have significant impact on health-related quality of life (HRQOL). Patient-centered, disease-specific instruments for assessing HRQOL in these patients are lacking. To this end, the authors sought to develop and validate a meningioma-specific HRQOL questionnaire through a standardized, patient-centered questionnaire development methodology. METHODS: The development of the questionnaire involved three main phases: item generation, item reduction, and validation. Item generation consisted of semistructured interviews with patients (n = 30), informal caregivers (n = 12), and healthcare providers (n = 8) to create a preliminary list of items. Item reduction with 60 patients was guided by the clinical impact method, multiple correspondence analysis, and hierarchical cluster analysis. The validation phase involved 162 patients and collected evidence on extreme-groups validity; concurrent validity with the SF-36, FACT-Br, and EQ-5D; and test-retest reliability. The questionnaire takes on average 11 minutes to complete. RESULTS: The meningioma-specific quality-of-life questionnaire (MQOL) consists of 70 items representing 9 domains. Cronbach's alpha for each domain ranged from 0.61 to 0.91. Concurrent validity testing demonstrated construct validity, while extreme-groups testing (p = 1.45E-11) confirmed the MQOL's ability to distinguish between different groups of patients. CONCLUSIONS: The MQOL is a validated, reliable, and feasible questionnaire designed specifically for evaluating QOL in meningioma patients. This disease-specific questionnaire will be fundamentally helpful in better understanding and capturing HRQOL in the meningioma patient population and can be used in both clinical and research settings.
ABSTRACT
BACKGROUND: As artificial intelligence (AI) approaches in research increase and AI becomes more integrated into medicine, there is a need to understand perspectives from members of the Canadian public and medical community. The aim of this project was to investigate current perspectives on ethical issues surrounding AI in health care. METHODS: In this qualitative study, adult patients with meningioma and their caregivers were recruited consecutively (August 2018-February 2019) from a neurosurgical clinic in Toronto. Health care providers caring for these patients were recruited through snowball sampling. Based on a nonsystematic literature search, we constructed 3 vignettes that sought participants' views on hypothetical issues surrounding potential AI applications in health care. The vignettes were presented to participants in interviews, which lasted 15-45 minutes. Responses were transcribed and coded for concepts, frequency of response types and larger concepts emerging from the interview. RESULTS: We interviewed 30 participants: 18 patients, 7 caregivers and 5 health care providers. For each question, a variable number of responses were recorded. The majority of participants endorsed nonconsented use of health data but advocated for disclosure and transparency. Few patients and caregivers felt that allocation of health resources should be done via computerized output, and a majority stated that it was inappropriate to delegate such decisions to a computer. Almost all participants felt that selling health data should be prohibited, and a minority stated that less privacy is acceptable for the goal of improving health. Certain caveats were identified, including the desire for deidentification of data and use within trusted institutions. INTERPRETATION: In this preliminary study, patients and caregivers reported a mixture of hopefulness and concern around the use of AI in health care research, whereas providers were generally more skeptical. These findings provide a point of departure for institutions adopting health AI solutions to consider the ethical implications of this work by understanding stakeholders' perspectives.
