Long used as a tool for medical compliance and adhering to treatment plans, behavior contracts have made their way into the in-patient healthcare setting as a way to manage the “difficult” patient and family. The use of this tool is even being adopted by healthcare ethics consultants (HECs) in US hospitals as part of their work in navigating conflict at the bedside. Anecdotal evidence of their increasing popularity among clinical ethicists, for example, can be found at professional bioethics meetings and (...) conversations and idea-sharing among practitioners on HEC social media. While there are a handful of papers gesturing toward a bioethical critique of behavior contracts of various types, the use of behavior contracts in the context of interpersonal conflict has not been vetted by bioethicists to determine their ethical legitimacy or efficacy. In this paper, we highlight a set of ethical concerns that we believe must be addressed before continuing or widespread implementation of behavior contracts to manage the “difficult” patient or family. (shrink)

Sedlakova and Traschel argue that conversational artificial intelligence (CAI) is more than a mere tool, but not quite an agent, as it “simulates having a therapeutic conversation [but] does not re...

The risk of tort liability for health maintenance organizations and other managed care plans has dramatically increased in recent years. This is due in part to the growing percentage of health care rendered through managed care plans. The cost-containment mechanisms commonly used by managed care plans, such as limiting access to services and/or choice of providers, creates a climate ripe for disputes that may end up in court. As dissatisfied patients and providers seek recourse in the courts, tort doctrines are (...) extended and new legal theories emerge as needed. For example, the concepts of direct and vicarious tort liability developed in the hospital context have been extended by courts to encompass HMOs. vicarious liability claims, based on ostensible agency or respondeat superior doctrines, have been brought against HMOs and managed care plans for negligent treatment by physicians selected to provide care to members. (shrink)

Background Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals’ judgments of stigma, empathy, and compassion. Methods We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments of stigma, (...) empathy, and compassion. Results Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists. Conclusions The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others. (shrink)

Breastfeeding advocates and global health agencies have been sounding alarms about the dangers of digital marketing practices of the formula-feeding industry. This study comprised a feminist discourse analysis of materials produced (blog, social media posts, comments) in a paid partnership between baby formula brand Enfamil and an influencer, Marilou Bourdon from Trois fois par jour. Our analysis reveals a sophisticated marketing campaign that co-opts feminist critiques of breastfeeding promotion discourse while carefully avoiding explicitly violating the International Code of Marketing of (...) Breast-milk Substitutes. Underlying this campaign is a rhetoric reliant on pathos to assuage maternal guilt and shame for feeding formula. (shrink)