Effects of Emotion on Medical Decisions Involving Tradeoffs

Perceived Social Norms Guide Health Care Decisions for Oneself and Others: A Cross-Sectional Experiment in a US Online Panel

BACKGROUND

Global aging has increased the reliance on surrogates to make health care decisions for others. We investigated the differences between making health care decisions and predicting health care decisions, self-other differences for made and predicted health care decisions, and the roles of perceived social norms, emotional closeness, empathy, age, and gender.

METHODS

Participants (N = 2037) from a nationally representative US panel were randomly assigned to make or to predict a health care decision. They were also randomly assigned to 1 of 5 recipients: themselves, a loved one 60 y or older, a loved one younger than 60 y, a distant acquaintance 60 y or older, or a distant acquaintance younger than 60 y. Hypothetical health care scenarios depicted choices between relatively safe lower-risk treatments with a good chance of yielding mild health improvements versus higher-risk treatments that offered a moderate chance of substantial health improvements. Participants reported their likelihood of choosing lower- versus higher-risk treatments, their perceptions of family and friends' approval of risky health care decisions, and their empathy.

RESULTS

We present 3 key findings. First, made decisions involved less risk taking than predicted decisions, especially for distant others. Second, predicted decisions were similar for others and oneself, but made decisions were less risk taking for others than oneself. People predicted that loved ones would be less risk taking than distant others would be. Third, perceived social norms were more strongly associated than empathy with made and predicted decisions.

LIMITATIONS

Hypothetical scenarios may not adequately represent emotional processes in health care decision making.

CONCLUSIONS

Perceived social norms may sway people to take less risk in health care decisions, especially when making decisions for others. These findings have implications for improving surrogate decision making.

HIGHLIGHTS

People made less risky health care decisions for others than for themselves, even though they predicted others would make decisions similar to their own. This has implications for understanding how surrogates apply the substituted judgment standard when making decisions for patients.Perceived social norms were more strongly related to decisions than treatment-recipient (relationship closeness, age) and decision-maker (age, gender, empathy) characteristics. Those who perceived that avoiding health care risks was valued by their social group were less likely to choose risky medical treatments.Understanding the power of perceived social norms in shaping surrogates' decisions may help physicians to engage surrogates in shared decision making.Knowledge of perceived social norms may facilitate the design of decision aids for surrogates.

Opportunities for theory-informed decision science in cancer control

Cancer prevention and control involves navigation of complex clinical decisions, often laden with uncertainty and/or intricate interpersonal dynamics, which have implications for both physical health and quality of life. Cancer decision-making research in recent decades has primarily focused on working to improve the quality of decisions by providing patients with detailed information about their choices and through an increased emphasis in medicine on the importance of shared decision making. This emphasis is reflective of a model of decision making that emphasizes knowledge, options, and deliberative synthesis of information as primary to decision making; yet, decades of research in psychology, decision science, and behavioral economics have taught us that our decisions are not influenced only by our objective knowledge of facts, but by our emotions, by the influence of others, and by biased cognitive processes. We present a conceptual framework for a future of research in decision science and cancer that is informed by decision science theories. Our framework incorporates greater recognition of the interpersonal dynamics of shared decision making, including the biases (including cognitive heuristics and race-based bias) that may affect multiple actors in the decision-making process, and emphasizes study of the interaction between deliberative and affective psychological processes as they relate to decision making. This work should be conducted with an eye toward informing efforts to improve decision making across the cancer care continuum, through interventions that are also informed by theory.

Exploring condom use decision-making among adolescents: the synergistic role of affective and rational processes

BACKGROUND

Condom use remains the most effective behavioural method for the prevention of HIV and unplanned pregnancies. However, condom use remains inconsistent among young people. Exploring the condom use decision-making processes that adolescents engage in might provide information that would assist in the prevention of many challenges related to poor sexual and reproductive health outcomes. This study therefore aimed to explore the factors that influenced decision-making about sexual debut and condom use of adolescents from two schools in the Western Cape, South Africa.

METHODS

A sample of 16 adolescents were selected using purposive sampling. Data were collected using semi-structured, individual interviews. Thematic analysis was used to analyse the data generated.

RESULTS

The link between sexual debut and affective processes was frequently discussed in condom use decision-making. Decisions about sexual debut were influenced by the belief that sex was a perceived symbol of 'true love' on the one hand, and respect for perceived parental expectations of age-appropriate sex, on the other. Condom use decision-making was shaped by adolescents' concerns about their future and lack of stability in their lives. Adolescents' fears of pregnancy, parenthood and disease shaped their condom use decision-making. It became evident that rational and affective decision-making in condom use choice were not mutually exclusive, but that these processes happened simultaneously.

CONCLUSIONS

The study highlighted the role of affective states as part of the process of examining alternatives when deciding to use a condom or not. Interventions to strengthen condom use decision-making should therefore incorporate not only rational but also affective processes to improve adolescent sexual and reproductive outcomes.

Using a Foreign Language Changes Medical Judgments of Preventative Care

Every day, multilinguals around the world make important healthcare decisions while using a foreign language. The present study examined how the use of a native vs. non-native language shapes evaluations and decisions about preventative care. Bilinguals were randomly assigned to evaluate a series of medical scenarios in either their native or non-native language. Each scenario described potential adverse effects of a medical condition and a preventative treatment, as well as the population risk of disease- or treatment-related complications. Participants judged the perceived negativity and likelihood of experiencing adverse effects and indicated how willing they would be to accept the preventative treatment. We found that bilinguals using a foreign language perceived disease symptoms and treatment side effects to be less negative than those using their native tongue. Foreign language users were also more likely to account for the objective risks associated with medical conditions and treatments when making decisions about preventative care. We conclude that the use of a native vs. foreign language changes how people evaluate the consequences of accepting and declining preventative treatment, with potential implications for millions of providers and patients who routinely make medical choices in their non-native tongue.

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Background

Advanced analytics, such as artificial intelligence (AI), increasingly gain relevance in medicine. However, patients’ responses to the involvement of AI in the care process remains largely unclear. The study aims to explore whether individuals were more likely to follow a recommendation when a physician used AI in the diagnostic process considering a highly (vs. less) severe disease compared to when the physician did not use AI or when AI fully replaced the physician.

Methods

Participants from the USA (n = 452) were randomly assigned to a hypothetical scenario where they imagined that they received a treatment recommendation after a skin cancer diagnosis (high vs. low severity) from a physician, a physician using AI, or an automated AI tool. They then indicated their intention to follow the recommendation. Regression analyses were used to test hypotheses. Beta coefficients (ß) describe the nature and strength of relationships between predictors and outcome variables; confidence intervals [CI] excluding zero indicate significant mediation effects.

Results

The total effects reveal the inferiority of automated AI (ß = .47, p = .001 vs. physician; ß = .49, p = .001 vs. physician using AI). Two pathways increase intention to follow the recommendation. When a physician performs the assessment (vs. automated AI), the perception that the physician is real and present (a concept called social presence) is high, which increases intention to follow the recommendation (ß = .22, 95% CI [.09; 0.39]). When AI performs the assessment (vs. physician only), perceived innovativeness of the method is high, which increases intention to follow the recommendation (ß = .15, 95% CI [− .28; − .04]). When physicians use AI, social presence does not decrease and perceived innovativeness increases.

Conclusion

Pairing AI with a physician in medical diagnosis and treatment in a hypothetical scenario using topical therapy and oral medication as treatment recommendations leads to a higher intention to follow the recommendation than AI on its own. The findings might help develop practice guidelines for cases where AI involvement benefits outweigh risks, such as using AI in pathology and radiology, to enable augmented human intelligence and inform physicians about diagnoses and treatments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01596-6.

Decision Science Can Inform Clinical Trade-Offs Regarding Cardiotoxic Cancer Treatments

Cancer treatment-related cardiotoxicity (ie, heart failure, coronary artery disease, vascular diseases, arrhythmia) is a growing cancer survivorship concern within oncology practice; heart disease is the leading cause of noncancer death in cancer survivors and surpasses cancer as the leading cause of death for some cancers with higher survival rates. The issue of cardiotoxicity introduces a critical tradeoff that must be acknowledged and reconciled in clinical oncology practice: treating cancer aggressively and effectively in the present vs preventing future cardiotoxicity. Although many cancers must be treated as aggressively as possible, for others, multiple treatment options are available. Yet even when effective and less cardiotoxic treatments are available, they are not always chosen. Wariness to choose equally effective but less cardiotoxic treatment options may result in part from providers' and patients' reliance on "cognitive heuristics," or mental shortcuts that people (including, research shows, medical professionals) use to simplify complex judgments. These heuristics include delay discounting, availability and affect heuristics, and default bias. In the current commentary, we describe relevant research that illuminates how use of heuristics leads to biased medical decision making and translate how this research may apply when the tradeoff between aggressive cancer treatment and preventing future cardiotoxicity is considered. We discuss the implications of these biases in oncology practice, offer potential solutions to reduce bias, and call for future research in this area.

A Web Application About Herd Immunity Using Personalized Avatars: Development Study

BACKGROUND

Herd immunity or community immunity refers to the reduced risk of infection among susceptible individuals in a population through the presence and proximity of immune individuals. Recent studies suggest that improving the understanding of community immunity may increase intentions to get vaccinated.

OBJECTIVE

This study aims to design a web application about community immunity and optimize it based on users' cognitive and emotional responses.

METHODS

Our multidisciplinary team developed a web application about community immunity to communicate epidemiological evidence in a personalized way. In our application, people build their own community by creating an avatar representing themselves and 8 other avatars representing people around them, for example, their family or coworkers. The application integrates these avatars in a 2-min visualization showing how different parameters (eg, vaccine coverage, and contact within communities) influence community immunity. We predefined communication goals, created prototype visualizations, and tested four iterative versions of our visualization in a university-based human-computer interaction laboratory and community-based settings (a cafeteria, two shopping malls, and a public library). Data included psychophysiological measures (eye tracking, galvanic skin response, facial emotion recognition, and electroencephalogram) to assess participants' cognitive and affective responses to the visualization and verbal feedback to assess their interpretations of the visualization's content and messaging.

RESULTS

Among 110 participants across all four cycles, 68 (61.8%) were women and 38 (34.5%) were men (4/110, 3.6%; not reported), with a mean age of 38 (SD 17) years. More than half (65/110, 59.0%) of participants reported having a university-level education. Iterative changes across the cycles included adding the ability for users to create their own avatars, specific signals about who was represented by the different avatars, using color and movement to indicate protection or lack of protection from infectious disease, and changes to terminology to ensure clarity for people with varying educational backgrounds. Overall, we observed 3 generalizable findings. First, visualization does indeed appear to be a promising medium for conveying what community immunity is and how it works. Second, by involving multiple users in an iterative design process, it is possible to create a short and simple visualization that clearly conveys a complex topic. Finally, evaluating users' emotional responses during the design process, in addition to their cognitive responses, offers insights that help inform the final design of an intervention.

CONCLUSIONS

Visualization with personalized avatars may help people understand their individual roles in population health. Our app showed promise as a method of communicating the relationship between individual behavior and community health. The next steps will include assessing the effects of the application on risk perception, knowledge, and vaccination intentions in a randomized controlled trial. This study offers a potential road map for designing health communication materials for complex topics such as community immunity.

Artificial Intelligence and Surgical Decision-making

Importance

Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.

Observations

Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

Conclusions and Relevance

Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.

Moving beyond categorization to understand affective influences on real world health decisions

This paper provides an overview of affect and health decision-making research, with a focus on identifying gaps, opportunities, and challenges to guide future research. We begin by defining common categorical distinctions of affective processes that influence health decisions: integral (i.e., related to the decision) and incidental (i.e., normatively unrelated to the decision) influences, and current (experienced in the moment) and anticipated ("cognitive representations" of future affect) affect. We then summarize key discoveries within the most common categories of affective influences on health decision making: current integral affect, current incidental affect, and anticipated integral affect. Finally, we highlight research gaps, challenges, and opportunities for future directions for research aimed at translating affective and decision science theory to improve our understanding of, and ability to intervene upon, health decision making.