Making decisions in medical care is a difficult task, involving a variety of cognitive processes. Decision making is defined as the process of examining possibilities, risks, uncertainties, and options, comparing them, and choosing a course of action. Decisions based on erroneous assessments may result in incorrect patient and family expectations, and potentially inappropriate advice, treatment, or discharge planning (eg, longer length of hospitalization, long-term placement, and wasted resources). Rapid and accurate decision making is critical to stroke care, for which several factors have proven effect on outcomes. In brief, there are patient-level, hospital-level, and provider-level characteristics that directly affect stroke outcomes.

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The management of multiple sclerosis (MS) is rapidly changing by the introduction of new and moreeffective disease-modifying agents. The importance of risk stratification was confirmed by results on diseaseprogression predicted by different risk score systems. Despite these advances, we know very little about medicaldecisions under uncertainty in the management of MS. The goal of this study is to i) identify whetheroverconfidence, tolerance to risk/uncertainty, herding influence medical decisions, and ii) to evaluate the frequencyof therapeutic inertia (defined as lack of treatment initiation or intensification in patients not at goals of care) andits predisposing factors in the management of MS 

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Emotions play a critical role in our daily decisions. However, it remains unclear how and what sortof emotional expressions are associated with therapeutic decisions in multiple sclerosis (MS) care. Our goal wasto evaluate the relationship between emotions and affective states (as captured by muscle facial activity andemotional expressions) and TI amongst neurologists caring for MS patients when making therapeutic decisions

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Little is known about the real-life factors that clinicians use in selection of patients that would receive endovascular treatment (EVT) in the real world. We sought to determine patient, practitioner, and health system factors associated with therapeutic decisions around endovascular treatment 

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In behavioral economics, herding is a phenomenon by which individuals follow recommendations from others rather than deciding independently on the basis of their own private information. Herding can occur in multiple sclerosis (MS) when a neurologist follows a therapeutic recommendation by a colleague even though it is not supported by best practice clinical guidelines. Limited information is currently available on the role of herding in medical care. The objective of this study was to determine the prevalence (and its associated factors) of herding in the management of MS

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Limited information is available on physician-related factors influencing therapeutic inertia (TI) in multiple sclerosis (MS). Our aim was to evaluate whether phy-sicians’ risk preferences are associated with TI in MS care, by applying concepts from behavioral economics

Knowledge-to-action gaps influence therapeutic decisions in atrial fi-brillation (AF). Physician-related factors are common, but the least studied. Weevaluated the prevalence and determinants of physician-related factors and knowledge-to-action gaps among physicians involved in the management of AF patients

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There is growing interest in understanding and addressing factors that govern the decision-making process in multiple sclerosis (MS) care. Therapeutic inertia (TI) is the failure to escalate therapy when goals are unmet. Limited data are available on the prevalence of TI and factors affecting therapeutic decisions in the management of patients with MS worldwide

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Therapeutic inertia (TI) is a common phenomenon among physicians who care for patients with chronic conditions. We evaluated the efficacy of the traffic light system (TLS) educational intervention to reduce TI among neurologists with MS expertise.

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The landscape of multiple sclerosis (MS) treatment is constantly changing. Significant heterogeneity exists in the efficacy and risks associated with these therapies. Therefore, clinicians have the challenge to tailor treatment based on several factors (disease activity level, risk of progression, individual patient preferences and characteristics, personal expertise, etc.), to identify the optimal balance between safety and efficacy.

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