Screen-Based Simulation for Training and Automated Assessment of Teamwork Skills: Comparing 2 Modes With Different Interactivity.

INTRODUCTION: The need for teamwork training is well documented; however, teaching these skills is challenging given the logistics of assembling individual team members together to train in person. We designed 2 modes of screen-based simulation for training teamwork skills to assess whether interactivity with nonplayer characters was necessary for in-game performance gains or for player satisfaction with the experience. METHODS: Mixed, randomized, repeated measures study with licensed healthcare providers block-stratified and randomized to evaluation-participant observes and evaluates the team player in 3 scenarios-and game play-participant is immersed as the leader in the same 3 scenarios. Teamwork construct scores (leadership, communication, situation monitoring, mutual support) from an ontology-based, Bayesian network assessment model were analyzed using mixed randomized repeated measures analyses of variance to compare performance, across scenarios and modes. Learning was measured by pretest and posttest quiz scores. User experience was evaluated using χ2 analyses. RESULTS: Among 166 recruited and randomized participants, 120 enrolled in the study and 109 had complete data for analysis. Mean composite teamwork Bayesian network scores improved for successive scenarios in both modes, with evaluation scores statistically higher than game play for every teamwork construct and scenario (r = 0.73, P = 0.000). Quiz scores improved from pretest to posttest (P = 0.004), but differences between modes were not significant. CONCLUSIONS: For training teamwork skills using screen-based simulation, interactivity of the player with the nonplayer characters is not necessary for in-game performance gains or for player satisfaction with the experience.

Current indications for transplantation: stratification of severe heart failure and shared decision-making.

Heart failure (HF) is a complex clinical syndrome that results from structural or functional cardiovascular disorders causing a mismatch between demand and supply of oxygenated blood and consecutive failure of the body's organs. For those patients with stage D HF, advanced therapies, such as mechanical circulatory support (MCS) or heart transplantation (HTx), are potentially life-saving options. The role of risk stratification of patients with stage D HF in a value-based healthcare framework is to predict which subset might benefit from advanced HF (AdHF) therapies, to improve outcomes related to the individual patient including mortality, morbidity and patient experience as well as to optimize health care delivery system outcomes such as cost-effectiveness. Risk stratification and subsequent outcome prediction as well as therapeutic recommendation-making need to be based on the comparative survival benefit rationale. A robust model needs to (I) have the power to discriminate (i.e., to correctly risk stratify patients); (II) calibrate (i.e., to show agreement between the predicted and observed risk); (III) to be applicable to the general population; and (IV) provide good external validation. The Seattle Heart Failure Model (SHFM) and the Heart Failure Survival Score (HFSS) are two of the most widely utilized scores. However, outcomes for patients with HF are highly variable which make clinical predictions challenging. Despite our clinical expertise and current prediction tools, the best short- and long-term survival for the individual patient, particularly the sickest patient, is not easy to identify because among the most severely ill, elderly and frail patients, most preoperative prediction tools have the tendency to be imprecise in estimating risk. They should be used as a guide in a clinical encounter grounded in a culture of shared decision-making, with the expert healthcare professional team as consultants and the patient as an empowered decision-maker in a trustful safe therapeutic relationship.

Artificial heart pumps: bridging the gap between science, technology and personalized medicine by relational medicine.

In the US population of 300 million, 3 million have heart failure with reduced ejection fraction and 300,000 have advanced heart failure. Long-term mechanical circulatory support will, within the next decade, be recommended to 30,000 patients annually in the USA, 3000 undergo heart transplantation annually. What do these advances mean for persons suffering from advanced heart failure and their loved ones/caregivers? In this perspective article, we discuss - by exemplifying a case report of a 27-year-old man receiving a Total Artificial Heart - a practice concept of modern medicine that fully incorporates the patient's personhood perspective which we have termed Relational Medicine™. From this case study, it becomes apparent that the successful practice of modern cardiovascular medicine requires the person-person encounter as a core practice element.