Athanasios Tsalatsanis

Dual processing model of medical decision-making.

BackgroundDual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease.MethodsWe developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice.ResultsWe show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice.ConclusionsWe have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).

Vision Based Target Tracking and Collision Avoidance for Mobile Robots

A real-time object tracking and collision avoidance method is presented for mobile robot navigation in indoors environments using stereo vision and a laser sensor. Stereo vision is used to identify the target and to calculate its relative distance from the mobile robot while laser based range measurements are utilized to avoid collision with surrounding objects. The target is tracked by its predetermined or dynamically defined color. The mobile robot’s velocity is dynamically adjusted according to its distance from the target. Experimental results in indoor environments demonstrate the effectiveness of the method.

Extensions to Regret-based Decision Curve Analysis: An application to hospice referral for terminal patients

BackgroundDespite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed.MethodsWe present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care.ResultsThe web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regretDCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patients threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available.ConclusionsWe present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.

How do physicians decide to treat: an empirical evaluation of the threshold model

BackgroundAccording to the threshold model, when faced with a decision under diagnostic uncertainty, physicians should administer treatment if the probability of disease is above a specified threshold and withhold treatment otherwise. The objectives of the present study are to a) evaluate if physicians act according to a threshold model, b) examine which of the existing threshold models [expected utility theory model (EUT), regret-based threshold model, or dual-processing theory] explains the physicians’ decision-making best.MethodsA survey employing realistic clinical treatment vignettes for patients with pulmonary embolism and acute myeloid leukemia was administered to forty-one practicing physicians across different medical specialties. Participants were randomly assigned to the order of presentation of the case vignettes and re-randomized to the order of “high” versus “low” threshold case. The main outcome measure was the proportion of physicians who would or would not prescribe treatment in relation to perceived changes in threshold probability.ResultsFewer physicians choose to treat as the benefit/harms ratio decreased (i.e. the threshold increased) and more physicians administered treatment as the benefit/harms ratio increased (and the threshold decreased). When compared to the actual treatment recommendations, we found that the regret model was marginally superior to the EUT model [Odds ratio (OR) = 1.49; 95% confidence interval (CI) 1.00 to 2.23; p = 0.056]. The dual-processing model was statistically significantly superior to both EUT model [OR = 1.75, 95% CI 1.67 to 4.08; p < 0.001] and regret model [OR = 2.61, 95% CI 1.11 to 2.77; p = 0.018].ConclusionsWe provide the first empirical evidence that physicians’ decision-making can be explained by the threshold model. Of the threshold models tested, the dual-processing theory of decision-making provides the best explanation for the observed empirical results.

Optimized task allocation in cooperative robot teams

This paper presents a novel task allocation methodology based on supervisory control theory applicable to cooperative robot teams. A team of heterogeneous robots with different sensory capabilities is considered. The overall teams mission is decomposed into multiple tasks which are assigned to one or more robots. An evaluation function is utilized to provide optimal task allocation. The proposed controller design methodology demonstrates flexibility in task assignments and robot coordination, and it is tolerant to robot failures and repairs. A warehouse patrolling application is used to demonstrate the implementation aspect of the proposed methodology.

Evaluation of Physicians' Cognitive Styles.

Background. Patient outcomes critically depend on accuracy of physicians’ judgment, yet little is known about individual differences in cognitive styles that underlie physicians’ judgments. The objective of this study was to assess physicians’ individual differences in cognitive styles relative to age, experience, and degree and type of training. Methods. Physicians at different levels of training and career completed a web-based survey of 6 scales measuring individual differences in cognitive styles (maximizing v. satisficing, analytical v. intuitive reasoning, need for cognition, intolerance toward ambiguity, objectivism, and cognitive reflection). We measured psychometric properties (Cronbach’s α) of scales; relationship of age, experience, degree, and type of training; responses to scales; and accuracy on conditional inference task. Results. The study included 165 trainees and 56 attending physicians (median age 31 years; range 25–69 years). All 6 constructs showed acceptable psychometric properties. Surprisingly, we found significant negative correlation between age and satisficing (r = −0.239; P = 0.017). Maximizing (willingness to engage in alternative search strategy) also decreased with age (r = −0.220; P = 0.047). Number of incorrect inferences negatively correlated with satisficing (r = −0.246; P = 0.014). Disposition to suppress intuitive responses was associated with correct responses on 3 of 4 inferential tasks. Trainees showed a tendency to engage in analytical thinking (r = 0.265; P = 0.025), while attendings displayed inclination toward intuitive-experiential thinking (r = 0.427; P = 0.046). However, trainees performed worse on conditional inference task. Conclusion. Physicians capable of suppressing an immediate intuitive response to questions and those scoring higher on rational thinking made fewer inferential mistakes. We found a negative correlation between age and maximizing: Physicians who were more advanced in their careers were less willing to spend time and effort in an exhaustive search for solutions. However, they appeared to have maintained their “mindware” for effective problem solving.

Defining optimum treatment of patients with pancreatic adenocarcinoma using regret-based decision curve analysis.

Objective:To use regret decision theory methodology to assess three treatment strategies in pancreatic adenocarcinoma. Background:Pancreatic adenocarcinoma is uniformly fatal without operative intervention. Resection can prolong survival in some patients; however, it is associated with significant morbidity and mortality. Regret theory serves as a novel framework linking both rationality and intuition to determine the optimal course for physicians facing difficult decisions related to treatment. Methods:We used the Cox proportional hazards model to predict survival of patients with pancreatic adenocarcinoma and generated a decision model using regret-based decision curve analysis, which integrates both the patients prognosis and the physicians preferences expressed in terms of regret associated with a certain action. A physicians treatment preferences are indicated by a threshold probability, which is the probability of death/survival at which the physician is uncertain whether or not to perform surgery. The analysis modeled 3 possible choices: perform surgery on all patients; never perform surgery; and act according to the prediction model. Results:The records of 156 consecutive patients with pancreatic adenocarcinoma were retrospectively evaluated by a single surgeon at a tertiary referral center. Significant independent predictors of overall survival included preoperative stage [P = 0.005; 95% confidence interval (CI), 1.19–2.27], vitality (P < 0.001; 95% CI, 0.96–0.98), daily physical function (P < 0.001; 95% CI, 0.97–0.99), and pathological stage (P < 0.001; 95% CI, 3.06–16.05). Compared with the “always aggressive” or “always passive” surgical treatment strategies, the survival model was associated with the least amount of regret for a wide range of threshold probabilities. Conclusions:Regret-based decision curve analysis provides a novel perspective for making treatment-related decisions by incorporating the decision makers preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.

Dynamic Task Allocation in Cooperative Robot Teams

In this paper a dynamic task allocation and controller design methodology for cooperative robot teams is presented. Fuzzy logic based utility functions are derived to quantify each robots ability to perform a task. These utility functions are used to allocate tasks in real-time through a limited lookahead control methodology partially based on the basic principles of discrete event supervisory control theory. The proposed controller design methodology accommodates flexibility in task assignments, robot coordination, and tolerance to robot failures and repairs. Implementation details of the proposed methodology are demonstrated through a warehouse patrolling case study.

Designing patient-centric applications for chronic disease management

Chronic diseases such as diabetes and heart disease are the leading causes of disability and death in the developed world. Technological interventions such as mobile applications have the ability to facilitate and motivate patients in chronic disease management, but these types of interventions present considerable design challenges. The primary objective of this paper is to present the challenges arising from the design and implementation of software applications aiming to assist patients in chronic disease management. We also outline preliminary results regarding a self-management application currently under development targeting young adults suffering from type 1 diabetes.

Multiple sensor based UGV localization using fuzzy extended Kalman filtering

A method for localization of unmanned ground vehicles (UGVs) that are equipped with multiple sensors is presented and evaluated based on derivation of a fuzzy extended Kalman filter (EKF). The fuzzy EKF is used to fuse information acquired from the UGV odometer, stereo vision system and laser range finder in order to estimate the vehicle position and orientation. The noise distribution of the multiple sensor readings is identified via a set of fuzzy logic (FL) controllers also used to update the measurement covariance matrix of the EKF. Artificial landmarks are recognized by the stereo vision system and distances between the vehicle and the landmarks are computed by both the laser range finder and the stereo vision system. Each FL controller is dedicated to one sensor and its primary function is to adjust the parameters of the sensor readings noise distribution. Range information, odometer measurements and FL controller outputs are inputs to the EKF that estimates the current position of the vehicle. As a case study, experiments with a skid steering mobile robot navigating indoors and outdoors are performed, and obtained experimental results demonstrate that the fuzzy EKF performs better than the EKF in terms of position accuracy.