Improving Performance of Open Access Clinics

Open Access Scheduling has shown great promise in allowing health care practices to provide same-day access, and to match patients with their regular physicians. However, similarly to traditional clinics where appointments are pre-booked, open access clinics are also frustrated with long waits, long idle time and long overtime due to uncertainties such as patient no-shows, variable service time and variable daily demand. These aspects have not been studied previously in an open access setting. This study investigates different management options to improve clinical performance in terms of patient waiting time, doctor idle time and clinic overtime. Other factors studied with a simulation model include client load and placement of pre-booked slots. Results show that a proper panel size is critical to obtain good performance for open access clinics, and that good choices for management options depend on the client load.

Electrophysiological correlates of performance monitoring in middle and late adolescence

The ability to monitor and evaluate the consequences of ongoing behaviors and coordinate behavioral adjustments seems to rely on networks including the anterior cingulate cortex (ACC) and phasic changes in dopamine activity. Activity (and presumably functional maturation) of the ACC may be indirectly measured using the error-related negativity (ERN), an event-related potential (ERP) component that is hypothesized to reflect activity of the automatic response monitoring system. To date, no studies have examined the measurement reliability of the ERN as a trait-like measure of response monitoring, its development in mid- and late- adolescence as well as its relation to risk-taking and empathic ability, two traits linked to dopaminergic and ACC activity. Utilizing a large sample of 15- and 18-year-old males, the present study examined the test-retest reliability of the ERN, age-related changes in the ERN and other components of the ERP associated with error monitoring (the Pe and CRN), and the relations of the error-related ERP components to personality traits of risk propensity and empathy. Results indicated good test-retest reliability of the ERN providing important validation of the ERN as a stable and possibly trait-like electrophysiological correlate of performance monitoring. Ofthe three components, only the ERN was of greater amplitude for the older adolescents suggesting that its ACC network is functionally late to mature, due to either structural or neurochemical changes with age. Finally, the ERN was smaller for those with high risk propensity and low empathy, while other components associated with error monitoring were not, which suggests that poor ACe function may be associated with the desire to engage in risky behaviors and the ERN may be influenced by the extent of individuals' concern with the outcome of events.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Is more information always good? : investigating the impact of website interface features on e-retailer's sales performance

A number of frameworks have been suggested for online retailing, but still there exists little consensus among researchers and practitioners regarding the appropriate amount of information critical and essential to the improvement of customers' satisfaction and their purchase intention. Against this backdrop, this study contributes to the current practical and theoretical discussions and conversations about how information search and perceived risk theories can be applied to the management of online retailer website features. This paper examines the moderating role of website personalization in studying the relationship between information content provided on the top US retailers' websites, and customer satisfaction and purchase intention. The study also explores the role played by customer satisfaction and purchase intention in studying the relationship between information that is personalized to the needs of individual customers and online retailers' sales performance. Results indicate that the extent of information content features presented to online customers alone is not enough for companies looking to satisfy and motivate customers to purchase. However, information that is targeted to an individual customer influences customer satisfaction and purchase intention, and customer satisfaction in tum serves as a driver to the retailer's online sales performance.