Objective
The purpose of this study is to identify barriers that early-adopting dentists perceive as common and challenging when implementing recommendations from evidence-based (EB) clinical guidelines.
Method
This is a cross-sectional study. Dentists who attended the 2008 Evidence-based Dentistry Champion Conference were eligible for inclusion. Forty-three dentists (34%) responded to a 22-item questionnaire administered online. Two investigators independently coded and categorized responses to open-ended items. Descriptive statistics were computed to assess the frequency of barriers and perceived challenges.
Results
The most common barriers to implementation are difficulty in changing current practice model, resistance and criticism from colleagues, and lack of trust in evidence or research. Barriers perceived as serious problems have to do with lack of up-to-date evidence, lack of clear answers to clinical questions, and contradictory information in the scientific literature.
Conclusions
Knowledge of barriers will help improve translation of biomedical research for dentists. Information in guidelines needs to be current, clear, and simplified for use at chairside; dentists’ fears need to be addressed.
BackgroundA major challenge in designing useful clinical information systems in dentistry is to incorporate clinical evidence based on dentists' information needs and then integrate the system seamlessly into the complex clinical workflow. However, little is known about the actual information needs of dentists during treatment sessions. The purpose of this study is to identify general dentists' information needs and the information sources they use to meet those needs in clinical settings so as to inform the design of dental information systems.MethodsA semi-structured interview was conducted with a convenience sample of 18 general dentists in the Pittsburgh area during clinical hours. One hundred and five patient cases were reported by these dentists. Interview transcripts were coded and analyzed using thematic analysis with a constant comparative method to identify categories and themes regarding information needs and information source use patterns.ResultsTwo top-level categories of information needs were identified: foreground and background information needs. To meet these needs, dentists used four types of information sources: clinical information/tasks, administrative tasks, patient education and professional development. Major themes of dentists' unmet information needs include: (1) timely access to information on various subjects; (2) better visual representations of dental problems; (3) access to patient-specific evidence-based information; and (4) accurate, complete and consistent documentation of patient records. Resource use patterns include: (1) dentists' information needs matched information source use; (2) little use of electronic sources took place during treatment; (3) source use depended on the nature and complexity of the dental problems; and (4) dentists routinely practiced cross-referencing to verify patient information.ConclusionsDentists have various information needs at the point of care. Among them, the needs for better visual representation and patient-specific evidence-based information are mostly unmet. While patient records and support staff remain the most used information sources, electronic sources other than electronic dental records (EDR) are rarely utilized during patient visits. For future development of dental information or clinical decision-support systems, developers should consider integrating high-quality, up-to-date clinical evidence into comprehensive and easily accessible EDRs as well as supporting dentists' resource use patterns as identified in the study.
Multi-agent reinforcement learning has made substantial empirical progresses in solving games with a large number of players. However, theoretically, the best known sample complexity for finding a Nash equilibrium in general-sum games scales exponentially in the number of players due to the size of the joint action space, and there is a matching exponential lower bound. This paper investigates what learning goals admit better sample complexities in the setting of m-player general-sum Markov games with H steps, S states, and A i actions per player. First, we design algorithms for learning an ε-Coarse Correlated Equilibrium (CCE) in O(H 5 S max i≤m A i /ε 2 ) episodes, and an ε-Correlated Equilibrium (CE) in O(H 6 S max i≤m A 2 i /ε 2 ) episodes. This is the first line of results for learning CCE and CE with sample complexities polynomial in max i≤m A i . Our algorithm for learning CE integrates an adversarial bandit subroutine which minimizes a weighted swap regret, along with several novel designs in the outer loop. Second, we consider the important special case of Markov Potential Games, and design an algorithm that learns an ε-approximate Nash equilibrium within O(S i≤m A i /ε 3 ) episodes (when only highlighting the dependence on S, A i , and ε), which only depends linearly in i≤m A i and significantly improves over existing efficient algorithms in the ε dependence. Overall, our results shed light on what equilibria or structural assumptions on the game may enable sample-efficient learning with many players.
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