Optimal Panel Size Research Articles

APPLICATION OF DELPHI METHOD IN SPORTS

A well-liked and reputable technique for gathering data from experts in a field of expertise is the Delphi method. This approach may be used to conduct surveys with qualified respondents from a wide geographic area because face-to-face interaction is not necessary. The Delphi method survey is a practical tool for obtaining expert opinions through repetitive rounds of controlled feedback. To have the maximum response rate possible, investigators must actively engage in this area. Participant cooperation is essential for the Delphi process to be carried out successfully. The main goal of this paper is to offer insights which can be useful to other researchers and practitioners preparing to apply Delphi methodology. Moreover, the article contributes to the methodological debate by taking into consideration the introduction of new practices that can be used to overcome certain classical issues of the Delphi methodology: optimal panel size, objectivity, controlled feedback, participant response rate, etc.

Balancing Clinician Workload Through Strategic Patient Panel Designs.

Clinician workload is a key contributor to burnout and well-being as well as overtime and staff shortages, particularly in the primary care setting. Appointment volume is primarily driven by the size of patient panels assigned to clinicians. Thus, finding the most appropriate panel size for each clinician is essential to optimization of patient care. One year of appointment and panel data from the Department of Family Medicine were used to model the optimal panel size. The data consisted of 82 881 patients and 105 clinicians. This optimization-based modeling approach determines the panel size that maximizes clinician capacity while distributing heterogeneous appointment types among clinician groups with respect to their panel management time (PMT), which is the percent of clinic work. The differences between consecutive PMT physician groups in total annual appointment volumes per clinician for the current practice range from 176 to 348. The optimization-based approach for the same PMT physician group results in having a range from 211 to 232 appointments, a relative reduction in variability of 88%. Similar workload balance gains are also observed for advanced practice clinicians and resident groups. These results show that the proposed approach significantly improves both patient and appointment workloads distributed among clinician groups. Appropriate panel size has valuable implications for clinician well-being, patients' timely access to care, clinic and health system productivity, and the quality of care delivered. Results demonstrate substantial improvements with respect to balancing appointment workload across clinician types through strategic use of an optimization-based approach.

What Is the Optimal Primary Care Panel Size?: A Systematic Review.

Primary care for a panel of patients is a central component of population health, but the optimal panel size is unclear. To review evidence about the association of primary care panel size with health care outcomes and provider burnout. English-language searches of multiple databases from inception to October 2019 and Google searches performed in September 2019. English-language studies of any design, including simulation models, that assessed the association between primary care panel size and safety, efficacy, patient-centeredness, timeliness, efficiency, equity, or provider burnout. Independent, dual-reviewer extraction; group consensus rating of certainty of evidence. Sixteen hypothesis-testing studies and 12 simulation modeling studies met inclusion criteria. All but 1 hypothesis-testing study were cross-sectional assessments of association. Three studies each provided low-certainty evidence that increasing panel size was associated with no or modestly adverse effects on patient-centered and effective care. Eight studies provided low-certainty evidence that increasing panel size was associated with variable effects on timely care. No studies assessed the effect of panel size on safety, efficiency, or equity. One study provided very-low-certainty evidence of an association between increased panel size and provider burnout. The 12 simulation studies evaluated 5 models; all used access as the only outcome of care. Five and 2 studies, respectively, provided moderate-certainty evidence that adjusting panel size for case mix and adding clinical conditions to the case mix resulted in better access. No studies had concurrent comparison groups, and published and unpublished studies may have been missed. Evidence is insufficient to make evidence-based recommendations about the optimal primary care panel size for achieving beneficial health outcomes. Veterans Affairs Quality Enhancement Research Initiative.

Optimal Panel Size: Are We Asking the Right Question?

Paige and colleagues' systematic review found surprisingly thin evidence about the association between primary care panel size and aims of health care. The editorialists believe that the right ques...

Arbitration in classical Athens

The Classical Athenians developed two formal arbitration procedures. They assigned low stakes disputes to a panel of arbitrators, while high stakes cases were handled by a single arbitrator. Given the information aggregation benefit of collective decision making, one would have expected more individuals to be assigned to more important cases. I develop a theoretical model to provide an explanation for their design. Recognizing that arbitrator competence is endogenous, effort put into making a good decision takes time and effort. In larger groups free riding is a concern. Consequently, there exists environments where the free-riding loss is magnified in higher stakes disputes to the point where the socially optimal panel size is inversely related to the stakes involved.

Family Medicine Panel Size with Care Teams: Impact on Quality

The demand for comprehensive primary health care continues to expand. The development of team-based practice allows for improved capacity within a collective, collaborative environment. Our hypothesis was to determine the relationship between panel size and access, quality, patient satisfaction, and cost in a large family medicine group practice using a team-based care model. Data were retrospectively collected from 36 family physicians and included total panel size of patients, percentage of time spent on patient care, cost of care, access metrics, diabetic quality metrics, patient satisfaction surveys, and patient care complexity scores. We used linear regression analysis to assess the relationship between adjusted physician panel size, panel complexity, and outcomes. The third available appointments (P < .01) and diabetic quality (P = .03) were negatively affected by increased panel size. Patient satisfaction, cost, and percentage fill rate were not affected by panel size. A physician-adjusted panel size larger than the current mean (2959 patients) was associated with a greater likelihood of poor-quality rankings (≤25th percentile) compared with those with a less than average panel size (odds ratio [OR], 7.61; 95% confidence interval [CI], 1.13-51.46). Increased panel size was associated with a longer time to the third available appointment (OR, 10.9; 95% CI, 1.36-87.26) compared with physicians with panel sizes smaller than the mean. We demonstrated a negative impact of larger panel size on diabetic quality results and available appointment access. Evaluation of a family medicine practice parameters while controlling for panel size and patient complexity may help determine the optimal panel size for a practice.

The calculus of committee composition.

Modern institutions face the recurring dilemma of designing accurate evaluation procedures in settings as diverse as academic selection committees, social policies, elections, and figure skating competitions. In particular, it is essential to determine both the number of evaluators and the method for combining their judgments. Previous work has focused on the latter issue, uncovering paradoxes that underscore the inherent difficulties. Yet the number of judges is an important consideration that is intimately connected with the methodology and the success of the evaluation. We address the question of the number of judges through a cost analysis that incorporates the accuracy of the evaluation method, the cost per judge, and the cost of an error in decision. We associate the optimal number of judges with the lowest cost and determine the optimal number of judges in several different scenarios. Through analytical and numerical studies, we show how the optimal number depends on the evaluation rule, the accuracy of the judges, the (cost per judge)/(cost per error) ratio. Paradoxically, we find that for a panel of judges of equal accuracy, the optimal panel size may be greater for judges with higher accuracy than for judges with lower accuracy. The development of any evaluation procedure requires knowledge about the accuracy of evaluation methods, the costs of judges, and the costs of errors. By determining the optimal number of judges, we highlight important connections between these quantities and uncover a paradox that we show to be a general feature of evaluation procedures. Ultimately, our work provides policy-makers with a simple and novel method to optimize evaluation procedures.

Is Bigger Always Better? On Optimal Panel Size, with Evidence from the Supreme Court of Canada

The US Supreme Court typically sits en banc. Historically, the House of Lords in the UK sat in panels of five. Its new successor, the UK Supreme Court, now sits in panels of five, seven or nine justices. A similar practice has long been in place at the Supreme Court of Canada, which routinely sits in panels of five, seven, or nine justices. We develop a formal model of the optimal choice of panel size. The model suggests that in the presence of scarce judicial resources, panel sizes can be deliberately adjusted to improve allocational e ciency. Using data from appeals heard by the Supreme Court of Canada from 1984-2005, we uncover evidence that the Court may be using varied panel sizes in a manner consistent with the predictions of our model.

Panel perception of change in facial aesthetics following orthodontic treatment in adolescents

The aim of the study was to evaluate the influence of the characteristics of panel members, the effects of gender and Angle Class of adolescent patients on their change in facial aesthetics following orthodontic treatment, and to assess the optimal panel size for epidemiological studies on changes in facial aesthetics after orthodontic treatment. A panel of 74 adult laymen (35 males and 39 females) and a panel of 87 orthodontists (37 males and 50 females) evaluated sets of three post-treatment standardized photographs (one frontal, one three-quarter smiling, and one lateral) of 64 adolescent orthodontic patients in relation to the pre-treatment sets of the same patient on a five-point scale. The main effects of professional background, age, gender, and geographic region of the panel members on the aesthetic scores, as well as their first order interactions were evaluated by multilevel models. Professional background, age, gender, and geographical region of panel members have an influence on the evaluation of the change of facial aesthetics following orthodontic treatment. The effect of gender and Angle Class of the patients on the scores was evaluated by two-way analysis of variance. There was no difference in the mean scores for boys and girls. Improvement of facial aesthetics by orthodontic treatment was significant for Class I, Class II division 1, and Class II division 2 patients, but not for Class III patients. Based on the intraclass correlation coefficient, a panel of nine randomly selected orthodontists, a panel of 14 randomly selected laymen, or a mixed panel of 13 individuals is sufficient to obtain reliable results in the aesthetic evaluation of adolescent faces, using photographs and a five-point scale.

Influence of panel composition on aesthetic evaluation of adolescent faces

The objective of this study was to evaluate the influence of professional background, age, gender, and geographical region of panel members on their evaluation of the facial aesthetics of adolescents, and to assess the optimal panel size for epidemiological studies on facial aesthetics. A panel of 76 adult laymen from two different regions (Belgium and The Netherlands) and a panel of 89 orthodontists from the same two regions, evaluated photographic sets (one frontal, one three-quarter smiling, and one lateral view) of 64 adolescents (32 boys, 32 girls) on a visual analogue scale (VAS) in relation to a reference set of photographs. The effects of the characteristics of the panel members on the VAS scores for boys and girls separately, as well as their interactions, were evaluated by multilevel models. The adolescents entered the model as a random effect and four characteristics of the panel members were included in the model as fixed effects. The multilevel model with main effects and first-order interactions revealed that laymen rated adolescents as more attractive than orthodontists. This finding was significant for all laymen, except for older males, and Belgian laymen, when rating girls. Older panel members rated boys significantly more attractive than younger panel members. Males rated adolescents more attractive than females. The latter was significant for all male subgroups, except for the lay male subgroup. There were regional differences. Based on the intraclass correlation coefficient, a panel of seven randomly selected laymen and/or orthodontists is sufficient to obtain reliable results in the aesthetic evaluation of adolescent faces, using photographs and a VAS.