Comparing Network Physician Performance Using Proposed Index31

We find a strong positive correlation between RVU/hr Index and absolute RVUs/hr (r=0.7687, p-value < 0.001), confirming that a substantial portion of revenue potential (RVUs/hr) can be explained by the proposed index. It captures the “intrinsic” ability of a physician to potentially generate revenue across facilities since it weights relative performance by hours at the various facilities. The unexplained portion may be driven by patient characteristics. Thus, the index is an equitable measure of physician performance.

To provide a more comprehensive view and offer appropriate “carrots and sticks” for reward and remedy, we jointly consider both productivity and patient satisfaction by plotting the new RVU/hr Index (x-axis) and PG Index (y-axis) on the xy-plane. Depending on which quadrant a physician falls, management can easily identify the need for improvement with regard to productivity, patient satisfaction, or both, or if the physician should be rewarded for outstanding performance on both dimensions (Fig. 5).

Fig. 5 displays physicians’ performances, where each dot represents one physician. The horizontal and vertical lines signify average performance on each dimension, and thus divide physicians into four groups (quadrants). Management may choose cutoff values within each quadrant to further differentiate physicians within each group, e.g., awarding those who perform one standard deviation above the mean (center point) on both dimensions. Additionally, they may choose to weight the two measures differently depending on specific performance goals. The

“best” physicians (26%) who deliver above average performance in both productivity and patient satisfaction, i.e., RVUs/hr Index ≥ 1 and PG Index ≥ 1, are located in Quadrant I and may be used for benchmarking. Physicians who fall in Quadrant III (30%) are below average performers on productivity and patient experience, and require immediate attention from management. Quadrant II (26%) shows those short on productivity, while Quadrant IV (18%) depicts those with low patient experience; both II and IV are candidates for remedial training depending on the specific threshold set by management. It may be necessary to further address ways to balance patient experience and productivity in Quadrants II and IV.

Figure 5 Integrating Productivity and Patient Experience: Insights for Rewards and Improvement Training

3.5 Cluster Analysis to Manage Physician Segments

We have proposed a fairer, systematic, logical and comprehensible performance metric to objectively evaluate physicians working in an EPMN in §3.4. To identify individual traits that may affect physician performance, we group physicians based on non-performance attributes and examine the disparities among clusters. Such information can be used to guide and reward different groups of physicians; and more efficiently and effectively allocate physicians to facilities to meet the needs of the EPMN.

Due to known differences between pediatric and general EDs, we treat pediatric physicians as a separate group. Since pediatric physicians only account for 5% of the physicians in our data, we have excluded physicians from the cluster analysis if the average age of their patients was less than

0.0

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00

Press Ganey Index

18. Using the variables in Table 6, we group the 1,026 non-pediatric EPs into five fundamentally different clusters using K-means.

Table 6 Summary Statistics of Variables used in Cluster Analysis Average Patient Age ≥ 18 (N=1026)

Variable Mean Standard

Deviation

Physician Age 45.749 9.967

# Facilities Worked 2.518 2.334

% 6AM-3PM Hours 36.724 14.257

% 3PM-12PM Hours 47.139 11.342

% 12PM-6AM Hours 16.126 15.176

% Admitted 18.052 10.018

APP Support Ratio (%) 23.268 10.226

Variable Count Percent

Male 715 69.688

White 812 79.142

We have colloquially named clusters based on dominant characteristics (e.g., Night Owl, Veteran, etc.) Figs. 6(a) – (b) graphically display differences among the five clusters. The dashed line represents the overall average, while each boxplot shows the distribution of the variable on the y-axis. The plot provides the (1) minimum, (2) 1st quartile (25th percentile), (3) median (50th percentile), (4) 3rd quartile (75th percentile), and (5) maximum. Figs. 6(a) – (b) shows that Veteran physicians are older and tend to work fewer nights, while Night Owls work a disproportionately high number of night shifts relative to others. Minority and Female physicians are generally younger, consistent with medical school trends.

(a) (b) Figure 6 Distributions of age and night-shift by clusters

By zooming in on Fig. 5 and examining the five clusters separately, we can link the cluster membership with performance relative to the entire physician population. Fig. 7(a) shows a relatively even distribution of Veterans across the four quadrants, while Fig. 7(b) suggests that the majority of Night Owls have low RVUs/hr indices, with many also earning lower PG scores. One-way ANOVA has confirmed that the cluster differences in PG scores and RVUs/hr Index are statistically significant.

(a) 25% of Veterans (Quadrant I) achieve above average performance on both dimensions

(b) 75% (Quadrants II & III) of Night Owls earn below average RVUs/hr Index Figure 7 RVUs/hr Index (Productivity) versus PG Index (Patient Experience) by Cluster

Thus far, we have provided a structural, evidence-based approach to segment EPs within the network. The clusters suggest that EPs may have different priorities at different stages in their personal and professional lives. For instance, younger physicians (i.e., many female and minority physicians) may have different family and financial concerns than veteran doctors (Darcy et al.

2012; Dyrbye et al. 2013). Like most professions, the composition of EPs encompasses varying career ambitions and personal responsibilities. It is thus vital to understand such distinctions when matching physicians with divergent priorities to different shifts and multiple facilities.

clusters only help to develop insights for our study, instead of serving as the basis for compensation.

The clustering results lead us to reflect on current practices. Namely, management must appreciate the dynamics of doctors’ professional and personal development and recognize that physicians will respond distinctively to incentives. Measuring performance collectively for all clusters or offering identical incentives to all physicians may not be effective. For example, comparing Night Owls with Veterans who work fewer night shifts could be unfair due to differences in patient composition. In the following section, we use the cluster differences to motivate our selection of control variables.