Strategic Admission Behavior and Its Implications: Evidence from a Cardiac Surgery Department

Abstract

This paper examines a decentralized admission control system with partial capacity sharing in a hospital setting. The admission decision is made by each physician who is assigned a number of dedicated inpatient beds. A physician can “borrow” beds from other physicians if his dedicated beds are all occupied. We seek to understand the impact of the “borrowing cost” on physicians’ admission behavior. We find that (i) If the borrowing cost is low, a physician tends to admit lower-risk patients when either his or others’ capacity utilization is higher; (ii) If the borrowing cost is moderate, a physician tends to admit higher (lower)-risk patients when his (others’) capacity utilization is higher; and (iii) If the borrowing cost is high, a physician tends to admit higher-risk patients when either his or others’ capacity utilization is higher. We then empirically test and validate these findings. Our work demonstrates that when designing strategic admission control systems, it is important to quantify and perhaps then influence the magnitude of the borrowing cost to induce a proper level of competition without sacrificing the benefit of resource pooling.

Appendix

Our empirical analyses have several subjective definitions: the criteria of filtering samples, the construction of the dependent variable $R\_\textit{grade}$, and the construction of the independent variables $\textit{Self}\_\textit{State}$ and $\textit{Others}\_\textit{State}$. In this appendix, we will show that our findings are robust under alternative definitions.

1.1 Different criteria of filtering samples

In Sect. 4.1, we use the median number of daily occupied beds 77 as a filter and focus on patient admissions when at least 77 beds are already occupied. We show in Table 7 our main results continue to hold when the number used to filter samples takes values from 75 to 80.

Table 7 Different criteria of filtering samples

1.2 Different criteria to construct the dependent variable

In Sect. 4.2, we use $R\_\textit{grade}$ as a proxy for a patient’s medical risk. Here, we use an alternative criterion to test the robustness of our results. We define four buckets where the buckets are partitioned in such a way that the four buckets are patients with total surgery grades in [0, 5), [5, 6), [6, 9) and [9, 16], respectively. Table 8 shows that our empirical results are robust under the alternative definition.

1.2.1 Different criteria to construct independent variables

To check the robustness of cutoff point of $\textit{Self}\_\textit{Load}$, we fixed the cutoff point of $\textit{Others}\_\textit{Load}$ is fixed at quantile 0.9. In Table 9, our main conclusion continues to hold when the cutoff point of $\textit{Self}\_\textit{Load}$ takes values from 0.4 to 0.7.

Similarly, when check the robustness of cutoff point of $\textit{Others}\_\textit{Load}$, we fix the cutoff point of $\textit{Self}\_\textit{Load}$ at quantile 0.6. In Table 10, our main conclusion continues to hold when the cutoff point of $\textit{Others}\_\textit{Load}$ takes values from 0.6 to 0.9.

Table 8 Different criteria to construct $R\_\textit{grade}$

Table 9 Robustness check on cut-points of $\textit{Self}\_\textit{State}$

Table 10 Robustness check on cut-points of $\textit{Others}\_\textit{State}$