Decision on SIG-2026-0289

[MSOM Conference]

08-May-2026

Re: SIG-2026-0289, "Proactive Transfer Admission Control for Emergency Departments"

SIG Day Decision: Reject

Dear Author (this is to ensure anonymity):

We received many excellent submissions for the Healthcare Operations Management SIG-Day Conference. Unfortunately, we were unable to accept all of them to be included in the program, and we are sorry to say that your paper was not accepted to the SIG-Day conference.

If you also submitted an extended abstract of your paper to the main MSOM Conference, a decision on that submission will come separately.


Sincerely,

Healthcare Operations;SIG Co-Chairs

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Referee: 1
Please summarize the paper briefly. - Rev: This paper proposes a fluid-based approach for proactive transfer admission control in emergency departments. The key tradeoff is between waiting time in the ED and the rejection cost of transfer patients. A central feature of the model is that admission decisions are based not only on current congestion but also on predicted future arrival rates and staffing levels. This predictive information is naturally integrated through the congestion clearance time.
The fluid-optimal policy is a zero-one single switch policy with switching time depends on future congestion clearance time. The authors also show that the fluid-based stochastic policy achieves asymptotically optimal performance under a many-server scaling regime. Numerical experiments based on both synthetic and real hospital data show that the proposed policy significantly improves upon current (non-lookahead) policies along the congestion–rejection cost tradeoff curve.

Referee: 2
Please summarize the paper briefly. - Rev: This paper studies how to incorporate predictive information into the admission control problem for emergency departments. The problem is modeled as a multi-server queueing network that includes both patients waiting to be seen and boarding patients awaiting admission to inpatient wards. The optimal admission policy is derived for a fluid approximation of the original stochastic model. The authors also propose several simple and implementable heuristics based on the optimal policy for the fluid approximation. Comprehensive numerical studies demonstrate the strong performance of the proposed policies and highlight the value of incorporating forecast information.-


Referee: 1

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Referee: 2

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