Decision on SIG-2026-0106

[MSOM Conference]

08-May-2026

Re: SIG-2026-0106, "Optimal Call-In Policies under Travel-Induced Risk: Design and Control for Hybrid Hospitalization"

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 could not 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 be made separately.


Sincerely,

Healthcare Operations;SIG Co-Chairs

MSOM Healthcare Operations Management SIG-Day Co-Chair

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Referee: 1
Strengths SIG Only: This paper studies a hybrid hospital model where patients are either treated remotely (at home) or on-site, with remote patients potentially "called in" to the hospital if their condition worsens. The authors model individual health trajectories as drifted Brownian motions and derive optimal call-in thresholds that minimize long-run average cost, subject to clinical safety constraints and shared resource limits. Key results include the non-monotonicity of the optimal threshold in travel time, an equivalence between the capacitated and uncapacitated problems via a cost-shifting parameter, and an extension to heterogeneous patient types.

- The paper addresses a timely and practically important problem. Hybrid hospitalization models combining remote and on-site care are expanding rapidly.

- The modeling framework is well-constructed. By centering the analysis on individual health trajectories rather than adopting a purely queueing-theoretic perspective, the authors can explicitly link clinical severity, travel distance, and cost to the call-in decision.

- The analytical results are strong. The equivalence between the capacitated and uncapacitated problems is interesting and practically useful. The characterization of the non-monotonicity of the optimal threshold in travel time, along with the closed-form expressions via the Lambert-W function, provides clean and implementable policy guidance.

Referee: 2
Strengths SIG Only: The paper uses a Brownian motion stochastic model to represent the evolution of individuals' health. This model is then embedded into a decision framework to come up with the optimal threshold policy to decide when to bring patients in (from at-home hospitalization). The model captures patient deterioration while in transit.

Referee: 3
Strengths SIG Only: Hybrid hospitalization with travel-induced risk. Instead of standard queueing abstractions, the paper models patient health trajectories directly using drifted Brownian motion. The paper generates several non-trivial and policy-relevant insights.

Referee: 1
Limitations: - The choice of drifted Brownian motion is the paper's central modeling decision and drives all analytical tractability. Yet the justification is thin. The authors note that BM hitting times yield inverse Gaussian LOS distributions, which have appeared in healthcare modeling, but this is an indirect argument. The authors should discuss more explicitly which qualitative features of the optimal policy would or would not survive under alternative specifications (jump-diffusions, regime-switching), even without full re-derivation.

- The negative drift guarantees all patients eventually recover. The authors acknowledge this as "conservative," but in the hybrid care context it is a substantive limitation. The primary clinical concern in remote monitoring of acutely ill patients is precisely the risk of death or irreversible deterioration, especially during transport. Excluding this removes arguably the most important risk from the optimization.

- The numerical experiments are illustrative, with parameters assembled from disparate literature sources. The authors mention a collaboration with Sheba Beyond and cite its operational model repeatedly. A case study using real or realistic patient-level data would simultaneously validate the BM specification, calibrate parameters coherently, and demonstrate the practical magnitude of policy improvements.

- The central finding that distant patients may sometimes be better served on-site is interesting but becomes relatively intuitive once the clinical severity cap and travel deterioration are introduced. The paper would benefit from pushing further: quantifying the cost gap between the optimal policy and simpler heuristics (e.g., fixed-distance cutoffs), examining sensitivity to the severity limit, and offering practical guidance on how administrators who do not directly observe recovery rates should calibrate the policy over time.

Minor Comments:

- The linear travel deterioration model deserves a brief robustness discussion, since clinical deterioration during transport may be highly nonlinear.

- Typo on abstract: "telemedecine" → "telemedicine."

Referee: 2
Limitations: The decision framework uses operational costs as the goal to determine the call-in policy. I have concerns about this being a valid metric, especially in a context where there is significant heterogeneity in insurance (and hence in payments to the hospital).

Is there an industry collaborator? It would be useful to have a more realistic numerical analysis, potentially inferring some parameters from industry practice.

Referee: 3
Limitations: The model is fundamentally static (ex-ante threshold choice). Limited behavioral considerations.

Referee: 1

Comments to the Author
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Referee: 2

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Referee: 3

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