Decision on SIG-2026-0407

[MSOM Conference]

08-May-2026

Re: SIG-2026-0407, "The Impact of Information-Granularity and Prioritization on Patients' Care Modality Choices"

SIG Day Decision: Accept

Dear Author (this is to ensure anonymity):

Congratulations, your paper has been accepted for inclusion in the 2026 Healthcare Operations Management SIG-Day Conference.

We will follow up with you shortly with information regarding the conference schedule. If, for any reason, you are no longer able to participate in the SIG Day Conference, please notify us as soon as possible.

Sincerely,

Healthcare Operations;SIG Co-Chairs
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Referee: 1
Strengths SIG Only: The paper develops a queueing game model of telemedicine triage in which patients choose between remote and in person care when telemedicine may require follow up visits. The key message is that better individualized information does not necessarily improve system performance, because refined predictions of follow up risk stabilize the telemedicine system through self selection but can also worsen congestion by directing high risk patients straight to the in person queue. The comparison between crude population level information and refined individualized predictions clearly illustrates this tradeoff and leads to a threshold type equilibrium in the refined regime. The paper also proposes a priority mechanism that can move the system toward the first best allocation, and a hospital based case study helps demonstrate the practical relevance of these insights.

Referee: 2
Strengths SIG Only: The paper studies a clear and important question on how information availability affects telemedicine use and system-level performance. The insight that “more information is not always better” is a non-obvious and practically relevant insight. The case study strengthens the study’s practical contribution.

Managerially, the work cautions that providing patients with detailed follow-up predictions can worsen congestion unless paired with a priority policy. These insights are actionable for health-system triage and scheduling.

Although this is an analytical paper, the writing and model presentation are concise and easy to follow.

Referee: 3
Strengths SIG Only: The paper combines predictive analytics, information design, and strategic queueing behavior into a unified framework. The model is clean and tractable. The policy mechanism (priority rule) is well-motivated.

Referee: 1
Limitations: Patients are assumed to choose solely based on expected waiting time, which omits clinical factors, preferences, and provider guidance. Telemedicine outcomes are modeled in a simplified way, and the refined information regime assumes accurate prediction of follow up risk without examining prediction error. The model also focuses on steady state performance and stylized service assumptions, which may limit applicability to real healthcare settings.

Referee: 2
Limitations: I have read multiple of versions of this paper over the years and appreciate the robustness checks and the consideration of fixed costs and capacity in this version. I have only a few remaining comments:

1. Heterogeneity in patients’ knowledge and how E[X] is known
I’d encourage the authors to acknowledge heterogeneity in how patients form expectations about follow-up needs. First-time telemedicine users or patients seeking care for a new condition plausibly may have weaker priors than repeat patients, which could tilt choices toward the modality with faster initial access. Even if formally modeling this is out of scope, a short discussion of its likely implications would help (e.g., whether such heterogeneity shifts the equilibrium threshold toward telemedicine or magnifies the value of refined information for first-time users).

Relatedly, in the crude-information case, the assumption that patients act on an average follow-up probability E[X] deserves a bit more operational grounding. In practice, public dashboards that report a single rate by disease area—like the one you shared—are quite coarse. Realistic expectations would also depend critically on other factors like perceived severity or comorbidities. It may be helpful to discuss what practical signals might inform perceived E[X] and/or consider cases when E[X] is mis-perceived.

2. Aligning the empirical case with the model’s scope
The authors clarify that the framework applies to cases where telemedicine is feasible substitute for in-person visits. To align the case study with this scope, would it be possible to restrict encounters used in the case study to those that fit this framework (i.e., substitution-eligible encounters), excluding triage or information-oriented uses? It seems unreasonable to include, for example, encounters that inherently require a physical exam since those are not what the study concerns.

3. Fixed costs in the case study

Given the practical importance of the case study, is there a reasonable way to consider/incorporate fixed costs via simple cost proxies or a stylized sensitivity? For example, travel costs can often be proxied with ZIP-to-clinic distance. I believe they can make the study much more realistic and convincing, regardless of whether they materially change the results. If full implementation is infeasible, it would be valuable to comment on the impact of incorporating fixed costs into the case study.

Referee: 3
Limitations: Patients are assumed to: Be fully rational and optimize only expected waiting time. The prediction model is relatively simple: Uses logistic regression only.

Referee: 1

Comments to the Author
Please see my detailed report attached.

Referee: 2

Comments to the Author
(There are no comments.)

Referee: 3

Comments to the Author
(There are no comments.)

[Yue Hu]

Dear Jing and Nikos,

Thank you so much for the wonderful news! We are thrilled and grateful that our paper has been accepted for the Healthcare Operations SIG, and really appreciate the time and effort that you and the committee/reviewers put into reviewing the submissions.

If possible, we would like to switch the speaker from Lin Zang, who is currently registered as the speaker, to myself. We have filled out this form to change the speaker, but just wanted to email in case SIG Day uses a different process from the Main Conference.

Thanks again, and look forward to seeing both of you soon!

Best,
Yue

On Fri, May 8, 2026 at 1:55 PM MSOM Conference <onbeh...@manuscriptcentral.com> wrote:

08-May-2026 Re: SIG-2026-0407, "The Impact of Information-Granularity and Prioritization on Patients' Care Modality Choices" SIG Day Decision: Accept Dear Author (this is to ensure anonymity): Congratulations, your paper has been accepted for

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