Decision on SIG-2026-0316

[MSOM Conference]

08-May-2026

Re: SIG-2026-0316, "Efficiency at the Cost of Equity? Effects of Hospital Algorithm Adoption on Care Delivery and Outcomes"

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: Please see attached.

Referee: 2
Strengths SIG Only: 1. The work addresses a relevant research question in regards to health equity with algorithm adoption in healthcare, and focuses on a patient population that could benefit from risk-stratification algorithms – Acute Coronary Syndrome (ACS). The work shows that low-severity and non-White patients do not experience significant improvements in healthcare outcomes with algorithm adoption (risk-stratification algorithms), and low severity patients are less likely to access definitive therapy. High severity patients do benefit from a mortality view and are able to access therapies more easily. However, within the high-severity group, the benefits primarily land to White patients, and White patients are more likely to receive diagnostic testing and intensive treatments following adoption.
2. The work uses multiple large datasets that cover multiple hospitals in the United States – Healthcare Cost and Utilization Project State Inpatient Database, AHA IT Supplemental Survey. The paper also considers a variety of robustness checks, e.g. event study analysis for parallel trends, subsample analyses, analyses of changes in patient composition, alternative logit/probit specifications, and placebo tests.

Referee: 3
Strengths SIG Only: This paper addresses a question of practical and scholarly importance: does the adoption of clinical risk-stratification algorithms improve patient outcomes, and are those benefits equitably distributed across racial groups? The research question is crisp, the empirical setting is well-chosen, and the causal identification strategy is credibly executed. The central finding that survival benefits accrue exclusively to white patients following algorithm adoption is important, carrying clear implications for hospital administrators and policymakers.

Referee: 1
Limitations: Please see attached.

Referee: 2
Limitations: 1. As highlighted by the authors, the AHA IT Supplemental Survey does not specify the exact risk-stratification algorithms in use, and so there is an important assumption being made by the authors here in terms of “cardiology is among the most technology-intensive specialties, thus a hospital’s report of adopting risk-stratification algorithms likely reflects the implementation of these tools in this high-stakes setting” (page 2)
2. The main specification is a generalized DID, but from the economics literature, we know that staggered difference-in-differences setups can be prone to bias. The authors could consider, either for their main specification or robustness checks, additional estimators, e.g. Callaway and Sant’Anna 2021 and Borusyak et al 2024. Or, as done in some OM publications, also consider a matched sample to match hospitals with and without adoption on observables, and/or an IV approach to address endogeneity in adoption. The authors do consider matching for the heterogeneity analysis by race, but not for severity – it seems appropriate to do it for both analyses.
3. Additionally, another important robustness check to consider would be the definition of high versus low severity to see whether it is driven by a particular level of severity (e.g. only include MCC – major complication or comorbidity as the high severity group). Currently it is quite balanced. I also am curious as to whether the results on non-White patients may be driven by the higher sample of White patients (70%) per Section 3.3.
4. Writing at times could be improved, e.g. sometimes the text is not clear, for instance “We define Treatment Access as a binary indicator equal to 1 if a patient received any of these three treatments (PCI, CABG) and 0 otherwise” but only 2 types of treatments were highlighted

References
Borusyak K, Jaravel X, Spiess J (2024) Revisiting event study designs: Robust and efficient estimation. Rev. Econom. Stud.91(6):3253–3285
Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of econometrics, 225(2), 200-230.

Referee: 3
Limitations: The AHA adoption measure is a proxy that cannot rule out confounding from simultaneous quality improvement initiatives undertaken by adopting hospitals. The mechanism underlying racial disparities remains incompletely explained, the authors document the what but not convincingly the why. Finally, the grounding in operations management theory is relatively thin (OM literature is not built upon), with connections to care delivery operations remaining largely implicit. On balance, this is a carefully executed empirical study with a coherent story, and a reasonable candidate for presentation at the SIG.

Referee: 1

Comments to the Author
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Referee: 2

Comments to the Author
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Referee: 3

Comments to the Author
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