Decision on SIG-2026-0091

[MSOM Conference]

08-May-2026

Re: SIG-2026-0091, "Workload in Plain Sight: Actionable Workload Transparency and the Tradeoffs for Nurses and Patients"

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 study examines the implementation of a workload-visibility tool in a Midwestern hospital designed to help nurse managers make more informed patient-to-nurse assignments. The findings show that the tool led to more strategic assignment decisions but also increased workload imbalances across nurses. The study further investigates the implications of these changes for nurse attrition and patient outcomes.

A key strength of the paper is its close collaboration with nursing teams, along with the development of a tool that more accurately captures task-based workload compared to existing approaches.

Referee: 2
Strengths SIG Only: n/a

Referee: 3
Strengths SIG Only: The paper does a great job of highlighting a novel tool introduced in the hospital for measuring workload. As the paper shows, there exists substantial heterogeneity in nurse workload across units, and so the study is important in highlighting the impact of workload and ways we can mitigate its effects on patient care.

Referee: 1
Limitations: The authors report that the tool led to more strategic assignment decisions but also increased workload imbalances across nurses. They further suggest that tool usage is associated with reduced nurse attrition, while workload imbalances are associated with higher attrition and worse patient outcomes. This raises an important tension: once workload imbalances are accounted for, it is unclear through what mechanism the tool reduces nurse attrition. Moreover, patient outcomes are analyzed as a function of workload imbalances rather than tool usage itself, making it difficult to attribute these effects directly to the tool. As currently presented, the findings on attrition and patient outcomes appear conceptually disconnected and are not rigorously tied back to the tool.


The framing of Hypothesis 1 is also somewhat confusing. H1A and H1B appear to reflect competing predictions about the same relationship (the effect of tool usage on workload imbalance), yet H1B is framed as “purposeful differentiation,” which makes it seem like a distinct conceptual argument rather than an alternative mechanism. I recommend aligning the framing of H1B more closely with H1A—both here and throughout the manuscript—to clarify that these are competing hypotheses about the same underlying effect.

A similar issue arises with Hypotheses 2A and 2B. While the A/B notation typically denotes competing hypotheses, here H2B (workload imbalance increases attrition) seems to describe the mechanism underlying H2A (tool usage reduces attrition), rather than a competing prediction. This creates confusion about the intended theoretical structure.

More broadly, the development of Hypotheses 2 and 3 appears to depend on the direction of the effect established in Hypothesis 1. That is, understanding how the tool affects attrition and patient outcomes requires first establishing whether it increases or decreases workload imbalance. Given this dependence, it would be more coherent to either (i) defer the development of these hypotheses until after the main effect is established, or (ii) explicitly formulate competing hypotheses for each downstream outcome.

Finally, the identification strategy raises concerns. The study examines the impact of tool usage rather than tool availability, yet treats usage as effectively exogenous. The claim that “charge nurses appear to have decided independently whether to use the tool, with no consistent selection patterns” is not fully convincing. Given that the tool is explicitly designed to improve workload management, it is unlikely that usage decisions are unrelated to contemporaneous or anticipated workload conditions—precisely the factors that also drive imbalance and outcomes. This suggests that tool usage is endogenous, and the analysis would benefit from a more rigorous identification strategy that addresses this concern.

In light of the conflicting findings about the tool's effect and workload imbalance effects, it's not clear what managerial implications can be take away from this study at its current form.

Referee: 2
Limitations: n/a

Referee: 3
Limitations: The assumptions used to argue for quasi-randomness in use of the tool is fairly weak, especially for the first set of hypotheses. While it is often impossible to guarantee that omitted variable bias does not play a role in the identification strategy, in this particular case, the authors could do more to convince the reader that it is negligible. The use of this tool and workload imbalance are so closely related that the chance of an unobserved variable affecting both is quite high.

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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