Equipping mathematical models for hospital dynamics using information theory

We propose metrics from information theory to characterize the clinical and operational dynamics of hospitals. Ergodicity, the equality of time- and space-averages within a dynamical system, ensures its stationarity. Surprisal, the log-inverse of probability, and entropy, the average surprisal, provide mathematical insights into the nature of dynamical systems. We applied these metrics to lab test order times from 15 units in three hospitals from 2018 to 2021. We found hospital units were ergodic in the sense that each bed is typical of the unit, while hospitals were not, and that external forces like the COVID-19 pandemic can perturb hospital dynamics. These information-theoretic metrics can indicate systemic shifts, inefficiencies, and anomalies in patient care, providing granular and interpretable signals of change. Monitoring these metrics can expose data drift, where evolving inputs and outputs hinder generalizability, and label leakage, where clinician actions inadvertently corrupt predictive monitoring systems.

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Publication Details

Journal
npj Digital Medicine
Published
2025-11-12
DOI
https://doi.org/10.1038/s41746-025-02013-2
Primary Topic
Ecosystem dynamics and resilience
Type
article
Field-Weighted Citation Impact
0.00
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article

Equipping mathematical models for hospital dynamics using information theory

Jeremy A. Balch, Ian German Mesner, Benjamin Shickel, Benjamin L. Ranard et al.
npj Digital Medicine
Ecosystem dynamics and resilience
article

Equipping mathematical models for hospital dynamics using information theory

Jeremy A. Balch, Ian German Mesner, Benjamin Shickel, Benjamin L. Ranard, Daniel Nametz, Matthew Clark, Die Hu, Tyler John Loftus, Robert T. Andris, Jackson G. Brandberg, Matthew M. Ruppert, J. Randall Moorman, David J. Albers, J. B. Delos, Murad Megjhani, Soojin Park
article en

Abstract

We propose metrics from information theory to characterize the clinical and operational dynamics of hospitals. Ergodicity, the equality of time- and space-averages within a dynamical system, ensures its stationarity. Surprisal, the log-inverse of probability, and entropy, the average surprisal, provide mathematical insights into the nature of dynamical systems. We applied these metrics to lab test order times from 15 units in three hospitals from 2018 to 2021. We found hospital units were ergodic in the sense that each bed is typical of the unit, while hospitals were not, and that external forces like the COVID-19 pandemic can perturb hospital dynamics. These information-theoretic metrics can indicate systemic shifts, inefficiencies, and anomalies in patient care, providing granular and interpretable signals of change. Monitoring these metrics can expose data drift, where evolving inputs and outputs hinder generalizability, and label leakage, where clinician actions inadvertently corrupt predictive monitoring systems.

npj Digital MedicineVol. 8(1)
University of Central Florida (US), NewYork–Presbyterian Hospital (US), Columbia University Irving Medical Center (US), University of Virginia Health System (US), University of Florida (US), University of Virginia Medical Center (US), University of Virginia (US), University of Colorado Anschutz Medical Campus (US), Columbia University (US), Florida College (US)
Openalex Percentile: Top 69%
Ecosystem dynamics and resilience
DOIOpen Access PDFOpenAlex
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