Faculty mentor/PI email address
jim010@aol.com
Is your research Teaching and Learning based?
1
Keywords
Emergency Department operations; complex adaptive systems; Fourier analysis; wavelet analysis; system boundaries; signal-informed prediction; healthcare systems engineering.
Date of Presentation
5-6-2026 12:00 AM
Poster Abstract
Background:
We theorize that Emergency Departments (EDs) function as complex adaptive systems characterized by nonlinear interactions, oscillatory demand patterns, and abrupt phase transitions from stable flow to turbulent states. Traditional dashboards rely on threshold metrics that detect instability after degradation becomes visible. Many ED data streams are significantly retrospective as well as compression-averaged into temporal systems such as days, months, quarters or even years. Signal processing methods—particularly Fourier and wavelet analysis—offer the framework for detecting latent oscillatory shifts preceding overt operational change from laminar to transitional to turbulent flow states. This could lead to better methods of adaptation.
Conceptual Framework:
We propose a Boundary-Based Signal Model of ED Operations which conceptualizes the ED as a dynamical system defined by three signal domains: inbound demand across the external boundary, internal processing and subsystem coupling within the ED, and outbound throughput across the discharge/admission boundary. We propose conceptually that signal analysis using Fourier and wavelet analysis would enable detection of transitional instability within and between these domains.
Proposed Model:
Operational turbulence is theorized in this model by rising high-frequency energy in inbound demand, loss of coherence among internal subsystems, and declining permeability at the outbound boundary. Indices could be developed that could integrate spectral energy ratios, entropy measures, and cross-signal coherence to enable signal-informed anticipation and prediction of instability.
Conclusions:
This framework reframes ED monitoring from static threshold detection toward dynamic signal interpretation. Rather than asking whether capacity has been exceeded, the model evaluates whether underlying oscillatory structure is shifting toward turbulence.
Thus, we propose a model that integrates advanced mathematical analysis, complexity science, clinical operations and Fourier, wavelet pattern recognition. This would provide a rigorous conceptual foundation for empirical testing of advanced signal detection systems in Emergency Department care delivery.
Disciplines
Health and Medical Administration | Medical Education | Medicine and Health Sciences | Quality Improvement
Included in
Detecting Early Operational Turbulence in the Emergency Department Using Fourier and Wavelet Analysis: A Conceptual Framework for Signal-Informed Anticipation and Prediction in the ED Complex Adaptive System
Background:
We theorize that Emergency Departments (EDs) function as complex adaptive systems characterized by nonlinear interactions, oscillatory demand patterns, and abrupt phase transitions from stable flow to turbulent states. Traditional dashboards rely on threshold metrics that detect instability after degradation becomes visible. Many ED data streams are significantly retrospective as well as compression-averaged into temporal systems such as days, months, quarters or even years. Signal processing methods—particularly Fourier and wavelet analysis—offer the framework for detecting latent oscillatory shifts preceding overt operational change from laminar to transitional to turbulent flow states. This could lead to better methods of adaptation.
Conceptual Framework:
We propose a Boundary-Based Signal Model of ED Operations which conceptualizes the ED as a dynamical system defined by three signal domains: inbound demand across the external boundary, internal processing and subsystem coupling within the ED, and outbound throughput across the discharge/admission boundary. We propose conceptually that signal analysis using Fourier and wavelet analysis would enable detection of transitional instability within and between these domains.
Proposed Model:
Operational turbulence is theorized in this model by rising high-frequency energy in inbound demand, loss of coherence among internal subsystems, and declining permeability at the outbound boundary. Indices could be developed that could integrate spectral energy ratios, entropy measures, and cross-signal coherence to enable signal-informed anticipation and prediction of instability.
Conclusions:
This framework reframes ED monitoring from static threshold detection toward dynamic signal interpretation. Rather than asking whether capacity has been exceeded, the model evaluates whether underlying oscillatory structure is shifting toward turbulence.
Thus, we propose a model that integrates advanced mathematical analysis, complexity science, clinical operations and Fourier, wavelet pattern recognition. This would provide a rigorous conceptual foundation for empirical testing of advanced signal detection systems in Emergency Department care delivery.