Modeling and simulation framework for value-based healthcare systems

Abstract

Regardless of the coordination of its activities, a healthcare system is composed of a large number of distributed components that are interrelated by complex processes. Understanding the behavior of the overall system is becoming a major concern among healthcare managers and decision-makers. This paper presents a modeling and simulation framework to support a holistic analysis of healthcare systems through a stratification of the levels of abstraction into multiple perspectives and their integration in a common simulation framework. In each of the perspectives, models of different components of a healthcare system can be developed and coupled together. Concerns from other perspectives are abstracted as parameters, that is, we reflect the parameter values of other perspectives through explicit assumptions and simplifications in such models. Consequently, the resulting top model within each perspective can be coupled with its experimental frame to run simulations and derive results. Components of the various perspectives are integrated to provide a holistic view of the healthcare problem and system under study. The resulting global model can be coupled with a holistic experimental frame to derive results that cannot be accurately addressed in any of the perspectives taken alone. Furthermore, as we endeavored to allow perspective-specific experts to contribute to the modeling process, we took benefit of results originating from research efforts that Norbert Giambiasi initiated in the 2000s, which his PhD students further developed with their own PhD students.

A hybrid simulation model approach to examine bacterial genome sequencing during a hospital outbreak

“…Healthcare system modeling is trending towards increased use of hybrid modeling, as the focus shifts from operational and process questions (patient flow, logistics and healthcare operations) to resources and design issues (capacity planning, resource allocation and program evaluation) [6]. A holistic healthcare simulation model must incorporate four interacting perspectives: resource allocation; health diffusion; population dynamics; and individual behavior [30]. The OXA-181 hybrid model encompassed these perspectives through features such as bed capacity, ward population dynamics and patient specific transmission.…”

Towards Health System Resiliency: An Agile Systems Modelling Framework for Bed Resource Planning During COVID-19

“…The modelling framework was conceived to provide estimates of mortality, morbidity and the impact on waiting times to specialist appointments and elective surgeries, length of stay (LOS) in the emergency department (ED), bed -demands and inpatient LOS and the utilization of critical hospital facilities (e.g., operating theatres, hospital and intensive care beds, isolation wards, diagnostic equipment, laboratory services). The systems modelling methodology based on system dynamics (SD) simulation [17], [18] was used to develop the inpatient bed resource management module. Where more detailed modelling was required-for instance, the Emergency Department (ED) and surgical resource planning, discrete events simulation (DES) [19] and agent-based modelling (ABM) [20] approaches were used.…”

“…Such was the case in Singapore as most of the earlier demand projections were based on importation and secondary local transmission models in the community and were not able to predict the massive clusters of positive cases that were discovered in migrant workers' dormitories (12). On the other hand, systems modelling techniques (e.g., systems dynamics, or SD, and discrete events simulation, or DES, models) (13,14) have seen many applications for both predictable demand patterns (15)(16)(17) and situations where the surge in demand is less predictable (e.g., disaster planning and pandemics due to emerging infectious diseases) (18,19). These simulation methods have the ability to capture both the detailed behaviour of the system (dynamic complexity) and structure (causal relationship) to provide a risk-free virtualized experimentation platform to evaluate strategies in scenarios that are subjected to significant uncertainties (20).…”

Hybrid Simulation for Cyber Physical Systems - A Panel on where are we going regarding complexity, intelligence and adaptability of CPS using simulation

“…The MPMHS approach has been formalized and applied to healthcare systems (Djitog et al, 2017a;Djitog et al, 2017b;Traoré et al, 2018). It is applicable to other forms of CPS as well.…”