Optimization of hospital ward resources with patient relocation using Markov chain modeling

Andersen, Anders Reenberg; Nielsen, Bo Friis; Reinhardt, Line Blander

doi:10.1016/j.ejor.2017.01.026

Cited by 47 publications

(15 citation statements)

References 16 publications

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“…The utilization rate of wards ( G 4 ) is one of the important indicators, which reflects the condition of equipment usage and the operational condition of the hospital 35 ,. 36 Both in prior studies Wang 33 and Qi, 37 the utilization rate of wards is a typical indicator to evaluate the performance of the hospitals. According to the documents of the government or the hospital, DMs give the rating values in the form of q ‐ROFNs for the HDTP proposals with respect to attributes G j , and three decision matrices

A^{s} = {(a_{i j}^{s})}_{5 \times 4} (s = 1,2,3)

are given in Tables 1–3.…”

Section: Case Studymentioning

confidence: 99%

Evaluation for hierarchical diagnosis and treatment policy proposals in China: A novel multi‐attribute group decision‐making method with multi‐parametric distance measures

Xing

Wang

2021

Health Planning & Management

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A^{s} = {(a_{i j}^{s})}_{5 \times 4} (s = 1,2,3)

are given in Tables 1–3.…”

Section: Case Studymentioning

confidence: 99%

Evaluation for hierarchical diagnosis and treatment policy proposals in China: A novel multi‐attribute group decision‐making method with multi‐parametric distance measures

Xing

Wang

2021

Health Planning & Management

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“…Chiachío et al (2020) stated that a prognostics methodology, which relies on a stochastic damage model based on Markov chains, could deal with complex degradation processes in metallic materials. Andersen, Nielsen and Reinhardt (2017) proposed a mathematical method to ensure adequate hospital beds by redistributing the existing wards in the hospital. The continuous-time Markov chain was used to model the patient flow, and the optimization was carried out using local search heuristics.…”

Section: Literature Reviewmentioning

confidence: 99%

The Research on Distribution of Lead-Time Demand on the Basis of Multivariate Higher-Order Markov Chain

Liu

et al. 2021

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Inventory management is an important part of supply chain management: inventory shortages could result in reduced delivery speeds and response speeds while excess inventory could lead to increased inventory and operating costs. Therefore, finding ways to efficiently control inventory has become an issue companies are most concerned about. Choosing a proper inventory management method based on the lead-time demand distribution fitted from historical data has become the key criteria to solve this issue. However, it is difficult to determine the lead-time distribution based on the limited amount of historical data directly. Thus, the method this report introduces uses a multivariate higher-order Markov chain to reconstruct historical data in order to expand the amount of data used to fit the lead-time distribution of demand, which is significant for inventory management.

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“…A number of predictive models have been developed to determine the impact of changing a medical intervention on the health outcome for an individual, including: Markov chain models (Ayer et al, 2012;Mason et al, 2013;Deo et al, 2013;Andersen et al, 2017), dynamical systems models (Helm et al, 2015), decision tree models (Wu et al, 2013), graph-theoretic models (Fetta et al, 2018), bandit models (Negoescu et al, 2014), and dynamic programming models (Engineer et al, 2009). (This literature also studies the problem of designing optimal treatment plans, which we do not consider in our present paper.)…”

Section: Literature Reviewmentioning

confidence: 99%

Behavioral modeling in weight loss interventions

Aswani

Kaminsky

Mintz

et al. 2019

European Journal of Operational Research

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Designing systems with human agents is difficult because it often requires models that characterize agents' responses to changes in the system's states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals' weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution towards treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches which cannot.

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Optimization of hospital ward resources with patient relocation using Markov chain modeling

Cited by 47 publications

References 16 publications

Evaluation for hierarchical diagnosis and treatment policy proposals in China: A novel multi‐attribute group decision‐making method with multi‐parametric distance measures

Evaluation for hierarchical diagnosis and treatment policy proposals in China: A novel multi‐attribute group decision‐making method with multi‐parametric distance measures

The Research on Distribution of Lead-Time Demand on the Basis of Multivariate Higher-Order Markov Chain

Behavioral modeling in weight loss interventions

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