Modeling and analysis of hospital inpatient rescue process: A Markov chain approach

Xiaolei Xie; Jingshan Li; Colleen H. Swartz; Yue Dong

doi:10.1109/CoASE.2013.6653987

Modeling and analysis of hospital inpatient rescue process: A Markov chain approach

Xiaolei Xie, Jingshan Li, Colleen H. Swartz, Yue Dong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Improving patient safety is the top priority for hospital management. On the hospital floor, an inpatient may experience clinical deterioration during his/her stay. Quick and appropriate treatment from the nurse, physician, and rapid response team (RRT) is essential to rescue the patient. In this paper, we introduce an analytical method to model and analyze the hospital inpatient rescue (HIR) process. A continuous time Markov chain model is presented to characterize the patient status and analyze the transitions between different patient states, such as risk, non-risk, intervention by the care provider, or elevation to intensive care, etc. Closed formulas to calculate the probability of the patient staying in different states are developed for single patient case. An approximation method, referred to as the shared resource iteration (SRI) approach, is proposed to study the multiple patients scenario. It is shown that such an iteration is convergent and results in a high accuracy estimation of patient state probability. This method provides a quantitative tool to analyze the HIR process and investigate strategies to improve patient safety. © 2013 IEEE.

Original language	English
Title of host publication	2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
Pages	978-983
DOIs	https://doi.org/10.1109/CoASE.2013.6653987
Publication status	Published - 2013
Externally published	Yes
Event	2013 IEEE International Conference on Automation Science and Engineering, CASE 2013 - Madison, WI, United States Duration: 17 Aug 2013 → 20 Aug 2013

Publication series

Name	IEEE International Conference on Automation Science and Engineering
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
Place	United States
City	Madison, WI
Period	17/08/13 → 20/08/13

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work is supported in part by NSF Grants No. CMMI-1233807 and 1234636.

Research Keywords

clinical deterioration
continuous time Markov chain
patient rescue
Patient safety
shared resource iteration

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10.1109/CoASE.2013.6653987

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title = "Modeling and analysis of hospital inpatient rescue process: A Markov chain approach",

abstract = "Improving patient safety is the top priority for hospital management. On the hospital floor, an inpatient may experience clinical deterioration during his/her stay. Quick and appropriate treatment from the nurse, physician, and rapid response team (RRT) is essential to rescue the patient. In this paper, we introduce an analytical method to model and analyze the hospital inpatient rescue (HIR) process. A continuous time Markov chain model is presented to characterize the patient status and analyze the transitions between different patient states, such as risk, non-risk, intervention by the care provider, or elevation to intensive care, etc. Closed formulas to calculate the probability of the patient staying in different states are developed for single patient case. An approximation method, referred to as the shared resource iteration (SRI) approach, is proposed to study the multiple patients scenario. It is shown that such an iteration is convergent and results in a high accuracy estimation of patient state probability. This method provides a quantitative tool to analyze the HIR process and investigate strategies to improve patient safety. {\textcopyright} 2013 IEEE.",

keywords = "clinical deterioration, continuous time Markov chain, patient rescue, Patient safety, shared resource iteration",

author = "Xiaolei Xie and Jingshan Li and Swartz, {Colleen H.} and Yue Dong",

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Xie, X, Li, J, Swartz, CH & Dong, Y 2013, Modeling and analysis of hospital inpatient rescue process: A Markov chain approach. in 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013., 6653987, IEEE International Conference on Automation Science and Engineering, pp. 978-983, 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013, Madison, WI, United States, 17/08/13. https://doi.org/10.1109/CoASE.2013.6653987

Modeling and analysis of hospital inpatient rescue process: A Markov chain approach. / Xie, Xiaolei; Li, Jingshan; Swartz, Colleen H. et al.
2013 IEEE International Conference on Automation Science and Engineering, CASE 2013. 2013. p. 978-983 6653987 (IEEE International Conference on Automation Science and Engineering).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Dong, Yue

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AB - Improving patient safety is the top priority for hospital management. On the hospital floor, an inpatient may experience clinical deterioration during his/her stay. Quick and appropriate treatment from the nurse, physician, and rapid response team (RRT) is essential to rescue the patient. In this paper, we introduce an analytical method to model and analyze the hospital inpatient rescue (HIR) process. A continuous time Markov chain model is presented to characterize the patient status and analyze the transitions between different patient states, such as risk, non-risk, intervention by the care provider, or elevation to intensive care, etc. Closed formulas to calculate the probability of the patient staying in different states are developed for single patient case. An approximation method, referred to as the shared resource iteration (SRI) approach, is proposed to study the multiple patients scenario. It is shown that such an iteration is convergent and results in a high accuracy estimation of patient state probability. This method provides a quantitative tool to analyze the HIR process and investigate strategies to improve patient safety. © 2013 IEEE.

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Modeling and analysis of hospital inpatient rescue process: A Markov chain approach

Abstract

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Research Keywords

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