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Discovering the impact of preceding units' characteristics on the wait time of cardiac surgery unit from statistic data.

Liu J, Tao L, Xiao B - PLoS ONE (2011)

Bottom Line: Results show that: (i) wait time of CU has a direct positive impact on wait time of SU (β = 0.330, p < 0.01); (ii) capacity of CU has a direct positive impact on demand of SU (β = 0.644, p < 0.01); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system.Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, Hong Kong Baptist University, Hong Kong, Special Administrative Region, People's Republic of China. jiming@comp.hkbu.edu.hk

ABSTRACT

Introduction: Prior research shows that clinical demand and supplier capacity significantly affect the throughput and the wait time within an isolated unit. However, it is doubtful whether characteristics (i.e., demand, capacity, throughput, and wait time) of one unit would affect the wait time of subsequent units on the patient flow process. Focusing on cardiac care, this paper aims to examine the impact of characteristics of the catheterization unit (CU) on the wait time of cardiac surgery unit (SU).

Methods: This study integrates published data from several sources on characteristics of the CU and SU units in 11 hospitals in Ontario, Canada between 2005 and 2008. It proposes a two-layer wait time model (with each layer representing one unit) to examine the impact of CU's characteristics on the wait time of SU and test the hypotheses using the Partial Least Squares-based Structural Equation Modeling analysis tool.

Results: Results show that: (i) wait time of CU has a direct positive impact on wait time of SU (β = 0.330, p < 0.01); (ii) capacity of CU has a direct positive impact on demand of SU (β = 0.644, p < 0.01); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).

Conclusion: Characteristics of CU have direct and indirect impacts on wait time of SU. Specifically, demand and wait time of preceding unit are good predictors for wait time of subsequent units. This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system. Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU. This implies that the wait time management should carefully consider the relationship between priority triage and risk stratification, especially for cardiac surgery.

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PLS test results for extended two-layer wait time model with risk profiles in SU.(Cath: the abbreviation of catheterization; Surgery: the shorter form of cardiac surgery.)
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pone-0021959-g005: PLS test results for extended two-layer wait time model with risk profiles in SU.(Cath: the abbreviation of catheterization; Surgery: the shorter form of cardiac surgery.)

Mentions: There are various methods for calculating the value of risk for patients undergoing catheterization (e.g., SYNTAX, http://www.syntaxscore.com/) and cardiac surgery (e.g., EuroSCORE, http://www.euroscore.org/, and Higgins Score [76]) based on several risk factors. For example, the surgical risk factors for isolated coronary artery bypass graft (CABG) surgery include age, sex, precious CABG, left ventricular function, and coronary anatomy, etc. [51][77]. The Institute for Clinical Evaluative Science of Ontario has published data on the distribution of risk profiles in isolated CABG (i.e., the major type of cardiac surgery) in years of 2005 and 2006, in the Ontario hospitals [51]. Thus, by utilizing this published risk profile data (represented as the percentage of low-, medium-, and high-risk patients for catheterization in a hospital), we have further investigated the relationship between risk profiles and wait time. In doing so, the missing data of each hospital's risk profiles for the years of 2007 and 2008 is substituted by the mean value (a common method for handling missing data in statistical data analysis [78]–[79]) of its available risk data [51]. By integrating our original cardiac care data with the riskprofile data, we have conductd an additional PLS analysis to test the extended two-layer wait time model, with risk profiles added as an extra predictor of wait time in SU (see Figure 5).


Discovering the impact of preceding units' characteristics on the wait time of cardiac surgery unit from statistic data.

Liu J, Tao L, Xiao B - PLoS ONE (2011)

PLS test results for extended two-layer wait time model with risk profiles in SU.(Cath: the abbreviation of catheterization; Surgery: the shorter form of cardiac surgery.)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3139594&req=5

pone-0021959-g005: PLS test results for extended two-layer wait time model with risk profiles in SU.(Cath: the abbreviation of catheterization; Surgery: the shorter form of cardiac surgery.)
Mentions: There are various methods for calculating the value of risk for patients undergoing catheterization (e.g., SYNTAX, http://www.syntaxscore.com/) and cardiac surgery (e.g., EuroSCORE, http://www.euroscore.org/, and Higgins Score [76]) based on several risk factors. For example, the surgical risk factors for isolated coronary artery bypass graft (CABG) surgery include age, sex, precious CABG, left ventricular function, and coronary anatomy, etc. [51][77]. The Institute for Clinical Evaluative Science of Ontario has published data on the distribution of risk profiles in isolated CABG (i.e., the major type of cardiac surgery) in years of 2005 and 2006, in the Ontario hospitals [51]. Thus, by utilizing this published risk profile data (represented as the percentage of low-, medium-, and high-risk patients for catheterization in a hospital), we have further investigated the relationship between risk profiles and wait time. In doing so, the missing data of each hospital's risk profiles for the years of 2007 and 2008 is substituted by the mean value (a common method for handling missing data in statistical data analysis [78]–[79]) of its available risk data [51]. By integrating our original cardiac care data with the riskprofile data, we have conductd an additional PLS analysis to test the extended two-layer wait time model, with risk profiles added as an extra predictor of wait time in SU (see Figure 5).

Bottom Line: Results show that: (i) wait time of CU has a direct positive impact on wait time of SU (β = 0.330, p < 0.01); (ii) capacity of CU has a direct positive impact on demand of SU (β = 0.644, p < 0.01); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system.Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, Hong Kong Baptist University, Hong Kong, Special Administrative Region, People's Republic of China. jiming@comp.hkbu.edu.hk

ABSTRACT

Introduction: Prior research shows that clinical demand and supplier capacity significantly affect the throughput and the wait time within an isolated unit. However, it is doubtful whether characteristics (i.e., demand, capacity, throughput, and wait time) of one unit would affect the wait time of subsequent units on the patient flow process. Focusing on cardiac care, this paper aims to examine the impact of characteristics of the catheterization unit (CU) on the wait time of cardiac surgery unit (SU).

Methods: This study integrates published data from several sources on characteristics of the CU and SU units in 11 hospitals in Ontario, Canada between 2005 and 2008. It proposes a two-layer wait time model (with each layer representing one unit) to examine the impact of CU's characteristics on the wait time of SU and test the hypotheses using the Partial Least Squares-based Structural Equation Modeling analysis tool.

Results: Results show that: (i) wait time of CU has a direct positive impact on wait time of SU (β = 0.330, p < 0.01); (ii) capacity of CU has a direct positive impact on demand of SU (β = 0.644, p < 0.01); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).

Conclusion: Characteristics of CU have direct and indirect impacts on wait time of SU. Specifically, demand and wait time of preceding unit are good predictors for wait time of subsequent units. This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system. Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU. This implies that the wait time management should carefully consider the relationship between priority triage and risk stratification, especially for cardiac surgery.

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