A simulation analysis of a patient-centered surgical home to improve outpatient surgical processes of care and outcomes

Morrice, Douglas J.; Wang, Dongyang Ester; Bard, Jonathan F.; Leykum, Luci K.; Noorily, Susan H.; Veerapaneni, Poornachand

doi:10.1109/wsc.2013.6721603

Cited by 3 publications

(4 citation statements)

References 6 publications

(5 reference statements)

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“…Incidentally, our findings are in line with Alexopoulos et al (2008) and Rohleder et al (2011). Similar to these studies, we report on the superiority of the Johnson SU distribution over the Normal distribution for APC arrivals in Morrice et al (2013).…”

Section: Model For Patient Arrivalssupporting

confidence: 92%

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A patient-centered surgical home to improve outpatient surgical processes of care and outcomes

Morrice

Wang

Bard

et al. 2014

IIE Transactions on Healthcare Systems Engineering

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Preparing patients for surgery is critical for achieving the best possible surgical outcomes. To do this effectively, care must be coordinated across several types of specialists, and potentially across multiple settings. In this paper, we develop a Patient-Centered Surgical Home (PCSH) for outpatient surgery based on the concept of the Perioperative Surgical Home proposed by the American Society of Anesthesiologists. A key feature of the PCSH is to have an anesthesiology preoperative assessment clinic (APC) serve as system coordinator and information integrator. Based on a study of outpatient surgery at the University of Texas Health Science Center at San Antonio and its primary teaching hospital using statistical analysis and simulation, we demonstrate how this can be accomplished. We show that for the PCSH to succeed, APC must see the right patients with the right information by overcoming improper triaging of patients and patient information deficiencies. Our analysis shows that with the proper screening tool and modifications to the way triage is handled, it is possible to increase the number of patients that the APC sees each day with a modest increase in resources. Much of the potential benefits rest on the cooperation of the referring clinics as well as closing the gap between the current level of patient information and what is needed for optimizing medical decisions. Estimated cost savings are over one million dollars annually with a PCSH. Since APC-like clinics are common, our findings have great potential for widespread implementation of similar PCSH models with commensurate benefits.

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Section: Model For Patient Arrivalssupporting

confidence: 92%

“…A preliminary version of this model appears in Morrice et al (2013). We chose not to use queuing theory in our analysis like Zonderland et al (2009) because patients arriving on time and steady state analysis did not hold (even approximately) in APC.…”

Section: The Apc Simulationmentioning

confidence: 99%

A patient-centered surgical home to improve outpatient surgical processes of care and outcomes

Morrice

Wang

Bard

et al. 2014

IIE Transactions on Healthcare Systems Engineering

Self Cite

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“…For example, a team of researchers [ 27 ] used discrete event simulation modeling to represent perioperative processes and tested the effects of three scenarios on the number of surgical cancellations. Another team [ 28 ] simulated an anesthesiology preoperative assessment clinic to quantify the impact of patient information deficiency to mitigate the problem of surgery delay or cancellation. These studies used industrial engineering techniques to investigate means for reducing the number of surgical cancellations across the system but did not focus on identifying surgery with high cancellation risk.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, various studies have focused on reducing surgery cancellation [21,[27][28][29][30]. For example, a team of researchers [27] used discrete event simulation modeling to represent perioperative processes and tested the effects of three scenarios on the number of surgical cancellations.…”

Section: Introductionmentioning

confidence: 99%

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

Zhang

Cui

Gong

et al. 2021

Journal of Healthcare Engineering

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This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk.

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The impact of a patient-centered surgical home implementation on preoperative processes in outpatient surgery

Morrice

Bard

Leykum

et al. 2018

IISE Transactions on Healthcare Systems Engineering

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A simulation analysis of a patient-centered surgical home to improve outpatient surgical processes of care and outcomes

Cited by 3 publications

References 6 publications

A patient-centered surgical home to improve outpatient surgical processes of care and outcomes

A patient-centered surgical home to improve outpatient surgical processes of care and outcomes

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

The impact of a patient-centered surgical home implementation on preoperative processes in outpatient surgery

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