Reorganizing patient care and workflow in the operating room: a cost-effectiveness study

Stahl, James E.; Sandberg, Warren S.; Daily, Bethany; Wiklund, Richard A.; Egan, Marie T.; Goldman, Julian M.; Isaacson, Keith; Gazelle, Scott; Rattner, David W.

doi:10.1016/j.surg.2005.12.006

Cited by 107 publications

(79 citation statements)

References 14 publications

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“…RM performed at an aggregate level). Out of the 10 articles, 5 studies applied RM at an organisational level [19][20][21][22][23]] and 5 at a national level [24,[26][27][28][29].…”

Section: Searches and Siftingmentioning

confidence: 99%

“…There is a diversity of outcomes assessed alongside traditional CEA (defined as those that include QALYs, disability-adjusted life year (DALYs) or life-year saved (LYs) [30]). As presented in table 2, the majority of the studies solely reported CEA, either by measuring the effects using QALYs [21-23, 27, 29] or LYs [24], while the rest of the studies focused on broader outcomes such as patient throughput per day [19,20]. Out of the 10 articles identified, only 4 of these studies assessed short-term outcomes (i.e.…”

Section: Searches and Siftingmentioning

confidence: 99%

“…Out of the 10 articles identified, only 4 of these studies assessed short-term outcomes (i.e. maximum number of cases treatable in 3, 8 or 10 hours) [19,20,28], while the rest of the studies only focus on long-term outcomes (e.g. costs and QALYS over 5-20 years).…”

Section: Searches and Siftingmentioning

confidence: 99%

“…The outcomes that were observed in these studies included total flow time [19,20,[27][28][29], waiting time [19][20][21][22][23][24][26][27][28][29], waiting probability [28], patient throughput [19-21, 23, 27, 28], resource utilisation [19-21, 23, 26, 28], queue length [21,23,24,28,29], probability of abandonment [28], and service level [28]. In one study observed [22], only one RM outcome was mentioned (waiting time); the remainder of these studies reported multiple outcomes.…”

Section: Searches and Siftingmentioning

confidence: 99%

“…That is, rather than modelling the capacity constraints of resources explicitly, the potential effect of resource constraints is modelled as limited patient throughput. Out of the 10 studies identified, 2 examined the effects against capacity constraints [19,20], 6 examined the effect against throughput constraints [21-24, 27, 29] and 2 studies examined the effects of both capacity and throughput constraints [26,28]. …”

Section: Searches and Siftingmentioning

confidence: 99%

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Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

et al. 2017

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OBJECTIVE:To conduct a systematic review of published research on the use of discrete event simulation (DES) for resource modelling in health technology assessment (HTA). Resource modelling (RM) is broadly defined as incorporating and measuring effects of constraints on physical resources (e.g. beds, doctors, nurses) in HTA models. METHODS: Systematic literature searches were conducted in academic databases (JSTOR, SAGE, SPRINGER, SCOPUS, IEEE, Science Direct, PUBMED, EMBASE) and grey literature (Google Scholar, NHS journal library), enhanced by manual searchers (i.e. reference list checking, citation searching and hand searching techniques). RESULTS:The search strategy yielded 4,117 potentially relevant citations. Following the screening and manual searches, 10 articles were included. Reviewing these articles provided insights into the applications of RM: firstly, different types of economic analyses, model settings, RM and costeffectiveness analysis (CEA) outcomes were identified. Secondly, variation in the characteristics of the constraints such as types and nature of constraints, sources of data for the constraints were identified. Thirdly, it was found that including the effects of constraints caused the CEA results to change in these articles. CONCLUSION:The review found that DES proved to be an effective technique for RM but there were only a small number of studies applied in HTA. However, these studies showed the important consequences of modelling physical constraints and point to the need for a framework to be developed to guide future applications of this approach. (230 words) Keywords Discrete event simulation, resource modelling, health technology assessment, simulation modelling 2 Key Points for Decision Makers  Economic evaluation studies in health care typically ignore the short-term constraints on physical resources (e.g. doctors, nurses) which can lead to incorrect results.  Discrete Event Simulation is an effective tool for modelling the effects of constraints but there were only a small number of studies applied in health technology assessment (HTA).  Further research is required to examine the possible developments for detailed modelling of the resource constraints in HTA.

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“…RM performed at an aggregate level). Out of the 10 articles, 5 studies applied RM at an organisational level [19][20][21][22][23]] and 5 at a national level [24,[26][27][28][29].…”

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

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Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

et al. 2017

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Workflow Analysis, Design, Modeling and Simulation for the Multimodality Imaging Therapy Operating System (MITOS)

Fernández‐Gutiérrez

Houston

Elle

et al. 2013

Intraoperative Imaging and Image-Guided Therapy

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Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow

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Manataki

Papanastasiou

et al. 2021

Biomedical Engineering Systems and Technologies

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Intelligent systems play an increasingly central role in healthcare systems worldwide. Nonetheless, operational friction represents an obstacle to full utilization of scarce resources and improvement of service standards. In this paper we address the challenge of developing data-driven models of complex workflow systems -a prerequisite for harnessing intelligent technologies for workflow improvement. We present a proof-of-concept model parametrized using real-world data and constructed based on domain knowledge from the Royal Infirmary of Edinburgh, demonstrating how off-the-shelf process mining, machine learning and stochastic process modeling tools can be combined to build predictive models that capture complex control flow, constraints, policies and guidelines.

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Reorganizing patient care and workflow in the operating room: a cost-effectiveness study

Cited by 107 publications

References 14 publications

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

Workflow Analysis, Design, Modeling and Simulation for the Multimodality Imaging Therapy Operating System (MITOS)

Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow

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