Evidence‐Based Medicine in Otolaryngology Part 10: Cost‐Effectiveness Analyses in Otolaryngology

Decision making in health care is complex, and substantial uncertainty can be involved. Structured, systematic approaches to the integration of available evidence, assessment of uncertainty, and determination of choice are of significant benefit in an era of “value-based care.” This is especially true for otolaryngology–head and neck surgery, where technological advancements are frequent and applicable to an array of subspecialties. Decision analysis aims to achieve these goals through various modeling techniques, including (1) decision trees, (2) Markov process, (3) microsimulation, and (4) discrete event simulation. While decision models have been used for decades, many clinicians and researchers continue to have difficulty deciphering them. In this review, we present an overview of various decision analysis modeling techniques, their purposes, how they can be interpreted, and commonly used syntax to promote understanding and use of these approaches. Throughout, we provide a sample research question to facilitate discussion of the advantages and disadvantages of each technique.

Section: Decision Treesmentioning

confidence: 99%

Decision Modeling for Economic Evaluation in Otolaryngology–Head and Neck Surgery: Review of Techniques

Forner

Hoit

Noel

et al. 2020

Section: Decision Uncertaintymentioning

confidence: 99%

“…33,40,48,52,57 The WTP threshold is a country-specific reference that denotes the maximum amount that an individual would be willing to pay to gain an additional QALY. 50,58,59 Figure 3 presents an example of a cost-effectiveness acceptability curve, which allows decision makers to estimate their maximum WTP (ie, the maximum acceptability) for a QALY. 60 In this example of management of early-stage oropharyngeal squamous cell carcinoma with TORS as compared with radiation therapy, radiation therapy was determined to be cost-effective in 54% of iterations at a WTP threshold of $100,000 per QALY gained.…”

Section: Decision Uncertaintymentioning

confidence: 99%

“…Uncertainty in decision modeling can arise from the underlying data, the type or structure of a decision model, and the validity of the results for intended populations. 1,12,[48][49][50] It is important for analysts to unambiguously describe their decision methods and assumptions, to be cognizant of the influence of uncertainty and heterogeneity in decision models, and to understand the role of sensitivity analyses. 12 The model structure should be determined by the problem of interest and not constrained by data availability or quality.…”

Section: Decision Uncertaintymentioning

confidence: 99%

“…That is, TORS was found to be a cost-effective strategy, at an incremental cost-effectiveness ratio of $57,380 per QALY gained, when the age of diagnosis was decreased, suggesting that TORS may yield the greatest value for younger patients. 1,50 Simultaneous changes in selected variables may be evaluated through 2-and n-way sensitivity analyses. 12,52 In a 2-way sensitivity analysis, the effect of simultaneous changes in 2 variable values is evaluated, while all remaining variables are held constant.…”

Section: Sources Of Uncertainty and Heterogeneitymentioning

confidence: 99%

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Evidence‐Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions

Caulley

Hunink

Randolph³

et al. 2020

Self Cite

Objective To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. Data Sources Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. Review Methods Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. Conclusions Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. Implications for Practice Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

Application of Time‐Driven Activity‐Based Costing for Head and Neck Microvascular Free Flap Reconstruction

Yalamanchi,

Marentette,

Fendrick

et al. 2024

ObjectiveTraditional hospital accounting fails to provide an accurate cost of complex surgical care. Here we describe the application of time‐driven activity‐based costing (TDABC) to characterize costs of head and neck oncologic procedures involving free tissue transfer.Study DesignRetrospective cohort study.SettingSingle tertiary academic medical center.MethodsAn analysis of head and neck oncologic procedures involving microvascular free flap reconstruction from 2018 to 2020 (n = 485) was performed using TDABC methodology to measure cost across operative case and postoperative admission, using quantity of time and cost per unit of each resource to characterize resource utilization. Univariate and generalized linear mixed models were used to examine associations between patient and hospital characteristics and cost of care delivery.ResultsThe total cost of care delivery was $41,905.77 ± 21,870.27 with operating room (OR) supplies accounting for only 10% of the total cost. Multivariable analyses identified significant cost drivers including operative time, postoperative length of stay, number of return trips to the OR, postoperative complication, number of free flaps performed, and patient transfer from another hospital or via emergency department admission (P < .05).ConclusionOperative time and postoperative length of stay, but not operative supplies, were primary drivers of cost of care for head and neck oncology cases involving free tissue transfer. TDABC offers granular cost characterization to inform cost optimization through unused capacity identification and postoperative admission efficiencies.