Decision-Making Framework for Evaluating Physicians’ Preference Items Using Multi-Objective Decision Analysis Principles

Shbool, Mohammad A.; Rossetti, Manuel D.

doi:10.3390/su12166415

Cited by 4 publications

(3 citation statements)

References 18 publications

(30 reference statements)

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“…The choice of the attributes (K) and corresponding levels (L k ) to be included in the experiment were inspired by previous studies on PPIs 5,8,12,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] and particularly referring to some of the factors (i.e., clinical evidence, cost of implant, and physicians' past experience with suppliers or device manufacturers) tested in US-based surveys. 8,12,47 The list of attributes and levels was finalized after consultation with the Italian Society of Orthopaedics and Traumatology (SIOT) Directors' Council and especially with one of its members, the clinical expert author (F.B.…”

Section: Selection Of Attributes/levelsmentioning

confidence: 99%

Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopedists Using 2 Different Stated Preference Methods

Armeni,

Meregaglia,

Borsoi

et al. 2023

Med Decis Making

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Objectives Physician preference items (PPIs) are high-cost medical devices for which clinicians express firm preferences with respect to a particular manufacturer or product. This study aims to identify the most important factors in the choice of new PPIs (hip or knee prosthesis) and infer about the existence of possible response biases in using 2 alternative stated preference techniques. Methods Six key attributes with 3 levels each were identified based on a literature review and clinical experts’ opinions. An online survey was administered to Italian hospital orthopedists using type 1 best-worst scaling (BWS) and binary discrete choice experiment (DCE). BWS data were analyzed through descriptive statistics and conditional logit model. A mixed logit regression model was applied to DCE data, and willingness-to-pay (WTP) was estimated. All analyses were conducted using Stata 16. Results A sample of 108 orthopedists were enrolled. In BWS, the most important attribute was “clinical evidence,” followed by “quality of products,” while the least relevant items were “relationship with the sales representative” and “cost.” DCE results suggested instead that orthopedists prefer high-quality products with robust clinical evidence, positive health technology assessment recommendation and affordable cost, and for which they have a consolidated experience of use and a good relationship with the sales representative. Conclusions The elicitation of preferences for PPIs using alternative methods can lead to different results. The BWS of type 1, which is similar to a ranking exercise, seems to be more affected by acquiescent responding and social desirability than the DCE, which introduces tradeoffs in the choice task and is likely to reveal more about true preferences. Highlights Physician preference items (PPIs) are medical devices particularly exposed to physicians’ choice with regard to type of product and supplier. Some established techniques of collecting preferences can be affected by response biases such as acquiescent responding and social desirability. Discrete choice experiments, introducing more complex tradeoffs in the choice task, are likely to mitigate such biases and reveal true physicians’ preferences for PPIs.

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Section: Selection Of Attributes/levelsmentioning

confidence: 99%

Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopedists Using 2 Different Stated Preference Methods

Armeni,

Meregaglia,

Borsoi

et al. 2023

Med Decis Making

View full text Add to dashboard Cite

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“…delivery time, quality, cost, expertise, service innovation, information system and flexibility) [ 8 ], whereas other researchers have studied different MCDM algorithms that can be employed to assess 3PLPs [ 9 ]. Qualitative as well as quantitative approaches have been utilized in the related literature [ 10 ]. For example, a simple assessment form was proposed in order to allow manager to obtain more information about the distribution facilities from tour visits [ 11 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A new framework for warehouse assessment using a Genetic-Algorithm driven analytic network process

et al. 2021

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A novel way of integrating the genetic algorithm (GA) and the analytic network process (ANP) is presented in this paper in order to develop a new warehouse assessment scheme, which is developed through various stages. First, we define the main criteria that influence a warehouse performance. The proposed algorithm that integrates the GA with the ANP is then utilized to determine the relative importance values of the defined criteria and sub-criteria by considering the interrelationships among them, and assign strength values for such interrelationships. Such an algorithm is also employed to linguistically present the relative importance and the strength of the interrelationships in a way that can circumvent the use of pairwise comparisons. Finally, the audit checklist that consists of questions related to the criteria is integrated with the proposed algorithm for the development of the warehouse assessment scheme. Validated on 45 warehouses, the proposed scheme has been shown to be able to identify the warehouse competitive advantages and the areas where more improvements can be achieved.

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“…Authors in (Shbool, 2016) focused on healthcare expenditures and causes of physicians' preferences in medical item selection. Multi-objective decision-making on physicians' preference items based on value modeling principles is investigated by (Shbool & Rossetti, 2017), and an extension of the model is provided in (Shbool & Rossetti, 2020).…”

Section: Introductionmentioning

confidence: 99%

An integrated multi-criteria decision-making framework for a medical device selection in the healthcare industry

et al. 2021

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Medical devices used in healthcare organizations are costly, and the process of selecting these devices requires considering multiple criteria such as effectiveness and ease of use. Careful selection of these devices is daunting since it entails the evaluation of various measures. This research investigates the selection process of the same type of medical devices, especially when alternatives are available, and the organization needs to make a good selection. A Multi-Criteria Decision-Making (MCDM) framework based on the integration of the Analytical Hierarchy Process (AHP) and ELimination Et Choice Translating Reality (ELECTRE) method is developed. The framework model includes 10 criteria, which are selected based on real-life inputs from professional physicians. Seven Ultrasound machines (referred to as alternatives) are evaluated using the developed

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Decision-Making Framework for Evaluating Physicians’ Preference Items Using Multi-Objective Decision Analysis Principles

Cited by 4 publications

References 18 publications

Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopedists Using 2 Different Stated Preference Methods

Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopedists Using 2 Different Stated Preference Methods

A new framework for warehouse assessment using a Genetic-Algorithm driven analytic network process

An integrated multi-criteria decision-making framework for a medical device selection in the healthcare industry

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