Sage Journals: Discover world-class research

Abstract

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.

Keywords

physician preference items discrete choice experiment best-worst scaling response bias preference elicitation health technology assessment orthopaedics Italy

Introduction

In several countries, policymakers are increasingly adopting evidence-based decision-making models such as health technology assessment (HTA) and value-based procurement to decide whether innovations enter routine practice.^1–3 However, clinicians’ preferences in selecting health care technologies are still relevant, especially in the field of surgical devices. For example, a recent study⁴ showed that in England, surgeons, rather than hospital purchasers, are the main drivers of the increase in laparoscopic colectomy use. Some medical technologies are more sensitive to physicians’ choices and therefore are called physician preference items (PPIs). For PPIs, it is the physician who chooses the product and the supplier, typically based on personal experience with the device and relationships with the vendor’s sales representative.^5,6 PPIs usually include orthopedic prostheses (e.g., hip and knee replacement components), coronary stents, and some spine surgery devices, and they are responsible for up to 60% of a hospital’s total expenditures on supplies.⁷ The medical decision-making process is inherently multidimensional, encompassing medical, technological, economic, and experiential elements. Therefore, a deep understanding of the factors that influence physicians’ preferences for PPIs is of paramount importance for policy makers and hospital managers, who must guarantee high-quality health care and efficient allocation of scarce resources at the same time.⁸

In recent years, the literature has started exploring the variables that affect clinicians’ preferences for medical devices but mostly through qualitative interviews or Likert-type scales using direct questions (e.g., how important is cost?).^8–12 A relevant issue in measuring noncognitive characteristics through Likert scales is that, in case of social pressures, respondent’s answers are susceptible to response biases¹³ such as social desirability (i.e., the tendency of respondents to reply in a manner that will be viewed favorably by others)¹⁴ and acquiescent responding (i.e., the tendency for survey respondents to agree with statements regardless of their content if disagreeing is seen as problematic).^15,16 In PPI surveys, physicians may be influenced by their scientific community, hospital managers, and health care policy makers, with the risk of providing incorrect and misleading information to decision makers about the factors that truly influence their choices.

The objective of this study was to collect clinicians’ preferences on PPIs and, relying on the existing literature, infer about the existence of possible response biases in using two alternative stated preference techniques, one of which makes socially desirable answers more evident to the respondent.

Methods

We designed an online survey using 2 different stated preference methods, namely, best-worst scaling (BWS) and discrete choice experiment (DCE). Their common basic assumption is that choices are made within the random utility model, according to which the frequency of choices between 2 (or more) alternatives provides an estimate of the utility associated with each element on a latent scale of preferences.¹⁷ For both BWS and DCE, the options that are relevant to a choice situation are first described by their attributes. The BWS of type 1 (object case) simply asks the participant to identify the couple of attributes considered, respectively, “best” and “worst” (or “more important” and “least important”) within a single option.^18,19 BWS is similar to Likert ratings but with the advantage that participants are not required to calibrate their responses to the scale range and there are no differences in scale use or interpretation between participants.²⁰ In this study, we compared DCE with BWS to provide a benchmark method that is less affected by other biases typically concerning Likert scales such as the tendency to choose the middle option or to avoid selecting the extreme ends of the scale (i.e., end-of-scale bias). Moreover, the BWS forces respondents to select explicitly both the best and the worst items, thus encouraging more careful consideration of the attributes and reducing the tendency to provide biased responses. This is particularly valuable in case all attributes appear socially desirable and responses on the Likert scale might be skewed toward the upper extreme.

In the DCE, the attributes are further described by their associated levels, and participants are required to choose among 2 (or more) hypothetical choice options (or scenarios) including all attributes but with different levels.²¹ By forcing respondents to tradeoff some elements for others, the DCE incorporates opportunity costs into the elicitation process²² and reveals more about “true” preferences in the welfarist sense.²³ The DCE is even more effective than the BWS in mitigating response biases since the increased task’s complexity makes it difficult for the respondent to simply opt for the most socially desirable response. In preference elicitation exercises, indeed, the ease of identification of a socially desirable behavior increases the probability of being affected by this bias. Unlike the BWS (of type 1), which simply requires to rank a list of items, the DCE adds levels to each attribute, thus reducing the influence of acquiescent responding. Moreover, preferences are expressed for a combination of attribute levels (scenarios) instead of single attributes, thus making respondents feel less judgeable about their choices. So far, few studies have addressed the impact of the design of choice experiments on social desirability; among them, Huls et al.²⁴ found poor evidence of social desirability when using a DCE.

In this study, we used, first, BWS (object case) to capture direct preferences (without tradeoffs), thus leaving physicians potentially exposed to the influence of socially desirable or acquiescent responding and, second, a binary DCE that, exposing participants to tradeoffs among choice dimensions, makes response biases less identifiable and might reduce their impact on stated preferences.

Case Study

The experiment targeted the orthopedist community by focusing on hip and knee implants, which in 2019 accounted for 3.6% of public expenditures (182 million Euros) on medical devices in Italy.²⁵ Moreover, orthopedic clinicians in Italy are potentially exposed to well-identifiable pressures on cost containment by hospital managers and policy makers on one hand and, on the other, on independence from industry and evidence-based decision making by the scientific societies.^26–29 The online survey was administered to Italian orthopedists over a 2-month period to identify the most relevant factors in the choice of a new hip or knee prosthesis. All participants were required to complete a 12-item questionnaire on professional information before responding to a single BWS question followed by a DCE task composed of 10 questions.

Selection of Attributes/Levels

The choice of the attributes (K) and corresponding levels (L_k) to be included in the experiment were inspired by previous studies on PPIs^{5,8,12,30–47} 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.), who also piloted the survey. The final design was balanced with each attribute (K = 6) having the same number of levels (L_K = 3), as reported in Table 1.

Table 1

Attributes and Levels

Attribute	Level	Wording
Clinical evidence	1	There is a safety study, which shows that the device is safe.
	2	There is a safety study, which shows that the device is safe, and a noncomparative efficacy trial/observational study, which shows that the device is effective.
	3	There is a safety study, which shows that the device is safe, and a randomized controlled trial/observational study (with bias balance) of comparative efficacy, which shows that the device is superior to the gold standard.
Quality of supplier’s products	1	In the past, you had some problems with the supplier’s devices.
	2	There have never been any major problems with the quality of the supplier’s products, considered “average.”
	3	The supplier’s devices are excellent and always working.
Relationship with the supplier’s sales rep	1	The relationship with the supplier’s sales rep is problematic: in the past, you had some problems.
	2	The relationship with the supplier’s sales rep is neutral. There have never been any significant problems; the sales rep is considered “average.”
	3	The relationship with the supplier’s sales rep is particularly good: the support provided is excellent, and the sales rep is always reliable.
Previous experience	1	The device has very different technical characteristics compared with the previous ones; therefore, a particular learning is required to implant it.
	2	The device has quite similar technical characteristics compared with the previous ones; therefore, an average learning period is required to learn the implantation technique.
	3	You have already used devices with the same characteristics of use, so you do not need any particular learning to implant the new device.
Cost	1	The cost of the device is €2,500.
	2	The cost of the device is €1,500.
	3	The cost of the device is €1,000.
Health technology assessment (HTA) recommendations	1	There is a negative HTA recommendation for the device.
	2	There are no HTA recommendations for the device.
	3	There is a positive HTA recommendation for the device.

Study Design: BWS

The first task was designed as a type 1 BWS (object case) that does not involve the decomposition of attributes into levels and is frequently adopted to analyze the characteristics of a new product or service. In this survey, a single question provided the list of the 6 mutually exclusive attributes (K) identified together with a brief description of each, asking participants to indicate the 2 considered respectively the “best” (i.e., most important in adopting PPIs) and the “worst” (i.e., least important in adopting PPIs), that is, the best-worst pair of preference (Appendix Figure A1).¹⁸

Study Design: DCE

The second task was designed as a binary DCE, in which respondents were invited to imagine themselves in a situation in which they had to decide whether to adopt a new hip or knee prosthesis and to choose their preferred option from a series of pairwise unlabeled alternatives (A and B) obtained by a unique combination of different attributes’ levels (Appendix Figure A2). The scenarios were conceived to reflect the previous literature^8,12 and everyday clinical practice. A fractional factorial design was applied to obtain a manageable number of choice questions from all possible combinations of attributes and levels (L_K^K = 3⁶ = 729).⁴⁸ The dcreate command was run in Stata⁴⁹ to create an efficient design using the modified Fedorov algorithm.⁵⁰ The choice set was reduced to 20 paired scenarios, split into 2 blocks of 10. The respondents were randomly assigned to each block using the function provided by the Web-based survey tool used for the survey administration. The questions within each block were randomized as well to rule out any possible effects that ordering may have on the estimation.

Data Collection

The study was approved by the Ethics Committee Review of Bocconi University on October 2, 2020. The online survey was designed via Qualtrics XM software (Qualtrics, Provo, UT) and consisted of a self-administered questionnaire. A mailing list was created by mapping all the orthopedic units in Italy through the analysis of the 2016 Italian National Hospital Discharge Records and manually searching hospitals’ Web site and Google to identify the orthopedists operating in each unit and their contacts. The potential respondents (n = 2,202) were invited to participate by an e-mail including a brief description of the research and the survey link. They were assured of confidentiality and anonymity and required to provide their informed consent. The survey was conducted between October and November 2020, with reminder e-mails being sent at 2- to 3-wk intervals.

Data Analysis: BWS

The completed questionnaires were downloaded in .csv format by Qualtrics, and the database was structured for statistical analysis conducted using Stata/SE (version 16, StataCorp LLC, College Station, TX). A P value of 0.05 was considered statistically significant.

First, the answers to the BWS question were analyzed through descriptive statistics by counting the number of times each attribute was selected, respectively, as the “most important” (i.e., best total score) and the “least important” (i.e., worst total score). Therefore, a best-minus-worst score (B-W score) was calculated as the difference between the best total score and the worst total score.

Second, we ran a conditional logit model (clogit in Stata) that treats each best-worst pair as a possible outcome of the respondent’s decision-making process (model 1). In a question containing K = 6 attributes, there are K (K − 1) = 30 possible best-worst combinations to choose from.⁵¹ The dependent variable is equal to 1 for the selected pair and 0 otherwise. The explanatory variables (i.e., the attributes) are coded, for each possible pair, as 1 for the best, −1 for the worst, and 0 otherwise. The attribute most frequently chosen as the least important is taken as a reference value (equal to 0) in the model, with respect to which all other coefficients must be interpreted. The statistically significant coefficients indicate the importance of each attribute in determining the overall utility for the participant.⁵² Akaike information criterion (AIC), Bayesian information criterion (BIC), and conditional Akaike information criterion (CAIC) statistics were computed to assess models’ fit.

Lastly, a further model (model 2) was performed to investigate heterogeneity in preferences, by adding to the explanatory variables the participants’ characteristics (e.g., gender) that interacted with the experiment’s attributes. The interaction terms represented the additional utility of each attribute for the subgroup under consideration (e.g., males v. females). Continuous variables and categorical variables with 3 or more response options in the questionnaire were dichotomized to increase the subsamples’ size; for example, 2 classes were created for age and professional experience (in years) around their median value. A univariate regression analysis was preliminarily performed to identify the characteristics that showed at least 1 significant interaction. Then, a backward selection was applied to identify the final model including only significant interactions.

Data Analysis: DCE

The DCE data were analyzed using a mixed logit regression model (gmnl command in Stata, with the specification mixl) in which the dependent variable was the dichotomous choice of the hypothetical scenarios, and independent variables were the factor/level combinations (model 1). One hundred iterations using Halton draws were applied. A dummy variable coding was applied to all attributes except device cost, which was treated as a continuous variable. The worst level of each attribute (e.g., the least robust clinical evidence, the lowest product quality) was considered as the reference case and omitted from the regression. The regression coefficients should be interpreted as the increment in utility associated with moving from the reference level of each attribute (the worst level) to the other levels. Under the assumption that clinicians may have heterogeneous preferences for a new device’s characteristics, we specified all factors as random parameters with normal distributions. As for BWS, interaction terms between the respondents’ observable characteristics and attribute levels were added to the model (model 2). AIC, BIC, and CAIC statistics were computed to assess the models’ fit. A likelihood ratio test was performed to assess whether the extended model (model 2) improved the explanatory power compared with model 1. The willingness-to-pay (WTP) for a change in each attribute level was computed as the ratio of mean attributes’ coefficients to cost coefficient from model 1.⁵³ Marginal rates of substitution between attributes were computed to investigate the rate at which clinicians would be willing to give up high-quality clinical evidence or high-quality products to obtain a gain in “softer” attributes (i.e., relationship with the supplier and experience of use). The responses to DCE were analyzed along 2 dimensions to assess their internal validity: 1) frequency of choice of the same scenario (A or B) by each respondent or 2) attribute dominance (i.e., whether respondents choose the alternative with the better level of one attribute in all or nearly all choice questions).⁵⁴

Results

A total of 240 questionnaires were collected from the online survey. Of these, 121 were discarded because they were unfinished and a further 11 because participants did not give consent to participation and/or did not implant hip or knee prosthesis in the last year. Finally, 108 questionnaires were in usable form and robust to validity checks; therefore, they were all retained for the analyses. The completion rate was 45%. We collected responses from 85 Italian hospitals, mostly public (63), located in 18 regions (out of 21), and accounting for 22% of hip implants and 18% of knee implants performed in Italy in 2016 (based on 2016 Italian hospital discharge records).

Sample Description

The mean age of the participants was 52.8 (±10.1) years, and the great majority (93.5%) were men. The average postgraduate work experience was 25.9 (±10.4). Of the orthopedists, 42.6% were second-level medical managers (i.e., head of the orthopedics unit) and 45.4% were first-level medical managers (i.e., fixed-term clinicians in an orthopedics unit) in public hospitals or covered equivalent positions in private hospitals. In the last year, 47.2% and 34.3% performed more than 50 hip and knee implants, respectively. A small percentage had an experience as prosthesis designer or proctor (Table 2).

Table 2

Respondents’ Characteristics (N = 108)

Age (y), $\bar{x}$ ±s (range)	52.8 ± 10.1 (33–72)
Age (y), median (IQR)	54.5 (17)
Gender: male, n (%)	101 (93.5%)
Years of experience (postgraduate), $\bar{x}$ ±s (range)	25.9 ± 10.4 (2–45)
Years of experience (postgraduate), median (IQR)	28 (19.5)
Qualification, n (%)
I level medical manager^a	49 (45.4%)
II level medical manager^b	46 (42.6%)
Other	13 (12.0%)
Geographic area, n (%)
North	70 (64.8%)
Center	18 (16.7%)
South	20 (18.5%)
Hip implants in the past year, n (%)
None	3 (2.8%)
Between 1 and 9	9 (8.3%)
Between 10 and 19	20 (18.5%)
Between 20 and 39	20 (18.5%)
Between 40 and 49	5 (4.6%)
≥50	51 (47.2%)
Knee implants in the past year, n (%)
None	14 (13.0%)
Between 1 and 9	16 (14.8%)
Between 10 and 19	20 (18.5%)
Between 20 and 39	14 (13.0%)
Between 40 and 49	7 (6.5%)
≥50	37 (34.3%)
Designer, n (%)
No	102 (94.4%)
Only for hip prostheses	3 (2.78%)
Only for knee prostheses	2 (1.9%)
For both hip and knee prostheses	1 (0.9%)
Proctor, n (%)
No	101 (93.5%)
Only for hip prostheses	1 (0.9%)
Only for knee prostheses	5 (4.6%)
For both hip and knee prostheses	1 (0.9%)
Participation in tender commissions, n (%)
No	79 (73.2%)
Only for hip prostheses	4 (3.7%)
Only for knee prostheses	5 (4.6%)
For both hip and knee prostheses	20 (18.5%)
Perceived autonomy in hip prostheses selection,^c $\bar{x}$ ±s	3.6 ± 1.5
Perceived autonomy in knee prostheses selection,^c $\bar{x}$ ±s	3.5 ± 1.6

This includes both first-level managers in public hospitals and similar professional figures working in private hospitals.

This includes both second-level managers in public hospitals and similar professional figures working in private hospitals.

Respondents were asked to express their perceived autonomy on a scale from 0 (no autonomy) to 5 (complete autonomy).

BWS Results

In BWS, the highest valued attribute was “clinical evidence” followed by “quality of products,” whereas the item with the lowest B-W score was “cost,” followed by “relationship with the sales representative.” The 2 remaining attributes obtained an equal number of “best” and “worst” responses (Appendix Table A1).

The conditional logit regression coefficients (model 1) were aligned with the BW frequency counts. The importance of each attribute was estimated relative to “cost,” which was most frequently selected as “worst.” All attributes had coefficients that were significantly different from 0 and of expected positive sign. The attribute presenting the highest utility coefficient was “clinical evidence” followed by “quality of products,” while “relationship with the sales representative” was the lowest rated item before “cost.” The conditional logit model with the addition of interaction terms (model 2) showed that “HTA recommendations” was particularly important for first-level medical managers and for those who implanted more than 50 hip prostheses in the past year. Conversely, “quality of products” and “previous experience” were valued as less important by clinicians who implanted more than 50 knee prostheses compared with those who implanted fewer than that (Table 3).

Table 3

Best-Worst Scaling Results from Conditional Logit Regression

	Model 1					Model 2
	Coefficient	SE	P	95% CI		Coefficient	SE	P	95% CI
Clinical evidence	2.730	0.271	<0.001	2.198	3.262	2.897	0.288	<0.001	2.332	3.462
Quality of supplier’s products	2.139	0.275	<0.001	1.600	2.677	2.663	0.328	<0.001	2.019	3.306
Relationship with the supplier’s sales rep	0.471	0.214	0.028	0.051	0.892	0.487	0.218	0.026	0.059	0.914
Previous experience	1.385	0.258	<0.001	0.879	1.891	1.915	0.330	<0.001	1.269	2.562
Health technology assessment (HTA) recommendations	1.347	0.257	<0.001	0.844	1.850	0.402	0.429	0.35	−0.439	1.242
Cost	0 (ref.)					0 (ref.)
First-level medical manager × HTA						1.146	0.470	0.015	0.224	2.067
No. of hip prostheses ≥50 × HTA						1.072	0.470	0.023	0.151	1.994
No. of knee prostheses ≥50 × Quality						−1.170	0.438	0.008	−2.030	−0.311
No. of knee prostheses ≥50 × Experience						−1.201	0.450	0.008	−2.084	−0.318
AIC	593.8					581.1
BIC	624.2					635.8
CAIC	632.3					643.9

AIC, Akaike information criterion; BIC, Bayesian information criterion; CAIC, conditional Akaike information criterion; CI, confidence interval; SE, standard error.

In the DCE, all attributes/levels (except for “average learning needed”) had a significant influence on the orthopedist’s decision to adopt a new hip or knee prosthesis (Table 4, model 1). The directions of the coefficients were in accordance with our hypotheses (e.g., positive sign for all attributes’ levels with respect to their reference level; negative sign for cost). In model 2 (with interaction terms), 2 categories of more experienced participants (i.e., those who implanted ≥50 prostheses over the past year and those acting as “proctor”) reported significantly different preferences in relation to “clinical evidence” and “HTA recommendations,” respectively (Table 4).

Table 4

Discrete Choice Experiment Results^a

	Statistic	Model 1					Model 2
	Statistic	Coefficient	SE	P	95% CI		Coefficient	SE	P	95% CI
Clinical evidence
Safety		Ref.
Safety + noncomparative efficacy/observational	$\bar{x}$	0.3538	0.1511	0.019	0.0576	0.6499	0.2910	0.1560	0.062	−0.0148	0.5968
Safety + noncomparative efficacy/observational	s	−0.4179	0.2526	0.098	−0.9130	0.0772	−0.7113	0.2249	0.002	−1.1520	−0.2705
Safety + RCT/observational (with bias balance)	$\bar{x}$	0.8486	0.1531	<0.001	0.5486	1.1487	0.6116	0.1929	0.002	0.2336	0.9896
Safety + RCT/observational (with bias balance)	s	0.6928	0.2010	0.001	0.2989	1.0866	−0.4354	0.2666	0.102	−0.9579	0.0872
Quality of supplier’s products
Problematic		Ref.
Average	$\bar{x}$	0.7016	0.1532	<0.001	0.4014	1.0019	0.7366	0.1799	<0.001	0.3841	1.0891
Average	s	0.1810	0.4264	0.67	−0.6546	1.0167	0.4425	0.2636	0.093	−0.0741	0.9592
Excellent	$\bar{x}$	0.8041	0.1480	<0.001	0.5140	1.0943	0.8474	0.1636	<0.001	0.5268	1.1680
Excellent	s	0.3115	0.3801	0.41	−0.4334	1.0564	−0.5178	0.3090	0.094	−1.1233	0.0877
Relationship with the supplier’s sales rep
Problematic		Ref.
Neutral	$\bar{x}$	1.0258	0.1626	<0.001	0.7070	1.3445	1.0979	0.1773	<0.001	0.7504	1.4453
Neutral	s	0.2515	0.2486	0.31	−0.2357	0.7387	−0.0974	0.3342	0.77	−0.7524	0.5575
Excellent	$\bar{x}$	1.3188	0.1826	<0.001	0.9610	1.6766	1.4490	0.2174	<0.001	1.0229	1.8751
Excellent	s	−0.5364	0.2619	0.041	−1.0496	−0.0231	0.5910	0.2799	0.035	0.0423	1.1396
Previous experience
Particular learning needed		Ref.
Average learning needed	$\bar{x}$	0.1031	0.1390	0.46	−0.1694	0.3755	0.0862	0.1492	0.56	−0.2062	0.3785
Average learning needed	s	0.6514	0.2179	0.003	0.2244	1.0785	0.7268	0.2688	0.007	0.2000	1.2537
No learning needed	$\bar{x}$	0.4435	0.1381	0.001	0.1728	0.7141	0.4910	0.1510	0.001	0.1950	0.7871
No learning needed	s	−0.2826	0.3381	0.40	−0.9453	0.3801	−0.5510	0.2329	0.018	−1.0074	−0.0945
HTA recommendation
Negative		Ref.
None	$\bar{x}$	1.0590	0.1499	<0.001	0.7652	1.3528	1.1016	0.1850	<0.001	0.7390	1.4642
None	s	−0.4037	0.2449	0.099	−0.8837	0.0762	0.3923	0.3096	0.21	−0.2146	0.9991
Positive	$\bar{x}$	1.6215	0.1870	<0.001	1.2549	1.9881	1.8325	0.2588	<0.001	1.3253	2.3397
Positive	s	−0.8086	0.2335	0.001	−1.2663	−0.3509	0.9407	0.2185	<0.001	0.5126	1.3689
Cost	$\bar{x}$	−0.0005	0.0001	<0.001	−0.0007	−0.0002	−0.0005	0.0001	<0.001	−0.0007	−0.0002
Cost	s	0.0006	0.0001	<0.001	0.0004	0.0009	0.0008	0.0002	<0.001	0.0004	0.0011
No. of hip prostheses ≥50 × evidence (safety + noncomparative efficacy/observational)	$\bar{x}$						0.6435	0.2588	0.013	0.1363	1.1507
Proctor × HTA recommendation (none)	$\bar{x}$						1.0119	0.5064	0.046	0.0194	2.0044
Observations		2,160					2,160
Log likelihood		−546.14					−541.91
Prob > χ²		<0.001					<0.001
Likelihood ratio test model 2 versus model 1 χ² (P value)							8.47 (0.014)
AIC		1,136.29					1,131.82
BIC		1,248.18					1,253.89
CAIC		1,270.18					1,277.89

AIC, Akaike information criterion; BIC, Bayesian information criterion; CAIC, conditional Akaike information criterion; CI, confidence interval; HTA, health technology assessment; RCT, randomized controlled trial; SE, standard error.

The sign of the estimated standard deviations is irrelevant; they should be interpreted as being positive.

Table 5 reports the mean WTP estimates for changes in attributes’ levels calculated from the restricted model (model 1). The marginal WTP for a device with robust clinical evidence (i.e., safety study + randomized controlled trial (RCT)/observational study with bias balance) compared with a device with only 1 safety study available was €1,829. The WTP for a high-quality product was €1,733, €2,843 for a good relationship with the supplier’s sales representative, and €3,495 for a device with a positive HTA recommendation. These findings suggest that, in contrast with the BWS, the existence of a positive HTA recommendation and the relationship with the supplier’s sales representative are more important than having a high-quality product or a product with an RCT/observational study (with bias balance) of comparative efficacy. The differences between BWS and DCE preferences rankings are highlighted in Figure 1 (cost is not reported for DCE because it is used to calculate the WTP).

Table 5

Mean Willingness-to-Pay Estimates for Changes in Attributes’ Levels

	Willingness-to-Pay Estimates (€)
	Coefficient	SE	P	95% CI
Clinical evidence
Safety	Ref.
Safety + noncomparative efficacy/observational	763	354	0.031	68	1,457
Safety + RCT/observational (with bias balance)	1,829	501	<0.001	847	2,812
Quality of supplier’s products
Problematic	Ref.
Average	1,512	439	0.001	652	2,373
Excellent	1,733	461	<0.001	829	2,637
Relationship with the supplier’s sales rep
Problematic	Ref.
Neutral	2,211	564	<0.001	1,106	3,316
Excellent	2,843	704	<0.001	1,463	4,222
Previous experience
Particular learning needed	Ref.
Average learning needed	222	304	0.46	−373	817
No learning needed	956	358	0.008	254	1,658
HTA recommendation
Negative	Ref.
None	2,283	599	<0.001	1,110	3,456
Positive	3,495	858	<0.001	1,813	5,177

CI, confidence interval; HTA, health technology assessment; RCT, randomized controlled trial; SE, standard error.

Figure 1

Differences between best-worst scaling and discrete choice experiment items ranking.

Marginal rates of substitution between attributes are reported in Appendix Table A2. They revealed that respondents would be willing to bear a higher sacrifice in the products’ quality or in the evidence level (negative sign) to obtain a gain in the relationship with the supplier’s sales representative (all coefficients in absolute values are greater than 1) than to obtain a gain in experience of use (almost all coefficients in absolute values are lower than 1).

Discussion

Synthesis of Results

The process of uptake and diffusion of technological innovations in health care, starting with marketing authorization normed by regulation systems in different jurisdictions and ending with purchasing decisions at the local level, encompasses a broad range of stakeholders including HTA agencies, physicians, purchasers, providers, and patients’ associations. The new EU Medical Device and HTA Regulations place the provision of robust clinical evidence at the heart of the approval procedure to make the whole market-access process more evidence based, less fragmented, and, therefore, less influenced by local and/or specific stakeholders’ expectations.^55,56 Nevertheless, physicians are the end users of medical technologies and, especially for PPIs, undoubtedly keep playing a pivotal role at the time of purchase, which is also the most decisive as to the diffusion.

So far, studies dealing with physician’s preferences have mainly used traditional rating scales and implicitly assumed that no distortions affected the survey responses, so that true preferences coincided with the ones declared in the survey. This study inferred that simply asking physicians to rank the importance of choice dimensions bears the risk of collecting preferences affected by acquiescent responding and social desirability, while exposing them to repeated tradeoffs in the choice of multifactorial scenarios allows to capture the true perceived relative importance of several dimensions. Therefore, we collected preferences from Italian orthopedists within the same choice context (i.e., the adoption of a new hip or knee prosthesis) but using 2 distinct stated preference methods, of which the BWS might expose them more to potential responses biases.¹⁵ The sample size (N = 108) was in line with most DCE studies, which enroll between 100 and 300 participants.⁵⁷ First, BWS asked respondents to simply rank the importance of attributes in the choice to adopt a new prosthesis in orthopedics. The BWS object case is less cognitively demanding (compared with DCE and other more complex stated preference techniques) and increasingly adopted in health care surveys, together with less sophisticated approaches to analyze data (e.g., best-worst count analysis).^18,58 We retrieved all attributes from the literature on PPI except for “HTA recommendations,” which was deemed crucial for our study as it conveys a different concept with respect to “clinical evidence” and “cost.” In fact, despite HTA recommendations being based on the assessment of both clinical and economic evidence, each of these elements influences decisions in different directions. Indeed, people generally have a negative preference for costs and a positive preference for clinical evidence, while preference direction for HTA is less clear and depends, for example, on the trust in the HTA authority that issued recommendations. In detail, “clinical evidence” and “HTA” differ under several dimensions, which are likely to impact differently on the overall judgment about a medical technology. For example, they use different value domains and related measures (i.e., efficacy and safety for the former, a broader range for the latter—including economic, social, ethical, and organizational implications, as outlined in the HTA core model by EUnetHTA⁵⁹), evidence standards (i.e., RCTs for the former, real-world studies for the latter), and time horizon (i.e., shorter for the former, longer for the latter). In addition, it should also be noticed that cost and HTA are not correlated in principle. The cost attribute, indeed, refers to the individual product’s cost, while HTA considers incremental costs over a long time (typically lifetime) horizon (and, occasionally, net costs in the short-term for a budget impact analysis) but also broader cost categories including family’s costs and productivity losses. Thus, a credible scenario can include low-cost, good evidence, and a negative HTA recommendation if an alternative product has even lower costs and at least noninferior clinical evidence. For example, in the United Kingdom, a technology appraisal guidance from the National Institute for Health and Care Excellence (NICE) comparing 8 different biological drugs for rheumatoid arthritis, all presenting a comparable cost-effectiveness profile, recommended starting treatment with the least expensive product considering a variety of costs (i.e., price per dose needed and administrative costs).⁶⁰

The analysis of BWS responses revealed that clinicians assigned the highest value to ‘clinical evidence’, and the lowest to ‘cost’. Moreover, participants with a higher volume of activity (i.e., ≥50 prostheses implanted over the last year) had significantly different preferences compared to clinicians performing less implants, while ‘HTA recommendations’ were particularly considered by first level medical managers that, likely due to their younger age, are more sensitive to pharmacoeconomic and HTA topics.

Second, DCE forced physicians to simultaneously evaluate different attribute/level combinations in randomly assigned hypothetical scenarios, with questions in a random order, thus making the choice more complex and less directly exposed to response biases. In DCE, clinicians’ choices revealed different preferences than those assessed through the BWS. In fact, “clinical evidence” was not the most important factor, and device “cost” had a small but significant influence on the choice of adopting a new prosthesis. Moreover, orthopedists would be willing to pay more for a good relationship with the supplier’s sales representative than for a high-quality product or for a product with robust clinical evidence, and HTA recommendations play a major role in driving their decisions. Overall, a low degree of heterogeneity was observed in physicians’ responses collected with both techniques, as revealed by the small number of significant interactions.

Policy and Research Implications

This study has several managerial and policy implications, as well as implications for future research. First, it showed that collecting preferences through ranking exercises, as done so far in research on PPIs, might produce results that are more aligned with the socially accepted opinions of different stakeholders (e.g., scientific community, hospital managers, and policy makers)^25–28 but, at the same time, might not necessarily reflect individuals’ true preferences.²³ Previous studies comparing DCE and BWS reported different preference estimates regardless of the health context, thus suggesting that the 2 methods may be measuring different constructs. However, no comparison was made with BWS of type 1⁶¹ that is even more different from the DCE since attributes are not articulated into levels. This study attempted to fill this literature gap. In our study, the differences between BWS and DCE results can be explained in 2 different ways according to the respondent’s willingness. If physicians intentionally completed the BWS task not following their real thought, this means that they are aware of what they prefer but choose not to disclose it to get into alignment with hospital managers and policy makers’ expectations. Otherwise, physicians might not be fully aware of what they really prefer. If simply asked about the importance of choice factors, they honestly believe they are driven by clinical evidence and quality of products whereas, when exposed to a more complex choice (such as the real multidimensional one), they show themselves to be more sensitive to HTA and the relationship with the sales representative. Indeed, the DCE, compared with the BWS of type 1, provides participants with more information (for example, asking to consider a range of realistic costs instead of “cost” as an abstract concept), which inevitably influences their individual preferences. Further research is needed to understand to what extent the difference between BWS and DCE choices is intentional and the main drivers underlying this difference.

Second, DCE results highlighted the importance of the sales representative’s reliability. A potential interpretation of this finding is that physicians could use the relationship as a “proxy” for product quality, although in principle, the 2 dimensions are not necessarily correlated. This is one way of acknowledging the value of the service component in the medical technology supply, which can make a difference in manufacturers’ competitive advantage.⁶² The “servitization” process (i.e., a company shifting from a product-centric to a service-centric business model and logic) in the medical technology industry is usually slower compared with other industries, and the regulatory framework can both favor and hinder this transition.⁶³ In Italy, public tenders for the purchase of medical devices mostly focus on purchasing goods rather than integrated bundles of goods and services, with the result of considering service a “nice to have” or a “given for granted” optional and leading physicians to conceal (or not be aware of) their sensitivity to this component. This is mainly due to complexities in managing tenders for public health authorities rather than to legal constraints, as the guidelines of the Italian Ministry of Health on public procurement recommend splitting the product price from the service price, within the same offer.⁶⁴ This study suggests using new contractual models that integrate service as an explicit component of products’ quality in public procurement, in line with existing guidelines.

Third, HTA is a much-needed guidance for clinical decision makers, although currently very few examples of a direct link with public procurement have been reported, mainly in gray literature and conference proceedings. HTA represents a crucial component of the rising value-based paradigm, whose final aim is to improve decision-making processes in health care by identifying the alternative that brings the highest value for the system as a whole. This trend is particularly evident and tangible for the MedTech ecosystem, as witnessed by the recent approval of the new EU Medical Device and HTA Regulations^55,56 and the ongoing debate on HTA in the United States.⁶⁵ The results of this study show that a positive (full) HTA recommendation would help clinicians to select the best option for the patient and at the same time deal with cost-containment pressures from hospital managers and policy makers without directly negotiating the tradeoff between additional costs and benefits. In this perspective, the Italian National Program for HTA of Medical Devices is a promising decisional framework whose importance is implicitly claimed by physicians too, with the potential to foster the integration of HTA into medical device procurement and turn the current purchasing system into a value-based procurement approach.⁶⁶

Study Limitations

This study presents several limitations. First, the sample was based on voluntary participation, and the individual response rate was only 5% (the hospital response rate was 15%). Therefore, the participants might not be fully representative of Italian orthopedists, despite being at sufficiently different ages and career stages and coming from all 3 geographical areas (i.e., north, center, south) and 18 different regions. The male-female ratio was comparable to national data (female SIOT members were 11.4% in 2021). Second, the choice of attributes was limited to 6, with 3 levels each, to avoid an excessive cognitive burden to respondents,²² although these may not entirely capture the complexity of the decision-making process. Third, clinicians are usually not familiar with BWS and DCE question formats, and this might have led some potential participants to opt out the survey (i.e., nonresponse bias).⁵⁸ Fourth, since respondents always completed the BWS before the DCE, we cannot exclude that responses were somehow affected by ordering effects. However, this order of tasks was chosen to allow participants to become familiar with the attributes before performing the much more complex DCE involving attribute-level combinations. Moreover, only few studies in the literature randomized which task (BWS or DCE) was presented first.⁶¹ Fifth, experiments relying on stated preference techniques investigate hypothetical behaviors instead of actual ones and therefore may lack of external validity.⁶⁷ Thus, this study, results could gain more credibility if compared with real-world data (e.g., about in-hospital purchasing procedures) or evidence collected in broader contexts.⁶⁸ Lastly, the tradeoffs that are intrinsic in stated preferences techniques (and particularly in DCEs, where choices require weighing multiple attributes at a time) can help mitigate response biases but not completely avoid them. Indeed, respondents might still choose the more socially accepted options, especially in case of sensitive choice tasks including health-related attributes.

Conclusions

This study suggested that collecting physicians’ preferences with different methods can lead to considerably divergent conclusions. In BWS (object 1), which is like a ranking exercise, clinicians might be influenced by acquiescent responding and social desirability. The DCE, instead, by introducing tradeoffs in the choice task, is likely to reveal more about true preferences (and indeed is generally preferred by economists). Therefore, the use of DCE is encouraged, although more research is needed to identify the most appropriate methods to collect undistorted preferences for medical devices that can ultimately facilitate the HTA process and the diffusion of a value-based health care.

Supplemental Material

sj-docx-1-mdm-10.1177_0272989X231201805 – Supplemental material for Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopaedists Using 2 Different Stated Preference Methods

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231201805 for Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopaedists Using 2 Different Stated Preference Methods by Patrizio Armeni, Michela Meregaglia, Ludovica Borsoi, Giuditta Callea, Aleksandra Torbica, Francesco Benazzo and Rosanna Tarricone in Medical Decision Making

Footnotes

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The financial support for this study was provided by an unrestricted grant from the Directorate General of Medical Devices and Pharmaceutical Service, Ministry of Health, Italy. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. This work was presented at the following conferences: International Pharmacoeconomics and Outcome Research (ISPOR), May 17–20, 2021 (virtual event); International Health Economics Association (iHEA), July 12–15, 2021 (virtual event); and Italian Health Economics Association (AIES), December 2–3, 2021 (Milan).

ORCID iDs

Michela Meregaglia

Francesco Benazzo

References

Sorenson

Kanavos

Medical technology procurement in Europe: a cross-country comparison of current practice and policy. Health Policy. 2011;100:43–50. DOI: 10.1016/j.healthpol.2010.08.001

Callea

Armeni

Marsilio

Jommi

Tarricone

The impact of HTA and procurement practices on the selection and prices of medical devices. Soc Sci Med. 2017;174:89–95. DOI: 10.1016/j.socscimed.2016.11.038

Torbica

Fornaro

Tarricone

Drummond

MF.

Do social values and institutional context shape the use of economic evaluation in reimbursement decisions? An empirical analysis. Value Health. 2020;23:17–24. DOI: 10.1016/j.jval.2019.11.001

Barrenho

Miraldo

Propper

Walsh

The importance of surgeons and their peers in adoption and diffusion of innovation: an observational study of laparoscopic colectomy adoption and diffusion in England. Soc Sci Med. 2021;272:113715. DOI: 10.1016/j.socscimed.2021.113715

Montgomery

Schneller

ES.

Hospitals’ strategies for orchestrating selection of physician preference items. Milbank Q. 2007;85:307–35. DOI: 10.1111/j.1468-0009.2007.00489.x

Wilson

Schneller

Montgomery

Bozic

KJ.

Hip and knee implants: current trends and policy considerations. Health Aff. 2008;27:1587–98. DOI: 10.1377/hlthaff.27.6.1587

Schneller

Smeltzer

LR.

Strategic Management of the Health Care Supply Chain. San Francisco (CA): Jossey-Bass; 2006.

Burns

Housman

Booth

Koenig

AM.

Physician preference items: what factors matter to surgeons? Does the vendor matter?

Med Devices (Auckl). 2018;11:39–49. DOI: 10.2147/MDER.S151647

Felgner

Henschke

Physicians’ decision making on adoption of new technologies and role of coverage with evidence development: a qualitative study. Value Health. 2018;21:1069–76. DOI: 10.1016/j.jval.2018.03.006

10.

Gold

Pitrelli

Hayes

Murphy

MM.

Decision to adopt medical technology: case study of breast cancer radiotherapy techniques. Med Decis Making. 2014;34:1006–15. DOI: 10.1177/0272989X14541679

11.

Hatz

Schreyogg

Torbica

Boriani

Blankart

CR.

Adoption decisions for medical devices in the field of cardiology: results from a European survey. Health Econ. 2017;26(suppl 1):124–44. DOI: 10.1002/hec.3472

12.

Burns

Housman

Booth

Jr Koenig

Implant vendors and hospitals: competing influences over product choice by orthopedic surgeons. Health Care Manage Rev. 2009;34:2–18. DOI: 10.1097/01.HMR.0000342984.22426.ac

13.

Lavrakas

PJ.

Encyclopedia of Survey Research Methods. Thousand Oaks (CA): Sage; 2008.

14.

Paulhus

DL.

Measurement and control of response bias. In: Robinson

Shaver

Wrightsman

, eds. Measures of Personality and Social Psychological Attitudes. San Diego (CA): Academic Press; 1991. p 17–59.

15.

Kreitchmann

Abad

Ponsoda

Nieto

Morillo

. Controlling for response biases in self-report scales: forced-choice vs. psychometric modeling of Likert items. Front Psychol. 2019;10:2309. DOI: 10.3389/fpsyg.2019.02309

16.

Pimentel

Some biases in Likert scaling usage and its correction. Int J Sci Basic Appl Res. 2019;45:183–91.

17.

Flynn

Marley

AAJ

. Best-worst scaling: theory and methods. In: Hess

Daly

, eds. Handbook of Choice Modelling. Cheltenham (UK): Edward Elgar Publishing; 2014. p 178–201.

18.

Cheung

Wijnen

Hollin

, et al. Using best-worst scaling to investigate preferences in health care. Pharmacoeconomics. 2016;34:1195–209. DOI: 10.1007/s40273-016-0429-5

19.

Muhlbacher

Kaczynski

Zweifel

Johnson

FR.

Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health Econ Rev. 2016;6:2. DOI: 10.1186/s13561-015-0079-x

20.

Burton

Rigby

Sutherland

CAM

Rhodes

Best-worst scaling improves measurement of first impressions. Cogn Res Princ Implic. 2019;4:36. DOI: 10.1186/s41235-019-0183-2

21.

Louviere

Flynn

Carson

RT.

Discrete choice experiments are not conjoint analysis. J Choice Modell. 2010;3:57–72. DOI: 10.1016/S1755-5345(13)70014-9

22.

Torbica

Fattore

Understanding the impact of economic evidence on clinical decision making: a discrete choice experiment in cardiology. Soc Sci Med. 2010;70:1536–43. DOI: 10.1016/j.socscimed.2009.12.030

23.

Flynn

Peters

Coast

Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data. J Choice Modell. 2013;6:34–43. DOI: 10.1016/j.jocm.2013.04.004

24.

Huls

van Exel

de Bekker-Grob

How to decrease social desirability bias in stated preference data?

An attempt gone to (food) waste. 2022. DOI: 10.2139/ssrn.4150154. Available from: https://ssrn.com/abstract=4150154

25.

Ministero della Salute. Appendice al “Rapporto sulla spesa rilevata dalle strutture sanitarie pubbliche del SSN per l’acquisto di dispositivi medici” per l’anno 2019: dataset Spesa rilevata per Azienda Sanitaria in formato Open Data. 2019. Available from: www.dati.salute.gov.it/dataset/dispositivi_medici_spesa_2019.jsp

26.

Italian Society of Orthopedics and Traumatology. Ethical code. 2013. Available from: https://siot.it/il-codice-etico/#:~:text=Il%20Codice%20definisce%20le%20regole,con%20gli%20organi%20di%20informazione [Accessed 15 January, 2022].

27.

Fineberg

HV.

Conflict of interest: why does it matter?

JAMA. 2017;317:1717–8. DOI: 10.1001/jama.2017.1869

28.

Italian Procurement Code. Art. 42 Conflict of interest. 2016.

29.

Health Systems and Policy Monitor. Health systems in transition (HiT) profile of Italy. Available from: https://eurohealthobservatory.who.int/monitors/health-systems-monitor/countries-hspm/hspm/italy-2014/overview/

30.

Burns

Lee

JA.

Hospital purchasing alliances: utilization, services, and performance. Health Care Manage Rev. 2008;33:203–15. DOI: 10.1097/01.HMR.0000324906.04025.33

31.

Lee

Losing preferential treatment. Physicians face limited choice in medical device selection as hospitals push to slash supply-chain costs. Mod Healthc. 2013;43:28–30.

32.

McIlhargey

Contracting for physician-preference items. J Healthc Contract. 2017. https://www.jhconline.com/contracting-for-physician-preference-items.html

33.

Islam

Turki

Murad

Karim

Do sustainable procurement practices improve organizational performance?

Sustain. 2017;9:2281.

34.

Lerner

Fox

Nelson

Reiss

JB.

The consequence of secret prices: the politics of physician preference items. Health Aff (Millwood). 2008;27:1560–5. DOI: 10.1377/hlthaff.27.6.1560

35.

Lingg

Merida-Herrera

Wyss

Durán-Arenas

Attitudes of orthopedic specialists toward effects of medical device purchasing. Int J Technol Assess Health Care. 2017;33:46–53. DOI: 10.1017/S0266462317000101

36.

Lingg

Wyss

Duran-Arenas

Effects of procurement practices on quality of medical device or service received: a qualitative study comparing countries. BMC Health Serv Res. 2016;16:362. DOI: 10.1186/s12913-016-1610-4

37.

Lingg

Wyss

Durán-Arenas

How does the knowledge environment shape procurement practices for orthopaedic medical devices in Mexico?

BMC Med Inform Decis Mak. 2016;16:85. DOI: 10.1186/s12911-016-0324-1

38.

Ivlev

Vacek

Kneppo

Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty. Eur J Oper Res. 2015;247:216–28. DOI: 10.1016/j.ejor.2015.05.075

39.

Sharkey

Sethuraman

Hozack

Rothman

Stiehl

JB.

Factors influencing choice of implants in total hip arthroplasty and total knee arthroplasty: perspectives of surgeons and patients. J Arthroplasty. 1999;14:281–7. DOI: 10.1016/s0883-5403(99)90052-9

40.

Robinson

JC.

Value-based purchasing for medical devices. Health Aff (Millwood). 2008;27:1523–31. DOI: 10.1377/hlthaff.27.6.1523

41.

Olson

Obremskey

Bozic

KJ.

Healthcare technology: physician collaboration in reducing the surgical cost. Clin Orthop Relat Res. 2013;471:1854–64. DOI: 10.1007/s11999-013-2828-7

42.

DeJohn

. The last frontier: saving on M.D. preference items. Hosp Mater Manage. 2005;30:1, 9–11.

43.

Sanderson

Lonsdale

Mannion

Matharu

Towards a Framework for Enhancing Procurement and Supply Chain Management Practice in the NHS: Lessons for Managers and Clinicians from a Synthesis of the Theoretical and Empirical Literature. Southampton (UK): NIHR Journals Library; 2015.

44.

Koopmanschap

Stolk

Koolman

Dear policy maker: have you made up your mind? A discrete choice experiment among policy makers and other health professionals. Int J Technol Assess Health Care. 2010;26:198–204. DOI: 10.1017/S0266462310000048

45.

ECRI Institute. Wasting millions by making purchases based solely on physician preference? Not in my hospital!2009.

46.

Shbool

. Essays in physicians preference items and inventory management within the healthcare supply chain. 2016. https://scholarworks.uark.edu/etd/1566/

47.

Siddel

Hospitals losing on physician preference items. OR Manager. 2012;28(1):20.

48.

Lancsar

Louviere

Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics. 2008;26:661–77. DOI: 10.2165/00019053-200826080-00004

49.

Hole

. DCREATE: stata module to create efficient designs for discrete choice experiments. 2017. https://ideas.repec.org/c/boc/bocode/s458059.html

50.

Carlsson

Martinsson

Design techniques for stated preference methods in health economics. Health Econ. 2003;12:281–94. DOI: 10.1002/hec.729

51.

Marti

A best-worst scaling survey of adolescents’ level of concern for health and non-health consequences of smoking. Soc Sci Med. 2012;75:87–97. DOI: 10.1016/j.socscimed.2012.02.024

52.

Meregaglia

Cairns

Alfieri

, et al. Eliciting preferences for clinical follow-up in patients with head and neck cancer using best-worst scaling. Value Health. 2017;20:799–808. DOI: 10.1016/j.jval.2017.01.012

53.

Lancsar

Fiebig

Hole

AR.

Discrete choice experiments: a guide to model specification, estimation and software. Pharmacoeconomics. 2017;35:697–716. DOI: 10.1007/s40273-017-0506-4

54.

Johnson

Yang

J-C

Reed

SD.

The internal validity of discrete choice experiment data: a testing tool for quantitative assessments. Value Health. 2019;22:157–60. DOI: 10.1016/j.jval.2018.07.876

55.

European Parliament and Council of the European Union. Regulation (EU) 2021/2282 of the European Parliament and of the Council of 15 December 2021 on health technology assessment and amending Directive 2011/24/EU. 2021. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32021R2282

56.

European Parliament and Council of the European Union. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC. 2017. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32017R0745

57.

de Bekker-Grob

Donkers

Jonker

Stolk

EA.

Sample size requirements for discrete-Choice experiments in healthcare: a practical guide. Patient. 2015;8:373–84. DOI: 10.1007/s40271-015-0118-z

58.

Feig

Cheung

Hiligsmann

Evers

SMAA

Simon

Mayer

Best-worst scaling to assess the most important barriers and facilitators for the use of health technology assessment in Austria. Expert Rev Pharmacoecon Outcomes Res. 2018;18:223–32. DOI: 10.1080/14737167.2017.1375407

59.

EUnetHTA. HTA core model. Available from: https://www.eunethta.eu/hta-core-model/

60.

National Institute for Health and Care Excellence (NICE). Adalimumab, etanercept, infliximab, certolizumab pegol, golimumab, tocilizumab and abatacept for rheumatoid arthritis not previously treated with DMARDs or after conventional DMARDs only have failed [Technology appraisal guidance]. 2016. Available from: https://www.nice.org.uk/guidance/ta375/resources/adalimumab-etanercept-infliximab-certolizumab-pegol-golimumab-tocilizumab-and-abatacept-for-rheumatoid-arthritis-not-previously-treated-with-dmards-or-after-conventional-dmards-only-have-failed-pdf-82602790920133

61.

Whitty

Oliveira Goncalves

AS.

A systematic review comparing the acceptability, validity and concordance of discrete choice experiments and best-worst scaling for eliciting preferences in healthcare. Patient. 2018;11:301–17. DOI: 10.1007/s40271-017-0288-y

62.

Bustinza

Bigdeli

Baines

Elliot

Servitization and competitive advantage: the importance of organizational structure and value chain position. Res Technol Manag. 2015;58:53–60. DOI: 10.5437/08956308X5805354

63.

Schröter

Lay

Manufacturers of medical technology: servitization in regulated markets. In: Lay

, ed. Servitization in Industry. Cham (UK): Springer; 2014. p 165–76.

64.

Ministero della Salute. DECRETO 10 agosto 2018 - Documento d’indirizzo per la stesura di capitolati di gara per l’acquisizione di dispositivi medici. GU Serie Generale. 2018. https://www.gazzettaufficiale.it/eli/id/2018/10/30/18A06933/sg

65.

Tarricone

Robinson

Harmonization of health technology assessment across the European Union: lessons for the United States. Health Aff Blog. 2021. DOI: 10.1377/hblog20211130.24462

66.

Tarricone

Amatucci

Armeni

, et al. Establishing a national HTA program for medical devices in Italy: overhauling a fragmented system to ensure value and equal access to new medical technologies. Health Policy. 2021;125(5):602–8. DOI: 10.1016/j.healthpol.2021.03.003

67.

Torbica

De Allegri

Belemsaga

Medina-Lara

Ridde

What criteria guide national entrepreneurs’ policy decisions on user fee removal for maternal health care services? Use of a best-worst scaling choice experiment in West Africa. J Health Serv Res Policy. 2014;19:208–15. DOI: 10.1177/1355819614533519

68.

Tarricone

Ciani

D’Acunto

Scalzo

The rise of rules: will the new EU regulation of medical devices make us safer?

Eur J Intern Med. 2020;80:117–20. DOI: 10.1016/j.ejim.2020.07.012

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.71 MB

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

Abstract

Objectives

Methods

Results

Conclusions

Highlights

Keywords

Introduction

Methods

Case Study

Selection of Attributes/Levels

Study Design: BWS

Study Design: DCE

Data Collection

Data Analysis: BWS

Data Analysis: DCE

Results

Sample Description

BWS Results

Discussion

Synthesis of Results

Policy and Research Implications

Study Limitations

Conclusions

Supplemental Material

sj-docx-1-mdm-10.1177_0272989X231201805 – Supplemental material for Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopaedists Using 2 Different Stated Preference Methods

Footnotes

ORCID iDs

References

Supplementary Material