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International Journal of Women's Health logoLink to International Journal of Women's Health
. 2026 Oct 1;18:637727. doi: 10.2147/IJWH.S637727

Preferences for Prenatal Care Among Pregnant Women: A Systematic Review of Discrete Choice Experiment Studies

Hui Xie 1,2,3, Jingbo Qiu 1,2,3, Ruo Jiang 4, Pengjie Zhang 1,2,3, Yugang Li 5, Xinhua Ji 1,2,3,✉, Wei Zhu 1,2,3,✉
PMCID: PMC13637504  PMID: 42836087

Abstract

Background

Prenatal care and its utilization can reduce the risks of adverse outcomes associated with pregnancy. Incorporating pregnant women’s preferences into care delivery can better meet their needs and improve outcomes. Our study aimed to systematically review evidence regarding preferences for prenatal care among pregnant women with a focus on how the preferences were elicited through a discrete choice experiment (DCE).

Methods

We searched DCE studies published up to May 2025 in PubMed, Web of Science, and Scopus. Two collaborators independently screened titles and abstracts, and then performed full-text reviews. Eligible studies were cross-sectional DCEs that elicited pregnant women’s preferences for prenatal care. One collaborator extracted data, and another verified the data extraction. We used the PREFS checklist for quality assessment. We summarized the findings using a narrative synthesis. We grouped attributes into categories and described their range-based relative importance (RI).

Results

Of 748 studies identified, 19 were finally included. Among these, 17 focused on preferences for specific prenatal testing and 2 on general prenatal care delivery. From the 17 DCEs on prenatal testing, 75 attributes were identified and grouped into eight categories: (1) information; (2) waiting time for test results; (3) cost; (4) test performance; (5) safety; (6) contents and procedures; (7) timing of test; and (8) staff. At the attribute level, the minimum RI (1.29%) was reported for “time to wait for results”; the maximum was 74.17% for “likelihood of getting a result”. At the category level, “safety” had the highest median RI (30.76%), followed by “staff” (27.08%) and “test performance” (23.72%).

Conclusion

Current DCE-based preference studies concentrate on specific prenatal testing, leaving preferences for non-medical aspects of prenatal care delivery underexplored. Pregnant women seemed to value care safety, care provider types and care efficacy more highly, and these elements may be prioritized in future DCEs.

Keywords: prenatal care, pregnancy, preference, review

Introduction

Although maternal mortality has declined recently, significant numbers of maternal and neonatal deaths persist globally.1–5 As a key measure to address this challenge and advance Sustainable Development Goal for 2030 on maternal and reproductive health, ensuring access to timely and quality health care for pregnant women has been prioritized by key stakeholders.6–8 WHO defines quality care as services that are safe, effective, timely, efficient, equitable, and people-centered.8 For healthcare to be truly universal, care coordination should be patient-centered, considering patients’ needs, respecting their preferences, and allowing their participation, alongside equity and quality assurance.9 This conceptualization is also addressed by various studies.10,11

Regarding patient-centered care and maternal care, a previous study highlights shared elements between these two concepts, including respect and dignity, effective communication, and social relationships.12 Moreover, high-quality maternal care has its own attributes, such as continuity of care, physical environment, and providers’ educational levels.12 Prenatal care, as an overarching pillar of maternal care, serves as the most essential preventive service to ensure the physical and mental health of pregnant women and their babies.13,14 By receiving such preventive services, pregnant women can prevent complications and gain awareness of their current conditions, ultimately improving both maternal and neonatal outcomes.15,16

Although prenatal care utilization has increased, its delivery continues to face significant challenges that pose threats to care quality. These challenges include inadequate prenatal care infrastructure, substandard quality of care, long waiting times, inconvenient waiting facilities, and long travel distances to healthcare centers.17 Additionally, structured models of prenatal care delivery remain largely unchanged since their initial formalization in the early 20th century, and the American College of Obstetricians and Gynecologists has developed a clinical consensus guiding tailored prenatal care delivery for pregnant women.14 Hence, pregnant women’s preferences regarding prenatal care are essential for enhancing patient-centeredness in prenatal care services.

The discrete choice experiment (DCE) is a well-established method to elicit preferences for health services and has a long history.18 By presenting at least two alternatives to participants and instructing them to choose one, preferences for and trade-offs among attributes associated with the alternatives can be measured.19 Although there are systematic reviews of preferences for prenatal care, they have merely focused on narrow aspects of prenatal care, such as clinical features of prenatal testing for specific diseases,20 non-DCE-elicited preferences,21 or preferences for postpartum care.22 Furthermore, these previous studies rarely explored preferences for various components of prenatal care, such as nutritional counseling and lifestyle guidance, and experiences of prenatal care utilization, which have been demonstrated to be effective in reducing the risks of adverse pregnancy outcomes.15,16 Thus, to the best of our knowledge, there are no systematic evidence summaries on preferences for prenatal care delivery—particularly for non-medical features of prenatal care provision and utilization—as elicited using DCEs among pregnant women. Of particular note, prenatal care and prenatal testing are two different concepts. General prenatal care is a comprehensive system covering multiple dimensions, such as routine screening, diagnostic testing, nutritional counseling, lifestyle guidance, and health education. Although prenatal testing plays a vital role in clinical decisions and is of high technical value, it serves only as one component of prenatal care. Hence, understanding pregnant women’s preferences should not be limited only to technical attributes, but should be extended to multiple dimensions or components of prenatal care. However, how current DCE evidence captures this diversity remains unclear. We conducted this systematic review to address this gap by examining: (1) how DCEs were conducted in this field; (2) what medical and non-medical attributes/levels pregnant women chose and valued most; and (3) how patient preferences were expressed.

Methods

We prospectively registered our systematic review protocol on PROSPERO (ID: CRD420251037096). Discrepancies between the protocol and our conduct of the review are documented in Appendix 1. We reported this systematic review following PRISMA guidelines. As our study is a review of secondary data, informed consent and ethical review are deemed exempt.

Searches

We systematically searched for studies in PubMed, Web of Science, and Scopus. We consulted previous studies that systematically reviewed DCEs23,24 to inform our search strategies. We finalized our search strategies after group discussions. Our search terms included prenatal care, DCEs, and related terms. We tailored search formulas to each database. The search date range was from database inception to May 2025. We only considered studies published in English. No other filters were applied. We also checked the reference list of the included studies for more eligible studies. Full search strategies are provided in Appendix 1.

Eligibility Criteria

We included primary studies if they: (1) recruited pregnant women or used proxy responses from caregivers; (2) were cross-sectional DCEs; (3) provided valid preference data; and (4) were related to prenatal care. We excluded studies if they: (1) were non-empirical (eg, reviews, perspectives, correspondence); (2) were duplicates; (3) used the same study population; (4) reported no preference data; and (5) were unrelated to prenatal care or patient preferences. We imposed no restrictions on study settings. We considered healthcare facilities, including hospitals, primary care clinics, and community-based settings. We also considered online panels managed by commercial agencies.

Selection of Study

We exported identified records to Zotero (version 7.0.15) for literature management. Two collaborators independently screened study titles and abstracts against eligibility criteria to identify potentially eligible studies for full-text review. We then retrieved the full texts of these studies, and two collaborators independently performed full-text reviews. Any disagreement regarding study selection was resolved through group discussions.

Data Extraction

We developed a structured electronic form to extract data using Excel 2016. We extracted data from the first three included studies to further refine data items initially defined in our protocol. One collaborator extracted data. A second collaborator verified all the data extracted by the first collaborator. The extracted data generally included publication-related characteristics, characteristics of study conduct and design, participants’ characteristics, attribute and level developments, attributes and levels, choice sets and analyses, and preference outcomes (eg, model coefficients, relative importance [RI]). Full extracted data can be found in OSF (which will be made publicly available upon final acceptance).

Quality Appraisal

We used the PREFS checklist to rate the quality of the included studies with reference to previous DCE reviews.25,26 The PREFS checklist assesses a DCE in five dimensions: (1) purpose (Is the purpose of the study in relation to preferences clearly stated?); (2) respondents (Are the responders similar to the non-responders?); (3) explanation (Are methods of assessing preferences clearly explained?); (4) findings (Were all respondents included in the reported findings and analysis of preference results?); and (5) significance (Were significance tests used to assess the preference results?).25 The PREFS checklist also specifies how to determine whether each dimension meets the criterion. Finally, each dimension is rated on a binary scale (presence, assigned 1 point; absence, assigned 0 points). A score ranging from 0 to 5 points is derived for the DCE quality outcome. Quality assessment was performed by one collaborator, with a second collaborator independently verifying a randomly selected subset of five studies.

Data Analysis

We summarized the findings of the included studies using a narrative synthesis, and presented study characteristics via summary statistics. We did not perform a meta-analysis due to expected heterogeneity across studies in designs, population characteristics, development methods of attributes/levels, and cultural contexts. To answer the review questions, we focused on summarizing study design characteristics, attributes/level selection, content and RI of attributes, and willingness to pay and other types of marginal rate of substitution (MRS).

Following prior DCE systematic reviews,23,24 we categorized attributes using an inductive approach with open coding. We visualized distributions of the categories across studies, and the connections between identified categories and attributes. We extracted the range-based RI of each attribute from the included studies. We calculated RIs for the studies that did not report these statistics. We summarized RIs for each identified attribute category.

We converted all reported monetary values into 2024 US dollars (USD) using the American annual Consumer Price Index and annual average exchange rates between USD and other currencies. We visualized distributions of cost levels across studies. We used Excel 2016 for data management, and R (4.0) for data analyses and visualizations.

Results

The study selection process can be found in Figure 1. Of 748 studies identified, 19 were finally included in this systematic review. The quality assessment results can be found in Figure S1 in Appendix 1. In summary, the mean score of the quality assessment was 3.53 out of 5, with a range of 3 to 5.

Figure 1.

A flowchart of the study selection process, detailing identification, screening and inclusion steps. The flowchart outlines the study selection process in three main stages: identification, screening and inclusion. In the identification stage, records were identified from PubMed (117), Web of Science Core Collection (325) and Scopus (306). Duplicate records removed before screening totaled 340. During screening, 408 records were screened, leading to 68 reports sought for retrieval. Of these, 340 reports were not retrieved. Reports assessed for eligibility numbered 68, with exclusions including non-DCE or DCE-like studies (7), non-prenatal care (23) and other specific categories such as prenatal physical health conditions (1), labor-related care (8), miscarriage or abortion care (4), HIV-related care (3), fertility care (3), emergency obstetric care (1), determinants of maternal health promotion (1), ward care after delivery (2), non-pregnant women (16) and only abstract (3). Finally, 19 studies were included in the review.

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Study selection process.

Participant-Level Characteristics

A total of 8235 participants were investigated across the included studies, with minimum, median and maximum sample sizes of 43,27 27228 and 266629 respectively. Two (10.53%) studies did not report participants’ age data;27,30 three (15.79%) reported median age (all 31 years);28,31,32 and 14 (73.68%) reported mean age.29,33–45 Among those reporting mean age, the range was between 27.1039 and 32.8033 years. Five (26.32%) studies did not report participants’ gestational age data;27,28,30,32,35 one (5.26%) reported a median value;39 two (10.53%) reported ranges;31,37 and 11 (57.89%) reported mean values.29,33,34,36,38,40–45 Among those reporting mean gestational age, the range was between 10.4038 and 29.20.42

Characteristics of Study Conduct and Design

Details can be found in Table 1. In brief, the earliest year when any studies were conducted was 2001,40 and the latest year was 2023.5,46 The most reported method of determining samples was convenience sampling (n = 16, 84.21%).27–30,32,33,35–44 The most reported study setting was hospitals (n = 13, 68.42%).27–29,31,34–38,40–43 The most reported administration mode was self-administration (n = 13, 68.42%).27–33,37,40,42–45 The most reported experimental design was a D-efficient design (n = 7, 36.84%).28,30,33,38,39,41,45

Table 1.

Characteristics of the Conduct and Designs of Included Studies (n = 19)

Variables No. (%) or Mean (SD)
Conduct year
Min~max range 2001~2023
Study locations
UK 5 (26.32%)
Singapore 2 (10.53%)
Netherlands 2 (10.53%)
Mainland China 2 (10.53%)
Canada 2 (10.53%)
US 1 (5.26%)
Tanzania 1 (5.26%)
Mixed 1 (5.26%)
Hong Kong China 1 (5.26%)
Denmark 1 (5.26%)
Australia 1 (5.26%)
Sampling methods
Convenience sampling 16 (84.21%)
Not reported 3 (15.79%)
Study settings
Hospitals 13 (68.42%)
Mixed 2 (10.53%)
Online recruitment 2 (10.53%)
Health facilities 1 (5.26%)
Maternity centers 1 (5.26%)
Determination of sample sizes
Johnson and Orme’s formula 5 (26.32%)
Reference to previous research 4 (21.05%)
Not reported 10 (52.63%)
Administration
Face-to-face interview 3 (15.79%)
Self-administration 13 (68.42%)
Not reported 3 (15.79%)
Experiment designs
D-efficient design 7 (36.84%)
Main effects OFFD 5 (26.32%)
OFFD 5 (26.32%)
Not reported 2 (10.53%)
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Abbreviation: OFFD, orthogonal fractional factorial design.

Three studies (15.79%) did not report any methods of developing attributes;31,32,36 five studies each used one method;27,34,40,42,43 two studies each used two methods;29,38 nine studies each used three methods.28,30,33,35,37,39,41,44,45 The most mentioned method was a literature review (n = 11, 30.56%), followed by expert consultations (n = 5, 13.89%), a reference to previous research (n = 4, 11.11%) and semi-structured interviews (n = 4, 11.11%, Figure 2a and b).

Figure 2.

Composite image of attribute methods, study categories and attribute mapping. The image consists of four parts: A bar chart labeled ′a′ shows frequency of attribute selection methods, with literature review at 11, expert consultations at 5 and others ranging from 4 to 1. A bar chart labeled ′b′ shows frequency of attribute level selection methods, with literature review at 6, expert consultations at 5 and others ranging from 3 to 1. A chart labeled ′c′ maps studies to attribute categories, including Information, Waiting time for test results, Cost, Test performance, Timing of test, Safety, Staff, Contents and procedures and Not reported. A network diagram labeled ′d′ maps connections between identified attribute categories and primary attributes, with node types including Attributes and Categories. Attributes such as Timing of test, Cost, Safety, Test performance and Information are connected to various subpoints like time of test, cost of test, risk of miscarriage, accuracy and information gained from test results.

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Characteristics of development methods and contents of attributes. (a) distributions of attribute selection methods; (b) distributions of attribute level selection methods; (c) distributions of identified attribute categories across studies; (d) mapping connections between identified attribute categories and primary attributes.

Attributes, Attribute Levels and Choice Sets

All studies had at least two attributes in their DCEs. The median number of attributes was 4, ranging from 327,31,36,40,45 to 7.28,44 The minimum number of attribute levels was 2;27–29,31–35,37–39,41–43 the maximum was 6.28 Of the 19 included studies, 17 (89.47%) elicited preferences for prenatal testing of specific diseases,27–34,36–38,40–45 and two focused on general prenatal care services.35,39 Among the 17 studies, a total of 75 attributes were reported. Eight categories were identified from the 75 attributes: (1) information: the comprehensiveness of information provided by the test, eg, comprehensive vs simple information, or whether non-medical information is included in the test report; (2) waiting time for test results: the time needed to get results after the test is completed, eg, one week vs two weeks; (3) cost: the cost of the test that needs to be paid out of pocket or by the patient’s insurance, eg, £10; (4) test performance: the ability of the test to detect or diagnose a specific disease, eg, Down Syndrome; (5) safety: the risk of adverse outcomes that a test carries for pregnant women, eg, miscarriage; (6) contents and procedures: what conditions the test is designed to detect and how the test is performed, eg, invasive vs non-invasive, the number of blood draws; (7) timing of test: when the test is conducted during pregnancy, eg, at the 7th vs 10th week of gestation; (8) staff: who conducts and explains the test, eg, genetics specialist vs general obstetrician. The category “test performance” consisted of 23 attributes, followed by “information” (11 attributes), “waiting time for test results” (11 attributes) and “safety” (10 attributes, Figure 2c and d).

The two studies eliciting preferences for general prenatal care services examined different attribute sets; one focused on type of care provider and place, payment, number of appointments, content of care visits, telephone advice line provided, and Doppler screening provided;35 and the other examined preferences for transport, cleanliness, comfort, content, and staff attitude.39

Eighteen studies (94.74%) reported the number of choice sets per participant.27–39,41–45 Among these studies, the median number was 10.5, ranging from 741 to 18.44 After subtracting choice sets for the consistency test in 10 studies,28–30,32,33,36,38,42–44 the median was 9, ranging from 828,38 to 17.44 The median number of choice sets used for the consistency test was 1, ranging from 128,29,32,33,36,38,42–44 to 2.30

Relative Importance

Range-based RI was reported or calculated for 11 studies (57.89%).28,30,33–35,37–39,41,43,45 At the attribute level, the minimum RI of 1.29% was reported for “time to wait for results” in Wu et al’s study;34 the maximum was 74.17% for “likelihood of getting a result” in Siranosian et al’s study.33 RI estimates for each attribute category can be found in Figure 3a and Table S1 in Appendix 1. In brief, the attribute category “safety” had the highest median RI (30.76%), followed by “staff” (27.08%) and “test performance” (23.72%).

Figure 3.

Four-part infographic showing relative importance, cost levels, willingness-to-pay and reported outcomes. A) Bar chart showing relative importance (median/range, percent) of attribute categories: Safety, Staff, Test performance, Information, Contents and procedures, Cost, Timing of test, Waiting time for test results. B) Bubble chart displaying cost levels across studies, with values ranging from 0 dollars to 1514 dollars. Studies include Yu et al, Siranosian et al and others. C) Scatter plot illustrating willingness-to-pay (USD 2024) for attributes like test performance, safety and timing of test. Categories are marked with different line styles. D) Bar chart showing percentage of reported outcomes, with categories like model estimates, subgroups and latent class model. Outcomes are marked as reported or not reported.

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Characteristics of relative importance, cost attributes, and analysis outcomes. (a) median relative importance of each identified attribute category; (b) distributions of converted cost levels across studies; (c) reported willingness-to-pay of attributes; (d) distributions of reported analysis outcomes.

Cost Attributes, Willingness to Pay and Other MRS

Ten studies (52.63%) included a cost attribute in their DCEs.27,28,30,31,34,35,37,38,41,44 The median number of cost levels was 4, ranging from 330 to 6.28 Reported cost levels are presented in Table S2 in Appendix 1. Converted cost levels and their distributions across studies are shown in Figure 3b. The converted cost levels ranged from $0 to $4559.

Thirteen studies (68.42%) reported at least one MRS.27–31,34–38,40,43,44 Among these, nine (69.23%) reported WTP.27,28,30,31,34,35,37,38,44 After converting to 2024 USD, the median WTP was $619.87, ranging from $21.12 to $5771.72. Distributions of WTP values across attributes are shown in Figure 3c. For other types of MRS, Deverill et al reported the number of visits women prepared to sacrifice to get preferred service attribute levels.35 Chan et al reported that participants were willing to wait an additional 41.03 days for rapid aneuploidy and 59.75 days for karyotype.31 Other studies also reported similar willingness-to-wait or willingness-to-accept trade-offs for preferred attribute levels.27,29,36,40,43,44

Analysis and Outcomes Reported

In brief, the most employed model was a conditional logit regression model (n = 6, 31.58%); the most used coding scheme was effects coding (n = 7, 36.84%). All studies reported at least one preference outcome. The most reported outcome was model coefficients (n = 19, 100%), followed by MRS (n = 13, 68.42%) and subgroup analysis results (n = 7, 36.84%, Figure 3d).

Discussion

Our systematic review revealed that most DCEs focused on preferences for prenatal testing of specific diseases, rather than on non-medical aspects of prenatal care that are significant to pregnant women. Moreover, the most frequently studied attributes were test performance, comprehensiveness of test outcome information, and waiting time for test outcomes. However, the safety of the test, test provider types, and test performance appeared to be valued more highly based on the RI estimates.

It is unsurprising that prenatal testing constitutes the focus of most current DCE research, as prenatal testing and its utilization involve high-risk decisions and technical complexity.46 Prenatal testing has distinct technological attributes, such as test accuracy and procedure-related risks, which readily lend themselves to quantification in DCE design. Additionally, certain prenatal tests (eg, screening for fetal abnormalities) carry substantial risks and require strong reliance on patients’ informed consent.47 Using DCEs to elicit preferences for these tests can inform clinical resource planning and allocation to better meet patients’ needs.

Nevertheless, prenatal testing is merely one component of prenatal care. As recommended by WHO, prenatal care for a positive pregnancy experience should incorporate comprehensive services, such as nutritional counseling, psychological support, and system-level efforts to improve healthcare access and utilization among pregnant women.48 Current DCEs may fail to adequately elicit preferences for these key elements of prenatal care delivery or for how pregnant women access the care, and these elements have been demonstrated to be associated with utilization of and adherence to prenatal care.49,50 Crucially, the core value of prenatal care utilization lies in preventing adverse pregnancy outcomes. However, preference assessments for various preventive care services remain limited in the included DCE studies. Previous evidence shows that nutritional interventions and lifestyle guidance, which are the core elements of preventive prenatal care, can effectively reduce the risks of adverse outcomes associated with pregnancy.51–53 Nevertheless, eliciting preferences for these services represents a critical research gap. This gap is one of the most important findings of our review. Future studies should place greater focus on these non-medical dimensions of prenatal care to advance truly people-centered prenatal care delivery.

According to our quality appraisal outcomes, most included studies demonstrated weak handling of non-respondents in DCE administration and analysis. This may introduce selection bias and informative missing data, potentially distorting true preferences and compromising study validity and policy relevance. For instance, non-respondents typically have lower educational attainment and/or belong to ethnic minority groups.54,55 This population is more likely to decline participation due to digital access disparities, challenges in understanding DCE tasks, and language barriers — all while representing higher-risk pregnancies and underutilizing prenatal care.56,57 Failure to account for systematic differences between respondents and non-respondents would compromise study generalizability and conceal unmeasured preference heterogeneity.

Most of the included studies reported at least one method for attribute development; however, this proportion was lower than that in previous systematic reviews of DCEs focused on other medical conditions.23 We also found varying utilization of primary qualitative methods for the development of attributes and levels across studies, which is consistent with previous systematic reviews of DCEs.18,24 Notably, the top three attribute development methods identified in our review included two secondary data-driven approaches, namely, literature review and reference to previous research. While these approaches enable cross-cultural comparisons of DCE studies, overreliance on them risks overlooking local perspectives and reducing cultural relevance. Primary qualitative methods remain essential for developing population-specific attributes and levels.58

Our systematic review has implications for prenatal care policy and research. Current DCEs are overly focused on pregnant women’s preferences for medical testing attributes, yet women also value other non-medical prenatal care aspects that can help improve care utilization and pregnancy outcomes. Policies should call for the delivery of care that fully considers valued aspects such as healthcare facilities, distance to services, and care continuity. Future DCE studies on prenatal care should incorporate these non-medical aspects to address the gaps. Findings from our systematic review also reveal that future DCEs in this domain should consider employing qualitative methods to develop attributes and levels, which could help capture precise preferences for attributes associated with prenatal care among local pregnant women. Notably, we found that the attribute categories “safety”, “staff”, and “test performance” achieved the highest RI values. These three categories of attributes may be prioritized in future DCEs focused on prenatal care. First, pregnant women are far more sensitive to safety and the types of healthcare providers than to other attributes. Overlooking such preferences may lead to service designs that diverge from patients’ real-world needs, and may in turn reduce service uptake. Second, incorporating the attributes from these three highly valued categories into a DCE can deliver concrete and actionable targets for policymakers.

Several limitations should be acknowledged. First, as mentioned earlier, the quality appraisal results showed that most included studies inadequately addressed the handling of non-respondents, which may limit participant diversity and generalizability of our findings. Furthermore, most included studies employed a convenience sampling method and were based on hospital settings, which could further impair the generalizability of our findings. Second, while we referenced previous studies for the development of attribute categories, we may still face randomness and arbitrariness, possibly ruling out other insightful categories. Third, we only included studies that were published in English, possibly overlooking studies published in other languages that could provide unique perspectives on preferences for non-medical attributes of prenatal care. This could affect the global representativeness of our conclusions. Fourth, RI values were available from only a fraction of the included studies, and there was heterogeneity across studies in DCE designs and population characteristics. Given this, we cannot treat them as inferential conclusions, but rather as a descriptive summary. Finally, we only identified two DCEs that examined preferences for general prenatal care delivery, which could result from limited coverage of databases in our search strategy.

Conclusions

Our systematic review suggests that current preference studies using DCEs concentrate on prenatal testing for specific diseases, underexploring non-medical aspects of prenatal care delivery that matter to pregnant women. Pregnant women seemed to value care safety, care provider types, and care efficacy more highly, and these elements may be prioritized in future DCEs.

Funding Statement

This study is funded by the Scientific Research Funding Program of the China Hospital Development Institute, Shanghai Jiao Tong University (CHDI-2025-Z-40). The funder has no role in the conduct of this study.

Data Sharing Statement

All data used in this study are available from a publicly available database. No new datasets were generated during the current study.

Disclosure

Dr Hui Xie reports grants or contracts from China Hospital Development Institute, Shanghai Jiao Tong University, outside the submitted work. The author(s) report no conflicts of interest in this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data used in this study are available from a publicly available database. No new datasets were generated during the current study.

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