Potential users’ preferences towards cardiac telemedicine: A discrete choice experiment investigation in Sardinia

Stakeholders' preferences for the design and delivery of virtual care services: A systematic review of discrete choice experiments

This systematic review aimed to synthesise evidence from discrete choice experiments (DCEs) eliciting preferences for virtual models of care, as well as to assess the quality of those DCEs and compare the relative preferences for different stakeholder groups. Articles were included if published between January 2010 and December 2022. Data were synthesised narratively, and attributes were assessed for frequency, significance, and relative importance using a semi-quantitative approach. Overall, 21 studies were included encompassing a wide range of virtual care modalities, with the most common setting being virtual consultations for outpatient management of chronic conditions. A total of 135 attributes were identified and thematically classified into six categories: service delivery, service quality, technical aspects, monetary aspects, health provider characteristics and health consumer characteristics. Attributes related to service delivery were most frequently reported but less highly ranked. Service costs were consistently significant across all studies where they appeared, indicating their importance to the respondents. All studies examining health providers' preferences reported either system performance or professional endorsement attributes to be the most important. Substantial heterogeneity in attribute selection and preference outcomes were observed across studies reporting on health consumers' preferences, suggesting that the consideration of local context is important in the design and delivery of person-centred virtual care services. In general, the experimental design and analysis methods of included studies were clearly reported and justified. An improvement was observed in the quality of DCE design and analysis in recent years, particularly in the attribute development process. Given the continued growth in the use of DCEs within healthcare settings, further research is needed to develop a standardised approach for quantitatively synthesising DCE findings. There is also a need for further research on preferences for virtual care in post-pandemic contexts, where emerging evidence suggests that preferences may differ to those observed in pre-pandemic times.

Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review

BACKGROUND

Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices.

OBJECTIVES

Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare.

METHODS

This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both.

RESULTS

Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity.

CONCLUSIONS

Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.

Would you zoom with your doctor? A discrete choice experiment to identify patient preferences for video and in-clinic consultations in German primary care

INTRODUCTION

The popularity of video consultations in healthcare has accelerated during the COVID-19 pandemic. Despite increased availability and obvious benefits, many patients remain hesitant to use video consultations. This study investigates the relative importance of the consultation mode compared to other attributes in patients' appointment choices in Germany.

METHODS

A discrete choice experiment was conducted to examine the influence of appointment attributes on preferences for video over in-clinic consultations. A total of 350 participants were included in the analysis.

RESULTS

The level of continuity of care (46%) and the waiting time until the next available appointment (22%) were shown to have higher relative importance than consultation mode (18%) and other attributes. Participants with fewer data privacy concerns, higher technology proficiency, and more fear of COVID-19 tended to prefer video over in-clinic consultations. The predicted choice probability of a video over a typical in-clinic consultation and opting out increased from <1% to 40% when the video consultation was improved from the worst-case to the best-case scenario.

CONCLUSION

This study provides insight into the effect of the consultation mode on appointment choice at a time when telemedicine gains momentum. The results suggest that participants preferred in-clinic over video consultations. Policymakers and service providers should focus on increasing the level of continuity of care and decreasing the time until the next available appointment to prompt the adoption of video consultations. Although participants preferred to talk to their physician in person over consulting via video per se, the demand for video consultations can be increased significantly by improving the other appointment attributes of video consultations such as the level of continuity of care.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.

METHODS

An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.

RESULTS

Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.

CONCLUSIONS

Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.