Using a Discrete-Choice Experiment to Estimate the Preferences of Clinical Practitioners for a Novel Non-invasive Device for Diagnosis of Peripheral Arterial Disease in Primary Care

Systematic review and validity assessment of methods used in discrete choice experiments of primary healthcare professionals

INTRODUCTION

Discrete choice experiments (DCEs) have been used to measure patient and healthcare professionals preferences in a range of settings internationally. Using DCEs in primary care is valuable for determining how to improve rational shared decision making. The purpose of this systematic review is to assess the validity of the methods used for DCEs assessing the decision making of healthcare professionals in primary care.

MAIN BODY

A systematic search was conducted to identify articles with original data from a discrete choice experiment where the population was primary healthcare professionals. All publication dates from database inception to 29th February 2020 were included. A data extraction and validity assessment template based on guidelines was used. After screening, 34 studies met the eligibility criteria and were included in the systematic review. The sample sizes of the DCEs ranged from 10 to 3727. The published DCEs often provided insufficient detail about the process of determining the attributes and levels. The majority of the studies did not involve primary care healthcare professionals outside of the research team in attribute identification and selection. Less than 80% of the DCEs were piloted and few papers investigated internal or external validity.

CONCLUSIONS

For findings to translate into improvements in rational shared decision making in primary care DCEs need to be internally and externally valid and the findings need to be able to be communicated to stakeholders in a way that is understandable and relevant.

Patients' preferences for health insurance coverage of new technologies for treating chronic diseases in China: a discrete choice experiment

OBJECTIVES

Our study aimed to inform insurance decision-making in China by investigating patients' preferences for insurance coverage of new technologies for treating chronic diseases.

DESIGN

We identified six attributes of new medical technologies for treating chronic diseases and used Bayesian-efficient design to generate choice sets for a discrete choice experiment (DCE). After conducting the DCE, we analysed the data by mixed logit regression to examine patient-reported preferences for each attribute.

SETTING

The DCE was conducted with patients in six tertiary hospitals from four cities in Jiangsu province.

PARTICIPANTS

Patients aged 18 years or older with a history of diabetes or hypertension and taking medications regularly for more than 1 year were recruited (n=408).

RESULTS

The technology attributes regarding expected gains in health outcomes from the treatment, high likelihood of effective treatment and low incidence of serious adverse events were significant, positive predictors of choice by the study patients (p<0.01). The out-of-pocket cost was a significant, negative attribute for the entire study sample (β = -0.258, p<0.01) and for the patients with Urban-Rural Residents Basic Medical Insurance (URRBMI) (β = -0.511, p<0.01), but not for all the patients with Urban Employees Basic Medical Insurance (UEBMI) (β = -0.071, p>0.05). The severity of target disease was valued by patients with lower EQ-5D-5L index value as well as URRBMI enrollees.

CONCLUSIONS

Patients highly valued the health benefits and risks of new technologies, which were closely linked to their feelings of disease and perceptions of health-related quality of life. However, there existed heterogeneity in preferences between URRBMI and UEBMI patients. Further efforts should be made to reduce the gap between insurance schemes and make safe and cost-effective new technologies as a priority for health insurance reimbursement.