Patients' choice and preference for common disease diagnosis and diabetes care: A discrete choice experiment

2019 ◽  
Vol 34 (4) ◽  
Author(s):  
Jingrong Zhu ◽  
Jinlin Li ◽  
Zengbo Zhang ◽  
Hao Li
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Diabetes Care ◽  
Choice Experiment ◽  
Disease Diagnosis ◽  
Common Disease

scholarly journals Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint)

2020 ◽  
Author(s):  
Taoran Liu ◽  
Winghei Tsang ◽  
Fengqiu Huang ◽  
Oi Ying Lau ◽  
Yanhui Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Disease Diagnosis ◽  
Class Model ◽  
Class 1 ◽  
Time Accuracy

BACKGROUND Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. OBJECTIVE This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. METHODS A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, <i>P</i> value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. RESULTS A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, <i>P</i>&lt;.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, <i>P</i>&lt;.001; class 3: OR 1.958, 95% CI 1.769-2.167, <i>P</i>&lt;.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, <i>P</i>=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. CONCLUSIONS Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

scholarly journals A discrete choice experiment investigating preferences for funding drugs used to treat orphan diseases: an exploratory study

2010 ◽  
Vol 6 (3) ◽  
pp. 405-433 ◽  
Author(s):  
Emmanouil Mentzakis ◽  
Patricia Stefanowska ◽  
Jeremiah Hurley
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Rare Diseases ◽  
Choice Experiment ◽  
Treatment Effectiveness ◽  
Public Funding ◽  
Common Disease ◽  
Orphan Diseases ◽  
Convenience Sample ◽  
Common Diseases

AbstractPolicy debate about funding criteria for drugs used to treat rare, orphan diseases is gaining prominence. This study presents evidence from a discrete choice experiment using a convenience sample of university students to investigate individual preferences regarding public funding for drugs used to treat rare diseases and common diseases. This pilot study finds that: other things equal, the respondents do not prefer to have the government spend more for drugs used to treat rare diseases; that respondents are not willing to pay more per life year gained for a rare disease than a common disease; and that respondents weigh relevant attributes of the coverage decisions (e.g. costs, disease severity and treatment effectiveness) similarly for both rare and common diseases. The results confirm the importance of severity and treatment effectiveness in preferences for public funding. Although this is the first study of its kind, the results send a cautionary message regarding the special treatment of orphan drugs in coverage decision-making.

scholarly journals Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment

10.2196/22841 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e22841
Author(s):  
Taoran Liu ◽  
Winghei Tsang ◽  
Fengqiu Huang ◽  
Oi Ying Lau ◽  
Yanhui Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Disease Diagnosis ◽  
Class Model ◽  
Class 1 ◽  
Time Accuracy

Background Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. Objective This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. Methods A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. Results A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. Conclusions Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

scholarly journals Preferences of people with Type 2 diabetes for diabetes care: a discrete choice experiment

2019 ◽  
Vol 37 (11) ◽  
pp. 1807-1815 ◽  
Author(s):  
D. F. L. Hertroijs ◽  
A. M. J. Elissen ◽  
M. C. G. J. Brouwers ◽  
M. Hiligsmann ◽  
N. C. Schaper ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Diabetes Care ◽  
Choice Experiment

scholarly journals Patients’ preferences for diabetes care: a discrete choice experiment

2018 ◽  
Vol 18 (s2) ◽  
pp. 283 ◽  
Author(s):  
D. F.L. Hertroijs ◽  
M. C.G.J. Brouwers ◽  
A. M.J. Elissen ◽  
M. Hiligsmann ◽  
N. C. Schaper ◽  
...  
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Diabetes Care ◽  
Choice Experiment

The essential ingredients of research supervision: A discrete-choice experiment.

2019 ◽  
Vol 111 (7) ◽  
pp. 1243-1260 ◽  
Author(s):  
Alex Roach ◽  
Bruce K. Christensen ◽  
Elizabeth Rieger
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Research Supervision

INDIVIDUALS' PREFERENCES FOR ESOPHAGEAL CANCER SCREENING STRATEGIES: A DISCRETE CHOICE EXPERIMENT

2019 ◽  
Author(s):  
Y Peters ◽  
E van Grinsven ◽  
M van de Haterd ◽  
D van Lankveld ◽  
J Verbakel ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Cancer Screening ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Screening Strategies

Preferences for antiviral therapy of chronic hepatitis C: a discrete choice experiment

2016 ◽  
Vol 18 (2) ◽  
pp. 155-165 ◽  
Author(s):  
Axel C. Mühlbacher ◽  
John F. P. Bridges ◽  
Susanne Bethge ◽  
Ch.-Markos Dintsios ◽  
Anja Schwalm ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Antiviral Therapy ◽  
Chronic Hepatitis C ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment
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