Title |
Implementing personalized medicine with asymmetric information on prevalence rates
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Published in |
Health Economics Review, August 2016
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DOI | 10.1186/s13561-016-0113-7 |
Pubmed ID | |
Authors |
Fernando Antoñanzas, Carmelo A. Juárez-Castelló, Roberto Rodríguez-Ibeas |
Abstract |
Although personalized medicine is becoming the new paradigm to manage some diseases, the economics of personalized medicine have only focused on assessing the efficiency of specific treatments, lacking a theoretical framework analyzing the interactions between pharmaceutical firms and healthcare systems leading to the implementation of personalized treatments. We model the interaction between the hospitals and the manufacturer of a new treatment as an adverse selection problem where the firm does not have perfect information on the prevalence across hospitals of the genetic characteristics of the patients making them eligible to receive a new treatment. As a result of the model, hospitals with high prevalence rates benefit from the information asymmetry only when the standard treatment is inefficient when applied to the patients eligible to receive the new treatment. Otherwise, information asymmetry has no value. Personalized medicine may be fully or partially implemented depending on the proportion of high prevalence hospitals. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 3 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 26 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 23% |
Student > Bachelor | 4 | 15% |
Researcher | 4 | 15% |
Student > Doctoral Student | 2 | 8% |
Student > Postgraduate | 2 | 8% |
Other | 4 | 15% |
Unknown | 4 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Economics, Econometrics and Finance | 6 | 23% |
Business, Management and Accounting | 3 | 12% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 8% |
Biochemistry, Genetics and Molecular Biology | 2 | 8% |
Agricultural and Biological Sciences | 2 | 8% |
Other | 6 | 23% |
Unknown | 5 | 19% |