Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-22211| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Portfolio frontier analysis : applying mean-variance analysis to health technology assessment for health systems under pressure |
| Autor/-in: | Baines, Darrin Disegna, Marta Hartwell, Christopher |
| et. al: | No |
| DOI: | 10.1016/j.socscimed.2021.113830 10.21256/zhaw-22211 |
| Erschienen in: | Social Science & Medicine |
| Band(Heft): | 276 |
| Heft: | 113830 |
| Erscheinungsdatum: | Mai-2021 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 1873-5347 0277-9536 |
| Sprache: | Englisch |
| Schlagwörter: | Decision-making; HTA; Health economics; Mean variance; Portfolio |
| Fachgebiet (DDC): | 362: Gesundheits- und Sozialdienste |
| Zusammenfassung: | The COVID-19 pandemic is challenging how healthcare technologies are evaluated, as new, more dynamic methods are required to test the cost effectiveness of alternative interventions during use rather than before initial adoption. Currently, health technology assessment (HTA) tends to be static and a priori: alternatives are compared before launch, and little evaluation occurs after implementation. We suggest a method that builds upon the current pre-launch HTA procedures by conceptualizing a mean-variance approach to the continuous evaluation of attainable portfolios of interventions in health systems. Our framework uses frontier analysis to identify the desirability of available health interventions so decision makers can choose diverse portfolios based upon information about expected returns and risks. This approach facilitates the extension of existing methods and assessments beyond the traditional concern with pre-adoption data, a much-needed innovation given the challenges posed by COVID-19. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22211 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Departement: | School of Management and Law |
| Organisationseinheit: | International Management Institute (IMI) |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Baines-etal_Portfolio-frontier-analysis.pdf | 727.99 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Baines, D., Disegna, M., & Hartwell, C. (2021). Portfolio frontier analysis : applying mean-variance analysis to health technology assessment for health systems under pressure. Social Science & Medicine, 276(113830). https://doi.org/10.1016/j.socscimed.2021.113830
Baines, D., Disegna, M. and Hartwell, C. (2021) ‘Portfolio frontier analysis : applying mean-variance analysis to health technology assessment for health systems under pressure’, Social Science & Medicine, 276(113830). Available at: https://doi.org/10.1016/j.socscimed.2021.113830.
D. Baines, M. Disegna, and C. Hartwell, “Portfolio frontier analysis : applying mean-variance analysis to health technology assessment for health systems under pressure,” Social Science & Medicine, vol. 276, no. 113830, May 2021, doi: 10.1016/j.socscimed.2021.113830.
BAINES, Darrin, Marta DISEGNA und Christopher HARTWELL, 2021. Portfolio frontier analysis : applying mean-variance analysis to health technology assessment for health systems under pressure. Social Science & Medicine. Mai 2021. Bd. 276, Nr. 113830. DOI 10.1016/j.socscimed.2021.113830
Baines, Darrin, Marta Disegna, and Christopher Hartwell. 2021. “Portfolio Frontier Analysis : Applying Mean-Variance Analysis to Health Technology Assessment for Health Systems under Pressure.” Social Science & Medicine 276 (113830). https://doi.org/10.1016/j.socscimed.2021.113830.
Baines, Darrin, et al. “Portfolio Frontier Analysis : Applying Mean-Variance Analysis to Health Technology Assessment for Health Systems under Pressure.” Social Science & Medicine, vol. 276, no. 113830, May 2021, https://doi.org/10.1016/j.socscimed.2021.113830.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.