65569.pdf

Since 2011, an international group of health policy experts has been working on a value-framework to be used for pharmaceutical policy decisions based on multicriteria decision analysis (MCDA). This tool can be easily adapted to a local decision-making context through a facilitated workshop format....

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Γλώσσα:English
Έκδοση: InTechOpen 2021
id oapen-20.500.12657-49344
record_format dspace
spelling oapen-20.500.12657-493442021-11-23T13:49:51Z Chapter Value-Based Evaluation of Chinese Provincial Health Insurance Policy Schemes Hu, Shanlian Holtorf, Anke-Peggy Wijaya, Kalman He, Jiangjiang Brixner, Diana China, multicriteria decision analysis, MCDA, insurance policy, decision-making, stakeholder engagement, medical savings account bic Book Industry Communication::M Medicine::MB Medicine: general issues::MBN Public health & preventive medicine::MBNH Personal & public health::MBNH9 Health psychology Since 2011, an international group of health policy experts has been working on a value-framework to be used for pharmaceutical policy decisions based on multicriteria decision analysis (MCDA). This tool can be easily adapted to a local decision-making context through a facilitated workshop format. Several workshops have been conducted in emerging markets to test the acceptance and feasibility of using MCDA in local decision-making. In China, national policy goals for expanding health-care coverage pressure the provincial governments to implement more comprehensive coverage schemes. This chapter demonstrates the adaptation of a global value-framework to the local policy environment. In September 2018, nine leaders from provincial health insurance bureaus responsible for the urban employee basic medical insurance (UEBMI) participated in a 1-day workshop to build a consensus on the most important objectives for the health-care reform and to translate these into measurable criteria. The participants ranked the criteria by importance and MCDA methodology was used for weighing the importance of each criterion in the final decision. The model driving this process will be presented and discussed by comparing two policy options for health-care reform. 2021-06-02T10:12:53Z 2021-06-02T10:12:53Z 2020 chapter ONIX_20210602_10.5772/intechopen.84373_458 https://library.oapen.org/handle/20.500.12657/49344 eng application/pdf n/a 65569.pdf InTechOpen 10.5772/intechopen.84373 10.5772/intechopen.84373 09f6769d-48ed-467d-b150-4cf2680656a1 FP7-ENV-2012-two-stage 308428 open access
institution OAPEN
collection DSpace
language English
description Since 2011, an international group of health policy experts has been working on a value-framework to be used for pharmaceutical policy decisions based on multicriteria decision analysis (MCDA). This tool can be easily adapted to a local decision-making context through a facilitated workshop format. Several workshops have been conducted in emerging markets to test the acceptance and feasibility of using MCDA in local decision-making. In China, national policy goals for expanding health-care coverage pressure the provincial governments to implement more comprehensive coverage schemes. This chapter demonstrates the adaptation of a global value-framework to the local policy environment. In September 2018, nine leaders from provincial health insurance bureaus responsible for the urban employee basic medical insurance (UEBMI) participated in a 1-day workshop to build a consensus on the most important objectives for the health-care reform and to translate these into measurable criteria. The participants ranked the criteria by importance and MCDA methodology was used for weighing the importance of each criterion in the final decision. The model driving this process will be presented and discussed by comparing two policy options for health-care reform.
title 65569.pdf
spellingShingle 65569.pdf
title_short 65569.pdf
title_full 65569.pdf
title_fullStr 65569.pdf
title_full_unstemmed 65569.pdf
title_sort 65569.pdf
publisher InTechOpen
publishDate 2021
_version_ 1771297595119894528