Health policy has been defined as a macro decision for population. It results from a complex interplay among various sources. Once a policy was made and implemented, the relevant governors cannot avoid facing a difficult position, which is the ability of understanding and management of the future affected by the policy. Studies of failures or successes of health policies and how to develop future implementations are valuable to increase the ability. However, there has been much less attention given to how to do policy research in academy. In Taiwan, most studies of health policy are descriptive health policy researches which use national health insurance data in the perspective of disease or organization. Small number of studies is analytical health policy research which applies multivariate statistical analysis based on large data sets. The scarcity of experimental health policy research which focuses on examining how policy changes might affect healthcare delivery was found. Applications of mixed methods have shown the trend of methodology in health policy research. The methods replies on the complementary support from one method (qualitative method or quantitative method) to the other. In recent years, the World Health Organization advocates applying systems thinking in health policy research and health systems research. In addition to systems thinking, the Bayesian network is emphasized by researchers for the study of decision support analysis. Bayesian networks are directed acyclic graphs whose nodes represent random variables in the Bayesian sense. It is capable to predict outcomes based on the inferential cause-effect relationships, hence, to support decision making toward an adequate strategy. This study aims to develop a mixed-method methodology for health policy researches based on the integration of the two methods: systems thinking and Bayesian network, and take the Taiwan’s long-term respiratory care system of ventilation dependent patients as a study case.
|Effective start/end date||8/1/13 → 7/31/14|
- health policy
- systems thinking
- Bayesian network
- mixed methods