Non-urgent emergency department visits have been increasing in recent years in Taiwan. In order to explore the optimal policy design for the implementation of the referral system, this project first studies the decision rules patients hinge upon when confronting tradeoffs among co-payment rates, transportation costs, and quality of healthcare from hospitals of different size.Using the 2005-2015 NIH dataset, this project estimates non-urgent patients’ discrete choices on the emergency services, via the maximum score estimation from the perspective of revealed preference analysis. Our estimation strategy has the following two advantages. First, the agents’ choice sets are constructed by the way markets are defined. Therefore, complexity of computation grows exponentially with the size of markets during estimation, rendering multinomial logit and probit models practically infeasible.Furthermore, the NHI data is highly sensitive, and hence only hospital choices made by agents are observed, whereas many important characteristics of agents in the markets might not be disclosed. In this regard, revealed preference analysis serves as a good solution to address the bias arising from the unobserved heterogeneity. In this project, the counterfactual analysis will be conducted based on the estimated coefficients, where changes in the non-urgent use of emergency room at medical centers will be examined under various counterfactual co-payment rates and transportation costs. The project will first focus on observations in the Taipei metropolitan, and data from all six metropolitans in Taiwan will then be incorporated into analyses in the future.
|Effective start/end date||1/08/18 → 30/04/20|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Referral System
- Maximum Score Estimation
- Revealed Preference
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