Effects of Medicaid disease management programs on medical expenditures: Evidence from a natural experiment in Georgia

How does digital infrastructure affect residents' healthcare expenditures? Evidence from Chinese microdata

Healthcare expenditure is only one of the heavy burdens that families face in developing countries. Current research mainly focuses on analyzing the effects of financial policy. There is a lack of studies that examine the understanding and assessment of the impact of digital infrastructure on this issue. In this study, we used the Broadband China policy as a quasi-natural experiment to explore the impact of digital infrastructure on residents' healthcare expenditures in China. Using the differences-in-differences (DID) model and micro-survey data, we found that digital infrastructure has a positive impact on reducing the burden of healthcare expenditure in China. Our findings indicate that residents in cities can save up to 18.8% on healthcare expenses following large-scale digital infrastructure construction. Through mechanism analysis, we found that digital infrastructure reduces residents' healthcare expenditures by improving both commercial insurance availability and the healthcare efficiency of residents. In addition, the effects of digital infrastructure on reducing healthcare expenditure are more pronounced among middle-aged individuals, those with low levels of education, and those with low incomes, which indicates this digital construction wave helps bridge the social gap between the poor and the rich. This study provides compelling evidence of the positive impact of digital society construction on social health and wellbeing.

Does disease management for diabetic nephropathy reduce medical expenditure? Evidence from a three-period difference-in-differences analysis

Background

To discriminatively evaluate the cost-saving effects of a disease management program for diabetic nephropathy patients through care process rectification and, subsequently, improved health outcomes.

Methods

This study links public medical insurance claims data to the health records of a disease management program for diabetic nephropathy patients. To account for selection bias caused by the non-randomized allocation of the disease management program for diabetes patients, we adopted a fixed-effect model of panel data analysis. To discriminatively evaluate the cost-saving effects of the disease management program for diabetic nephropathy patients through care process rectification and, subsequently, improved health outcomes, we expanded the difference-in-differences analysis from the traditional two-period model to a three-period model, comprising the before-intervention, during-intervention, and after-intervention periods. Data were extracted from municipal public insurers in Kure, Japan.

Results

The cost-reduction effect in terms of treatment costs from the before-intervention period to the during-intervention period (the rectification effect) was 4.02%, and the cost-saving effect from the during-intervention period to the after-intervention period (the health improvement effect) was 2.95%.

Conclusions

A disease management program for diabetes patients organized by local public insurers in Japan reduced costs both by amending treatment processes and by subsequently improving the prognosis of the disease.

The effect of predictive analytics-driven interventions on healthcare utilization

This paper studies a commercial insurer-driven intervention to improve resource allocation. The insurer developed a claims-based algorithm to derive a member-level healthcare utilization risk score. Members with the highest scores were contacted by a care management team tasked with closing gaps in care. The number of members outreached was dictated by resource availability and not by severity, creating a set of arbitrary cutoff points, separating treated and untreated members with very similar predicted risk scores. Using a regression discontinuity approach, we find evidence that predictive analytics-driven interventions directed at high-risk individuals reduced emergency room and specialist visits, yet not hospitalizations.

Who benefits most in disease management programs: Improving target efficiency

Disease management programs aim to reduce cost by improving the quality of care for chronic diseases. Evidence of their effectiveness is mixed. Reducing health care spending sufficiently to cover program costs has proved particularly challenging. This study uses a difference in differences design to examine the impact of a diabetes disease management program for high risk patients on preventive tests, health outcomes, and cost of care. Heterogeneity is examined along the dimensions of severity (measured using the proxy of poor glycemic control) and preventive testing received in the baseline year. Although disease management programs tend to focus on the sickest, the impact of this program concentrates in the group of people who had not received recommended tests in the preintervention period. If confirmed, such findings are practically important to improve cost-effectiveness in disease management programs by targeting relevant subgroups defined both based on severity and on (missing) test information.