The cost-saving effect of continuity of care (COC): an analysis of institutional COC on diabetic treatment costs using panel 2SLS regressions.

BACKGROUND: The high costs of chronic conditions call for new treatment approaches that reduce costs while ensuring desirable health outcomes. There has been a growing transformation of care delivery models from conventional referral systems to integrated care models. This study seeks to evaluate the cost-saving impact of integrated care delivery model under pay-for-performance (P4P) scheme with continuity of care at institution level (ICOC). METHODS: We analyzed the Taiwan National Health Insurance claim data of 21,725 diabetic patients who visited clinics and/or hospitals at least four times a year for 8 years. Using average local provider P4P participation rate (for each accreditation level) as an instrumental variable in two-stage least squares (2SLS) regressions, we have estimated consistent estimates of the ICOC elasticities for all-cause inpatient and outpatient costs. RESULTS: Our results show that ICOC significantly reduced inpatient costs but increased outpatient costs with the elasticity for treatment costs of -11.6 and 1.03, respectively. The decrease in inpatient costs offset the increase in outpatient costs and the resulting total cost saving showed significant association with ICOC. The saving effect of ICOC is especially robust among patients who used clinics as their principal source of care. CONCLUSIONS: Institutional continuity of care has a substantial impact on the treatment costs of diabetes patients. In the context where inpatient care costs are significantly higher than that of the outpatient care, ICOC would lead to a meaningful cost-saving effect. For new diabetes patients, care by clinics demonstrated the strongest saving effect.

A Tale of Two Social Insurance Systems in South Korea and Taiwan: A Financial Risk Protection Perspective.

Universal Health Coverage (UHC) is a widespread policy goal in the 21st century. The aim is to protect people from financial risk while promoting their access to good-quality care. This study examined the social insurance systems of South Korea and Taiwan to explore the critical challenges of achieving effective UHC. By assessing the impact of UHC on financial risk protection (measured by out-of-pocket payment share and catastrophic payment headcount), we found that when South Korea inaugurated its National Health Insurance (NHI) program with a limited benefits package and high cost sharing, it did not reduce the financial burden. Meanwhile, we observed a drop of 5 to 6 percentage points in the catastrophic payment headcount in Taiwan, which offered a universal and rather comprehensive benefits package with a modest cost-sharing design under its single-payer NHI system. The political-economic context of the UHC policy evolution was further explored through an in-depth discussion. We conclude that to provide sufficient financial risk protection against unexpected medical expenses, the design of the insurance scheme, in particular the risk-sharing mechanism, not only matters but is also the key to success.

Deep Learning and Explainable Artificial Intelligence to Predict Patients' Choice of Hospital Levels in Urban and Rural Areas.

Maldistribution of healthcare resources among urban and rural areas is a significant challenge worldwide. People living in rural areas may have limited access to medical resources, and often neglect their health problems or receive insufficient care services. This research uses a deep learning approach to predict patient choices regarding hospital levels (primary, secondary or tertiary hospitals) and interpret the model decision using explainable artificial intelligence. We proposed an autoencoder-deep neural network framework and trained region-based models for the urban and rural areas. The models achieve an area under the receiver operating characteristics curve (AUC) of 0.94 and 0.95, and an accuracy of 0.93 and 0.92 for the urban and rural areas, respectively. This result indicates that region-based models are effective in improving the performance. The result is potentially leading to appropriate policy planning. Further interpretation can be done to investigate the explicit differentiation of the rural and urban scenarios.

Predicting the Travel Distance of Patients to Access Healthcare Using Deep Neural Networks.

OBJECTIVE: Improving geographical access remains a key issue in determining the sufficiency of regional medical resources during health policy design. However, patient choices can be the result of the complex interactivity of various factors. The aim of this study is to propose a deep neural network approach to model the complex decision of patient choice in travel distance to access care, which is an important indicator for policymaking in allocating resources. METHOD: We used the 4-year nationwide insurance data of Taiwan and accumulated the possible features discussed in earlier literature. This study proposes the use of a convolutional neural network (CNN)-based framework to make predictions. The model performance was tested against other machine learning methods. The proposed framework was further interpreted using Integrated Gradients (IG) to analyze the feature weights. RESULTS: We successfully demonstrated the effectiveness of using a CNN-based framework to predict the travel distance of patients, achieving an accuracy of 0.968, AUC of 0.969, sensitivity of 0.960, and specificity of 0.989. The CNN-based framework outperformed all other methods. In this research, the IG weights are potentially explainable; however, the relationship does not correspond to known indicators in public health. CONCLUSIONS: Our results demonstrate the feasibility of the deep learning-based travel distance prediction model. It has the potential to guide policymaking in resource allocation. Clinical and Translational Impact Statement- Deep learning technology is feasible in investigating the distance that patients would travel while accessing care. It is a tool that integrates complex interactive variables with highly imbalanced data distributions.

The relationship between healthy lifestyle and hospital utilization among adults with diabetes: results from a national cohort in Taiwan.

The aim of this study was to investigate whether adopting healthy lifestyle habits, such as engaging in leisure time physical activity (LTPA), adopting recommended dietary patterns, and not smoking, are associated with reduced hospitalizations over 1 year among adults with diabetes. We analyzed data from a national sample of people aged 18 years and above with self-reported physician-diagnosed diabetes (n = 664) through linkage to the 2001 National Health Interview Survey in Taiwan and the 2002 National Health Insurance claims data. Multivariate analysis showed that participants reporting greater than 150 min/wk of moderate-intensity activity had a significantly lower chance for hospitalization (odds ratio = 0.52; 95% confidence interval [CI] = 0.27-0.98), fewer admissions (incidence rate ratio [IRR] = 0.58; 95% CI = 0.33-1.00), and fewer hospital bed days (IRR = 0.42; 95% CI = 0.20-0.92) compared with inactive individuals. Diet control and smoking status did not significantly predict hospital use after controlling for other factors. Our findings indicate that increased LTPA results in reduced hospitalization among adults with diabetes.

The spillover effect of National Health Insurance on household consumption patterns: evidence from a natural experiment in Taiwan.

While providing financial risk protection was one of the major aims of introducing a National Health Insurance (NHI) program in Taiwan in 1995, one may also wonder how the households may exploit resources freed up and available to them as a result of reduced exposure to risk due to health insurance. This paper aims at studying and evaluating the impact of social insurance on these differing household consumption patterns. A differences-in-differences estimation model was applied to a sample of 17,899 households from the 1993-2000 Taiwan Survey of Family Income and Expenditure to assess the NHI's impact. This effect was evaluated by the changes in the proportion of the consumption expenditure devoted to medical items and non-medical items in the post-NHI period (1996-2000) compared to the pre-NHI period (1993-1994). Our study found that spending related to the improvement of housing conditions (rental and water bills) had the most significant increase, 1.87% (in the share). Furthermore, examining the NHI impact across socioeconomic status (SES) strata (in terms of income and education levels), our study found that households with the lowest SES experienced the largest increase in spending share (2.16%) for rental and water bills, and the least drop (0.64%) on education items. Recognizing how households can exploit the potential benefits associated with NHI provision could enable the government to devise specific policy tools to facilitate better targeting of investment decisions with limited resources available for less well-off households.

Geriatric conditions as predictors of increased number of hospital admissions and hospital bed days over one year: findings of a nationwide cohort of older adults from Taiwan.

The main aim of the present study was to determine whether geriatric conditions independently predict hospital utilizations after controlling for chronic diseases and disability among community dwelling older adults. We analyzed data from a nationally representative sample of older adults aged 65 years and above by linkage of 2005 Taiwan National Health Interview Survey data (including demographic characteristics, chronic diseases, disability, and geriatric conditions such as depressive symptoms, cognitive impairment, falls, and urinary incontinence), and 2006 National Health Insurance (NHI) claims data (including hospital admissions and hospital bed days). A total of 1598 participants who consented to data linkage, were successfully linked to NHI data, and had complete data for geriatric conditions were eligible for analysis. The prevalence of depressive symptoms, cognitive impairment, falls, and urinary incontinence were 20.6%, 26.1%, 21.3% and 23.9%, respectively. Overall, 18.2% (291/1598) of participants had at least one hospital admission during 2006. After adjustment for demographics, prior hospitalization, chronic diseases and functional disability, participants with geriatric conditions had significantly more hospital admissions (incidence rate ratio=1.34; 95% confidence interval=[1.02-1.75]) and more hospital bed days (incidence rate ratio=1.72; 95% confidence interval=[1.11-2.66]) than participants without geriatric conditions. Our results highlight the high prevalence (56.3%) of one or more geriatric conditions and their independent association with excess hospital utilizations. Thus, it is of critical importance to develop programs aimed at preventing or improving these conditions to reduce hospital use in this population.

Impact of physical activity on hospitalization in older adults: a nationwide cohort from Taiwan.

The aim of the present study was to explore the effect of baseline physical activity on hospitalization over one year in a representative sample of older adults in Taiwan. We conducted a prospective study of persons aged 65 and above (N = 2064) participating in the National Health Interview Survey in Taiwan, 2001. A total of 1521 participants had complete data on physical activity and were successfully linked to 2002 National Health Insurance claims data. Participants reporting physical activity of ≥ 1000 kcal per week had a significantly lower risk of hospitalization, fewer admissions, and fewer hospital bed days compared with inactive individuals. However, the strength of these associations was substantially reduced after adjustment for number of chronic diseases, activities of daily living (ADL) limitation and self-rated health. A possible effect of physical activity between 500 and 999 kcal per week on reducing hospitalization was also demonstrated. Moreover, this association was not altered by adjustment for other health factors.