Assessing the relationship between healthcare market competition and medical care quality under Taiwan's National Health Insurance programme

Hospital competition and health outcomes: Evidence from acute myocardial infarction admissions in Germany

Countries increasingly rely on competition among hospitals to improve health outcomes. However, there is limited empirical evidence on the effect of competition on health outcomes in Germany. We examined the effect of hospital competition on quality of care, which is assessed using health outcomes (risk-adjusted in-hospital and post-hospitalization mortality and cardiac-related readmissions), focusing on acute myocardial infarction (AMI) treatment. We obtained data on all hospital utilizations and mortality of 13.2% of the population from a large statutory health insurer and all AMI admission records from Diagnosis-Related Groups Statistic from 2015-19. We constructed the measures of hospital competition, which mitigates the possibility of endogeneity bias. The relationships between health outcomes and competition measures are estimated using linear probability models. Intense competition was associated with lower quality of care in terms of mortality and cardiac-related readmissions. Patients treated in hospitals facing high competition were 0.9 (1.2) percentage points more likely to die within 90 days (2 years) of admission, and 1.4 (1.6) percentage points more likely to be readmitted within 90 days (2 years) of discharge than patients treated in hospitals facing low competition. Our results indicate that hospital competition does not lead to better health outcomes for AMI patients in Germany. Therefore, additional measures are necessary to achieve quality improvement.

Does competition support integrated care to improve quality?

Introduction

This work investigates the compatibility of integrated care and competition in China and analyses the impact of integrated care on regional care quality (DeptQ) within a competitive framework.

Method

The study was built on multivariate correspondence analysis and a two-way fixed-effects model. The data were collected from Xiamen's Big Data Application Open Platform and represent nine specialised departments that regularly performed inter-institutional referrals between 2016 and 2019.

Results

First, care quality for referred patients (ReferQ) and the relative scale of referred patients (ReferScale) and competition have an antagonistic but not completely mutually exclusive relationship. Second, ReferQ and competition both have a significant effect on DeptQ, but only when competition is weak can ReferQ and competition act synergistically on DeptQ. When competition is fierce, competition will weaken the impact of ReferQ on DeptQ.

Conclusion

Changes in the intensity of integrated care and competition ultimately affect care quality.

Changes in healthcare use by age groups of patients and locations of healthcare institutions after the COVID-19 pandemic in Korea: Analyzing healthcare big data

Objectives

The COVID-19 pandemic affected healthcare use globally. However, there have been few studies examining how it affected age-specific healthcare use by patients as related to the locations of healthcare institutions. We explore changes in healthcare use while focusing on age-specific patient groups and facility locations after the COVID-19 pandemic.

Methods

We compared two databases of cross-sectional outpatient health-insurance claims that have equivalent time points yearly and quarterly both before and after the COVID-19 pandemic. We categorized patients of healthcare institutions into five age groups and two facility locations.

Results

The number of claims in 2020 significantly decreased by about 15% compared to 2019. The greatest reduction was for patients aged under 20 (-43.7%), followed by the 20-39 group (-15.0%) and the 40-59 group (-11.9%). Moreover, the number of claims significantly decreased in both urban and rural areas (p< 0.001); however, the magnitude of this decrease was greater in urban areas (-15.2%) than in rural areas (-10.8%). The annual decrease in healthcare use by age groups and location of facility was still supported even after controlling for institutional covariates, except for the patient group aged 80 or over in rural areas.

Conclusions

We found that the COVID-19 pandemic critically affected healthcare use across age-specific population groups and different locations of healthcare institutions. It suggests there is a need for further research and policy implications as to whether the declining healthcare use among those age groups is in core health care, and as to whether there are any unmet healthcare needs.

Was size of healthcare institution a factor affecting changes in healthcare utilisation during the COVID-19 pandemic in Korea? A retrospective study design analysing national healthcare big data

OBJECTIVES

Many small-sized healthcare institutions play a critical role in communities by preventing infectious diseases. This study examines how they have been impacted by the global COVID-19 pandemic compared with large hospitals.

DESIGN

This study adopted a retrospective study design looking back at the healthcare utilisation of medical facilities according to size after the COVID-19 pandemic. The dependent variable was change in the number of outpatient health insurance claims before and after onset of the COVID-19 pandemic. The independent variable was an observation time point of the year 2020 compared with 2019.

SETTING AND PARTICIPANTS

The study was conducted in Korea having a competitive medical provision environment under the national health insurance system. The units of analysis are hospitals and clinics: tertiary hospitals (42), general hospitals (293), small hospitals (1272) and medical clinics (27 049). This study analysed all the health insurance claim data from 1 January 2019 to 31 December 2020.

RESULTS

Compared with 2019, in 2020, there were significant decreases in the number of claims (-14.9%), particularly in small hospitals (-16.8%) and clinics (-16.3%), with smaller decreases in general hospitals (-8.9%) and tertiary hospitals (-5.3%). The reduction in healthcare utilisation increased as the size of institutions decreased. The magnitude of decrease was significantly greatest in small hospitals (absolute risk (AR): 0.8317, 0.7758 to 0.8915, p<0.0001; relative risk (RR): 0.8299, 0.7750 to 0.888, p<0.0001) followed by clinics (AR: 0.8369, 0.8262 to 0.8478, p<0.0001; RR: 0.8362, 0.8255 to 0.8470, p<0.0001) even after controlling institutional covariates.

CONCLUSION

The external impact of the pandemic increased incrementally as the size of healthcare institutions decreased. Healthcare policy-makers need to keep in mind the possibility that small hospitals and clinics may experience reduced healthcare utilisation in the infectious disease pandemic. This fact has political implications for how healthcare policy-makers should prepare for the next infectious disease pandemic.

The Response of Different-Levels Public Hospitals to Regional Global Budget with a Floating Payment System: Evidence from China

BACKGROUND

Regional Global Budget with a Floating Payment System (RGB-FPS) is a global budget widely used in medical insurance payments. However, existing studies on hospitals' responses to RGB-FPS have limitations. First, existing studies have paid little attention to RGB-FPS's macro effects. Theoretical studies did not analyze differences between different levels of hospitals. Secondly, studies did not reveal whether RGB-FPS has the same impact on the public-hospital-dominated market.

METHODS

First, we refine the research hypotheses through theoretical analysis. We then test the hypotheses empirically through interrupted time series analysis.

RESULTS

Theoretical analysis found that small hospitals were easier to transfer costs. The empirical analysis found that after RGB-FPS, the proportion of inpatients (PI)and the average times of inpatients in large hospitals increased (p < 0.001), and the proportion of non-reimbursable expenses (PNE) remained stable (p > 0.05). PI in secondary hospitals decreased (p < 0.01), and PNE increased (p < 0.01). PI in the primary hospital decreased (p < 0.05), and PNE increased (p < 0.001).

CONCLUSION

This study verifies theoretically and empirically that large hospitals are easier to increase service volume and small hospitals are easier to transfer costs under the influence of RGB-FPS. Chinese public hospitals' response to RGB-FPS is similar to that of private hospitals.

Factors of Hospitals Associated With an Independent Hospice Care Unit: A Quantitative Empirical Study

Few studies have examined why some hospitals operate independent hospice units (IHUs) and others do not. This study aims to investigate the organizational factors of hospitals having IHU. The units of analysis were individual hospitals. The study had a cross-sectional design and used health insurance claims data of 349 hospitals in Korea from January 1 to December 31, 2019. The dependent variable was whether a hospital operated an IHU. The independent variables were the percentage of patients with cancer, overall severity of patients, percentage of patients 60 years and older, health care cost per patient, percentage of inpatients, and nurse staffing level. Independent hospice units were present at 21.2% of the hospitals (74/349). The odds of having an IHU were significantly associated with the following factors: the percentage of patients with cancer (odds ratio [OR], 1.228; 95% confidence interval [CI], 1.071-1.408; P = .003), disease severity (OR, 5.129; 95% CI, 2.477-10.622; P < .001), percentage of patients 60 years and older (OR, 1.053; 95% CI, 1.015-1.092; P = .006), health care cost per patient (OR, 1.018; 95% CI, 1.009-1.027; P < .001), and nurse staffing level (OR, 0.439; 95% CI, 0.292-0.661; P < .001). The organizational factors of hospitals operating IHUs were similar to those of hospice facilities.

The effects of market concentration on health care price and quality in hospital markets in Ibadan, Nigeria

Evidence about the Nigerian health indicators show that the quality of health care in Nigeria is low and inflation of health care prices also persists. Theoretically, by observing the market concentration, inferences can be drawn as to how hospitals conduct themselves, which allows the evaluation of the market performance. Therefore, the effects of market concentration on the health care price and quality were examined. Market concentration was measured by Herfindahl Hirschman Index (HHI) and four hospital concentration ratios (CR₄). The values of HHI were disaggregated into the less and more concentrated markets. Quality of health care was measured by the staff-nurse-patient ratio. Ordinary Least Square (OLS) was used to estimate the effects of market concentration on price and quality of health care. The price of health care was found to be 13.4% lower in the less concentrated markets than in the more concentrated market. Income significantly and positively influenced health care prices by 17.8%. Also, a low HHI lead to 33.4% increase in Staff-nurse Patient Ratio (SPR) indicating that the quality of health care was higher in less concentrated markets as hospitals increased the treatment intensity via staff-nurse patient ratio. A less concentrated market is linked with higher health care quality and lower health care prices. Therefore, a strategy that will reduce market concentration so as to enhance consumer welfare in terms of price and quality is recommended.

Hospital Competition and Unplanned Readmission: Evidence from a Systematic Review

Competition has been widely introduced among hospitals in the hope of improving health-care quality. However, whether competition leads to higher-quality health care is a topic of considerable debate. We conducted a systematic review to assess the impact of hospital-market competition on unplanned readmission. We searched six electronic databases (PubMed, EmBase, Wiley Online Library, Web of Science, Scopus, and JSTOR) and reference lists of screened articles for relevant studies, and strictly followed methods proposed by the Cochrane Collaboration. Finally, nine observational studies with 2,241,767 patients were included. For the primary outcome, pooled results of three studies showed that it was uncertain whether or not hospital competition reduces readmission (β=0.02, P=0.06; very low certainty of evidence, as they were all observational studies with high heterogeneity). Inconsistent results were found in the remaining six studies, and they were assessed as very low–certainty evidence, downgraded for either inconsistency or indirectness or both. As for secondary outcomes, seven of the nine studies reported on the impact of competition on the risk of mortality, and two reported on length of stay (LOS). It was uncertain whether competition had an effect on mortality or LOS. The relevant studies were limited and of very low certainty, which means there is currently no reliable evidence showing that hospital competition reduces quality of health care in terms of readmission/mortality/LOS. There is a need for rigorous studies to assess the impact of hospital competition on the quality of health care.