Big Data-Enabled Analysis of DRGs-Based Payment on Stroke Patients in Jiaozuo, China

Abstract

Stroke is the first leading cause of mortality in China with annual 2 million deaths. According to the National Health Commission of the People’s Republic of China, the annual in-hospital costs for the stroke patients in China reach ¥20.71 billion. Moreover, multivariate stepwise linear regression is a prevalent big data analysis tool employing the statistical significance to determine the explanatory variables. In light of this fact, this paper aims to analyze the pertinent influence factors of diagnosis related groups- (DRGs-) based stroke patients on the in-hospital costs in Jiaozuo city of Henan province, China, to provide the theoretical guidance for medical payment and medical resource allocation in Jiaozuo city of Henan province, China. All medical data records of 3,590 stroke patients were from the First Affiliated Hospital of Henan Polytechnic University between 1 January 2019 and 31 December 2019, which is a Class A tertiary comprehensive hospital in Jiaozuo city. By using the classical statistical and multivariate linear regression analysis of big data related algorithms, this study is conducted to investigate the influence factors of the stroke patients on in-hospital costs, such as age, gender, length of stay (LoS), and outcomes. The essential findings of this paper are shown as follows: (1) age, LoS, and outcomes have significant effects on the in-hospital costs of stroke patients; (2) gender is not a statistically significant influence factor on the in-hospital costs of the stroke patients; (3) DRGs classification of the stroke patients manifests not only a reduced mean LoS but also a peculiar shape of the distribution of LoS.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

“…With limited healthcare resources, large, routinely collected data on the use of healthcare services are instrumental for eliciting potential opportunities for cost containment. Costs after stroke, or associated factors, were reported in four studies conducted in Canada, China, Sweden, and the USA (n = 1,073 to 29,673) [55][56][57][58]. Among patients with stroke and systemic embolism with non-valvular atrial fibrillation taking oral anticoagulants, healthcare costs were greater among those with major bleeding (US$81,414; mean 343 days after stroke) than those without (US$32,607; mean 327 days after stroke) [58].…”

Current research progress of isoflurane in cerebral ischemia/reperfusion injury: a narrative review

“…These cascading injuries leave the central nervous system vulnerable, eventually leads to the occurrence of serious adverse events. 12 After cerebral ischemia, cerebral ischemia hypoxia resulting in a large number of brain cell death and neural function defect, 13 primary injury caused by ischemia has been unable to recover, and the secondary brain injury caused by restored blood flow, resulting in the release of various neurotoxic molecules, the expansion of inflammation, cell electrolyte accumulation, brain disorder environment. All of these factors interact to result in a cascade of brain tissue damage, a breakdown of the blood- brain barrier, a weakened nerve function, and ultimately a poor overall prognosis for patients.…”

Building a Performance Management System for Hospitals Based on Diagnosis-Related Group (DRG) Payment

“…Jian et al applied a total of 1.3 million inpatient records from 154 hospitals in Beijing in 2008 to explore the performance of DRG systems [8]. Qiao et al analyzed the factors associated with the influence of DRG-based stroke patients on in-hospital costs in Jiaozuo, Henan province, and offered theoretical instructions for healthcare payment and healthcare resource allocation [9]. Wang et al adopted multiple linear regression analysis to examine the correlation between the total medical costs of inpatients and the age, gender, length of stay, region, and economic level of the hospital, thus better researching DRG payment systems [10].…”