What are the risk factors of hospital length of stay in the novel coronavirus pneumonia (COVID-19) patients? A survival analysis in southwest China

Background The global epidemic of novel coronavirus pneumonia (COVID-19) has resulted in substantial healthcare resource consumption. Since patients’ hospital length of stay (LoS) is at stake in the process, an investigation of COVID-19 patients’ LoS and its risk factors becomes urgent for a better understanding of regional capabilities to cope with COVID-19 outbreaks. Methods First, we obtained retrospective data of confirmed COVID-19 patients in Sichuan province via National Notifiable Diseases Reporting System (NNDRS) and field surveys, including their demographic, epidemiological, clinical characteristics and LoS. Then we estimated the relationship between LoS and the possibly determinant factors, including demographic characteristics of confirmed patients, individual treatment behavior, local medical resources and hospital grade. The Kaplan-Meier method and the Cox Proportional Hazards Model were applied for single factor and multi-factor survival analysis. Results From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment, including 271 (50%) patients aged ≥ 45, 285 (53%) males, and 450 patients (84%) with mild symptoms. The median LoS was 19 (interquartile range (IQR): 14–23, range: 3–41) days. Univariate analysis showed that age and clinical grade were strongly related to LoS (P<0.01). Adjusted multivariate analysis showed that the longer LoS was associated with those aged ≥ 45 (Hazard ratio (HR): 0.74, 95% confidence interval (CI): 0.60–0.91), admission to provincial hospital (HR: 0.73, 95% CI: 0.54–0.99), and severe illness (HR: 0.66, 95% CI: 0.48–0.90). By contrast, the shorter LoS was linked with residential areas with more than 5.5 healthcare workers per 1,000 population (HR: 1.32, 95% CI: 1.05–1.65). Neither gender factor nor time interval from illness onset to diagnosis showed significant impact on LoS. Conclusions Understanding COVID-19 patients’ hospital LoS and its risk factors is critical for governments’ efficient allocation of resources in respective regions. In areas with older and more vulnerable population and in want of primary medical resources, early reserving and strengthening of the construction of multi-level medical institutions are strongly suggested to cope with COVID-19 outbreaks.

ML-Enabled Informed Intervention for Crowdsourcing-Based Optimization of Medical Resources

It is said that every adversity presents the opportunity to grow. The current pandemic is a lesson to all healthcare infrastructure stakeholders to look at existing setups with an open mind. This chapter's proposed solution offers technology assistance to manage patient data effectively and extends the hospital data management system's capability to predict the upcoming need for healthcare resources. Further, the authors intend to supplement the proposed solution with crowdsourcing to meet hospital demand and supply for unprecedented medical emergencies. The proposed approach would demonstrate its need in the current pandemic scenario and prepare the healthcare infrastructure with a more streamlined and cooperative approach than before.

A Decision Support System with Artificial Intelligence and Natural Language Processing to Mitigate the Deduction Rate of Health Insurance Claims

Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan’s National Health Insurance Bureau (TNHIB) deducts the weight that medical resources carry if regarded as unnecessary or abused when examining health insurance claims. The ratio of the deducted weight to the total weight claimed by a hospital is defined as the health insurance claim deduction rate (HICDR). A high HICDR increases the operating expenses of the hospital. In addition, it takes the hospital many resources to prepare and file appeals for the deduction. This study aims to: (1) minimize the weight deducted by the TNHIB for a hospital; and (2) facilitate efficient appeals to claim denials. It is expected that HICDR will be reduced through big data analytics. In this study, evidence-based medicine (EBM) is involved to clarify the debate, dilemmas, conflicts of interests in examining health insurance claims. A natural language method—latent Dirichlet allocation (LDA), was used to analyze patients’ medical records. The topics derived from the LDA are used as factors in the logistic regression model to estimate the probability of each claim to be deducted. The experimental results on various medical departments show that the proposed predictive model can produce accurate results, and lead to more than 41.7% reduction to the deduction of the health insurance claims. It is equivalent to more than a 750 thousand NT dollars saving per year. The efficiency of application is validated compared to the manual process that is time-consuming and labor intensive. Moreover, it is expected that this study will supplement the insufficiency of traditional methods and propose a new and effective solution to reduce the deduction rate.

Reliability Aware Medical Resource Allocation for Health Care Industrial Internet of Things (IIoT) Using Tabu Search and Alo Algorithm

The medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.

Increasing Rate of Hospitalization For Severe Peptic Ulcer After The Pandemic: Digestive Emergency Data From Two Regional Hospitals.

With the introduction of the coronavirus disease 2019 (COVID-19) vaccine, the pandemic has abated. However, the virus has not been completely contained, and some of the potential effects of the outbreak have not been thoroughly studied. We collected data from two regional emergency centers from May to November 2015-2019, before the outbreak, and from May to November 2020, after the outbreak. We evaluated the incidence of each major type of digestive disease before and after the pandemic in adults at two hospitals, which experienced COVID-19 outbreaks with varying severity. A total of 11,336 patients were enrolled in the study (PUTIAN, n=5503, UNION, n= 5891). From 2015 to 2019, the numbers of patients at the two hospitals increased steadily, but in 2020, the number of patients at UNION declined. The constituent ratios of diseases in each year in the two hospitals differed. The number of patients with peptic ulcer in 2020 was significantly different from that in each year from 2015 to 2019 (PUTIAN 2015-2020, 16.6%, 20.0%, 16.6%, 18.3%, 21.1%, 37.1%; UNION 2015-2020, 31.5%, 34.6%, 31.6%, 31.3%, 31.7%, 43.7%, respectively). The rates of peptic ulcer increased dramatically in both hospitals in 2020. An increase in the incidence of severe peptic ulcer was observed after the pandemic compared to the same period in previous nonpandemic years. Therefore, these factors should be considered in the formulation of public health strategies and the allocation of medical resources in the postepidemic era.