Antecedents and Consequences of Patient Satisfaction in the Context of Private Hospitalization Services

This study aims to investigate the impacts of perceived quality and perceived value on patient satisfaction as well as the influence of patient satisfaction on hospital’s brand image, patient loyalty and word-of-mouth intention in the context of private hospitalization services. With regards to the conceptualization of perceived quality, this study also attempts to uncover the underlying dimensions of hospitalization quality in the specific context of private hospital. This study surveyed 254 patients who were admitted for at least three days at private hospital in Malaysia, revealing that patient satisfaction with hospitalization services could be explained directly or indirectly by five hospitalization quality domains namely outcome quality, rights and privacy, medical quality, service quality, and servicescape. The findings of this study also show that patient satisfaction has significant impacts on all the three consequences variables – brand image, patient loyalty and WoM intention. The two major contributions of this study include the conceptualization of hospitalization quality domains and the newly developed measurement of perceived value in the context of profit-oriented healthcare institutions.

Security of Cloud based Medical of Internet Things (MIoTs)

In this digital era expectations for medical quality have increased. As the number of patients continues to increase, conventional health care methods are having to deal with new complications. In light of these observations, researchers suggested a hybrid combination of conventional health care methods with IoT technology and develop MIoT. The goal of IoMT is to ensure that patients can respond more effectively and efficiently to their treatment. But preserving user privacy is a critical issue when it comes to collecting and handling highly sensitive personal health data. However, IoMTs have limited processing power; hence, they can only implement minimal security techniques. Consequently, throughout the health data transfer through MIoT, patient’s data is at risk of data leakage. This manuscript per the authors emphasizes the need of implementing suitable security measures to increase the IoMT's resilience to cyberattacks. Additionally, this manuscript per the authors discusses the main security and privacy issues associated with IoMT and provide an overview of existing techniques.

Analysis of Perioperative Patient Adverse Event Reports and Anesthesia Safety Management in an Anesthesia Department of a Tertiary A Hospital in 2019-2020

Objective: To analyze the characteristics of anesthesia adverse events in our department and propose measures for anesthesia safety management, to achieve better improvement of medical quality and promote medical safety. Methods: A total of 589 cases of anesthesia adverse events were collated and analyzed, including the time period of anesthesia adverse events and ASA (American Society of Aneshesiologists) classification of anesthesia adverse events. Results: The anesthesia induction and awakening periods were the main time periods for the occurrence of anesthesia adverse events, other human factors were the main reasons for the occurrence of anesthesia adverse events, and ASA grading II and III surgical patients accounted for the main proportion (mainly because of the heavy proportion of II and III surgical patients). Conclusion: Understanding the causes of adverse anesthesia events and implementing strict anesthesia safety management measures are conducive to reducing the occurrence of adverse anesthesia events and improving the safety of anesthesia.

Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis

The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through core technologies such as Internet of Things, Big Data Analytics, Artificial Intelligence, and microservice framework, to improve patient safety, medical quality, clinical efficiency, and operational benefits. Among them, how to use computers and deep learning technology to assist in the diagnosis of tongue images and realize intelligent tongue diagnosis has become a major trend. Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems’ identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. In this paper, we do systematic and in-depth researches and propose an improved U-Net network for image semantic segmentation of fissured tongue. By introducing the Global Convolution Network module into the encoder part of U-Net, it solves the problem that the encoder part is relatively simple and cannot extract relatively abstract high-level semantic features. Finally, the method is verified by experiments. The improved U-Net network has a better segmentation effect and higher segmentation accuracy for fissured tongue image dataset. It can be used to design a computer-aided tongue diagnosis system.

An Intelligent Patient Admission Model of Day Surgery Using Heterogeneous Data with Semi-Supervised Learning: A Case Study of Laparoscopic Cholecystectomy (Preprint)

BACKGROUND Day surgery has many advantages including shortening hospital stay, decreasing the risk of hospital-associated infections, and increasing cost efficiency over traditional surgery, it has gained a great reputation and popularity in recent years. However, the patients’ admission criteria of day surgery at present were mainly based on expert experience, which was a lack of scientific evidence. OBJECTIVE Our study is to investigate the day surgery patient’s admission criteria and build an intelligent machine learning model of day surgery patients who underwent laparoscopic cholecystectomy, to ensure patients’ safety and medical quality, providing reference and inspiration for other day surgery admission decisions. METHODS We analyzed the clinical data of day surgery patients who underwent laparoscopic cholecystectomy at West China Hospital from Jan 1st 2009 to Dec 31st 2021 and developed a semi-supervised artificial intelligence algorithm, SDSPA algorithm, which is built by self-training and uses both structured data like patient characteristics and unstructured clinical diagnosis to assist surgeons to make quick admission decisions. RESULTS After comparing several classifiers with self-training in our experiment, the performance of LightGBM with unstructured text processed by BERT were the best, obtaining an accuracy of 0.85 and an f1-score of 0.83, as well as reaching 0.97 on the precision score, which is an important indicator related to patients’ safety. CONCLUSIONS The application of our SDSPA algorithm can make the patient admission of day surgery more intelligent, and maximize the utilization of medical resources while ensuring patients’ safety.

Trends of Antibiotic Use and Expenditure After an Intensified Antimicrobial Stewardship Policy at a 2,200-Bed Teaching Hospital in China

Objective: This study aimed to evaluate the effects of intensified Chinese special rectification activity on clinical antibiotic use (CSRA) policy on a tertiary-care teaching hospital.Methods: A 48-month longitudinal dataset involving inpatients, outpatients, and emergency patients were collected. Study period included pre-intervention stage (adopting soft measures like systemic training) and post-intervention stage (applying antibiotic control system to intensify CSRA policy). Antibiotic use was evaluated by antibiotic use rate (AUR) or antibiotic use density (AUD). Economic indicator was evaluated by antibiotic cost in prescription or antibiotic expenditure in hospitalization. Data was analyzed by interrupted time series (ITS) analysis.Results: The medical quality indicators remained stable or improved during the study period. AUR of inpatients (AURI) declined 0.553% per month (P = 0.025) before the intervention and declined 0.354% per month (P = 0.471) after the intensified CSRA policy was implemented. AUD, expressed as defined daily doses per 100 patients per day (DDDs/100PD), decreased by 1.102 DDDs/100PD per month (P = 0.021) before and decreased by 0.597 DDDs/100PD per month (P = 0.323) thereafter. The ratio of antibiotic expenditure to medication expenditure (AE/ME) decreased by 0.510% per month (P = 0.000) before and fell by 0.096% (P = 0.000) per month thereafter. AE per patient decreased by 25.309 yuan per month (P = 0.002) before and decreased by 7.987 yuan per month (P = 0.053) thereafter. AUR of outpatient (AURO) decreased by 0.065% per month before (P = 0.550) and decreased by 0.066% per month (P = 0.994) thereafter. The ratio of antibiotic cost to prescription cost in outpatient (ACO/PCO) decreased by 0.182% per month (P = 0.506) before and decreased by 0.216% per month (P = 0.906) thereafter. AUR of emergency patient (AURE) decreased by 0.400% per month (P = 0.044) before and decreased by 0.092% per month (P = 0.164) thereafter. The ratio of antibiotic cost to prescription cost in emergency patient (ACE/PCE) decreased by 0.616% per month (P < 0.001) before and decreased by 0.151% per month (P < 0.001) thereafter.Conclusions: Implementation of CSRA policy was associated with declining antibiotic use and antibiotic expenditure in inpatients, outpatients, and emergency patients. However, it is also important to note that the declining trend of antibiotic consumption slowed due to the limited capacity for decline in the later stages of CSRA intervention.

Spatial Pattern and Spatial Heterogeneity of Chinese Elite Hospitals: A Country-Level Analysis

Elite hospitals represent the highest level of Chinese hospitals in medical service and management, medical quality and safety, technical level and efficiency, which are also one of the important indicators reflecting high-quality medical resources in the region, and their spatial allocation is directly related to the fairness of health resource allocation. We explored the allocation pattern of high-quality resources and its influencing factors in the development of China's health system using geographic weighted regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), GWR and MGWR with Spatial Autocorrelation(GWR-SAR and MGWR-SAR), spatial lag model (SLM), and spatial error model (SEM). The results of OLS regression showed that city level, number of medical colleges, urbanization rate, permanent population and GDP per capita were its significant variables. And spatial auto-correlation of elite hospitals in China is of great significance. Further, its spatial agglomeration phenomenon was confirmed through SLM and SEM. Among them, the city level is the most important factor affecting the spatial allocation of elite hospitals in China. Its action intensity shows a solid and weak mosaic trend in the Middle East, relatively concentrated in some areas with medium intensity and concentrated in the West China. Obviously, China's elite hospitals are unevenly distributed and have evident spatial heterogeneity. Therefore, we suggest that we should pay attention to the spatial governance of high-quality medical resources, attract medical elites in the region, increase investment in medical education in the scarce areas of elite hospitals and develop tele-medicine service.

Trading quality for quantity? Evidence from patient level data in China

In China, overcrowding at hospitals increases the workload of medical staff, which may negatively impact the quality of medical services. This study empirically examined the impact of hospital admissions on the quality of healthcare services in Chinese hospitals. Specifically, we estimated the impact of the number of hospital admissions per day on a patient’s length of stay (LOS) and hospital mortality rate using both ordinary least squares (OLS) and instrumental variable (IV) methods. To deal with potential endogeneity problems and accurately identify the impact of medical staff configuration on medical quality, the daily air quality index was selected as the IV. Furthermore, we examined the differential effects of hospital admissions on the quality of care across different hospital tiers. We used the data from a random sample of 10% of inpatients from a city in China, covering the period from January 2014 to June 2019. Our final regression analysis included a sample of 167 disease types (as per the ICD-10 classification list) and 862,722 patient cases from 517 hospitals. According to our results, the LOS decreased and hospital mortality rate increased with an increasing number of admissions. Using the IV method, for every additional hospital admission, there was a 6.22% (p < 0.01) decrease in LOS and a 1.86% (p < 0.01) increase in hospital mortality. The impact of healthcare staffing levels on the quality of care varied between different hospital tiers. The quality of care in secondary hospitals was most affected by the number of admissions, with the average decrease of 18.60% (p < 0.05) in LOS and the increase of 6.05% (p < 0.01) in hospital mortality for every additional hospital admission in our sample. The findings suggested that the supply of medical services in China should be increased and a hierarchical diagnosis and treatment system should be actively promoted.

Effects of seasonal and climate variations on in-hospital mortality and length of stay in patients with type A aortic dissection

Objective To investigate the effects of seasonal and climatic changes on postoperative in-hospital mortality and length of stay (LOS) in patients with type A acute aortic dissection (AAD). Methods Patients undergoing implantation of the modified triple-branched stent graft to replace the descending aorta in addition to aortic root reconstruction for type A AAD in our hospital from January 2016 to December 2019 were included. Relevant data were retrospectively collected and analyzed. Results A total of 404 patients were included in our analyses. The multivariate unconditional logistic regression analysis showed that patients admitted in autumn (OR 4.027, 95% CI 1.023–17.301, P = 0.039) or with coronary heart disease (OR 8.938, 95% CI 1.991–29.560, P = 0.049) were independently associated with an increased risk of postoperative in-hospital mortality. Furthermore, patients admitted in autumn (OR 5.956, 95% CI 2.719–7.921, P = 0.041) or with hypertension (OR 3.486, 95% CI 1.192–5.106, P = 0.035) were independently associated with an increased risk of longer LOS. Conclusion Patients admitted in autumn or with coronary heart disease are at higher risk of in-hospital mortality following surgery for type A AAD. Also, patients admitted in autumn or with hypertension have a longer hospital LOS. In the autumn of the temperature transition, we may need to strengthen the management of medical quality after surgery for type A AAD.