Big data analytics in medical engineering and healthcare: methods, advances and challenges

Prevalence and characteristics of chronic kidney disease in people with type 2 diabetes mellitus in the Autonomous Community of Aragon

AIMS

The main objective in this study was to determine the prevalence of Chronic Kidney Disease (CKD) in people with Type 2 Diabetes Mellitus (T2DM) in the Autonomous Community (AC) of Aragon (Spain) and to detect whether or not there is under-registration in the patient's history. As a secundary objetive, it was proposed to study the most relevant demographic and clinical characteristics of people with CKD.

METHODS

Observational and retrospective real world data study of the population over 18 years of age with a diagnosis of T2DM, between January 2017 and December 2021. A descriptive analysis of qualitative and quantitative variables, and a comparison using the parametric Student's t-test or the non-parametric Mann-Whitney U-test between both groups was performed.

RESULTS

The prevalence of T2DM was 8.07 % and that of CKD 31.4 %, with an under-reporting of 47 %. The main risk factor associated with CKD was arterial hypertension (p<0.001), followed by dyslipidemia (p<0.001). The main treatment used for diabetes control was metformin, both in patients with and without CKD (p<0.001). A total of 56.81 % of people with T2DM and CKD did not undergo annual monitoring of their renal function (glomerular filtration rate) or determination of albuminuria.

CONCLUSIONS

The prevalence of CKD increases in patients with T2DM (31.4 %), and in almost half of patients the diagnosis is not registered (47 %). This under-reporting delays the implementation of measures needed to prevent CKD progression.

Development of a universal cutting guide for raising deep circumflex iliac artery flaps

PURPOSE

The deep circumflex iliac crest flap (DCIA) is used for the reconstruction of the jaw. For fitting of the transplant by computer-aided planning (CAD), a computerized tomography (CT) of the jaw and the pelvis is necessary. Ready-made cutting guides save a pelvic CT and healthcare resources while maintaining the advantages of the CAD planning.

METHODS

A total of 2000 CTs of the pelvis were divided into groups of 500 by sex and age (≤ 45 and > 45 years). Three-dimensional (3D) pelvis models were aligned and averaged. Cutting guides were designed on the averaged pelvis for each group and an overall averaged pelvis. The cutting guides and 50 randomly selected iliac crests (10 from each group and 10 from the whole collective) were 3D printed. The appropriate cutting guide was mounted to the iliac crest and a cone beam CT was performed. The thickness of the space between the iliac crest and the cutting guide was evaluated.

RESULTS

Overall the mean thickness of the space was 2.137 mm and the mean volume of the space was 4513 mm3. The measured values were significantly different between the different groups. The overall averaged group had not the greatest volume, maximum thickness and mean thickness of the space.

CONCLUSION

Ready-made cutting guides for the DCIA flap fit to the iliac crest and make quick and accurate flap raising possible while radiation dose and resources can be saved. The cutting guides fit sufficient to the iliac crest and should keep the advantages of a standard CAD planning.

Editorial Topical Collection: "Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment"

The integration of biomedical imaging techniques with advanced data analytics is at the forefront of a transformative era in healthcare [...].

Nurse-Sensitive Indicators as Predictors of Trauma Patient Discharge Disposition

BACKGROUND

About 3.5 million trauma patients are hospitalized every year, but 35%-40% require further care after discharge. Nurses' ability to affect discharge disposition by minimizing the occurrence of nurse-sensitive indicators (catheter-associated urinary tract infection [CAUTI], central line-associated bloodstream infection [CLABSI], and hospital-acquired pressure injury [HAPI]) is unknown. These indicators may serve as surrogate measures of quality nursing care.

OBJECTIVE

The purpose of this study was to determine whether nursing care, as represented by three nurse-sensitive indicators (CAUTI, CLABSI, and HAPI), predicts discharge disposition in trauma patients.

METHODS

This study was a secondary analysis of the 2021 National Trauma Data Bank. We performed logistic regression analyses to determine the predictive effects of CAUTI, CLABSI, and HAPI on discharge disposition, controlling for participant characteristics.

RESULTS

A total of n = 29,642 patients were included, of which n = 21,469 (72%) were male, n = 16,404 (64%) were White, with a mean (SD) age of 44 (14.5) and mean (SD) Injury Severity Score of 23.2 (12.5). We created four models to test nurse-sensitive indicators, both individually and compositely, as predictors. While CAUTI and HAPI increased the odds of discharge to further care by 1.4-1.5 and 2.1 times, respectively, CLABSI was not a statistically significant predictor.

CONCLUSIONS

Both CAUTI and HAPI are statistically significant predictors of discharge to further care for patients after traumatic injury. High-quality nursing care to prevent iatrogenic complications can improve trauma patients' long-term outcomes.

Management of Rheumatoid Arthritis in Primary Care: A Scoping Review

Rheumatoid arthritis (RA) can lead to severe joint impairment and chronic disability. Primary care (PC), provided by general practitioners (GPs), is the first level of contact for the population with the healthcare system. The aim of this scoping review was to analyze the approach to RA in the PC setting. PubMed, Scopus, and Web of Science were searched using the MESH terms "rheumatoid arthritis" and "primary care" from 2013 to 2023. The search strategy followed the PRISMA-ScR guidelines. The 61 articles selected were analyzed qualitatively in a table and discussed in two sections, namely criticisms and strategies for the management of RA in PC. The main critical issues in the management of RA in PC are the following: difficulty and delay in diagnosis, in accessing rheumatological care, and in using DMARDs by GPs; ineffective communication between GPs and specialists; poor patient education; lack of cardiovascular prevention; and increase in healthcare costs. To overcome these criticisms, several management strategies have been identified, namely early diagnosis of RA, quick access to rheumatology care, effective communication between GPs and specialists, active patient involvement, screening for risk factors and comorbidities, clinical audit, interdisciplinary patient management, digital health, and cost analysis. PC appears to be the ideal healthcare setting to reduce the morbidity and mortality of chronic disease, including RA, if a widespread change in GPs' approach to the disease and patients is mandatory.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Increased Risk of Coronary Artery Disease in People with Diagnosis of Neuromuscular Disorders: A Nationwide Retrospective Population-Based Case-Control Study

The existing literature has explored carpal tunnel syndrome (CTS) and determined that it could be a risk for coronary artery disease (CAD), but there has been little research comparing the relevance of CAD with other neuromuscular disorders (NMDs) to CTS. This case-control study explored the association between CTS, stenosing tenosynovitis (ST), and ulnar side NMDs and CAD. The study utilized data from Taiwan's National Health Insurance Research Database, focusing on health insurance claims. Between January 2000 and December 2011, we employed the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes to identify 64,025 CAD patients as the case group. The control group consisted of an equal number of individuals without CAD, matched for age, sex, and index year of CAD. Logistic regression analysis was employed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for each variable. Multivariate analysis, after adjusting for sociodemographic factors and comorbidities, revealed a significantly higher likelihood of a previous diagnosis of CTS in the CAD group compared to the comparison control group. However, neither ST nor the ulnar side NMDs had any statistical significance. These results indicated that median nerve injury, rather than other NMDs, may uniquely serve as a predisposing factor of CAD.

National Cross-Sectional Epidemiological Analysis of the Impact of Pandemic COVID-19 on Vascular Procedures in Public Health System: 521,069 Procedures Over 4 Years

BACKGROUND

During the COVID-19 pandemic, there was a dramatic increase in healthcare demand. Resources were redirected to care patients with COVID-19. Therefore, surgical treatments were affected, including those of vascular diseases. There are no studies evaluating the whole impact of the COVID-19 pandemic, considering all types of vascular procedures, both elective and urgent, in a large country. The aim of the present study was to analyze the impact on all types of vascular procedures performed in Brazilian public hospitals during the COVID-19 pandemic.

METHODS

Cross-sectional population-based analysis of publicly available data referring to vascular procedures. Surgeries 2 years before the pandemic onset (2018-2019) and 2 years during pandemic (2020-2021) were included.

RESULTS

We observed a total of 521,069 procedures. Decrease was observed in elective abdominal aortic aneurysm repairs both open surgery (P = 0.001) and endovascular surgery (P < 0.001), emergency open abdominal repairs (P = 0.005), elective thoracic aortic aneurysm repairs (P = 0.007), elective open peripheral aneurysm repairs (P = 0.038), carotid endarterectomies (P < 0.001) and angioplasties (P = 0.001), open revascularizations for peripheral arterial disease (P < 0.001), surgical treatment of chronic venous disease (P < 0.001) and sympathectomies for hyperhidrosis (P < 0.001). However, there was an increase of lower limb amputations (P = 0.027) and vena cava filter placements (P = 0.005). There was a reduction of almost US$17 million in financial investments.

CONCLUSIONS

The reorganization of health systems led to a significant reduction in vascular procedures and decrease in financial investments. On the other hand, there was a significant increase in the number of lower limb amputations and vena cava filter placements.

Healthcare analytics-A literature review and proposed research agenda

This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.

Challenges and opportunities of big data analytics in healthcare

Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.

Cross-disciplinary training of nursing informatics and nursing engineering at the postgraduate level: A feasibility analysis based on the qualitative method

BACKGROUND

The trend of interdisciplinary education is becoming increasingly prominent. Nursing informatics and nursing engineering have received much attention and development at different levels of nursing education in many Western countries. Meanwhile, in China, the cultivation of interdisciplinary nursing talents has either not been initiated or has only entered an initial stage.

OBJECTIVES

This study aims to explore experts' opinions from nursing, informatics and engineering on the feasibility of interdisciplinary education at graduate master's level in nursing through interview.

DESIGN

This was a descriptive qualitative study.

SETTING

Interviews were conducted online or face to face.

PARTICIPANTS

Experts in the fields of nursing, informatics, and engineering who met the study qualifications were enrolled.

METHODS

This study used a purposive sampling method and collected data via semi-structured interviews. A total of 14 experts were involved based on data saturation, which eight were interviewed face-to-face and six were interviewed online. A content analysis method was used to summarize and analyze the attitudes, opinions, and suggestions of experts.

RESULTS

A total of 579 min of interviews with 66,387 words were transcribed and analyzed after 30-50 min time range of each interview, and 4 themes were established. A consensus was obtained on the necessity and importance of interdisciplinary education. Policy guidance, financial support, and mutual recognition were the prerequisites for the cultivation. Moreover, feasibility of interdisciplinary education depends on multi-cooperation, including society, university, and hospital. Finally, a linkage mechanism among relevant stakeholders was required.

CONCLUSION

The necessity and feasibility of such integrated training was concluded. Learning from the experience of relevant countries, China should launch an interdisciplinary training model suitable for its national condition.

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead

The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices.

Data Analytics in Healthcare: A Tertiary Study

The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer's disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25-100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT

Knowledge extraction from rich text in online health communities can supplement and improve the existing knowledge base, supporting evidence-based medicine and clinical decision making. The extracted time series health management data of users can help users with similar conditions when managing their health. By annotating four relationships, this study constructed a deep learning model, BERT-BiGRU-ATT, to extract disease-medication relationships. A Chinese-pretrained BERT model was used to generate word embeddings for the question-and-answer data from online health communities in China. In addition, the bidirectional gated recurrent unit, combined with an attention mechanism, was employed to capture sequence context features and then to classify text related to diseases and drugs using a softmax classifier and to obtain the time series data provided by users. By using various word embedding training experiments and comparisons with classical models, the superiority of our model in relation to extraction was verified. Based on the knowledge extraction, the evolution of a user's disease progression was analyzed according to the time series data provided by users to further analyze the evolution of the user's disease progression. BERT word embedding, GRU, and attention mechanisms in our research play major roles in knowledge extraction. The knowledge extraction results obtained are expected to supplement and improve the existing knowledge base, assist doctors' diagnosis, and help users with dynamic lifecycle health management, such as user disease treatment management. In future studies, a co-reference resolution can be introduced to further improve the effect of extracting the relationships among diseases, drugs, and drug effects.

The development path of the medical profession in China's engineering universities from the perspective of the 'four new' disciplines

In recent years, China has actively promoted the construction of first-class universities and disciplines of the world ('Double First-Class'), and built a new model of university development to solve Chinese problems and support high-quality economic and social development. In the context of China's efforts to promote the construction of new engineering, new medicine, new agriculture, and new liberal arts (referred to as the 'four new' disciplines), these disciplines are developing rapidly. As a specialty dealing with major life issues, medical education has become increasingly prominent. To enhance the comprehensive strength of universities, corresponding to the 'four new' disciplines strategy, engineering universities are building and developing medical specialties one after another. At present, the greatest problem in the medical specialty of engineering universities is the tendency to blindly follow trends and integrate new concepts with traditional methods. However, to date, the integration of medical and nonmedical specialties has been superficial and thus has not been successful. To address this problem, this paper, guided by the policies aimed at developing the 'four new' disciplines, analyses the current situation of traditional medicine education and professional development in engineering universities and proposes measures to enhance the competitiveness of new medicine in engineering universities, thereby promoting the development of universities.KEY MESSAGESThe implementation of the 'four new' disciplines is a strategic choice for higher education.Engineering technology is an efficient path and hands-on approach to solving medical problems.Interdisciplinary and comprehensive educational approaches play an important role in the development of medical science.

Sema3d Restrained Hepatocellular Carcinoma Progression Through Inactivating Pi3k/Akt Signaling via Interaction With FLNA

Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors worldwide due to the high incidence rate of metastasis and recurrence. Semaphorin 3d (Sema3d) has been shown to play a critical role in vascular development during early embryogenesis and several forms of cancer progression via regulating cell migration. However, the function of Sema3d in hepatocellular carcinoma (HCC) remains elusive. This study aimed to explore the function and mechanisms of Sema3d in HCC. In our study, Sema3d expression was significantly downregulated in HCC tissues and cell lines. Downregulated Sema3d was closely correlated with aggressive clinicopathological features and poor clinical outcomes in HCC patients. Moreover, overexpression of Sema3d in HCCLM3 cells was significantly inhibited and knockdown of Sema3d in PLC/PRF/5 cells promoted proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) of HCC cells in vitro and tumor growth, EMT, and metastasis in vivo. Furthermore, the RNA sequencing and gene set enrichment analysis (GSEA) indicated that these phenotypic and functional changes in Sema3d-interfered HCC cells were mediated by the Pi3k/Akt signaling pathway, and co-IP–combined mass spectrometry indicated Sema3d might interact with FLNA. Finally, we proved that Sema3d exerted its tumor-restraining effect by interacting with FLNA to inactivate the Pi3k/Akt signaling pathway and remodel the cytoskeleton. Our data showed that Sema3d restrained hepatocellular carcinoma proliferation, invasion, and metastasis through inactivating Pi3k/Akt via interaction with FLNA, which may serve as a novel prognostic predictor and a potential therapeutic target for HCC patients.

Financial Data Analysis and Application Based on Big Data Mining Technology

We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students' performance; firstly, we need to obtain the plain Bayesian analysis model of college students' learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students' learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method.

How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective

From a technical perspective, for electronic medical records (EMR), this paper proposes an effective confidential management solution on the cloud, whose basic idea is to deploy a trusted local server between the untrusted cloud and each trusted client of a medical information management system, responsible for running an EMR cloud hierarchical storage model and an EMR cloud segmentation query model. (1) The EMR cloud hierarchical storage model is responsible for storing light EMR data items (such as patient basic information) on the local server, while encrypting heavy EMR data items (such as patient medical images) and storing them on the cloud, to ensure the confidentiality of electronic medical records on the cloud. (2) The EMR cloud segmentation query model performs EMR related query operations through the collaborative interaction between the local server and the cloud server, to ensure the accuracy and efficiency of each EMR query statement. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed solution for confidentiality management of electronic medical records on the cloud, i.e., which can ensure the confidentiality of electronic medical records on the untrusted cloud, without compromising the availability of an existing medical information management system.

Open-source electronic health record systems: A systematic review of most recent advances

Open-source Electronic Health Records (OS-EHRs) are of pivotal importance in the management, operations, and administration of any healthcare organization. With the advancement of health informatics, researchers and healthcare practitioners have proposed various frameworks to assess the maturation of Open-source EHRs. The significance of OS-EHRs stems from the fact that vendor-based EHR implementations are becoming financially burdensome, with some vendors raking in more than $1 billion with one contract. Contrarily, the adoption of OS-EHRs suffers from a lack of systematic evaluation from the standpoint of a standard reference model. To this end, the Healthcare Information and Management Systems Society (HIMSS) has presented a strategic road map called EMR Adoption and Maturity (EMRAM). The HIMSS-EMRAM model proposes a stage-wise model approach that is globally recognized and can be essentially applied as a benchmark evaluation criteria for open-source EHRs. This paper offers an applied descriptive methodology over the frequently studied open-source EHRs currently operational worldwide or has the potential of adoption in healthcare settings. Besides, we also present profiling (User Support, Developer' Support, Customization Support, Technical details, and Diagnostic help) of studied OS-EHRs from developer's and user's perspectives using updated standard metrics. We carried out multi-aspect objective analysis of studied systems covering EHR functions, software based features and implementation. This review portrays systematic aspects of electronic medical record standards for open-source software implementations. As we observed in the literature, prevalent research and working prototypes lack systematic review of the HIMSS-EMRAM model and do not present evolving software features. Therefore, after the application of our assessment measures, the results obtained indicate that OS-EHRs are yet to acquire standard compliance and implementation. The findings in this paper can be beneficial in the planning and implementation of OS-EHRs projects in the future.

Challenges of Utilizing Medical Big Data in Reproductive Health Research

In the background of the “Three-Child Policy” introduced by the Chinese government, reproductive health has become one of the most important public health issues. With the promotion of digitization management of medical care institutions for women and children in the country, there will be chances to acquire medical big data of obstetrics and pediatrics. Here the authors are presenting their opinions on the challenges of the management and utilization of reproductive big data.

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients' compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group (P < 0.05). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment (P < 0.05), and the scores of the quality of life in the intervention group were higher than those in the control group (P < 0.05). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group (P < 0.05). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group (P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group (P < 0.05).

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.