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On-site supervision is a risk-based regulatory system that requires the scientific development of supervision plans for quality risks and hidden dangers in pharmaceutical enterprises， the rational allocation of supervision resources based on their risk levels， and the implementation of classified supervision measures. In this study， the quality risk monitoring business support system is set up for pharmaceutical enterprises by establishing the quality risk expert database and quality risk monitoring index system for pharmaceutical enterprises based on the difficulty analysis of on-site drug supervision. Based on this support system， the quality risk classification method， the differentiated spot check strategy and business auxiliary visualization system are established. This support system is used to learn the risk level of pharmaceutical enterprises， so as to innovate supervision methods and optimize monitoring strategies. Taking Jiangxi Province as an example， it is verified that the support system can guide the risk assessment of sample enterprises， can improve the targeting of on-site drug supervision in the process of technical review， scheme editing， on-site implementation and comprehensive evaluation， and can effectively improve the quality and efficiency of supervision.
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Traditional Chinese medicine has been paid more and more attention in the development of modern healthcare,and its clinical diagnosis and treatment have broad prospects and enormous potential.However,the current traditional Chinese medicine diagnosis and treatment model have serious shortcomings in service capacity and,diagnosis,and treatment effect.The rapid development of big data and artificial intelli-gence technology provides an opportunity for the iterative upgrade of traditional Chinese medicine diagnosis and treatment models.This article reviewed the current situation of artificial intelligence empowering tradi-tional Chinese medicine clinical diagnosis and treatment,clarified the problems and challenges faced by artifi-cial intelligence technology in data integration,data quality,and data analysis in traditional Chinese medicine clinical diagnosis and treatment,and proposed to empower from the aspects of disciplinary integration,data quality optimization,data privacy protection,and promotion and application,so as to provide reference for im-proving the effectiveness of traditional Chinese medicine clinical diagnosis and treatment.
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Objective With the focus on emerging infectious diseases and diseases of unknown cause,the study aims to realize multi-point trigger monitoring of infectious diseases through key monitoring sites and key populations.Methods Using ar-tificial intelligence,deep learning,big data and other information technologies to build an intelligent information center for infec-tious diseases with patients'disease files as the core,construct a core capacity of infectious disease surveillance,early warning and situation prediction,and predict and evaluate the importance of infectious disease warning signals.Results The system cov-ered 1 425 primary-level medical institutions,18 hospitals,2 580+schools,4 134 pharmacies,4 laboratories and civil affairs departments,detected 55 kinds of infectious diseases and 6 kinds of syndrome monitoring signals.Since its launch,121 000 ac-tive notification cards have been issued,more than 54 000 new notification cards have been added,35.256 million times of multi-source monitoring and 14.4 million disease files have been recorded.Conclusion By expanding monitoring content and chan-nels,we realized early monitoring,auxiliary investigation and multi-mode visual early warning of infectious diseases,built a multi-point trigger mechanism,and moved forward the infectious disease surveillance.
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Big data has emerged as a critical technological focus in medical treatment and public health.Big data and mining techniques can significantly enhance the productivity of medical institutions,ensure the quality of healthcare services,strengthen the core competitiveness of hospitals,and guarantee the optimal utilization of various medical resources.This paper ex-amines the distinctive characteristics of medical big data.Moreover,through the application analysis of big data,it explores the effect of big data in disease prevention,diagnosis,treatment,pharmaceutical research and development,evaluation,as well as in medical research and investigation.
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Purpose/Significance By integrating clinical and biological sample information,a big data research platform for biologi-cal sample information resources is built to provide one-stop data retrieval,integration and analysis services for researchers,and a data governance system is established,so as to improve the level of hospital clinical research infrastructure construction.Method/Process Common data model and data governance technology are adopted to integrate data sources from different vendors through extraction,trans-formation,loading and other steps to provide a unified data access portal.Result/Conclusion The big data research platform for biologi-cal sample information resources has the advantages of multi-dimensional data screening and rapid integrated analysis,which can pro-vide support for clinical research.
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Objective:To construct a multi-dimensional surgical equipment management and control platform based on artificial intelligence and Internet of Things(AIoT)to assist with the refinement and intelligent management medical equipment in hospital operating rooms.Methods:A multi-dimensional surgical equipment control platform based on AIoT was established by integrating the Internet of Things(IoT),big data analysis,indoor positioning technology,artificial intelligence(AI)technology and other technologies to collect real-time process data of surgical equipment such as endoscopy and electrosurgical,and to open up the relationships among information systems relating to surgical equipment,such as hospital information system(HIS),laboratory information system(LIS),radiology information system(RIS)and operation anesthesia management system(OAMS),so as to provide technical support for efficiency analysis,benefit analysis and assets management of surgical equipment.The platform was composed of 3 layers:data extraction layer,data engine layer and AI data analysis layer,including 4 functional modules:automatic data acquisition,deep data fusion,data mining and analysis and data visualization.Results:This platform was launched in Shanghai Municipal Hospital of Traditional Chinese Medicine in June 2022,and had realized achieving intelligent daily management such as indoor positioning of operating room equipment,one click inventory.A set of performance analysis method based on IoT and integrated with information systems was established to automatically count the utilization efficiency and cost-effectiveness of key surgical equipment to realize intelligent service,intelligent management,and digital operation.Conclusion:The construction and application of this platform improved the efficiency of medical equipment in operating rooms,reduced the cost and increased the efficiency,assisted in the refinement and intelligent management of hospital surgical equipment,and provided data support for scientific decision-making of hospital managers.
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ObjectiveTo provide more basic information of comparative medicine for the study of biological changes and pathogenesis of COVID-19 by systematical sorting and analyzing the transcriptome data.MethodsFollowing a retrieval strategy, using COVID-19 and SARS-CoV-2 as key words, transcriptome datasets related to COVID-19 from January 2020 to May 2023 were collected from GEO, ArrayExpress and GEN Transcriptome databases. The composition, distribution, and research application of COVID-19 transcriptome data resources were analyzed. Data distribution was visually displayed and correlation analysis was performed. The research applications and limitations of existing COVID-19 transcriptome data were analyzed from the perspectives of clinical medicine and comparative medicine, focusing on clinical-related molecular mechanisms, biomarkers and related immune responses, treatment intervention strategies, etc. The research value and application prospects were discussed.Results A total of 975 sets of COVID-19 transcriptome data were included. Among three databases, datasets from humans accounted for the highest proportion, namely 71.9%, 77.9%, and 90%, respectively. Species other than humans, such as mice, were the main sources of data, with the respiratory and nervous systems having the highest distribution of data. Twenty-seven datasets were associated with clinical significance. Analysis revealed that respiratory tract injury and other related molecular mechanisms were obtained through transcriptome data mining. Biomarkers such as cfDNA could be used as therapeutic targets. The severity of COVID-19 infection was associated with cell changes and disorders represented by M1 macrophages. Comparative medical analysis showed that mice, hamsters, and other animals were susceptible to SARS-CoV-2. Rhesus monkeys and cynomolgus monkeys exhibited infection characteristics highly similar to human. Apart from respiratory symptoms, hamsters also exhibited digestive system symptoms. SARS-CoV-2 can replicate in the respiratory organs of various susceptible animals, the intestines of ferrets and the ears of minks, resulting in interstitial pneumonia, diffuse lung injury and other pathological changes of varying degrees. Based on the differences in immune responses, hamsters can be used for neutralizing antibody reaction research.Conclusion Currently there is a wealth of COVID-19 transcriptome data, but there is a lack of comparative transcriptome research. Transcriptomics can be combined with comparative medicine to further explore the differences in comparative medicine of COVID-19.
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With the development of digital technology， an increasing number of artificial intelligence （AI） technologies are being applied in the field of public health， significantly improving the efficiency of healthcare systems. However， such technological advancement also introduces a series of ethical risks. In this article， we conducted a systematic review by searching nine domestic and international databases and analyzing the ethical issues related to AI in public health， ultimately including 158 articles. Based on the analysis of the included literature， ethical risks were categorized into four aspects： data， algorithms， rights and responsibilities， and social impact. A total of 15 key issues were identified， among which privacy and confidentiality， informed consent， data security， and fairness， justice and inclusion emerged as the most prominent issues. The ethical challenges posed by AI in the field of public health cannot be ignored， and it is necessary to formulate ethical guidelines and practical recommendations for AI in this field， establish sound regulatory and review mechanisms， thereby ensuring the healthy development of AI research in public health.
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Background: Competitive intelligence (CI) involves monitoring competitors and providing organizations with actionable and meaningful intelligence. Some studies have focused on the role of CI in other industries post-COVID-19 pandemic. Objectives: This article aims to examine the impact of COVID-19 on the South African insurance sector and how the integration of CI and related technologies can sustain the South African insurance sector post-COVID-19 epidemic. Method: Qualitative research with an exploratory-driven approach was used to examine the impact of the COVID-19 pandemic on the South African insurance sector. Qualitative secondary data analyses were conducted to measure insurance claims and death benefits paid during the COVID-19 pandemic. Results: The research findings showed that the COVID-19 pandemic significantly impacted the South African insurance industry, leading to a reassessment of pricing, products, and risk management. COVID-19 caused disparities in death benefits and claims between provinces; not everyone was insured. Despite challenges, South African insurers remained well-capitalised and attentive to policyholders. Integrating CI and analytical technologies could enhance the flexibility of prevention, risk management, and product design. Conclusion: COVID-19 requires digital transformation and CI for South African insurers' competitiveness. Integrating artificial intelligence (AI), big data (BD), and CI enhances value, efficiency, and risk assessments. Contribution: This study highlights the importance of integrating CI strategies and related technologies into South African insurance firms' operations to aid in their recovery from the COVID-19 crisis. It addresses a research gap and adds to academic knowledge in this area.
الموضوعات
Humans , Male , Female , Artificial Intelligence , COVID-19الملخص
ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.
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Purpose: To describe the demographics and clinical profile of pseudoexfoliation syndrome (PXF or PES) in patients presenting to a multi?tier ophthalmology hospital network in India. Methods: This cross?sectional hospital?based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of PXF in at least one eye were included as cases. The data were collected using an electronic medical record system. Results: Overall, 23,223 (0.75%) patients were diagnosed with PXF. The majority of the patients were male (67.08%) and had unilateral (60.96%) affliction. The most common age group at presentation was during the seventh decade of life with 9,495 (40.89%) patients. The overall prevalence was higher in patients from a lower socio?economic status (1.48%) presenting from the urban geography (0.84%) and in retired individuals (3.61%). The most common location of the PXF material was the pupillary margin (81.01%) followed by the iris (19.15%). The majority of the eyes had mild or no visual impairment (<20/70) in 12,962 (40.14%) eyes. PXF glaucoma was documented in 7,954 (24.63%) eyes. Krukenberg’s spindle was found in 64 (0.20%) eyes, phacodonesis in 328 (1.02%) eyes, and lens subluxation in 299 (0.93%) eyes. Among the surgical interventions, cataract surgery was performed in 8,363 (25.9%) eyes, trabeculectomy was performed in 966 (2.99%) eyes, and a combined procedure in 822 (2.55%) eyes. Conclusion: PXF more commonly affects males presenting during the seventh decade of life from lower socio?economic status and is predominantly unilateral. A quarter of the affected eyes are associated with glaucoma and the majority of the eyes have mild or no visual impairment.
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Purpose: To describe the demographics, clinical characteristics, and presentation of solar retinopathy in patients who presented to a multi?tier ophthalmology hospital network in India. Methods: This cross?sectional, hospital?based study included 3,082,727 new patients presenting to the hospital between August 2010 and December 2021. Patients with a clinical diagnosis of solar retinopathy in at least one eye were included in the study. All the data was collected using an electronic medical record system. Results: Three hundred and forty?nine eyes of 253 (0.01%) patients were diagnosed with solar retinopathy and included in the study, and 157 patients (62.06%) had a unilateral affliction. Solar retinopathy was noted to be significantly more common in males (73.12%) and adults (98.81%). The most common age group at presentation was during the sixth decade of life with 56 (22.13%) patients. They were more commonly from the rural geography (41.9%). Among the 349 eyes, 275 (78.8%) eyes had mild or no visual impairment (<20/70), which was followed by moderate visual impairment (>20/70–20/200) found in 45 (12.89%) eyes. The most commonly associated ocular comorbidity was cataract in 48 (13.75%) eyes, followed by epiretinal membrane in 38 (10.89%) eyes. The most common retinal damage seen was interdigitation zone (IZ) disruption (38.68%), followed by inner segment–outer segment (IS–OS) disruption (33.52%). Foveal atrophy was seen in 105 (30.09%) eyes. Conclusion: Solar retinopathy is predominantly unilateral and is more common in males. It usually presents during the sixth decade of life and rarely causes significant visual impairment. The most common retinal damage seen was disruption of the outer retinal layers
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Purpose: To describe the demographics, clinical profile, and outcomes of ocular siderosis in patients presenting to a multi?tier ophthalmology hospital network in India. Methods: This cross?sectional and hospital?based study included 3,082,727 new patients who presented between August 2010 and December 2021. Patients with a clinical diagnosis of ocular siderosis in at least one eye were included. Results: Overall, 58 eyes of 57 patients (0.002%) were diagnosed with ocular siderosis. The majority were men (96.49%) and had unilateral (98.25%) affliction. The most common age group at presentation was during the third decade of life with 24 patients (42.11%). A clear history of ocular trauma was documented in 47 patients (81.03%). Major clinical signs included corneal pigment deposition in nearly half of the eyes (27/58 eyes, 46.55%), corneal scar (20/58 eyes, 34.48%), cataract (22/58 eyes, 37.93%) and retinal detachment (11/58 eyes, 18.96%). The intraocular foreign body (IOFB) was anatomically localized in a majority of the eyes (i.e., 45/58 eyes, 77.59%). The most common location of the IOFB was in the posterior segment (22/58 eyes, 37.93%). The eyes that underwent a vitreoretinal surgery with removal of IOFB had a slightly better BCVA (1.0 ± 1.01) when compared to eyes with non?removal of IOFB (1.58 ± 1.00). Conclusion: Ocular siderosis is a rare sight?threatening entity, with half of the affected eyes exhibiting severe visual impairment. Majority of the eyes in ocular siderosis will have a detectable IOFB. Surgical removal of IOFB may lead to a better visual gain when compared to non?removal.
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With the continuous progress of research methodology in the real world and the growing maturity of artificial intelligence technology， a method for conducting “quantitative” research to guide clinical practice based on traditional Chinese medicine （TCM） diagnosis and treatment data was gradually developed. However， there is still a need for further improvements in the overall design of studies and the transformation of findings into clinical practice. Based on this， we put forward a comprehensive overall design concept and application approach for real-world study and artificial intelligence research based on clinical diagnosis and treatment data of TCM. This approach consists of five steps： Constructing a research-based database with a large sample size and high data quality； Mining and classification of core prescriptions； Conducting cohort studies to evaluate the effectiveness of core prescriptions； Utilizing case-control studies to clarify the dominant population； Establishing predictive models to achieve precision medicine. Additionally， it is imperative for researchers to establish a standardized system for collecting TCM variables and processing data， optimize the determination and measurement methods of confounding factors， further improve and promote methodologies， and strengthen the training of interdisciplinary talents. By following this research method， we anticipate that the clinical translation of research findings will be facilitated， leading to advancements in TCM precision medicine. Real-world study and artificial intelligence research share similar research foundations， and clinical applications complement each other. In the future， the two will merge together.
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With the development of Internet technology and big data, artificial intelligence has been widely used in the field of clinical anesthesia. In the field of clinical teaching, artificial intelligence has also led to a series of innovations and changes in teaching model, contents, and evaluation. With reference to the current status of the application of artificial intelligence in the field of anesthesia, this article analyzes the possible impact of artificial intelligence on teaching model, teaching effect evaluation, teaching management, and ethical issues in clinical anesthesia teaching, so as to provide a theoretical basis for integrating artificial intelligence into clinical anesthesia teaching practice in the future.
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Objective It combines medical big data and machine learning techniques to explore clinical outcomes based clinical physician performance evaluation method.Methods The non-negative principal component analysis(NPCA)was used in cases.Based on the non-negative sparse principal component analysis(NSPCA),a comprehen-sive index fitting was performed on 11 clinical performance indicators of 170 clinicians treating cardiovascular diseases.At the same time,confidence intervals were constructed based on root cause assessment techniques to calculate the range of indicators for each clinician.Results The coincidence rate of outpatient discharge diagnosis,the rate of grade A healing of surgical incision,the proportion of surgical patients,the rate of 3-day diagnosis,the proportion of third-grade and fourth-grade surgery,the completion of surgery and the number of operations were significant in dis-tinguishing the work performance of clinicians.However,the average length of hospital stays before surgery,the rate of unplanned readmission within 30 days,the average length of hospital stays of discharged patients,the main diag-nosis and cure/improvement,and the number of patients admitted were not significant in distinguishing the clinical work performance of clinicians.The overall work performance of all clinicians can be ranked through comprehensive index fitting,and the further evaluation of high,middle and low performance of each specific index can reveal the potential reconstruction dimensions of each clinician.Conclusion It utilizes machine learning techniques to achieve a comprehensive evaluation of clinical performance,utilizing medical big data as the foundation.It holds the potential to provide important support for a more scientific and objective assessment of clinical performance.
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The medical big data platform for mental illness was constructed by analyzing the clinical characteris-tics and application needs of mental disorders,which was built on the Hadoop ecological construction and used com-mon data model and standard data element dictionary.The construction of the medical big data platform distributes computing framework which provides real-time and full-scale computing,machine learning,and map computing ca-pabilities of medical data,provides efficient inquiries,multi-dimensional statistics and analysis,and supports for scientific research,medical service quality management and operation management.
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Clinical pathway management is an important part of public hospital reform, providing great significance for regulating medical behavior, improving medical quality, and controlling unreasonable medical expenses. In December 2020, a tertiary hospital conducted clinical pathway management practices based on clinical decision support systems. Through segmented design of clinical pathways, dynamic adjustment of pathway execution process, quality control throughout the entire process, and performance evaluation and continuous improvement, the management level of clinical pathways was comprehensively improved. After one year of practice, the hospital clinical pathway management based on clinical decision support systems had achieved good results. The average days of stay of patients decreased from 6.58 d in 2020 to 6.32 d in 2021, the proportion of admitted patients to discharged patients increased from 63.10% to 67.47%, the admission rate increased from 88.32% to 90.43%, and the variation rate decreased from 20.03% to 15.00%. This management practice provided a reference for public hospitals in China to improve the quality and efficiency of clinical pathway management.
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The establishment of mental health assessment system provides a new way for the early diagnosis of mental health problems, in view of the growing population of mental diseases and problems and the uneven distribution of mental health resources. In the mental health assessment system, intelligent assistant diagnosis can assist or help psychiatrists improve their work efficiency. Intelligent assistant diagnosis provides technical support for predictive screening and auxiliary diagnosis of mental health problems. It is an intelligent diagnosis research based on big data analysis and machine learning in mental health assessment system. This article mainly reviews the application methods, the application progress in the field of mental health, as well as related technical issues and safety issues, and prospects the future research development.
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Objective To introduce and evaluate the practice of “Internet Plus” new technology for health management of chronic diseases in community in Yichang, and to provide reference for chronic disease patients' health management in community. Methods Data of hypertensive patients were collected from the national basic public health service system, the big data intelligent sorting system for chronic disease patients in Yichang City, and the basic public health service system in urban areas in Yichang from 2016 to 2020. Data on the discovery, sorting and filing, standardized management rate and blood pressure control of urban hypertension patients were analyzed. The application effect of “Internet Plus” new technology in chronic disease community health management was evaluated. Results From 2016 to 2020, 15 934 patients with hypertension were found and their health records were established through big and intelligent data in Yichang City, accounting for 93.54% (15 934 / 17 035) of the total. The rate of standardized management in each district increased year by year, with an increase of 8.71% in 2020 compared with 2016, and the difference was statistically significant (χ2=1273.30, P2=867.14, P<0.001). Conclusion Data exchange and sharing among medical institutions at all levels can strengthen the health management of chronic diseases in the community. The “Internet Plus” new technology, integrating the Internet, big data, cloud computing and intelligent terminal technology, can effectively improve the detection, management and treatment rate of chronic diseases, and provide a new direction for the health management of chronic diseases.