Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures.

Manual microscopy of Gram stains from positive blood cultures (PBCs) is crucial for diagnosing bloodstream infections but remains labor intensive, time consuming, and subjective. This study aimed to evaluate a scan and analysis system that combines fully automated digital microscopy with deep convolutional neural networks (CNNs) to assist the interpretation of Gram stains from PBCs for routine laboratory use. The CNN was trained to classify images of Gram stains based on staining and morphology into seven different classes: background/false-positive, Gram-positive cocci in clusters (GPCCL), Gram-positive cocci in pairs (GPCP), Gram-positive cocci in chains (GPCC), rod-shaped bacilli (RSB), yeasts, and polymicrobial specimens. A total of 1,555 Gram-stained slides of PBCs were scanned, pre-classified, and reviewed by medical professionals. The results of assisted Gram stain interpretation were compared to those of manual microscopy and cultural species identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The comparison of assisted Gram stain interpretation and manual microscopy yielded positive/negative percent agreement values of 95.8%/98.0% (GPCCL), 87.6%/99.3% (GPCP/GPCC), 97.4%/97.8% (RSB), 83.3%/99.3% (yeasts), and 87.0%/98.5% (negative/false positive). The assisted Gram stain interpretation, when compared to MALDI-TOF MS species identification, also yielded similar results. During the analytical performance study, assisted interpretation showed excellent reproducibility and repeatability. Any microorganism in PBCs should be detectable at the determined limit of detection of 105 CFU/mL. Although the CNN-based interpretation of Gram stains from PBCs is not yet ready for clinical implementation, it has potential for future integration and advancement.

CProtMEDIAS: clustering of amino acid sequences encoded by gene families by MErging and DIgitizing Aligned Sequences.

Protein phylogenetic analysis focuses on the evolutionary relationships among related protein sequences and can help researchers infer protein functions and developmental trajectories. With the advent of the big data era, the existing protein phylogenetic methods, including distance matrix and character-based methods, are facing challenges in both running time and application scope. Here, we developed an R package that we call CProtMEDIAS that is useful for protein phylogenetic analysis. In contrast to existing phylogenetic analysis methods, CProtMEDIAS utilizes dimensionality reduction algorithms to digitize multiple sequence alignments and quickly conduct phylogenetic analysis with a large number of amino acid sequences from similarly distant protein families and species. We used CProtMEDIAS to perform a dimensionality reduction, clustering, pseudotime, specific residue and evolutionary trajectory analysis of the plant homeobox superfamily. We found that CProtMEDIAS delivers consistent clustering, fast running and elegant presentation and thus provides powerful new tools and methods for protein clustering and evolutionary analysis.

SurvdigitizeR: an algorithm for automated survival curve digitization.

BACKGROUND: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. METHODS: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. RESULTS: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. CONCLUSIONS: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

The individuals' awareness and adoption of electronic health records in China: a questionnaire survey of 1,337 individuals.

BACKGROUND: Electronic health records (EHRs) are digital records of individual health information. However, their adoption and utilization remain low. This study explores the factors influencing the implementation of EHRs through a questionnaire survey to enhance individual awareness and adoption of EHRs. METHODS: A questionnaire and an expert rating scale were developed sequentially, and the consistency of the scores from five experts was calculated using Kendall's W to generate a final questionnaire. A non-parametric test was utilized to analyze differences in continuous data that did not follow a normal distribution. Categorical variables were expressed as percentages (%), the chi-square test was employed for group comparisons, and multiple logistic regression was implemented to assess individuals' awareness and adoption of EHRs. RESULTS: In total, 1,341 survey questionnaires were distributed between January and December 2022, with 1,337 valid responses (99.7%). The results indicated that the proportion of participants who were aware of EHRs and had a bachelor's degree or higher education, an income of ≥$700 per month, residence in urban areas, possessed self-care abilities, and underwent annual physical examinations was significantly higher than that without awareness of EHRs (P < 0.05), while in hearing problems and walking abilities was markedly lower than that of participants without awareness of EHRs (P < 0.05). Additionally, the proportion of individuals willing to self-manage EHRs was significantly higher than those reluctant to do so (P < 0.05) among participants with a bachelor's degree or higher education, an income of ≥$700 per month, residence in urban areas, possession of self-care abilities, annual physical examinations, hearing problems, and poor walking abilities. Age (Odds Ratio [OR] = 1.104, 95% Confidence Interval [CI] 1.001-1.028, P = 0.033), hearing problems (OR = 0.604, 95% CI 0.377-0.967, P = 0.036), self-care ability (OR = 5.881, 95% CI 1.867-18.529, P = 0.002), and annual physical examinations (OR = 3.167, 95% CI 2.31-4.34, P < 0.001) were independently associated with willingness to self-manage EHRs. Annual physical examination (OR = 2.507, 95%CI 1.585-2.669, P < 0.001) also independently made a difference to the awareness of EHRs. CONCLUSIONS: Our findings suggest that annual physical examinations, age, hearing problems, and self-care abilities are significant factors in assessing individuals' awareness and adoption of EHRs. Understanding the characteristics of individuals who are aware of or are willing to take advantage of EHRs plays a positive role in promoting their popularization and application.

The impact of chronic diseases on the health-related quality of life of middle-aged and older adults: the role of physical activity and degree of digitization.

BACKGROUND: The incidence of chronic diseases is on the rise worldwide, with a high mortality rate in China, posing a serious threat to the health-related quality of life (HRQoL) of middle-aged and older adults. This study explores the association between chronic diseases and the HRQoL of middle-aged and older adults, as well as the role of physical activity (PA) and degree of digitization in this relationship. METHODS: The data used in this study was obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS), which included 13,620 middle-aged and older Chinese adults (≥ 45 years). The study utilized correlation analysis, and bootstrapping to investigate the mediating role of PA and the moderating influence of the degree of digitization. Data analysis was conducted using SPSS 26.0. RESULTS: The study findings indicate that the severity of chronic disease has a significant negative predictive effect on HRQoL (PCS, physical component summary; MCS, mental component summary) (PCS: ß = -2.515, p < 0.01, MCS: ß = -0.735, p < 0.01). Further analysis revealed that PA plays a mediating and masking role in the impact of chronic disease on PCS and MCS. Additionally, the degree of digitization moderates the relationship between chronic disease and PA, chronic disease and PCS, and PA and PCS. CONCLUSION: For middle-aged and older persons, chronic diseases have a detrimental effect on their HRQoL; nevertheless, PA can help. Furthermore, proper internet usage can help older individuals to some extent in mitigating the negative impact of chronic diseases. Therefore, it is encouraged to promote PA among the elderly with chronic diseases to improve their physical health, as well as to guide them in the proper use of the Internet to establish healthy behaviors.

Evaluating free segmentation tools for CBCT-derived models: Cost-effective solutions.

OBJECTIVES: This study evaluated the segmentation accuracy and reliability of free software packages and compared them with commercial alternatives. MATERIAL AND METHODS: A total of 36 stone models were scanned using a desktop scanner and then imaged by cone beam computed tomography (CBCT). The CBCT volumes were segmented using 2 free software packages (3D Slicer and Blue Sky Plan) and 2 commercial software packages (Mimics and OnDemand3D). Stereolithography (STL) files generated by the desktop scanner were used as the control group (reference models). The accuracy of segmentation was evaluated by (1) comparing 6 linear measurements taken from each STL model generated by the 4 software packages with that obtained by the scanner, and (2) deviation analysis of each STL model generated by the 4 software packages with that obtained by the scanner. Absolute error and percentage error, repeated measures anova and Friedman test followed by post hoc analysis, intraclass correlation coefficient (ICC), and Pearson's r were used to evaluate the accuracy of the tested software packages. RESULTS: There was no statistically significant difference in all intra-arch measurements obtained using the four software packages. Measurements obtained using the free software packages and the scanner showed excellent positive correlation, ranging from 0.825 to 0.988, confirming equivalence with commercial software packages. CONCLUSION: Within the settings of the current study, accurate and time-saving segmentations with high positive correlation could be performed using the tested free segmentation software packages (3D Slicer and Blue Sky Plan). Nevertheless, further evaluation is necessary to gage their accuracy using different CBCT modalities.

Leveraging electronic health records to streamline the conduct of cardiovascular clinical trials.

Conventional randomized controlled trials (RCTs) can be expensive, time intensive, and complex to conduct. Trial recruitment, participation, and data collection can burden participants and research personnel. In the past two decades, there have been rapid technological advances and an exponential growth in digitized healthcare data. Embedding RCTs, including cardiovascular outcome trials, into electronic health record systems or registries may streamline screening, consent, randomization, follow-up visits, and outcome adjudication. Moreover, wearable sensors (i.e. health and fitness trackers) provide an opportunity to collect data on cardiovascular health and risk factors in unprecedented detail and scale, while growing internet connectivity supports the collection of patient-reported outcomes. There is a pressing need to develop robust mechanisms that facilitate data capture from diverse databases and guidance to standardize data definitions. Importantly, the data collection infrastructure should be reusable to support multiple cardiovascular RCTs over time. Systems, processes, and policies will need to have sufficient flexibility to allow interoperability between different sources of data acquisition. Clinical research guidelines, ethics oversight, and regulatory requirements also need to evolve. This review highlights recent progress towards the use of routinely generated data to conduct RCTs and discusses potential solutions for ongoing barriers. There is a particular focus on methods to utilize routinely generated data for trials while complying with regional data protection laws. The discussion is supported with examples of cardiovascular outcome trials that have successfully leveraged the electronic health record, web-enabled devices or administrative databases to conduct randomized trials.

Comparison of failure modes and effects analyses and time for brachytherapy ring and tandem applicator digitization between manual and solid applicator source placement methods.

PURPOSE: Ring and tandem (R&T) applicator digitization is currently performed at our institution by manually defining the extent of the applicators. Digitization can also be achieved using solid applicators: predefined, 3D models with geometric constraints. This study compares R&T digitization using manual and solid applicator methods through Failure Modes and Effects Analyses (FMEAs) and comparative time studies. We aim to assess the suitability of solid applicator method implementation for R&T cases METHODS: Six qualified medical physicists (QMPs) and two medical physics residents scored potential modes of failure of manual digitization in an FMEA as recommended by TG-100. Occurrence, severity, and detectability (OSD) values were averaged across respondents and then multiplied to form combined Risk Priority Numbers (RPNs) for analysis. Participants were trained to perform treatment planning using a developed solid applicator protocol and asked to score a second FMEA on the distinct process steps from the manual method. For both methods, participant digitization was timed. FMEA and time data were analyzed across methods and participant samples RESULTS: QMPs rated the RPNs of the current, manual method of digitization statistically lower than residents did. When comparing the unique FMEA steps between the two digitization methods, QMP respondents found no significant difference in RPN means. Residents, however, rated the solid applicator method as higher risk. Further, after the solid applicator method was performed twice by participants, the time to digitize plans was not significantly different from manual digitization CONCLUSIONS: This study indicates the non-inferiority of the solid applicator method to manual digitization in terms of risk, according to QMPs, and time, across all participants. Differences were found in FMEA evaluation and solid applicator technique adoption based on years of brachytherapy experience. Further practice with the solid applicator protocol is recommended because familiarity is expected to lower FMEA occurrence ratings and further reduce digitization times.

The impact of digital technology use on nurses' professional identity and relations of power: a literature review.

AIM: This study seeks to review how the use of digital technologies in clinical nursing affects nurses' professional identity and the relations of power within clinical environments. DESIGN: Literature review. DATA SOURCES: PubMed and CINAHL databases were searched in April 2023. METHODS: We screened 874 studies in English and German, of which 15 were included in our final synthesis reflecting the scientific discourse from 1992 until 2023. RESULTS: Our review revealed relevant effects of digital technologies on nurses' professional identity and power relations. Few studies cover outcomes relating to identity, such as moral agency or nurses' autonomy. Most studies describe negative impacts of technology on professional identity, for example, creating a barrier between nurses and patients leading to decreased empathetic interaction. Regarding power relations, technologically skilled nurses can yield power over colleagues and patients, while depending on technology. The investigation of these effects is underrepresented. CONCLUSION: Our review presents insights into the relation between technology and nurses' professional identity and prevalent power relations. For future studies, dedicated and critical investigations of digital technologies' impact on the formation of professional identity in nursing are required. IMPLICATIONS FOR THE PROFESSION: Nurses' professional identity may be altered by digital technologies used in clinical care. Nurses, who are aware of the potential effects of digitized work environments, can reflect on the relationship of technology and the nursing profession. IMPACT: The use of digital technology might lead to a decrease in nurses' moral agency and competence to shape patient-centred care. Digital technologies seem to become an essential measure for nurses to wield power over patients and colleagues, whilst being a control mechanism. Our work encourages nurses to actively shape digital care. REPORTING METHOD: We adhere to the JBI Manual for Evidence Synthesis where applicable. EQUATOR reporting guidelines were not applicable for this type of review. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Advanced Monitoring of Manufacturing Process through Video Analytics.

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.

Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual's health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.

RGB Color Model: Effect of Color Change on a User in a VR Art Gallery Using Polygraph.

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.

High Spatial Resolution Detector System Based on Reconfigurable Dual-FPGA Approach for Coincidence Measurements.

Time-resolved spectroscopic and electron-ion coincidence techniques are essential to study dynamic processes in materials or chemical compounds. For this type of analysis, it is necessary to have detectors capable of providing, in addition to image-related information, the time of arrival for each individual detected particle ("x, y, time"). The electronics capable of handling such sensors must meet requirements achievable only with time-to-digital converters (TDC) with a resolution on the order of tens of picoseconds and the use of a field-programmable gate array (FPGA) to manage data acquisition and transmission. This study introduces the design and implementation of an innovative TDC based on two FPGAs working symbiotically with different tasks: the first (AMD/Xilinx Artix® 7) directly implements a TDC, aiming for a temporal precision of 12 picoseconds, while the second (Intel Cyclone® 10) manages the acquisition and connectivity with the external world. The TDC has been optimized to operate on eight channels (+ sync) simultaneously but is potentially extendable to a greater number of channels, making it particularly suitable for coincidence measurements where it is necessary to temporally correlate multiple pieces of information from various measurement systems.

Digital stressors and resources perceived by emergency physicians and associations to their digital stress perception, mental health, job satisfaction and work engagement.

BACKGROUND: Digital technologies are increasingly being integrated into healthcare settings, including emergency departments, with the potential to improve efficiency and patient care. Although digitalisation promises many benefits, the use of digital technologies can also introduce new stressors and challenges among medical staff, which may result in the development of various negative work and health outcomes. Therefore, this study aims to identify existing digital stressors and resources among emergency physicians, examine associations with various work- and health-related parameters, and finally identify the potential need for preventive measures. METHODS: In this quantitative cross-sectional study, an online questionnaire was used to examine the relationship between digital stressors (technostress creators), digital resources (technostress inhibitors), technostress perception as well as mental health, job satisfaction and work engagement among 204 physicians working in German emergency medicine departments. Data collection lasted from December 2022 to April 2023. Validated scales were used for the questionnaire (e.g. "Technostress"-scale and the Copenhagen Psychosocial Questionnaire (COPSOQ). Descriptive and multiple regression analyses were run to test explorative assumptions. RESULTS: The study found medium levels of technostress perception among the participating emergency physicians as well as low levels of persisting technostress inhibitors. The queried physicians on average reported medium levels of exhaustion symptoms, high levels of work engagement and job satisfaction. Significant associations between digital stressors and work- as well as health-related outcomes were analyzed. CONCLUSION: This study provides a preliminary assessment of the persistence of digital stressors, digital resources and technostress levels, and their potential impact on relevant health and work-related outcomes, among physicians working in German emergency departments. Understanding and mitigating these stressors is essential to promote the well-being of physicians and ensure optimal patient care. As digitisation processes will continue to increase, the need for preventive support measures in dealing with technology stressors is obvious and should be expanded accordingly in the clinics. By integrating such support into everyday hospital life, medical staff in emergency departments can better focus on patient care and mitigate potential stress factors associated with digital technologies.

Additively manufactured CAD-CAM complete dentures with intraoral scanning and cast digitization: A controlled clinical trial.

PURPOSE: To assess, clinically, patient satisfaction of additively manufactured complete dentures with intraoral scanning and hybrid cast digitization in comparison with conventional complete dentures. MATERIALS AND METHODS: Participants who were edentulous in both arches were recruited and received three types of complete dentures (CDs): conventionally manufactured with conventional impression (CC), additively manufactured with intraoral scanning (AMI), and additively manufactured with cast digitization (AMH). Definitive impressions of the edentulous arches were made with medium viscosity polyvinyl siloxane (Hydrorise Monophase; Zhermack, Italy) for the CC group, intraoral scanning (TRIOS 4; 3Shape, Copenhagen, Denmark) for the AMI group, and laboratory scanning of the definitive casts (Ceramill Map400 AMANNGIRRBACH, Pforzheim, Deutschland) for the AMH group. The trial dentures of the CC group were scanned for occlusion registrations of the AMI and AMH groups and were used to guide the designing process (Exocad 3.0 Galway; Exocad GmbH). The AMI and AMH dentures were additively manufactured with a vat-polymerization 3D printer (Sonic XL 4K; phrozen, Taiwan). Patient satisfaction and clinical outcome were assessed with OHIP EDENT, and 14-factor criteria, respectively. Statistical analyses were performed with paired sample t-test and one-way repeated measure ANOVA for satisfaction, Wilcoxon signed rank test for clinical outcome, and Pearson's r (r) for effect size, with α = 0.05. RESULTS: A total of 20 participants were included. Satisfaction had no statistically significant difference within or among the groups (p < 0.105). Within-group comparison between the two arches showed no statistical significance for the clinical outcome except for a significantly higher maxillary AMI score (p = 0.01, r = -0.40 with medium effect size). For among group's comparison; AMI had a significantly lower score than CC for the maxillary and mandibular arches (p = 0.01, r = -0.40, medium effect size, and p = 0.003, r = -0.47, medium effect size), and significantly lower score than the mandibular AMH (p = 0.03, r = -0.47, medium effect size), with significantly lower quality in teeth arrangement and retention domains for the AMI, and teeth arrangement for the AMH, in comparison with CC. CONCLUSIONS: Patient satisfaction with both types of additively manufactured dentures is comparable to conventional dentures. The comparable overall clinical outcomes between hybrid and conventional dentures indicate that additive manufacturing is an acceptable clinical substitute for the conventional methods. However, additively manufactured dentures made with intraoral scanning have lower clinical quality and retention than hybrid and conventional dentures, particularly for the mandibular arch. Teeth arrangement of both additively manufactured dentures is clinically inferior to the conventional denture.

[Current situation and development trend of digital traditional Chinese medicine pharmacy].

The 21st century is a highly information-driven era, and traditional Chinese medicine(TCM) pharmacy is also moving towards digitization and informatization. New technologies such as artificial intelligence and big data with information technology as the core are being integrated into various aspects of drug research, manufacturing, evaluation, and application, promoting interaction between these stages and improving the quality and efficiency of TCM preparations. This, in turn, provides better healthcare services to the general population. The deep integration of emerging technologies such as artificial intelligence, big data, and cloud computing with the TCM pharmaceutical industry will innovate TCM pharmaceutical technology, accelerate the research and industrialization process of TCM pharmacy, provide cutting-edge technological support to the global scientific community, boost the efficiency of the TCM industry, and promote economic and social development. Drawing from recent developments in TCM pharmacy in China, this paper discussed the current research status and future trends in digital TCM pharmacy, aiming to provide a reference for future research in this field.

[The impact of digital technologies on labor organization in health care scope].

Enhancement of the health care system in Russia continues to be one of key directions of National development. In conditions of deficiency of personnel, systemic changes are needed to transit to qualitatively new level. The application of digital platforms permits to resolve a number of issues related to accessibility and quality of medical services. The paper characterizes current state of health care digitization and level of competitiveness of medical institutions. On the basis of analysis of corporate culture of medical institutions, conclusions are made that it contributes to successful implementation of unified medical information system and development of best corporate standards and traditions.

[DEVELOPMENT OF THE SECTOR of R&D development in Russian HIGHER medical education in digitization conditions].

The article considers results of study of the R&D sector directions development in system of Russian medical higher education in the context of digitization. The analysis made it possible to substantiate timeliness and importance of solving problems of improving medical services quality against the background of necessity for medical workers to systematically master breakthrough digital technologies in current medical practice. This conditions importance to ensure efficiency of R&D in Russian higher medical education in the context of digitization. The main models of organization of activities in the field of R&D are characterized, taking into account sources of financing and staffing. Based on on-line surveys of students in Google docs about their attitude to their further participation in R&D during education and after graduation, weak motivation to be engaged in R&D during their university education was revealed that is justified by non-obviousness of prospects of financial remuneration. Along with this, study established high degree of interest among students to mastering breakthrough digital technologies in order to further apply them in medical practice together with the latest medical technologies.

Digitization of Pathology Labs: A Review of Lessons Learned.

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.