Economic evaluations of big data analytics for clinical decision-making: a scoping review

Pure drug nanomedicines - where we are?

Pure drug nanomedicines (PDNs) encompass active pharmaceutical ingredients (APIs), including macromolecules, biological compounds, and functional components. They overcome research barriers and conversion thresholds associated with nanocarriers, offering advantages such as high drug loading capacity, synergistic treatment effects, and environmentally friendly production methods. This review provides a comprehensive overview of the latest advancements in PDNs, focusing on their essential components, design theories, and manufacturing techniques. The physicochemical properties and in vivo behaviors of PDNs are thoroughly analyzed to gain an in-depth understanding of their systematic characteristics. The review introduces currently approved PDN products and further explores the opportunities and challenges in expanding their depth and breadth of application. Drug nanocrystals, drug-drug cocrystals (DDCs), antibody-drug conjugates (ADCs), and nanobodies represent the successful commercialization and widespread utilization of PDNs across various disease domains. Self-assembled pure drug nanoparticles (SAPDNPs), a next-generation product, still require extensive translational research. Challenges persist in transitioning from laboratory-scale production to mass manufacturing and overcoming the conversion threshold from laboratory findings to clinical applications.

Metacognitive processes, situational factors, and clinical decision-making in nursing education: a quantitative longitudinal study

OBJECTIVE

This study examined the longitudinal development of metacognitive skills and clinical decision-making abilities in nursing students, focusing on the interactions between metacognitive processes, situational factors, and individual differences.

METHODS

A longitudinal, quantitative design was employed, following 185 third-year nursing students from a major university in China over one academic year. Data were collected at six time points using the Metacognitive Awareness Inventory, Nursing Decision-Making Instrument, and custom-designed clinical scenario assessments. Latent Growth Curve Modeling, Multilevel Modeling, and Moderation Analysis were used to analyze the data.

RESULTS

Significant positive growth trajectories were observed for both metacognitive awareness (mean slope = 0.07, p < .001) and decision-making skills (mean slope = 0.08, p < .001). Metacognitive regulation emerged as the strongest predictor of decision-making outcomes (β = 0.188, p < .001 for quality; β = 0.168, p < .001 for efficiency). Task complexity negatively impacted decision-making quality (β = -0.129, p < .001), while time pressure more strongly affected efficiency (β = -0.121, p < .001). Cognitive style and emotional intelligence moderated the relationship between metacognitive processes and decision-making outcomes. The effectiveness of metacognitive strategies in mitigating the negative effects of situational factors varied across individuals and over time.

CONCLUSION

This study provides robust evidence for the complex interplay between metacognitive processes, situational factors, and individual differences in the development of clinical decision-making skills among nursing students. The findings highlight the importance of tailoring educational interventions to enhance specific metacognitive skills, particularly regulation, while considering the impact of situational factors and individual cognitive styles. These insights can inform the design of more effective, personalized approaches to nursing education, potentially enhancing the preparation of nursing students for the complexities of clinical practice.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

Clinical Care of Hyperthyroidism Using Wearable Medical Devices in a Medical IoT Scenario

This paper presents an in-depth study and analysis of clinical care of patients with hyperthyroidism using wearable medical devices in the context of medical IoT scenarios. According to the use scenario of the gateway and the connectivity of the equipment, the hardware architecture, hardware interfaces, functionality, and performance of the gateway were briefly designed, so as to monitor patients with hyperthyroidism more comprehensively and save labor costs. The gateway can provide access to different devices and adaptation functions to different hardware interfaces and provide hardware support for the subsequent deployment of the proposed new medical communication protocols and related information systems. A medical data convergence information system based on multidevice management and multiprotocol parsing was designed and implemented. The system enables the management and configuration of different medical devices and access to data through the targeted parsing of the underlying medical device communication protocols. The system also provides the automatic adaptation of multiple types of underlying medical device communication protocols and automatic parsing of multiple versions and can provide multiple devices to process fused data streams or device information and data from a single device. The use of event-driven asynchronous communication eliminates the tight dependency on service invocation in the synchronous communication approach. The use of a metadata-based data model structure enables model extensions to accommodate the impact of iterative business requirements on the database structure. Real-time patient physiological data transmission for intraoperative monitoring based on the MQTT protocol and video transmission for intraoperative patient monitoring based on the RTMP protocol were implemented. The development of the intelligent medical monitoring service system was completed, and the system was tested, optimized, and deployed. The functionality and performance of the system were tested, the performance issue of slow query speed was optimized, and the deployment of the project using Docker containers was automated.

Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network

With the advent of the era of economic globalization, the world capital market is also facing financial risks. It is necessary to have a corresponding financial management early warning model to reduce economic losses. This paper uses the combination of ant colony algorithm and neural network algorithm to build a neural network improved by ant colony algorithm model. By setting relevant assumptions, the financial statements and annual report texts are predicted and analyzed and compared with the original static data forecasting model. Compared with traditional methods, the time series sequencing analysis used in this paper makes the result prediction more accurate. This allows one year's data to be used to predict the data for the next two years. This research can provide a corresponding reference for the optimization of financial management early warning system.

How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

BACKGROUND

Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments.

METHODS

The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions.

RESULTS

When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (- 0.5%, - €886) and to improve patient-ventilator interaction (- 3%, - €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings.

CONCLUSIONS

We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm

At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.

Human Skeleton Detection and Extraction in Dance Video Based on PSO-Enabled LSTM Neural Network

With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video.

The Clinical Information Systems Response to the COVID-19 Pandemic

OBJECTIVE

The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.

METHODS

PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.

RESULTS

CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.

CONCLUSION

Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

Purpose

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Design/methodology/approach

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

Findings

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

Research limitations/implications

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

Originality/value

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.