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Osteogenesis imperfecta (OI) is a rare bone fragility disorder caused by mutations in genes encoding collagen type I or that affect its processing. Alterations in osteoclasts were suggested to contribute to OI pathophysiology. We aimed to systematically identify studies reporting measures of osteoclast formation and function in patients and mouse models of OI, to quantify OI-induced changes. The systematic search of Medline, Ovid, and Web of Science identified 798 unique studies. After screening, we included 23 studies for meta-analysis, reporting osteoclast parameters in 310 patients with OI of 9 different types and 16 studies reporting osteoclast parameters in 406 animals of 11 different OI mouse models. The standardized mean difference with 95% confidence interval (CI) was used as the effect size, and random-effects meta-analysis was performed. In patients with OI, collagen degradation markers were significantly higher compared with age-matched controls, with an effect size of 1.23 (CI: 0.36, 2.10]. Collagen degradation markers were the most elevated in the 3- to 7-year-old age group and in patients with more severe forms of OI. Bone histomorphometry demonstrated the trends for higher osteoclast numbers (1.16; CI: −0.22, 2.55) and osteoclast surface (0.43; CI: −0.63, 1.49), and significantly higher eroded surface (3.24; CI: 0.51, 5.96) compared with age-matched controls. In OI mice, meta-analysis demonstrated significant increases in collagen degradation markers (1.59; CI: 1.07, 2.11), in osteoclast numbers (0.94; CI: 0.50, 1.39), osteoclast surface (0.73; CI: 0.22, 1.23), and eroded surface (1.31; CI: 0.54, 2.08). The largest differences were in OI mice with the mutations in Col1a1 and Col1a2 genes. There were no differences between males and females in clinical or animal studies. Quantitative estimates of changes in osteoclast indices and their variance for patients with OI are important for planning future studies. We confirmed that similar changes are observed in mice with OI, supporting their translational utility. Graphical Abstract [ABSTRACT FROM AUTHOR]

Summary In the present‐day scenario, multimedia technology serves as a vital medium for communication, facilitating the exchange of information among individuals across various sectors such as education, mass media, banking, and healthcare. In our proposal for a centralized system to store patient data, we aim to enhance healthcare services by enabling doctors to access the medical history of every patient seamlessly. To ensure hassle‐free services, we have devised a for authenticated key sharing protocol leveraging cryptographic hash functions for the Internet of Things healthcare system. A key advantage of our approach is that patients need not undergo medical tests repeatedly, unless it is deemed necessary due to diagnostic requirements. We have provided informal proof of the protocol's ability to safeguard against security threats. Furthermore, we have conducted simulations using the Scyther simulation tool to verify the strength of our protocol, affirming its safety and effectiveness. Our performance analysis section demonstrates significantly improved complexities compared to existing protocols, further emphasizing the reliability and efficiency of our proposed solution. [ABSTRACT FROM AUTHOR]

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BACKGROUND: Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions. OBJECTIVE: We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data. METHODS: Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data. RESULTS: Among 112 youths, (age: mean 16.1, SD 1.6 y; female: n=72, 64.3%), 83 (74.1%) agreed to share social media data. Female participants were more likely to share (P=.04). In all, 49 (43.8%) and 28 (25%) participants viewed and posted about rheumatic disease, respectively. Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02) and more pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P

The healthcare industry is increasingly reliant on access to large volumes of patient data, which is critical for improving patient outcomes, reducing costs, and ensuring regulatory compliance. However, there are significant challenges facing healthcare organizations in retrieving patient data from databases and ensuring the security and privacy of this data. Addressing these challenges is vital to the future success of the healthcare industry. This paper proposes two solutions to these challenges: a simplified ontology-based retrieval algorithm for healthcare multimedia data to enhance the retrieval process, and Hierarchical learning: ensemble model-based reinforcement learning to improve patient data security. An ontology-based retrieval algorithm was a method used to retrieve relevant information from large datasets. It filtered out irrelevant data and provided contextually relevant results. Hierarchical learning involved organizing learning tasks in a hierarchical structure, with higher-level policies guiding lower-level policies. Ensemble model-based reinforcement learning combined multiple models to improve the performance of a reinforcement learning system. The Simplified Ontology-Based Retrieval Algorithm had excellent performance with an accuracy of 92%, making it highly effective in information retrieval tasks. The paper highlights the importance of using advanced technologies and prioritizing cyber security in healthcare to maintain trust and optimal patient care. Cutting-edge technologies transformed healthcare by enhancing patient data retrieval and cyber security. Examples included encrypted computations, multi-party privacy, query result anonymization, real-time monitoring through IoMT, and secure data sharing. Adopting these solutions ultimately contributed to the protection of sensitive patient data and the overall security of healthcare organizations. [ABSTRACT FROM AUTHOR]

In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring patient behavior. Among patient-centric concerns, addressing data heterogeneity, extraction, and prediction challenges is crucial. To enhance patient monitoring using care indicators like cost and length of stay at healthcare centers, many researchers found a model for automated tools, but do not have the artificial intelligence (AI) based models as of now. Therefore, this research study will propose an AI and internet of things (IoT) integrated automated approach with smart sensors called the "PatientE" framework with heterogeneity and patient data. Employing certain rules for data extraction to form a distinct representation, our model integrates pretreatment information and employs a modified combined random forest, long-short term memory (LSTM), and bidirectional long-short term memory (BiLSTM) algorithm for predictive post-treatment monitoring. This framework, synergizing AI, IoT, and advanced neural networks, facilitates real-time health monitoring, especially focusing on breast cancer patients. Embracing pre-treatment, in-treatment, and post-treatment phases, our model aims for accurate diagnosis, improved cost-efficiency, and extended stays. The evaluation underscores scalability, reliability enhancement, and validates the framework's efficacy in transforming healthcare practices. [ABSTRACT FROM AUTHOR]

Digital health interventions offer a promising approach for monitoring during postoperative recovery. However, the effectiveness of these interventions remains poorly understood, particularly in children. The objective of this study was to assess the efficacy of digital health interventions for postoperative recovery in children. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the use of automation tools for searching and screening. We searched five electronic databases for randomised controlled trials or non-randomised studies of interventions that utilised digital health interventions to monitor postoperative recovery in children. The study quality was assessed using Cochrane Collaboration's Risk of Bias tools. The systematic review protocol was prospectively registered with PROSPERO (CRD42022351492). The review included 16 studies involving 2728 participants from six countries. Tonsillectomy was the most common surgery and smartphone apps (WeChat) were the most commonly used digital health interventions. Digital health interventions resulted in significant improvements in parental knowledge about the child's condition and satisfaction regarding perioperative instructions (standard mean difference=2.16, 95% confidence interval 1.45–2.87; z =5.98, P <0.001; I 2=88%). However, there was no significant effect on children's pain intensity (standard mean difference=0.09, 95% confidence interval −0.95 to 1.12; z =0.16, P =0.87; I 2=98%). Digital health interventions hold promise for improving parental postoperative knowledge and satisfaction. However, more research is needed for child-centric interventions with validated outcome measures. Future work should focus development and testing of user-friendly digital apps and wearables to ease the healthcare burden and improve outcomes for children. PROSPERO (CRD42022351492). [ABSTRACT FROM AUTHOR]

Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness of MKEE scales in tandem with the volume of information encapsulated within the ciphertext. MKEE substantially reduced the size of the ciphertext, achieving an 88% decrease when incorporating ten plaintext values. To further reduce the size of the ciphertext in our proposal, a Modular Multiplicative Inverse method (MMI) is introduced. MMI experiments were conducted, demonstrating that we achieved a commendable 50% reduction in the ciphertext size. To validate the practicality of the proposed method, a case study was conducted on a diabetes dataset. By integrating MKEE and MMI, this study showed a data storage reduction of 94%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. [ABSTRACT FROM AUTHOR]

Healthcare information security is becoming a significant responsibility for all healthcare organisations and individuals. Innovative medical equipment and healthcare apps are vital to patient care, yet they are often the target of hackers. Moreover, attackers are silently working against healthcare data. Once a hacker has gained access to a network, they might install ransomware to lock down essential services or encrypt files until a specified ransom is paid. Businesses are frequently compelled to pay the ransom, hoping the money is eventually recovered since the healthcare sector is time-sensitive. Although less common, network-connected devices can be taken over and used to distribute incorrect medications or alter a machine's functionality. So, there is a need to implement cyber security in healthcare to protect all information. In comparison to other industries, security duties in the healthcare industry are particularly broad and new. This is especially true given that data is accumulated and accessed from various destinations. Data on a specific patient is gathered from various sources, including hospital and lab records, insurance records, fitness apps, trackers and gadgets, health portals, and many more. It can be easily protected by using cybersecurity technology. This paper briefs about cybersecurity and its need in healthcare. Several tools, traits and roles of cybersecurity in the Healthcare Sector are studied. Finally, we identified and studied the applications of cybersecurity in healthcare. For hackers, a patient's aggregated data might be regarded as a goldmine, providing them with a detailed biography of an individual, including basic information, health trends, family history, and financial details. The importance of Data access in the healthcare sector emerges from numerous endpoints, which can be regarded as the weak spots of the healthcare data management system and can also open up an infringement in the medical data management infrastructure. [ABSTRACT FROM AUTHOR]

The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively. [ABSTRACT FROM AUTHOR]

Introduction Households are increasingly studied in population health research as an important context for understanding health and social behaviours and outcomes. Identifying household units of analysis in routinely collected data rather than traditional surveys requires innovative and standardised tools, which do not currently exist. Objectives To design a utility that identifies households at a point in time from pseudonymised Unique Property Reference Numbers (UPRNs) known as Residential Anonymised Linkage Fields (RALFs) assigned to general practitioner (GP) patient addresses in electronic health records (EHRs) in north east London (NEL). Methods Rule-based logic was developed to identify households based on GP registration, address date, and RALF validity. The logic was tested on a use case on the household clustering of childhood weight status, and bias in success of identifying households was examined in the use case cohort and in a full population cohort. Results 92.1% of the use case cohort was assigned a household. The most frequent dominant reason (55.3%) for a household not assigned was that a person had no valid household RALFs available across their patient registration address records. Other reasons are having none or multiple valid household RALFs, or not being alive at the event date. In the use case, children not assigned to a household were more likely to attend schools in City & Hackney and living in the third most deprived quintile of lower super output areas. 88.9% of the population cohort was assigned a household. Patients not assigned to a household were more likely to be aged 18 to 45 years, living in City & Hackney, and living in the second quintile of most deprived lower super output areas. Conclusions We have developed a method for deriving households from primary care EHRs that can be implemented quickly and in real-time, providing timely data to support population health research on households.

Background Recent randomized clinical trials have demonstrated that endovascular therapy for basilar artery occlusion is safe and potentially effective, predominantly in the non‐White population. The aim of this study was to identify predictors of good functional outcome in posterior circulation strokes in US population after mechanical thrombectomy from the TRACK (Trevo Stent‐Retriever Acute Stroke) and the NASA (North American Solitaire Stent Retriever Acute Stroke) registries from North America. Methods Patient‐level data from the TRACK and NASA registries were pooled, and patients with posterior circulation stroke were included in this analysis. Patients were dichotomized into those with 90‐day good functional outcome (modified Rankin scale [mRS] score 0–2) and poor functional outcome (mRS score ≥3). Baseline and procedural data were compared between the 2 cohorts. Multivariate logistic regression was performed to identify predictors of functional outcome. P

Healthcare information security is becoming a significant responsibility for all healthcare organisations and individuals. Innovative medical equipment and healthcare apps are vital to patient care, yet they are often the target of hackers. Moreover, attackers are silently working against healthcare data. Once a hacker has gained access to a network, they might install ransomware to lock down essential services or encrypt files until a specified ransom is paid. Businesses are frequently compelled to pay the ransom, hoping the money is eventually recovered since the healthcare sector is time-sensitive. Although less common, network-connected devices can be taken over and used to distribute incorrect medications or alter a machine's functionality. So, there is a need to implement cyber security in healthcare to protect all information. In comparison to other industries, security duties in the healthcare industry are particularly broad and new. This is especially true given that data is accumulated and accessed from various destinations. Data on a specific patient is gathered from various sources, including hospital and lab records, insurance records, fitness apps, trackers and gadgets, health portals, and many more. It can be easily protected by using cybersecurity technology. This paper briefs about cybersecurity and its need in healthcare. Several tools, traits and roles of cybersecurity in the Healthcare Sector are studied. Finally, we identified and studied the applications of cybersecurity in healthcare. For hackers, a patient's aggregated data might be regarded as a goldmine, providing them with a detailed biography of an individual, including basic information, health trends, family history, and financial details. The importance of Data access in the healthcare sector emerges from numerous endpoints, which can be regarded as the weak spots of the healthcare data management system and can also open up an infringement in the medical data management infrastructure.

Copyright of Innere Medizin (2731-7080) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces an innovative approach to antibiotic optimization and improved infectious disease management in healthcare facilities. Antibiotic stewardship and patient-specific outcomes are prioritized in the suggested strategy that uses neural networks to increase the precision and utility of antibiotic prescriptions. There are three primary algorithms at the heart of the technique. When it comes to identifying infectious illnesses from a picture, Algorithm 1 uses a Convolutional Neural Network (CNN). In order to provide educated antibiotic recommendations, Algorithm 2 uses a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) cells. The third algorithm integrates reinforcement learning to automatically modify therapies based on patient results and antibiotic use. The outcomes prove that the suggested strategy is better than the status quo. The F1 score, recall, and precision all increase dramatically, and the overall diagnostic accuracy is much higher. Antibiotic stewardship also improves noticeably, leading to fewer antibiotic prescriptions, more effective measures against antibiotic resistance, better health outcomes for patients, and lower overall healthcare expenditures. Addressing the difficulties of fluctuating patient states and changing disease patterns, the suggested methodology provides a comprehensive strategy for managing infectious diseases. Using this method, antibiotic prescriptions may be optimized while still meeting all legal and ethical requirements. The ethical use of AI in healthcare is further ensured by constant monitoring and flexibility. [ABSTRACT FROM AUTHOR]

Objective: We assessed headache clinicians' viewpoints on potential remote access to patients' digital headache diary data and the practicalities of data utilization. Background: With the ubiquitous nature of electronic medical records and the existence of remote monitoring (RM) for many medical conditions, there is now the potential for remote symptom monitoring for patients with headache disorders. While patients are asked to utilize headache diaries, clinicians may or may not have access to the data before patient visits, and their perspectives regarding this emerging technology are currently unknown. Methods: After recruiting participants from the National Institutes of Health Pain Consortium Network, the American Headache Society Special Interest Section listservs, and Twitter and Facebook social media platforms, we conducted 20 semi‐structured qualitative interviews of headache providers across the United States from various types of institutions and asked them their perspectives on remote access to patient headache diary data. We transcribed the interviews, which were then coded by two independent coders. Themes and sub‐themes were developed using inductive content analysis. Results: All clinicians felt the RM data needed to be integrated into the electronic medical record. Six themes emerged from the interviews: (i) Clinician perspectives on how RM could be beneficial but at other times could create obstacles/challenges, (ii) operationally, data integration could benefit headache care, (iii) there should be initial logistical considerations for bringing RM into clinical care, (iv) education may need to be provided to both patients and clinicians, (v) there are likely research benefits associated with RM, and (vi) additional suggestions for considering potential integration of RM into practice. Conclusions: While headache clinicians had mixed opinions on the benefits/challenges that RM presents to patient care, patient satisfaction, and visit time, new ideas emerged that may help advance the field. [ABSTRACT FROM AUTHOR]

The clinical environment is one of the most important sources of sensitive patient data in healthcare. These data have attracted cybercriminals who pursue the theft of this information for personal gain. Therefore, protecting these data is a critical issue. This paper focuses on an analysis of the clinical environment, presents its general ecosystem and stakeholders, and inspects the main protocols implemented between the clinical components from a security and privacy perspective. Additionally, this article defines a complete use case to describe the typical workflow within a clinical setting: the life cycle of a patient sample. Moreover, we present and categorize crucial clinical information and divide it into two sensitivity levels: High and Very Sensitive, while considering the severe risks of cybercriminal access. The threat model for the use case has also been identified, in conjunction with the use case’s security and privacy needs. This work served us as basis to develop the minimum security and privacy requirements to protect the use case. Accordingly, we have defined protection mechanisms for each sensitivity level with the enabling technologies needed to satisfy each requirement. Finally, the main challenges and future steps for the use case are presented.

In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.

Highlights Six machine learning methods, Logistic Regression (LR), Naïve Bayes, Support Vector Machine (SVM), Boosted Tree, Classification and Regression Tree (CART), and Bootstrap Forest were compared together in order to predict sepsis. Early stage of admission data including patient’s gender, age, severity level, mortality risk, admit type along with hospital length of stay were used for predicting sepsis. The Bootstrap Forest can be considered the best machine learning method in predicting sepsis regarding applied features in this research mainly because it showed superior performance and efficiency in two performance measures, i.e. AUC and R-square.

Background: Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions. Objective: We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data. Methods: Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data. for research. Sharers reported worse symptoms and functioning compared to those of nonsharers. Social media may offer a potent information source and engagement pathway for youth with rheumatic diseases, but differences between sharing and nonsharing youth merit consideration when designing studies and evaluating social media-derived findings. [ABSTRACT FROM AUTHOR]

India as a country of contrast and diversity has witnessed digital evolution in different waves and stages. The technology is already an integral part of lives of millions in India; however, its application in the health management remains limited unlike developed economies. COVID-19 pandemic has plunged the country into universal, regional, or local lockdowns repeatedly since the last year. An unexpected and unforeseen impact of this has been the usage of technology for doctor-patient interactions through telemedicine. Hitherto limited to certain pockets, virtual interactions with doctors, ordering laboratory investigations through an application or procuring medicines through internet, are now part of mainstream patient behavior. This is a crucial change in the mindset but requires a lot more to be done at various levels to tap its full potential with rheumatologists being at the forefront and leading the change in their specialty. The pool of rheumatologists is very small and mostly concentrated in few urban areas, leading to diagnostic delay, suboptimal treatment, and poor outcomes. Technology could, therefore, become a catalyst for change and harbinger for greater clinician access. There are plenty of discussions about the impact and potential of deep learning, artificial intelligence, remote monitoring with wearables, etc., but plenty of them may not be relevant to Indian patients in the current scenario. Hence, the context, relevance, and applicability are the key for rheumatologists when making a judgment. [ABSTRACT FROM AUTHOR]

Abstract Background The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. Results In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Conclusions Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

To determine delta check limits for immunoglobulins and complements in outpatients and inpatients based on patient data and biological variation due to the lack of relevant studies.Patient data for IgA, IgG, IgM, IgE, C3, and C4 from 1 January 2022 to 31 December 2023 was collected from laboratory information system (LIS) in our clinical laboratory of Wuhan Union Hospital, which includes both outpatients and inpatients. The delta difference (DD), delta percent change (DPC), and reference change value (RCV) were calculated based on patient data and biological variation.For DDs, there are significant differences between outpatients and inpatients in C4, IgE, IgG, and IgM. For DPCs, the corresponding analytes which are significantly different are C3, C4, IgE, IgG, and IgM. Two sources of CVI to calculate the RCV of IgA, IgG, IgM, C3, and C4 were applied in this study, which revealed that two kinds of RCVs based on different biological variation databases are similar to each other, but both were smaller than delta check limits based on patient data, except for C4.The delta check is a useful tool to monitor potential errors which may occur in total testing process. We hope our findings could be helpful for future studies focused on delta checks in immunological analytes. [ABSTRACT FROM AUTHOR]

India as a country of contrast and diversity has witnessed digital evolution in different waves and stages. The technology is already an integral part of lives of millions in India; however, its application in the health management remains limited unlike developed economies. COVID-19 pandemic has plunged the country into universal, regional, or local lockdowns repeatedly since the last year. An unexpected and unforeseen impact of this has been the usage of technology for doctor–patient interactions through telemedicine. Hitherto limited to certain pockets, virtual interactions with doctors, ordering laboratory investigations through an application or procuring medicines through internet, are now part of mainstream patient behavior. This is a crucial change in the mindset but requires a lot more to be done at various levels to tap its full potential with rheumatologists being at the forefront and leading the change in their specialty. The pool of rheumatologists is very small and mostly concentrated in few urban areas, leading to diagnostic delay, suboptimal treatment, and poor outcomes. Technology could, therefore, become a catalyst for change and harbinger for greater clinician access. There are plenty of discussions about the impact and potential of deep learning, artificial intelligence, remote monitoring with wearables, etc., but plenty of them may not be relevant to Indian patients in the current scenario. Hence, the context, relevance, and applicability are the key for rheumatologists when making a judgment.

Now and in the near future, blockchain improves the lives of patients. Hear how the patient journey transforms with the ability to control ownership of patient data, and how critical data is shared seamlessly between stakeholders. With blockchain, healthcare is becoming patient-centered, both clinically and administratively.

Patient-based real-time quality control (PBRTQC) has gained attention as an alternative/integrative tool for internal quality control (iQC). However, it is still doubted for its performance and its application in real clinical settings. We aim to generate a newly and easy-to-access patient-based real-time QC by machine learning (ML) traceable to standard reference data with assigned values by National Institute of Metrology of China (NIM), and to compare it with PBRTQC for clinical validity evaluation. For five representative biochemistry analytes, 1,195 000 patient testing results each were collected. After data processing, independent training and test sets were divided. Machine learning internal quality control (MLiQC) was set up by Random Forest in ML and was validated by way of both metrology algorithm traceability and 4 PBRTQC methods recommended by IFCC analytical working group. MLiQC were established. As an example of albumin (ALB) at the critical bias, the uncertainty of MLiQC was 0.14%, which was evaluated by standard reference data produced by NIM. Compared with four optimal PBRTQC methods at critical bias, the average of the number of patient samples from a bias introduced until detected (ANPed) of MLiQC averagely decreased from 600 to 20. The median and 95 quantiles of NPeds (MNPed and 95NPed) of MLiQC were superior to all optimal PBRTQCs above 90% for all test items. MLiQC is highly superior to PBRTQC and well-suited in real settings. The validation of the model from two aspects of algorithm traceability and clinical effectiveness confirms its satisfactory performance. [ABSTRACT FROM AUTHOR]

This article addresses the challenge of sharing finer scale protected health information (PHI) while maintaining patient privacy by using regionalization to create higher resolution Health Insurance Portability and Accountability Act (HIPAA)-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), and self-organizing map (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intraunit homogeneity, and maximizes the number of spatial units while meeting the minimum population threshold required for sharing PHI under HIPAA guidelines. The relative utility of each configuration was assessed with measures of model fit, compactness, homogeneity, and resolution. Adding the SOM procedure to max-p-regions resulted in statistically significant improvements for nearly all assessment measures, whereas the addition of SOM to REDCAP primarily degraded these measures. These differences can be attributed to the different impacts of SOM on top-down and bottom-up regionalization procedures. Overall, we recommend REDCAP, which outperformed on most measures. The SOM variant of max-p-regions (MSOM) could also be recommended, because it provided the highest resolution while maintaining suitable performance on all other measures. [ABSTRACT FROM AUTHOR]

Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia, urinary tract infection, and other illnesses. Some of its symptoms are fever, tachycardia, tachypnea, etc. Unfortunately, sepsis remains a critical problem at the hospitals and leads to many issues, such as increasing mortality rate, health care costs, and health care utilization. Early detection of sepsis in patients can help respond quickly, take preventive actions, and prevent major issues. The main aim of this study is to predict the risk of sepsis by utilizing the patient's demographic and clinical information, i.e., patient's gender, age, severity level, mortality risk, admit type along with hospital length of stay. Six machine learning approaches, Logistic Regression (LR), Naïve Bayes, Support Vector Machine (SVM), Boosted Tree, Classification and Regression Tree (CART), and Bootstrap Forest are used to predict the risk of sepsis. The results showed that different machine learning methods have other performances in terms of various measures. For instance, the Bootstrap Forest machine learning method exhibited the highest performance in AUC and R-square or SVM and Boosted Tree showed the highest performance in terms of misclassification rate. The Bootstrap Forest can be considered the best machine learning method in predicting sepsis regarding applied features in this research, mainly because it showed superior performance and efficiency in two performance measures: AUC and R-square. Highlights: Six machine learning methods, Logistic Regression (LR), Naïve Bayes, Support Vector Machine (SVM), Boosted Tree, Classification and Regression Tree (CART), and Bootstrap Forest were compared together in order to predict sepsis. Early stage of admission data including patient's gender, age, severity level, mortality risk, admit type along with hospital length of stay were used for predicting sepsis. The Bootstrap Forest can be considered the best machine learning method in predicting sepsis regarding applied features in this research mainly because it showed superior performance and efficiency in two performance measures, i.e. AUC and R-square. [ABSTRACT FROM AUTHOR]

Background: Recent legislation ensuring patient access to their electronic health records represents a promising national commitment to patient empowerment. Access and interoperability rules seek to empower individuals as well as increase opportunities for data sharing by hospitals, apps, and other parties for research and innovation. However, there are trade-offs between data accessibility and oversight. Some third-party apps may not be covered by federal regulations, and receiving records directly from individuals may render some services in possession of health data. To promote consumer trust, these services should follow ethical standards regardless of regulatory status.Actionable Principles: This Perspective proposes 3 actionable principles, grounded in medical ethics, for services making use of health data: services should (1) provide informed, dynamic, regular consent, including control over data sharing, (2) promote inclusivity and equity, and (3) intentionally focus on consumer trust and the perception of value in the service provided. [ABSTRACT FROM AUTHOR]

Patient-Reported Outcome (PRO) data are being widely mobilised as a means to implement clinical and governance decision-making systems based on measurement of "what matters to patients". Little is known, however, of how these—datified and calculative—versions of patients' embodied experiences become integrated in healthcare systems to achieve clinical, managerial and political goals. Drawing on data collected on a Danish national initiative to establish a questionnaire bank and a technical infrastructure for PRO data, this paper explores the epistemic and moral tensions of emerging infrastructures for patient-reported data. Our data reveals that those involved in crafting and developing the Danish PRO system seek to encode 'patient experience' in the Danish healthcare system by infrastructuring for the clinic, by infrastructuring for the organisation, and by infrastructuring for participation. The infrastructuring efforts in each of these domains involve tensions as the emerging phenomenon of PRO rubs up against existing ways of working clinically and of enhancing and evaluating the quality of clinical care. Thus, while we share critical social science concern about standardisation of patient experience, we show here how those concerns are already pragmatically present in the infrastructuring efforts that patients, clinicians and administrators engage in. [ABSTRACT FROM AUTHOR]

Background: Clinical pathways are sequences of events followed during the clinical care of a group of patients who meet pre-defined criteria. They have many applications ranging from healthcare evaluation and optimization to clinical decision support. These pathways can be discovered from existing healthcare data, in particular with machine learning which is a family of methods used to learn patterns from data. This review provides a comprehensive overview of the literature concerning the use of machine learning methods for clinical pathway discovery from patient data., Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method , we conducted a systematic review of the existing literature. We searched 6 databases, i.e., ACM Digital Library, ScienceDirect, Web of Science, PubMed, IEEE Xplore, and Scopus spanning from January 2004 to December 2023 using search terms pertinent to clinical pathways and their development. Subsequently, the retrieved papers were analyzed to assess their relevance to the scope of this study., Results: In total, 131 papers that met the specified inclusion criteria were identified. These papers expressed diverse motivations behind data-driven clinical pathway discovery ranging from knowledge discovery to conformance checking with established clinical guidelines (derived from existing literature and clinical experts). Notably, the predominant methods employed (67.2%, n=88) involved unsupervised machine learning techniques, such as clustering and process mining., Conclusions: Relevant clinical pathways can be discovered from patient data using machine learning methods, with the desirable potential to aid clinical decision-making in healthcare. However, to reach this objective, the methods used to discover pathways should be reproducible, and rigorous performance evaluation by clinical experts needs to be conducted for validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Diabetic retinopathy is one of the leading causes of avoidable blindness among adults globally, and screening programmes can enable early diagnosis and prevention of progression. Artificial intelligence (AI) diagnostic solutions have been developed to diagnose diabetic retinopathy. The aim of this review is to identify ethical concerns related to AI-enabled diabetic retinopathy diagnostics and enable future research to explore these issues further., Methods: This is a narrative review that uses thematic analysis methods to develop key findings. We searched two databases, PubMed and Scopus, for papers focused on the intersection of AI, diagnostics, ethics, and diabetic retinopathy and conducted a citation search. Primary research articles published in English between 1 January 2013 and 14 June 2024 were included. From the 1878 papers that were screened, nine papers met inclusion and exclusion criteria and were selected for analysis., Results: We found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated., Conclusions: While the technical developments in AI-enabled diabetic retinopathy diagnostics receive the bulk of the research focus, we found that insufficient attention is paid to how this technology is accessed equitably in different settings and which safeguards are needed against exploitative practices. Such ethical issues merit additional exploration and practical problem-solving through primary research. AI-enabled diabetic retinopathy screening has the potential to enable screening at a scale that was previously not possible and could contribute to reducing preventable blindness. It will only achieve this if ethical issues are emphasised, understood, and addressed throughout the translation of this technology to clinical practice., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Introduction: The importance of patient perspectives is increasingly appreciated in clinical practice and academia with formal engagement processes developing worldwide. Digital surgery encompasses intraoperative patient data (including surgical video) analysis and so requires public-patient involvement (PPI)., Methods: Engagement events were conducted based on NIHR and GRIPP2 LF guidelines. Following informative talks on digital surgery, invited patients and patient relatives were split into focus groups regarding 1) Research; 2) Data; 3) Industry Involvement; and 4) Artificial Intelligence in surgery. Scribed feedback was thematically analysed by two researchers independently. A pre and post event survey was sought voluntarily., Results: 36 participant perspectives were analysed. In general, patients were enthusiastic about having a voice in surgical research and sharing their journey, with most groups concluding that capturing this was most appropriate after treatment recovery. The use of patient data for surgical development (i.e. research and education) was endorsed unanimously for the purpose of future patient benefit when responsibly and transparently managed and the value of industry was acknowledged. From 30 pre/post surveys (all p > 0.05), participants afforded the greatest data (including video) ownership claim to the surgical team (52 %/48 %) versus patients (32 %/24 %) and the hospital (12 %/24 %). While most (73 %/80 %) agreed that AI should be applied in surgical care, most felt the surgeon most valuable (93 %/80 %) with participants disagreeing that AI should make diagnoses (57 %/64 %) or treat patients (70 %/70 %) without human input., Conclusion: Patients capably represent stable views and expectations that can strengthen modern and evolving surgical development involving data privacy, ownership and management., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Deirdre Nally reports financial support was provided by Irish Cancer Society. Ronan A Cahill reports a relationship with Stryker Corporation that includes: speaking and lecture fees. Ronan A Cahill reports a relationship with Olympus Corporation that includes: speaking and lecture fees. Ronan A Cahill reports a relationship with Ethicon Inc that includes: speaking and lecture fees. Ronan A Cahill reports a relationship with Intuitive Surgical Inc that includes: funding grants. Ronan A Cahill reports a relationship with Medtronic that includes: funding grants. Ronan A Cahill reports a relationship with IBM Research Europe - Ireland that includes: funding grants. Ronan A Cahill reports a relationship with Deciphex Ltd that includes: funding grants. Ronan A Cahill reports a relationship with Horizon Europe that includes: funding grants. Ronan A Cahill reports a relationship with Palliare that includes: board membership. Ronan A Cahill reports a relationship with Arthrex Inc that includes: consulting or advisory. Ronan A Cahill reports a relationship with Diagnostic Green that includes: consulting or advisory. Ronan A Cahill reports a relationship with Medtronic (Touch Surgery) that includes: consulting or advisory. Jeffrey Dalli is a recipient of the Tertiary Education Scholarship Scheme, Malta (EU). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.)

Healthcare systems capture patients' data using different medical equipment and store it in the databases with a continual increase in data volume. The continuous processing and sharing of this massive data are rising concerns in live data transferring over the networks. Sending patient data to the distant remote user without proper compressing format requires high latency in the communication channels. Any alternation of data transmitted via the communication medium may also cause issues in assuring data authentication and integrity. For solving the problems, watermarking method is being applied to ensure such security, which has a cheaper computational cost. Various watermarking mechanisms are available for ensuring health data security, especially for medical images. Watermarking on the text was not used yet due to the lack of efficient technique. This paper proposes a secured compression technique for patient live-text data while sharing them remotely over a bandwidth-deficient channel. To test the proposed system, we use patient data. The result indicates that the proposed strategy outperforms the existing compression methods and is robust enough to provide data integrity and authentication.

Technology is continually evolving, and Blockchain development in recent years has shown tremendous adaptability. Blockchain's emphasis lies mainly in the finance sector, but some latent fields such as healthcare still grow and transform the future. In this paper, we introduced a smart contract-based framework for Blockchain-based healthcare system. According to our hypotheses, the proposed framework in the healthcare system will be changed by leveraging the principles and technologies of a public ledger, which will change the healthcare industry's vision based on Blockchain. Health records, laboratory evaluation results, doctoral perceptions, and precise information about health care can be decentralized in the form of blocks in the form of transactions. These blocks can be linked in Blockchain as distributed ledgers according to the series of events. It can eliminate a highly complex process and manual intervention. Through adding an Identity Manager, fully open and secure applications can be based on Blockchain technologies. Based on the expected outcomes, we are optimistic that the proposed Blockchain-based framework will be helpful in the healthcare context; to evaluate the maturity level of our proposed framework we map the framework on Ethereum-based application and evaluated in the hospital setting, for the evaluation of the proposed framework. At the initial stage, we are confident that the proposed framework will be helpful in the hospital environment and contribute to enhancing the performance of the healthcare environment. [ABSTRACT FROM AUTHOR]