Medical Data Integration Research Articles

Utilization of basic medical insurance data in cancer-related researches in China

Objective: To explore the utilization of cancer-related data from basic medical insurance databases in China, and promote the application of medical insurance data in cancer prevention and treatment. Methods: Database PubMed, Web of Science, Wanfang, and CNKI were used to select related research papers using data from basic medical insurance system in China published by December 2021. Descriptive analysis was conducted in terms of the number of publications, types of cancer, primary research contents and author affiliations. Results: A total of 65 papers were included in the study. The number of publications increased rapidly after 2016. The most studied cancer type was lung cancer, and healthcare costs were the most common research contents. Fujian, Beijing, and Anhui have made a better use of cancer-related medical insurance databases compared to other provincial regions. The accessibility of the New Rural Cooperative Medical Scheme data was limited due to the high regional barriers, while the accessibility of the urban basic medical insurance data was relatively high. The researchers from Peking University and Fudan University had higher utilization of basic medical insurance data compared with those from other institutions. Conclusions: The utilization of cancer-related data from basic medical insurance databases in China is limited because of poor accessibility, insufficient data sharing, and regional restrictions. Thus, it is urgent to improve data accessibility and promote the integration and utilization of regional medical insurance data.

Bio-inspired swarm-intelligent with machine learning framework for prediction and classification of lung cancer

Early detection of lung cancer nodules, which is often dependent on a tomography scan filmic examination, greatly increases the probability of survival. Earlier tumor detection reduces lung cancer mortality by increasing the likelihood of successful therapy. Traditionally, radiologists have analyzed medical pictures for signs of lung cancer using a laborious and inaccurate systematic approach. The confidentiality and integrity of medical data have become a serious challenge for healthcare applications as a result of the tremendous improvement in the transmission of medical data in the healthcare sector. This research makes use of computational intelligence methods. In this research, a brand-new Enhanced vortex search algorithm optimized Support Vector Machine (EVSAO-SVM) is developed for detection and classification. Preprocessing using the Gaussian Filter, segmentation with Otsu thresholding, feature extraction with local binary patterns (LPB), and classification and prediction with the EVSAO-SVM are the processes that are simulated. Regarding performance criteria including accuracy, sensitivity, specificity, and F1-measure, this study indicates the suggested framework's superiority over the traditional approaches. The results of the tests conducted demonstrate that the suggested model can reach up to 95.42 percent sensitivity, 96.24 percent accuracy, 98.92 percent specificity, and 94.26 percent F1 measure.

Building an Electronic Medical Record System Exchanged in FHIR Format and Its Visual Presentation.

Currently, the Taiwan Electronic Medical Record Exchange Center uses the Clinical Document Architecture (CDA) framework, which is based on the international medical standard. The CDA R2 standard, defined in 2005, is used for cross-institution retrieval of electronic medical records (Ministry of Health and Welfare, Information Department, 2021). However, CDA R2 only supports the exchange of clinical documents and is limited to the XML format. Due to the lack of a standardized framework for medical data exchange in Taiwan, different standards and specifications result in different data interface methods between systems, requiring customization for each system by healthcare institutions or the government. The inconsistency in data formats requires healthcare institutions and the government to spend more time on data parsing and mapping, resulting in slow integration of medical data. In this study, we simulated healthcare institutions using Fast Healthcare Interoperability Resources (FHIR) for medical information exchange and utilized the exchanged medical information to create a dynamic dashboard to assist healthcare professionals in making medical decisions. To ensure information security, we employed Hyper Text Transfer Protocol Secure (HTTPS) for secure transmission, which encrypts the transmitted medical record data using the Transport Layer Security (TLS) protocol, preventing deliberate interception and tampering of medical record data between the two systems. Finally, to test the load and performance of static and dynamic resources and web applications, we conducted a system performance evaluation using Apache JMeter. The results of this study demonstrate that replacing the gateway of the Electronic Medical Record Exchange Center with an FHIR server effectively reduces the time and cost spent by developers on data format conversion while also mitigating the information security risks associated with the previous VPN solution. Additionally, by utilizing dynamic charts, healthcare professionals are assisted in making medical decisions.

ARCHITECTURAL HEALTH DATA STANDARDS AND SEMANTIC INTEROPERABILITY: A COMPREHENSIVE REVIEW IN THE CONTEXT OF INTEGRATING MEDICAL DATA INTO BIG DATA ANALYTICS.

The integration of medical data into Big Data analytics holds significant potential for advancing healthcare practices and research. However, achieving semantics interoperability, wherein data is exchanged and interpreted accurately among diverse systems, is a critical challenge. This study explores the impact of existing architectures on semantics interoperability in the context of integrating medical data into Big Data analytics. The study highlights the complexities involved in integrating medical data from various sources, each using different formats, data models, and vocabularies. Without a strong emphasis on semantic interoperability, data integration efforts can result in misinterpretations, inconsistencies, and errors, adversely affecting patient care and research outcomes. The significance of data standards and ontologies in establishing a common vocabulary and structure for medical data integration is underscored. Additionally, the importance of data mapping and transformation is discussed, as data discrepancies can lead to data loss and incorrect analysis results. The success of integrating medical data into Big Data analytics is heavily reliant on existing architectures that prioritize semantics interoperability. A welldesigned architecture addresses data heterogeneity, promotes semantic consistency, and supports data standardization, unlocking the transformative capabilities of medical data analysis for improved healthcare outcomes.

Protection of COVID-19 images using multiple elliptic curve cryptography

ABSTRACT COVID-19 pandemic has made the medical industry around the world under unprecedented and growing pressure. To preserve the integrity and protection of medical data for transmission and for secure diagnosis, this work suggests an encryption method for the protection of privacy of COVID-19 patient’s data by securing medical images based on elliptic curve cryptography. This is done through amalgamating three different types of stages by using elliptic curve cryptography. First, the coordinates of pixels of medical images are scrambled using elliptic curve and encrypt the coordinates of pixels, which help in decreasing the correlation between the image’s pixels. The second stage empowers the first stage by encrypting the group of pixels from previous outputs irrespective whether the pixel value lies on the curve or not based on the multiplication modulo operation of elliptic curve cryptography. Finally, the group of pixels from the previous output is encrypted by XORing with the key generated from ECC DHKE (Elliptic Curve Cryptography Diffie–Hellman Key Exchange). The NIST test suite examines and verifies the statistical characteristics of the binary sequences for 2 10 bits and 2 16 bits that are created by cryptographic random or pseudorandom number generators. The outcomes of the experiments indicate how the suggested approach satisfies the security requirements of COVID-19 images of patients in terms of high effectiveness in encryption quality, resilient against many statistical analyses and attacks and also performs better than some recent schemes. This work also evaluated comparisons of quantitative, differential, time consumptions and computational complexity with several recent schemes.

An end-to-end screen shooting resilient blind watermarking scheme for medical images

The rapid progress in artificial intelligence and network communication has led to the development of advanced regional medical systems, revolutionizing the way hospitals and patients connect. However, the security of these systems is threatened by the potential breach of medical data, especially sensitive medical images. Existing watermarking schemes have focused on optimizing either imperceptibility or robustness, creating a fundamental trade-off between these two aspects that poses a significant challenge in the field. To address this challenge and ensure the confidentiality and integrity of medical data, this paper proposes an end-to-end screen shooting resistant blind watermarking scheme for medical images. The proposed method offers a robust and reliable solution for the secure transmission and storage of medical images, particularly in the face of screen shooting attacks. To enhance both security and imperceptibility, the scheme employs a novel encryption technique based on BCH codes, resulting in a trinary sequence for watermark images. These watermarks are then embedded in the spatial domain, effectively protecting against unauthorized access while preserving the integrity of the original image. To further enhance robustness, the method introduces a neural network structure specifically designed to resilient screen shooting attacks. By simulating the screen shooting process and incorporating various distortions, this network combines the strengths of residual networks and generative adversarial networks. Moreover, the proposed method introduces a novel loss function that exploits the unique characteristics of the image transformation domain, optimizing both imperceptibility and robustness. In the subsequent experiment, the watermarked images achieved satisfactory results with the highest PSNR and SSIM values reaching 62.563 dB and 0.9999, respectively. The proposed scheme provides an effective solution to protect the privacy and copyright of medical images, effectively addressing a critical security concern prevalent in regional healthcare systems.

Enhancement in line of therapy (LoT) derivation from real-world data (RWD) from electronic health records (EHR) via integration of medical claims data.

6514 Background: Clinical RWD derived from EHRs is becoming increasingly important for clinical research, trial design, regulatory decisions etc. These applications require identification of lines of therapy (LoT) which are typically not captured in EHR and must be abstracted from other clinical and medication data. EHR data has significant missingness which can be complemented with other data sources such as medical claims data. In this study, we demonstrate how our proprietary line of therapy algorithms for solid cancers show significant improvements when built using integrated EHR and claims data when compared to EHR data alone. Methods: For this analysis, ConcertAI’s RWD360 dataset integrated with a large administrative open-claims dataset (>90% overlap) for 14 solid cancer indications (Breast, Bladder, Lung, Prostate, Pancreas, Melanoma, Liver, Head & Neck, Renal, Colorectal, Melanoma, Ovarian, Thyroid, Endometrial) was used. The date of advanced/metastatic diagnosis used as the index date for LoTs was derived from the EHR data and medications from both EHR and claims data were used. We ran our LoT algorithms on EHR data with and without claims data and evaluated the impact of integrating claims data on the quantity and quality of LoT output. Results: The inclusion of medication data from claims significantly increased (7-22%) the number of patients for which LoTs could be extracted from the EHR data. Furthermore, we observed increases in number of lines per patient, length of lines and medications per line across cohorts. The distance between index date and 1st line start date was shortened in a subset (2-12%) of patients as a result. In a small fraction of cases, we even observed removal of false lines as some of the lines moved to adjuvant/neoadjuvant setting by filling in missing medication from claims. Overall, 7-39% patients in the LoT cohorts were impacted by addition of claims. Results for a few cancer types are presented in Table 1. We also compared the top LoTs derived from the integrated dataset against the standard of care for that cancer and observed very good concordance. Conclusions: Deriving LoTs by integrating data from multiple data sources such as EHR and claims can significantly improve its accuracy. [Table: see text]

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital

BackgroundSecondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called “real world data” are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital.MethodsWe identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools.ResultsWe demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements.ConclusionThough the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study.

Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the "pAItient" (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value) project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center. The first part of the pAItient project aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations. In addition, patients and physicians were interviewed. The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium. The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications. RR2-10.2196/42208.

Lightweight certificateless privacy-preserving integrity verification with conditional anonymity for cloud-assisted medical cyber–physical systems

Cloud-assisted medical cyber–physical systems (MCPSs) leverage the power of cloud computing for scalable storage and management of outsourced medical data. Based on the crucial outsourced medical data, guardians could know patient’s health condition timely, doctors can make accurate diagnoses of patients, and medical research centers could also conduct medical big data analysis. However, medical data is highly sensitive and demands strong protection. Meanwhile, the integrity of outsourced medical data is vulnerable to hardware failures, software bugs or human errors. To date, a large number of data privacy-preserving integrity auditing schemes have been presented, but most of them suffer from the burden of complex public key certificate management or the key escrow problem of identity-based cryptography. In this paper, we propose a lightweight certificateless privacy-preserving integrity verification scheme (LCPPIV) with conditional anonymity for cloud-assisted MCPSs by developing an elliptic-curve digital signature and a key exchange mechanism, which can achieve the functionalities of medical data privacy protection, integrity verification of outsourced medical data, conditional anonymity, batch auditing, and secure compensation mechanism, simultaneously. Theoretical security analysis demonstrates that our scheme is provably secure in the random oracle model, with resistance to the public key replacement attack, malicious-but-passive-KGC attack, and response auditing proofs forgery. The performance evaluations indicate that LCPPIV is much more practical for cloud-assisted MCPSs compared with state-of-art data auditing schemes.

Design and Implementation of Intelligent Medical Mobile Collaborative Office Approval System

Medical affairs approval system is a hot issue in the field of smart medicine. The existing medical affairs approval process is unscientific and opaque, and the approval process is slow, resulting in untimely supply of medical resources. To address this issue, a design and implementation method of a smart medical mobile collaborative office approval system is proposed. Based on the Internet big data information collaboration method, it provides heterogeneous fusion and integration of smart medical mobile collaborative office approval data, strengthens the global efficiency of information data analysis and decision-making, and considers improving data security and privacy protection. The system provides multi-level and multi-type user permissions. The test results show that the proposed method improves the efficiency of medical emergency approval, reduces the approval process, and increases the transparency of the approval process.

A security model to protect the isolation of medical data in the cloud using hybrid cryptography

A cloud computing platform delivers a cost-efficient path for cloud users to store and access data privately over remote storage via an Internet connection. Medical data is stored in the cloud since it is scalable, secure, reliable, provides ubiquitous access, and is highly available. It is required to be isolated in terms of physical, network, and operational for protecting data from internal threats or external cyber-attacks. It can be achievable through hybrid cryptography in which symmetric, as well as asymmetric cryptosystem, is being utilized. To assure the confidentiality and integrity of medical data, an improved Robust S-box-based Advanced Encryption Standard (IRS-AES) is proposed with Runge-Kutta Optimization (RKO) algorithm. RKO generates an enhanced and secured RS-box through the computation of the Mackey-Glass equation. Initially, the medical data are compressed using Improved Huffman Coding (IHC). The Deoxyribonucleic Acid (DNA)-based Modified Elliptic Curve Cryptography (MECC) algorithm is introduced for key generation, and the best key is selected with Bald Eagle Search optimization (BES) algorithm. Finally, the medical data are encrypted IRS-AES algorithm and stored in the cloud. The proposed RS-box security strength is evaluated using non-linearity, Strict Avalanche Criterion (SAC), Differential Probability (DP), Bit Independence Criterion (BIC), and Linear Probability (LP) parameters. The efficiency of the proposed algorithm is evaluated with image, audio, and video dataset. The evaluation metrics such as communication overhead, file upload time, computational cost, Mean Square Error (MSE), encryption and decryption time, Peak Signal-to-Noise Ratio (PSNR), key generation time, and Signal-to-Noise Ratio (SNR) are used for validating the proposed approach. The performance of the proposed approach is enhanced with the communication overhead of 11.51 for the image dataset. MSE and PSNR obtained are 16.2 and 8.465 for an audio dataset, 75.21 and 3.5 for the video dataset.

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine.

This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization. Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data. This will be an efficient and highly scalable data analysis platform through embedding embedded Sparkmlib in the Hadoop ecosystem. With the concept of "Internet + medical integration," the overall architecture design of the big data platform for disease burden management was proposed based on the Spark engine and Python language. The main system composition and application scenarios are given at four levels: multisource data collection, data processing, data analysis, and the application layer, according to application scenarios and use requirements. The big data platform of disease burden management helps to promote the multisource convergence of disease burden data and provides a new path for the standardized paradigm of disease burden measurement. Provide methods and ideas for the deep integration of medical big data and the formation of a broader standard paradigm.

A Privacy-Preserving Distributed Medical Data Integration Security System for Accuracy Assessment of Cancer Screening: Development Study of Novel Data Integration System

Big data useful for epidemiological research can be obtained by integrating data corresponding to individuals between databases managed by different institutions. Privacy information must be protected while performing efficient, high-level data matching. Privacy-preserving distributed data integration (PDDI) enables data matching between multiple databases without moving privacy information; however, its actual implementation requires matching security, accuracy, and performance. Moreover, identifying the optimal data item in the absence of a unique matching key is necessary. We aimed to conduct a basic matching experiment using a model to assess the accuracy of cancer screening. To experiment with actual data, we created a data set mimicking the cancer screening and registration data in Japan and conducted a matching experiment using a PDDI system between geographically distant institutions. Errors similar to those found empirically in data sets recorded in Japanese were artificially introduced into the data set. The matching-key error rate of the data common to both data sets was set sufficiently higher than expected in the actual database: 85.0% and 59.0% for the data simulating colorectal and breast cancers, respectively. Various combinations of name, gender, date of birth, and address were used for the matching key. To evaluate the matching accuracy, the matching sensitivity and specificity were calculated based on the number of cancer-screening data points, and the effect of matching accuracy on the sensitivity and specificity of cancer screening was estimated based on the obtained values. To evaluate the performance, we measured central processing unit use, memory use, and network traffic. For combinations with a specificity ≥99% and high sensitivity, the date of birth and first name were used in the data simulating colorectal cancer, and the matching sensitivity and specificity were 55.00% and 99.85%, respectively. In the data simulating breast cancer, the date of birth and family name were used, and the matching sensitivity and specificity were 88.71% and 99.98%, respectively. Assuming the sensitivity and specificity of cancer screening at 90%, the apparent values decreased to 74.90% and 89.93%, respectively. A trial calculation was performed using a combination with the same data set and 100% specificity. When the matching sensitivity was 82.26%, the apparent screening sensitivity was maintained at 90%, and the screening specificity decreased to 89.89%. For 214 data points, the execution time was 82 minutes and 26 seconds without parallelization and 11 minutes and 38 seconds with parallelization; 19.33% of the calculation time was for the data-holding institutions. Memory use was 3.4 GB for the PDDI server and 2.7 GB for the data-holding institutions. We demonstrated the rudimentary feasibility of introducing a PDDI system for cancer-screening accuracy assessment. We plan to conduct matching experiments based on actual data and compare them with the existing methods.

Blockchain-based solutions for clinical trial data management: a systematic review

Blockchain technology can reduce the need for intermediaries in various types of transactions by providing a decentralized and secure ledger that can be accessed and updated by all parties involved in the transaction. Clinical trials are essential for bringing new drugs and therapies to market, but the current clinical research process is often marred by inefficiencies, data inaccuracies, and a lack of transparency. The implementation of blockchain technology in clinical trials has the potential to address these challenges by providing a secure and transparent platform for data management. By leveraging the power of blockchain, healthcare providers can improve the integrity and accuracy of clinical trial data, enhance trust in the clinical research process, and ultimately improve patient outcomes. In this article, we propose the use of blockchain technology in clinical trials and explore its potential benefits for the healthcare. The implementation of a blockchain-based data management system for clinical trials holds significant potential to address several challenges associated with the current clinical research process. By improving the integrity and security of medical data, enhancing trust, and easing regulatory burden, such a system can promote the efficient and effective conduct of clinical trials. The adoption of a blockchain-based solution for clinical trial data management has the potential to optimize costs, contributing to the sustainability of healthcare services. It also provides a model for future research and development of blockchain-based solutions in the field of clinical research.

Exploring Stakeholder Requirements to Enable the Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Protocol for a Multistep Mixed Methods Study

In recent years, research and developments in advancing artificial intelligence (AI) in health care and medicine have increased. High expectations surround the use of AI technologies, such as improvements for diagnosis and increases in the quality of care with reductions in health care costs. The successful development and testing of new AI algorithms require large amounts of high-quality data. Academic hospitals could provide the data needed for AI development, but granting legal, controlled, and regulated access to these data for developers and researchers is difficult. Therefore, the German Federal Ministry of Health supports the Protected Artificial Intelligence Innovation Environment for Patient-Oriented Digital Health Solutions for Developing, Testing, and Evidence-Based Evaluation of Clinical Value (pAItient) project, aiming to install the AI Innovation Environment at the Heidelberg University Hospital in Germany. The AI Innovation Environment was designed as a proof-of-concept extension of the already existing Medical Data Integration Center. It will establish a process to support every step of developing and testing AI-based technologies. The first part of the pAItient project, as presented in this research protocol, aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. We planned a multistep mixed methods approach. In the first step, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations to quantify qualitative findings and discover important aspects that were not mentioned by the interviewees. The questionnaires will be analyzed descriptively. In addition, patients and physicians were interviewed as well. No survey questionnaires were developed for this second group of participants. The study was approved by the Ethics Committee of the Heidelberg University Hospital (approval number: S-241/2021). Data collection concluded in summer 2022. Data analysis is planned to start in fall 2022. We plan to publish the results in winter 2022 to 2023. The results of our study will help in shaping the AI Innovation Environment at our academic hospital according to stakeholder requirements. With this approach, in turn, we aim to shape an AI environment that is effective and is deemed acceptable by all parties. DERR1-10.2196/42208.

Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology.

Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research. This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research. Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly. The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.

Harvesting metadata in clinical care: a crosswalk between FHIR, OMOP, CDISC and openEHR metadata

Metadata describe information about data source, type of creation, structure, status and semantics and are prerequisite for preservation and reuse of medical data. To overcome the hurdle of disparate data sources and repositories with heterogeneous data formats a metadata crosswalk was initiated, based on existing standards. FAIR Principles were included, as well as data format specifications. The metadata crosswalk is the foundation of data provision between a Medical Data Integration Center (MeDIC) and researchers, providing a selection of metadata information for research design and requests. Based on the crosswalk, metadata items were prioritized and categorized to demonstrate that not one single predefined standard meets all requirements of a MeDIC and only a maximum data set of metadata is suitable for use. The development of a convergence format including the maximum data set is the anticipated solution for an automated transformation of metadata in a MeDIC.

Investigation and analysis of four countries’ recalls of osteosynthesis implants and joint replacement implants from 2011 to 2021

BackgroundMedical devices are used in almost all orthopedic surgical subspecialties, and the frequency of adverse events is increasing, which should not be ignored. To provide suggestions on how to avoid implant recalls from the perspective of manufacturers, medical institutions and supervisions, as well as how to respond promptly to adverse events.MethodsThe research extracted recalls of osteosynthesis implants and joint replacement implants from January 1, 2011, to June 30, 2021, in the CNMPA, FDA, HC and ATGA websites and collected the information on device name, recall time, recall class, recall manufacturer, device classification and affected areas. Moreover, the McKinsey 7S model and fishbone diagram were used to analyze recall reasons.ResultsA total of 315 cases of osteosynthesis implants and 286 cases of joint replacement implants were reported in China, the USA, Canada and Australia. The recalls number from 2016 to 2021 was more than that from 2011 to 2015 for osteosynthesis implant (p = 0.012) and joint replacement implant (p = 0.002), and both mainly focused on class II (76.19% and 78.32%). There were statistical differences in the four countries for both implants (p = 0.000), especially osteosynthesis implant between China and the USA (p = 0.000), China and Canada (p = 0.001), the USA and Australia (p = 0.002), and joint replacement implant between China and Australia (p = 0.000).ConclusionsTo avoid the recalls of such implants, manufacturers should strictly select implant materials and components, develop detailed labels and instructions, severely control the packaging process and establish the integrity of medical device data. Medical institutions should standardize procurement procedures, use qualified equipment and train medical workers. It also requires supervisions to conduct premarket safety assessments. In addition, regulators should strengthen supervision and establish reporting systems to deal with the occurrence of adverse events promptly.

EP07.01-003 Metadata-Oriented Data Engineering Approach for Integration of Medical Data: A Pilot Study

Data Mining (DM) applications may benefit from artificial intelligence (AI) approaches that aim to reveal valuable information from data. The quantity and quality of data are important to receive accurate and reliable information. In recent years, research on the development of computer software programs under the title of metadata has been carried out to reach reliable data. In this study, it is aimed to evaluate the applicability of metadata-oriented data engineering methods in a relatively small medical dataset.