Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data

Purpose: Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). Materials and methods: R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database. Results: Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts. Conclusion: R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.ope

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making

BRHIM - Base de Registros Hospitalares para Informações e Metadados

Os riscos de reidentificação de dados hospitalares são altos e há uma demanda por eles em projetos de desenvolvimento e validação de Inteligência Artificial (IA). Este trabalho aborda os principais métodos de preparação de registros hospitalares usados para realizar estudos observacionais de maneira direcionada de avaliar o risco de reidentificação e o impacto da perda de informações que a anonimização produz nos resultados da IA. Uma revisão sobre o assunto é apresentada no início e após são apresentados dois artigos, sempre considerando o contexto da utilização de registros hospitalares em estudos epidemiológicos. O primeiro artigo propõe uma ontologia de domínio para definir um escopo para a tratar a anonimização. Os tipos de ataques, os tipos de dados e atributos, os modelos de privacidade, os tipos de uso da inteligência artificial e os diferentes delineamentos são apresentados. Foi feito um exemplo de instância da ontologia na ferramenta Web Protegé, disponível pela Universidade de Stanford para a construção de ontologias e que permite replica-la. O segundo artigo visa definir uma receita de preparação de prontuário hospitalar com 5 etapas para implementar a pseudo-anonimização, desidentificação e anonimização de dados e comparar os efeitos dessas etapas em uma aplicação da IA. Para isto, um evento Datathon foi realizado para desenvolver um preditor de IA de mortalidade hospitalar. Comparando os resultados da IA usando os dados originais e os dados anônimos, demonstrando uma diferenca inferior a 1% nos resultados da AUC-ROC, enquanto o risco de um paciente ser identificado foi reduzido em 95%, demonstrando que o preparo pode ser sistematizado agregando privacidade e computando a perda de informações, a fim de torná-los transparentes.The risks of re-identifying hospital data is high and there is a demand for them in projects for the development and validation of Artificial Intelligence (AI). This approach addresses the main methods of preparing hospital records used to carry out observational studies and in a directed way to assess the risk of re-identification and the impact of the loss of information that anonymization produces on AI results. A review of the review on the subject is presented at the beginning and after the literature is presented two articles, always considering the context of the use of hospital records in epidemiological studies. The first article proposes a domain ontology to define a scope for the search for anonymization. The types of attacks, the types of attacks, the types of data and attributes, the privacy models, the types of use that artificial intelligence devices and the different delineations are presented. An example of an ontology instance was made in the Web Protegé tool, made available by Stanford University for building ontologies and which allows replicating pregnant children and thus disseminating anonymization atology. The article aims to define a second hospital record preparation recipe with 5 steps for implementing pseudo-anonymization, de-identification and data anonymization and to compare the effects of these steps in an AI application. A Datathon event was conducted to develop an AI predictor of hospital mortality. Comparing the AI results using the original data and the anonymized data, which were identified as less than 1% results on the AUC-ROC, while the risk of a registered patient was recorded at 95%, demonstrating that the preparation can be systematized with privacy privacy and information loss in order to make them transparent

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe