Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 34
Filtrar
1.
Stud Health Technol Inform ; 287: 73-77, 2021 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-34795084

RESUMO

Adopting international standards within health research communities can elevate data FAIRness and widen analysis possibilities. The purpose of this study was to evaluate the mapping feasibility against HL7® Fast Healthcare Interoperability Resources® (FHIR)® of a generic metadata schema (MDS) created for a central search hub gathering COVID-19 health research (studies, questionnaires, documents = MDS resource types). Mapping results were rated by calculating the percentage of FHIR coverage. Among 86 items to map, total mapping coverage was 94%: 50 (58%) of the items were available as standard resources in FHIR and 31 (36%) could be mapped using extensions. Five items (6%) could not be mapped to FHIR. Analyzing each MDS resource type, there was a total mapping coverage of 93% for studies and 95% for questionnaires and documents, with 61% of the MDS items available as standard resources in FHIR for studies, 57% for questionnaires and 52% for documents. Extensions in studies, questionnaires and documents were used in 32%, 38% and 43% of items, respectively. This work shows that FHIR can be used as a standardized format in registries for clinical, epidemiological and public health research. However, further adjustments to the initial MDS are recommended - and two additional items even needed when implementing FHIR. Developing a MDS based on the FHIR standard could be a future approach to reduce data ambiguity and foster interoperability.


Assuntos
COVID-19 , Metadados , Atenção à Saúde , Registros Eletrônicos de Saúde , Nível Sete de Saúde , Humanos , Sistema de Registros , SARS-CoV-2
3.
Artigo em Alemão | MEDLINE | ID: mdl-34297162

RESUMO

Public health research and epidemiological and clinical studies are necessary to understand the COVID-19 pandemic and to take appropriate action. Therefore, since early 2020, numerous research projects have also been initiated in Germany. However, due to the large amount of information, it is currently difficult to get an overview of the diverse research activities and their results. Based on the "Federated research data infrastructure for personal health data" (NFDI4Health) initiative, the "COVID-19 task force" is able to create easier access to SARS-CoV-2- and COVID-19-related clinical, epidemiological, and public health research data. Therefore, the so-called FAIR data principles (findable, accessible, interoperable, reusable) are taken into account and should allow an expedited communication of results. The most essential work of the task force includes the generation of a study portal with metadata, selected instruments, other study documents, and study results as well as a search engine for preprint publications. Additional contents include a concept for the linkage between research and routine data, a service for an enhanced practice of image data, and the application of a standardized analysis routine for harmonized quality assessment. This infrastructure, currently being established, will facilitate the findability and handling of German COVID-19 research. The developments initiated in the context of the NFDI4Health COVID-19 task force are reusable for further research topics, as the challenges addressed are generic for the findability of and the handling with research data.


Assuntos
Pesquisa Biomédica/tendências , COVID-19 , Disseminação de Informação , Alemanha , Humanos , Metadados , Pandemias , SARS-CoV-2
4.
Stud Health Technol Inform ; 281: 88-92, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042711

RESUMO

Studies investigating the suitability of SNOMED CT in COVID-19 datasets are still scarce. The purpose of this study was to evaluate the suitability of SNOMED CT for structured searches of COVID-19 studies, using the German Corona Consensus Dataset (GECCO) as example. Suitability of the international standard SNOMED CT was measured with the scoring system ISO/TS 21564, and intercoder reliability of two independent mapping specialists was evaluated. The resulting analysis showed that the majority of data items had either a complete or partial equivalent in SNOMED CT (complete equivalent: 141 items; partial equivalent: 63 items; no equivalent: 1 item). Intercoder reliability was moderate, possibly due to non-establishment of mapping rules and high percentage (74%) of different but similar concepts among the 86 non-equal chosen concepts. The study shows that SNOMED CT can be utilized for COVID-19 cohort browsing. However, further studies investigating mapping rules and further international terminologies are necessary.


Assuntos
COVID-19 , Systematized Nomenclature of Medicine , Consenso , Humanos , Reprodutibilidade dos Testes , SARS-CoV-2
5.
Stud Health Technol Inform ; 281: 402-406, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042774

RESUMO

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.


Assuntos
Informática Médica , Registros Eletrônicos de Saúde , Hospitais Universitários , Humanos
6.
Stud Health Technol Inform ; 281: 1027-1028, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042834

RESUMO

The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine ("Netzwerk Universitätsmedizin (NUM)") tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.


Assuntos
COVID-19 , Aplicativos Móveis , Humanos , Pandemias , Padrões de Referência , SARS-CoV-2
7.
Stud Health Technol Inform ; 278: 19-26, 2021 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-34042871

RESUMO

The objectives of this paper are to analyze the terminologies SNOMED CT and Logical Observation Identifiers Names and Codes (LOINC) and to provide a guideline for the translation of LOINC concepts to SNOMED CT. Verified research data sets were used for this study, so this experiment is replicable with other research data. 50 LOINC concepts of frequently performed laboratory services were translated to SNOMED CT. Information would be lost with pre-coordinated mapping but the compositional grammar of SNOMED CT allows for the linking of individual concepts into complicated postcoordinated expressions including all embedded information in LOINC concepts. All information can thus be transferred smoothly to SNOMED CT.


Assuntos
Logical Observation Identifiers Names and Codes , Systematized Nomenclature of Medicine , Linguística , Traduções
8.
Stud Health Technol Inform ; 278: 156-162, 2021 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-34042889

RESUMO

Infectious diseases due to microbial resistance pose a worldwide threat that calls for data sharing and the rapid reuse of medical data from health care to research. The integration of pathogen-related data from different hospitals can yield intelligent infection control systems that detect potentially dangerous germs as early as possible. Within the use case Infection Control of the German HiGHmed Project, eight university hospitals have agreed to share their data to enable analysis of various data sources. Data sharing among different hospitals requires interoperability standards that define the structure and the terminology of the information to be exchanged. This article presents the work performed at the University Hospital Charité and Berlin Institute of Health towards a standard model to exchange microbiology data. Fast Healthcare Interoperability Resources (FHIR) is a standard for fast information exchange that allows to model healthcare information, based on information packets called resources, which can be customized into so-called profiles to match use case- specific needs. We show how we created the specific profiles for microbiology data. The model was implemented using FHIR for the structure definition, and the international standards SNOMED CT and LOINC for the terminology services.


Assuntos
Logical Observation Identifiers Names and Codes , Systematized Nomenclature of Medicine , Academias e Institutos , Atenção à Saúde , Humanos , Disseminação de Informação
9.
Stud Health Technol Inform ; 278: 231-236, 2021 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-34042899

RESUMO

Electronic documentation of medication data is one of the biggest challenges associated with digital clinical documentation. Despite its importance, it has not been consistently implemented in German university hospitals. In this paper we describe the approach of the German Medical Informatics Initiative (MII) towards the modelling of a medication core dataset using FHIR® profiles and standard-compliant terminologies. The FHIR profiles for Medication and MedicationStatement were adapted to the core dataset of the MIl. The terminologies to be used were selected based on the criteria of the ISO-standard for the Identification of Medicinal Products (IDMP). For a first use case with a minimal medication dataset, the entries in the medication chapter of the German Procedure Classification (OPS codes) were analyzed and mapped to IDMP-compliant medication terminology. OPS data are available at all German hospitals as they are mandatory for reimbursement purposes. Reimbursement-relevant encounter data containing OPS medication procedures were used to create a FHIR representation based on the FHIR profiles MedicationStatement and Medication. This minimal solution includes - besides the details on patient and start-/end-dates - the active ingredients identified by the IDMP-compliant codes and - if specified in the OPS code - the route of administration and the range of the amount of substance administered to the patient, using the appropriate unit of measurement code. With FHIR, the medication data can be represented in the data integration centers of the MII to provide a standardized format for data analysis across the MII sites.


Assuntos
Informática Médica , Registros Eletrônicos de Saúde , Humanos , Cooperação do Paciente
10.
Stud Health Technol Inform ; 278: 245-250, 2021 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-34042901

RESUMO

Medical data generated by wearables and smartphones can add value to health care and medical research. This also applies to the ECG data that is created with Apple Watch 4 or later. However, Apple currently does not provide an efficient solution for accessing and sharing ECG raw data in a standardized data format. Our method aims to provide a solution that enables patients to share their Apple Watch's ECG data with any health care institution via an iPhone application. We achieved this by implementing a parser in Swift that converts the Apple Watch's raw ECG data into a FHIR observation. Furthermore, we added the capability of transmitting these observations to a specified server and equipping it with the patient's reference number. The result is a user-friendly iPhone application, enabling patients to share their Apple Watch's ECG data in a widely known health data standard with minimal effort. This allows the personnel involved in the patient's treatment to use data that was previously difficult to access for further analyses and processing. Our solution can facilitate research for new treatment methods, for example, utilizing the Apple Watch for continuous monitoring of heart activity and early detection of heart conditions.


Assuntos
Eletrocardiografia , Software , Humanos
11.
J Med Internet Res ; 23(2): e25283, 2021 02 08.
Artigo em Inglês | MEDLINE | ID: mdl-33497350

RESUMO

BACKGROUND: The COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on many health care systems and the global economy. This devastating pandemic has brought together communities across the globe to work on this issue in an unprecedented manner. OBJECTIVE: This case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges posed by the COVID-19 pandemic. It aims to deliver a clear implementation road map for other organizations to follow. METHODS: This 4-day hackathon was conducted in April 2020, based on six COVID-19-related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (1) individual experience satisfaction, (2) level of interprofessional skills exchange, (3) maturity of the projects realized, and (4) overall quality of the event. At the end of the event, participants were invited to take part in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions that assessed their experience regarding the remote nature of the event and their individual project, interprofessional skills exchange, and their confidence in working on a digital health project before and after the hackathon. Mentors, who guided the participants through the event, also provided feedback to the organizers through an online survey. RESULTS: A total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and stated that they were likely to continue working on their projects. Of the participants who provided feedback, 60% (n=18) would not have started their project without this particular hackathon and indicated that the hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype. CONCLUSIONS: This study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful for other institutions and initiatives that are willing to introduce similar event formats in the fight against COVID-19.


Assuntos
COVID-19/terapia , Atenção à Saúde/organização & administração , Internet , Adulto , COVID-19/epidemiologia , Humanos , SARS-CoV-2/isolamento & purificação
12.
BMC Med Inform Decis Mak ; 20(1): 341, 2020 12 21.
Artigo em Inglês | MEDLINE | ID: mdl-33349259

RESUMO

BACKGROUND: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. METHODS: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. RESULTS: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. CONCLUSION: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.


Assuntos
Pesquisa Biomédica , COVID-19 , Conjuntos de Dados como Assunto , Medicina , Consenso , Humanos , Pandemias
13.
J Med Syst ; 44(8): 137, 2020 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-32642856

RESUMO

This paper presents an approach to enable interoperability of the research data management system XNAT by the implementation of the HL7 standards framework Fast Healthcare Interoperability Resources (FHIR). The FHIR implementation is realized as an XNAT plugin (Source code: https://github.com/somnonetz/xnat-fhir-plugin ), that allows easy adoption in arbitrary XNAT instances. The approach is demonstrated on patient data exchange between a FHIR reference implementation and XNAT.


Assuntos
Nível Sete de Saúde/organização & administração , Sistemas Computadorizados de Registros Médicos/organização & administração , Neuroimagem/métodos , Gerenciamento de Dados , Registros Eletrônicos de Saúde , Nível Sete de Saúde/normas , Humanos , Sistemas Computadorizados de Registros Médicos/normas , Integração de Sistemas
14.
Orphanet J Rare Dis ; 15(1): 145, 2020 06 09.
Artigo em Inglês | MEDLINE | ID: mdl-32517778

RESUMO

BACKGROUND: Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. METHODS: Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010-2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. RESULTS: Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and "omics" data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). CONCLUSION: Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.


Assuntos
Aprendizado de Máquina , Doenças Raras , Algoritmos , Humanos , Prognóstico , Máquina de Vetores de Suporte
15.
Stud Health Technol Inform ; 270: 8-12, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570336

RESUMO

The cryptographic method Secure Multi-Party Computation (SMPC) could facilitate data sharing between health institutions by making it possible to perform analyses on a "virtual data pool", providing an integrated view of data that is actually distributed - without any of the participants having to disclose their private data. One drawback of SMPC is that specific cryptographic protocols have to be developed for every type of analysis that is to be performed. Moreover, these protocols have to be optimized to provide acceptable execution times. As a first step towards a library of efficient implementations of common methods in health data sciences, we present a novel protocol for efficient time-to-event analysis. Our implementation utilizes a common technique called garbled circuits and was implemented using a widespread SMPC programming framework. We further describe optimizations that we have developed to reduce the execution times of our protocol. We experimentally evaluated our solution by computing Kaplan-Meier estimators over a vertically distributed dataset while measuring performance. By comparing the SMPC results with a conventional analysis on pooled data, we show that our approach is practical and scalable.


Assuntos
Segurança Computacional , Disseminação de Informação , Humanos , Informática Médica
16.
Eur J Hum Genet ; 28(5): 558-566, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32055015

RESUMO

Modern diagnostic methods (next-generation sequencing) are one of the current hopes with regard to a personalised medicine. By applying detailed genetic analysis, it is possible to not only improve the prediction of potential risks (as, e.g., concerning hereditary breast cancer) but also the precision of therapy by targeting it to a specific genetic variant. However, there is no international standard for creating, structuring and/or transferring the results of a genetic test report. This type of test report often contains large amounts of complex information, and a standardised and consistent structure would offer potential benefits to all. These include reduced expenditure of time (due to the elimination of information-conversion steps), improved safety (due to a reduction in the occurrence of transmission errors, misunderstanding or misinterpretation of content) and improved clinical information gathering (by the respective linkage to scientific data and literature). Especially in regard to secondary use, a standardised (electronic) format would improve the suitability of these data in retrospective studies and basic research. In this study, we analysed the format and content of 96 genetic testing reports (germline and somatic) from Germany, Switzerland and Austria. Based on these results, we summarised and discussed potentially critical data that were demonstrated to be reported inconsistently, and propose a baseline structure for reporting that would also ease future electronic conversion.


Assuntos
Registros Eletrônicos de Saúde/normas , Testes Genéticos/normas , Áustria , Testes Genéticos/métodos , Alemanha , Humanos , Registros Públicos de Dados de Cuidados de Saúde , Suíça
17.
Stud Health Technol Inform ; 267: 52-58, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483254

RESUMO

Fast Healthcare Interoperability Resources (FHIR), an international standard for exchanging digital health data, is increasingly used in health information technology. FHIR promises to facilitate the use of electronic health records (EHRs), enable mobile technologies and make health data accessible to large-scale analytics. Until now, there is no comprehensive review of scientific articles about FHIR and its use in digital health. Here, we aim to address this gap and provide an overview of the main topics associated with FHIR in the scientific literature. For this, we screened all articles about FHIR on Web of Science and PubMed and identified the main topics discussed in these articles. We also explored the temporal trend and geography of publications and performed some basic text mining on article abstracts. We found that the topics most commonly discussed in the articles were related to data models, mobile and web applications as well as medical devices. Since its introduction, the number of publications about FHIR have steadily increased until 2017, indicating an increasing popularity of FHIR in healthcare (in 2018, publication numbers remained stable). In sum, our study provides an overview of the scientific literature about FHIR and its current use in digital health.


Assuntos
Registros Eletrônicos de Saúde , Informática Médica , Mineração de Dados , PubMed
18.
Stud Health Technol Inform ; 267: 81-85, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483258

RESUMO

Disease management programs coordinate and manage treatment between physicians and across sectors of the healthcare system. The aim is to reduce existing care deficits (overuse, underuse and misuse) and thus improve the quality and cost-effectiveness of care. To facilitate the treatment of chronic diseases such as asthma, it is important to continuously document a patient's medical history. For this purpose, it is necessary to be able to integrate and exchange data from and between multiple different information systems. Aiming to ensure interoperability across electronic documentation systems, this paper proposes the standardization of the KBV's (National Association of Statutory Health Insurance in Germany) specification for the electronic Disease Management Program (eDMP) for bronchial asthma. Therefore, international standards like SNOMED CT, LOINC and UCUM were chosen to encode clinical information, while evaluating their suitability with the scoring system ISO/PRF TR 21564. The resulting analysis showed that most of the terms had either a complete or partial equivalent term in one of the terminology systems. Therefore, future implementations of the eDMP for bronchial asthma that utilize standard terminologies could benefit from data integration from different sources like electronic health records and reduce redundancies in data capture and storage.


Assuntos
Asma , Asma/terapia , Alemanha , Humanos , Logical Observation Identifiers Names and Codes , Systematized Nomenclature of Medicine
19.
Stud Health Technol Inform ; 264: 1574-1575, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438238

RESUMO

Infections are a global public health concern. For coordinated actions against infectious diseases, semantic interoperability between infection control systems is crucial. This requires a consistent use of standard terminologies such as SNOMED CT. Here, we compare two value sets of infectious agents annotated with SNOMED CT (WHONET 2018 vs. pathogens reported under the German Protection against Infection Act). Our comparison revealed several inconsistencies, highlighting the importance of the consistent and coordinated use of standard terminologies.


Assuntos
Semântica , Systematized Nomenclature of Medicine , Saúde Pública
20.
NPJ Digit Med ; 2: 79, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31453374

RESUMO

Digital data are anticipated to transform medicine. However, most of today's medical data lack interoperability: hidden in isolated databases, incompatible systems and proprietary software, the data are difficult to exchange, analyze, and interpret. This slows down medical progress, as technologies that rely on these data - artificial intelligence, big data or mobile applications - cannot be used to their full potential. In this article, we argue that interoperability is a prerequisite for the digital innovations envisioned for future medicine. We focus on four areas where interoperable data and IT systems are particularly important: (1) artificial intelligence and big data; (2) medical communication; (3) research; and (4) international cooperation. We discuss how interoperability can facilitate digital transformation in these areas to improve the health and well-being of patients worldwide.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...