Methods of Information in Medicine最新文献

{"title":"MAGICPL: A Generic Process Description Language for Distributed Pseudonymization Scenarios.","authors":"Galina Tremper,&nbsp;Torben Brenner,&nbsp;Florian Stampe,&nbsp;Andreas Borg,&nbsp;Martin Bialke,&nbsp;David Croft,&nbsp;Esther Schmidt,&nbsp;Martin Lablans","doi":"10.1055/s-0041-1731387","DOIUrl":"https://doi.org/10.1055/s-0041-1731387","url":null,"abstract":"<p><strong>Objectives: </strong> Pseudonymization is an important aspect of projects dealing with sensitive patient data. Most projects build their own specialized, hard-coded, solutions. However, these overlap in many aspects of their functionality. As any re-implementation binds resources, we would like to propose a solution that facilitates and encourages the reuse of existing components.</p><p><strong>Methods: </strong> We analyzed already-established data protection concepts to gain an insight into their common features and the ways in which their components were linked together. We found that we could represent these pseudonymization processes with a simple descriptive language, which we have called MAGICPL, plus a relatively small set of components. We designed MAGICPL as an XML-based language, to make it human-readable and accessible to nonprogrammers. Additionally, a prototype implementation of the components was written in Java. MAGICPL makes it possible to reference the components using their class names, making it easy to extend or exchange the component set. Furthermore, there is a simple HTTP application programming interface (API) that runs the tasks and allows other systems to communicate with the pseudonymization process.</p><p><strong>Results: </strong> MAGICPL has been used in at least three projects, including the re-implementation of the pseudonymization process of the German Cancer Consortium, clinical data flows in a large-scale translational research network (National Network Genomic Medicine), and for our own institute's pseudonymization service.</p><p><strong>Conclusions: </strong> Putting our solution into productive use at both our own institute and at our partner sites facilitated a reduction in the time and effort required to build pseudonymization pipelines in medical research.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 1-02","pages":"21-31"},"PeriodicalIF":1.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39084872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning.","authors":"Takuma Oda,&nbsp;Shih-Wei Chiu,&nbsp;Takuhiro Yamaguchi","doi":"10.1055/s-0041-1731388","DOIUrl":"https://doi.org/10.1055/s-0041-1731388","url":null,"abstract":"<p><strong>Objective: </strong> This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data normalization; feature extraction by distributed representation of dataset names, variable names, and variable labels; and supervised machine learning.</p><p><strong>Materials and methods: </strong> Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation: Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms-namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms-to identify the one that could generate the best prediction model.</p><p><strong>Results: </strong> The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format.</p><p><strong>Conclusion: </strong> By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 1-02","pages":"49-61"},"PeriodicalIF":1.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39164925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Clinical Data Sharing Framework Based on Blockchain Technology.","authors":"Karamo Kanagi,&nbsp;Cooper Cheng-Yuan Ku,&nbsp;Li-Kai Lin,&nbsp;Wen-Huai Hsieh","doi":"10.1055/s-0041-1727193","DOIUrl":"https://doi.org/10.1055/s-0041-1727193","url":null,"abstract":"<p><strong>Background: </strong>While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming.</p><p><strong>Objectives: </strong>This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers.</p><p><strong>Methods: </strong>Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data.</p><p><strong>Results: </strong>The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds.</p><p><strong>Conclusion: </strong>The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 6","pages":"193-204"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38975039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action.","authors":"Luis Fernandez-Luque,&nbsp;Andre W Kushniruk,&nbsp;Andrew Georgiou,&nbsp;Arindam Basu,&nbsp;Carolyn Petersen,&nbsp;Charlene Ronquillo,&nbsp;Chris Paton,&nbsp;Christian Nøhr,&nbsp;Craig E Kuziemsky,&nbsp;Dari Alhuwail,&nbsp;Diane Skiba,&nbsp;Elaine Huesing,&nbsp;Elia Gabarron,&nbsp;Elizabeth M Borycki,&nbsp;Farah Magrabi,&nbsp;Kerstin Denecke,&nbsp;Linda W P Peute,&nbsp;Max Topaz,&nbsp;Najeeb Al-Shorbaji,&nbsp;Paulette Lacroix,&nbsp;Romaric Marcilly,&nbsp;Ronald Cornet,&nbsp;Shashi B Gogia,&nbsp;Shinji Kobayashi,&nbsp;Sriram Iyengar,&nbsp;Thomas M Deserno,&nbsp;Tobias Mettler,&nbsp;Vivian Vimarlund,&nbsp;Xinxin Zhu","doi":"10.1055/s-0041-1726414","DOIUrl":"https://doi.org/10.1055/s-0041-1726414","url":null,"abstract":"<p><strong>Background: </strong>As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis.</p><p><strong>Aim: </strong>In this paper, we describe a joint call for action to use and leverage evidence-based health informatics as the foundation for the COVID-19 response and public health interventions. Tangible examples are provided for how the working groups and special interest groups of the International Medical Informatics Association (IMIA) are helping to build an evidence-based response to this crisis.</p><p><strong>Methods: </strong>Leaders of working and special interest groups of the IMIA, a total of 26 groups, were contacted via e-mail to provide a summary of the scientific-based efforts taken to combat COVID-19 pandemic and participate in the discussion toward the creation of this manuscript. A total of 13 groups participated in this manuscript.</p><p><strong>Results: </strong>Various efforts were exerted by members of IMIA including (1) developing evidence-based guidelines for the design and deployment of digital health solutions during COVID-19; (2) surveying clinical informaticians internationally about key digital solutions deployed to combat COVID-19 and the challenges faced when implementing and using them; and (3) offering necessary resources for clinicians about the use of digital tools in clinical practice, education, and research during COVID-19.</p><p><strong>Discussion: </strong>Rigor and evidence need to be taken into consideration when designing, implementing, and using digital tools to combat COVID-19 to avoid delays and unforeseen negative consequences. It is paramount to employ a multidisciplinary approach for the development and implementation of digital health tools that have been rapidly deployed in response to the pandemic bearing in mind human factors, ethics, data privacy, and the diversity of context at the local, national, and international levels. The training and capacity building of front-line workers is crucial and must be linked to a clear strategy for evaluation of ongoing experiences.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 6","pages":"183-192"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1726414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38889490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.","authors":"Mindy K Ross,&nbsp;Henry Zheng,&nbsp;Bing Zhu,&nbsp;Ailina Lao,&nbsp;Hyejin Hong,&nbsp;Alamelu Natesan,&nbsp;Melina Radparvar,&nbsp;Alex A T Bui","doi":"10.1055/s-0041-1729951","DOIUrl":"https://doi.org/10.1055/s-0041-1729951","url":null,"abstract":"<p><strong>Objectives: </strong>Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)-but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested computable phenotype for asthma.</p><p><strong>Methods: </strong>We compared two established asthma computable phenotypes: the Chicago Area Patient-Outcomes Research Network (CAPriCORN) and Phenotype KnowledgeBase (PheKB). We established a large-scale, consensus gold standard (<i>n</i> = 1,365) from the University of California, Los Angeles Health System's clinical data warehouse for patients 5 to 17 years old. Results were manually reviewed and predictive performance (positive predictive value [PPV], sensitivity/specificity, F1-score) determined. We then examined the classification errors to gain insight for future algorithm optimizations.</p><p><strong>Results: </strong>As applied to our final cohort of 1,365 expert-defined gold standard patients, the CAPriCORN algorithms performed with a balanced PPV = 95.8% (95% CI: 94.4-97.2%), sensitivity = 85.7% (95% CI: 83.9-87.5%), and harmonized F1 = 90.4% (95% CI: 89.2-91.7%). The PheKB algorithm was performed with a balanced PPV = 83.1% (95% CI: 80.5-85.7%), sensitivity = 69.4% (95% CI: 66.3-72.5%), and F1 = 75.4% (95% CI: 73.1-77.8%). Four categories of errors were identified related to method limitations, disease definition, human error, and design implementation.</p><p><strong>Conclusion: </strong>The performance of the CAPriCORN and PheKB algorithms was lower than previously reported as applied to pediatric data (PPV = 97.7 and 96%, respectively). There is room to improve the performance of current methods, including targeted use of natural language processing and clinical feature engineering.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 6","pages":"219-226"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113735/pdf/nihms-1774084.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39183444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health-Enabling Technologies for Telerehabilitation of the Shoulder: A Feasibility and User Acceptance Study.","authors":"Bianca Steiner,&nbsp;Lena Elgert,&nbsp;Birgit Saalfeld,&nbsp;Jonas Schwartze,&nbsp;Horst Peter Borrmann,&nbsp;Axel Kobelt-Pönicke,&nbsp;Andreas Figlewicz,&nbsp;Detlev Kasprowski,&nbsp;Michael Thiel,&nbsp;Ralf Kreikebohm,&nbsp;Reinhold Haux,&nbsp;Klaus-Hendrik Wolf","doi":"10.1055/s-0040-1713685","DOIUrl":"https://doi.org/10.1055/s-0040-1713685","url":null,"abstract":"<p><strong>Background: </strong>After discharge from a rehabilitation center the continuation of therapy is necessary to secure already achieved healing progress and sustain (re-)integration into working life. To this end, home-based exercise programs are frequently prescribed. However, many patients do not perform their exercises as frequently as prescribed or even with incorrect movements. The telerehabilitation system AGT-Reha was developed to support patients with shoulder diseases during their home-based aftercare rehabilitation.</p><p><strong>Objectives: </strong>The presented pilot study AGT-Reha-P2 evaluates the technical feasibility and user acceptance of the home-based telerehabilitation system AGT-Reha.</p><p><strong>Methods: </strong>A nonblinded, nonrandomized exploratory feasibility study was conducted over a 2-year period in patients' homes. Twelve patients completed a 3-month telerehabilitation exercise program with AGT-Reha. Primary outcome measures are the satisfying technical functionality and user acceptance assessed by technical parameters, structured interviews, and a four-dimensional questionnaire. Secondary endpoints are the medical rehabilitation success measured by the active range of motion and the shoulder function (pain and disability) assessed by employing the Neutral-0 Method and the standardized questionnaire \"Shoulder Pain and Disability Index\" (SPADI), respectively. To prepare an efficacy trial, various standardized questionnaires were included in the study to measure ability to work, capacity to work, and subjective prognosis of work capacity. The participants have been assessed at three measurement points: prebaseline (admission to rehabilitation center), baseline (discharge from rehabilitation center), and posttherapy.</p><p><strong>Results: </strong>Six participants used the first version of AGT-Reha, while six other patients used an improved version. Despite minor technical problems, all participants successfully trained on their own with AGT-Reha at home. On average, participants trained at least once per day during their training period. Five of the 12 participants showed clinically relevant improvements of shoulder function (improved SPADI score > 11). The work-related parameters suggested a positive impact. All participants would recommend the system, ten participants would likely reuse it, and seven participants would have wanted to continue their use after 3 months.</p><p><strong>Conclusion: </strong>The findings show that home-based training with AGT-Reha is feasible and well accepted. Outcomes of SPADI indicate the effectiveness of aftercare with AGT-Reha. A controlled clinical trial to test this hypothesis will be conducted with a larger number of participants.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 S 02","pages":"e90-e99"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1713685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38250295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools.","authors":"Vicent Giménez-Alventosa,&nbsp;José Damián Segrelles,&nbsp;Germán Moltó,&nbsp;Mar Roca-Sogorb","doi":"10.1055/s-0040-1712460","DOIUrl":"https://doi.org/10.1055/s-0040-1712460","url":null,"abstract":"<p><strong>Background: </strong>Scientific publications are meant to exchange knowledge among researchers but the inability to properly reproduce computational experiments limits the quality of scientific research. Furthermore, bibliography shows that irreproducible preclinical research exceeds 50%, which produces a huge waste of resources on nonprofitable research at Life Sciences field. As a consequence, scientific reproducibility is being fostered to promote Open Science through open databases and software tools that are typically deployed on existing computational resources. However, some computational experiments require complex virtual infrastructures, such as elastic clusters of PCs, that can be dynamically provided from multiple clouds. Obtaining these infrastructures requires not only an infrastructure provider, but also advanced knowledge in the cloud computing field.</p><p><strong>Objectives: </strong>The main aim of this paper is to improve reproducibility in life sciences to produce better and more cost-effective research. For that purpose, our intention is to simplify the infrastructure usage and deployment for researchers.</p><p><strong>Methods: </strong>This paper introduces Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open source extension for Jupyter to deploy deterministic virtual infrastructures across multiclouds for reproducible scientific computational experiments. To exemplify its utilization and how APRICOT can improve the reproduction of experiments with complex computation requirements, two examples in the field of life sciences are provided. All requirements to reproduce both experiments are disclosed within APRICOT and, therefore, can be reproduced by the users.</p><p><strong>Results: </strong>To show the capabilities of APRICOT, we have processed a real magnetic resonance image to accurately characterize a prostate cancer using a Message Passing Interface cluster deployed automatically with APRICOT. In addition, the second example shows how APRICOT scales the deployed infrastructure, according to the workload, using a batch cluster. This example consists of a multiparametric study of a positron emission tomography image reconstruction.</p><p><strong>Conclusion: </strong>APRICOT's benefits are the integration of specific infrastructure deployment, the management and usage for Open Science, making experiments that involve specific computational infrastructures reproducible. All the experiment steps and details can be documented at the same Jupyter notebook which includes infrastructure specifications, data storage, experimentation execution, results gathering, and infrastructure termination. Thus, distributing the experimentation notebook and needed data should be enough to reproduce the experiment.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 S 02","pages":"e33-e45"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1712460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38250294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data.","authors":"Mark David Walker,&nbsp;Mihály Sulyok","doi":"10.1055/s-0041-1726276","DOIUrl":"https://doi.org/10.1055/s-0041-1726276","url":null,"abstract":"<p><strong>Background: </strong>Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's \"Mobility Trends\" (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists.</p><p><strong>Objective: </strong>The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany?</p><p><strong>Methods: </strong>AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response.</p><p><strong>Results: </strong>Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with \"driving,\" 2511. \"transit,\" 2513. \"walking,\" 2508).</p><p><strong>Conclusion: </strong>These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 6","pages":"179-182"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1726276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25521272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Permissioned Blockchain Network for Security and Sharing of De-identified Tuberculosis Research Data in Brazil.","authors":"Vinícius Costa Lima,&nbsp;Filipe Andrade Bernardi,&nbsp;Domingos Alves,&nbsp;Afrânio Lineu Kritski,&nbsp;Rafael Mello Galliez,&nbsp;Rui Pedro Charters Lopes Rijo","doi":"10.1055/s-0041-1727194","DOIUrl":"https://doi.org/10.1055/s-0041-1727194","url":null,"abstract":"<p><strong>Background: </strong>Tuberculosis (TB) is an infectious disease and is among the top 10 causes of death in the world, and Brazil is part of the top 30 high TB burden countries. Data collection is an essential practice in health studies, and the adoption of electronic data capture (EDC) systems can positively increase the experience of data acquisition and analysis. Also, data-sharing capabilities are crucial to the construction of efficient and effective evidence-based decision-making tools for managerial and operational actions in TB services. Data must be held secure and traceable, as well as available and understandable, for authorized parties.</p><p><strong>Objectives: </strong>In this sense, this work aims to propose a blockchain-based approach to build a reusable, decentralized, and de-identified dataset of TB research data, while increasing transparency, accountability, availability, and integrity of raw data collected in EDC systems.</p><p><strong>Methods: </strong>After identifying challenges and gaps, a solution was proposed to tackle them, considering its relevance for TB studies. Data security issues are being addressed by a blockchain network and a lightweight and practical governance model. Research Electronic Data Capture (REDCap) and KoBoToolbox are used as EDC systems in TB research. Mechanisms to de-identify data and aggregate semantics to data are also available.</p><p><strong>Results: </strong>A permissioned blockchain network was built using Kaleido platform. An integration engine integrates the EDC systems with the blockchain network, performing de-identification and aggregating meaning to data. A governance model addresses operational and legal issues for the proper use of data. Finally, a management system facilitates the handling of necessary metadata, and additional applications are available to explore the blockchain and export data.</p><p><strong>Conclusions: </strong>Research data are an important asset not only for the research where it was generated, but also to underpin studies replication and support further investigations. The proposed solution allows the delivery of de-identified databases built in real time by storing data in transactions of a permissioned network, including semantic annotations, as data are being collected in TB research. The governance model promotes the correct use of the solution.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 6","pages":"205-218"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1727194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38879944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International Comparison of Six Basic eHealth Indicators Across 14 Countries: An eHealth Benchmarking Study.","authors":"Elske Ammenwerth,&nbsp;Georg Duftschmid,&nbsp;Zaid Al-Hamdan,&nbsp;Hala Bawadi,&nbsp;Ngai T Cheung,&nbsp;Kyung-Hee Cho,&nbsp;Guillermo Goldfarb,&nbsp;Kemal H Gülkesen,&nbsp;Nissim Harel,&nbsp;Michio Kimura,&nbsp;Önder Kırca,&nbsp;Hiroshi Kondoh,&nbsp;Sabine Koch,&nbsp;Hadas Lewy,&nbsp;Dara Mize,&nbsp;Sari Palojoki,&nbsp;Hyeoun-Ae Park,&nbsp;Christopher Pearce,&nbsp;Fernan G B de Quirós,&nbsp;Kaija Saranto,&nbsp;Christoph Seidel,&nbsp;Vivian Vimarlund,&nbsp;Martin C Were,&nbsp;Johanna Westbrook,&nbsp;Chung P Wong,&nbsp;Reinhold Haux,&nbsp;Christoph U Lehmann","doi":"10.1055/s-0040-1715796","DOIUrl":"https://doi.org/10.1055/s-0040-1715796","url":null,"abstract":"<p><strong>Background: </strong>Many countries adopt eHealth applications to support patient-centered care. Through information exchange, these eHealth applications may overcome institutional data silos and support holistic and ubiquitous (regional or national) information logistics. Available eHealth indicators mostly describe usage and acceptance of eHealth in a country. The eHealth indicators focusing on the cross-institutional availability of patient-related information for health care professionals, patients, and care givers are rare.</p><p><strong>Objectives: </strong>This study aims to present eHealth indicators on cross-institutional availability of relevant patient data for health care professionals, as well as for patients and their caregivers across 14 countries (Argentina, Australia, Austria, Finland, Germany, Hong Kong as a special administrative region of China, Israel, Japan, Jordan, Kenya, South Korea, Sweden, Turkey, and the United States) to compare our indicators and the resulting data for the examined countries with other eHealth benchmarks and to extend and explore changes to a comparable survey in 2017. We defined \"availability of patient data\" as the ability to access data in and to add data to the patient record in the respective country.</p><p><strong>Methods: </strong>The invited experts from each of the 14 countries provided the indicator data for their country to reflect the situation on August 1, 2019, as date of reference. Overall, 60 items were aggregated to six eHealth indicators.</p><p><strong>Results: </strong>Availability of patient-related information varies strongly by country. Health care professionals can access patients' most relevant cross-institutional health record data fully in only four countries. Patients and their caregivers can access their health record data fully in only two countries. Patients are able to fully add relevant data only in one country. Finland showed the best outcome of all eHealth indicators, followed by South Korea, Japan, and Sweden.</p><p><strong>Conclusion: </strong>Advancement in eHealth depends on contextual factors such as health care organization, national health politics, privacy laws, and health care financing. Improvements in eHealth indicators are thus often slow. However, our survey shows that some countries were able to improve on at least some indicators between 2017 and 2019. We anticipate further improvements in the future.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 S 02","pages":"e46-e63"},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1715796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38616509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}