scholarly journals Unlocking Data for Clinical Research – The German i2b2 Experience

2011 ◽  
Vol 02 (01) ◽  
pp. 116-117 ◽  
Author(s):  
S. Mate ◽  
K Helbing ◽  
U. Sax ◽  
H.U. Prokosch ◽  
T. Ganslandt
Keyword(s):  
Data Protection ◽  
Clinical Care ◽  
Use Cases ◽  
Entry Barriers ◽  
Objective Data ◽  
Query Performance ◽  
Data Query ◽  
Medical Standard ◽  
Data Collections ◽  
Cdisc Odm

Summary Objective: Data from clinical care is increasingly being used for research purposes. The i2b2 platform has been introduced in some US research communities as a tool for data integration and querying by clinical users. The purpose of this project was to assess the applicability of i2b2 in Germany regarding use cases, functionality and integration with privacy enhancing tools. Methods: A set of four research usage scenarios was chosen, including the transformation and import of ontology and fact data from existing clinical data collections into i2b2 v1.4 instances. Query performance was measured in comparison to native SQL queries. A setup and administration tool for i2b2 was developed. An extraction tool for CDISC ODM data was programmed. Interfaces for the TMF privacy enhancing tools (PID Generator, Pseudonymization Service) were implemented. Results: Data could be imported in all tested scenarios from various source systems, including the generation of i2b2 ontology definitions. The integration of TMF privacy enhancing tools was possible without modification of the platform. Limitations were found regarding query performance in comparison to native SQL and certain temporal queries. Conclusions: i2b2 is a viable platform for data query tasks in use cases typical for networked medical research in Germany. The integration of privacy enhancing tools facilitates the use of i2b2 within established data protection concepts. Entry barriers should be lowered by providing tools for simplified setup and import of medical standard formats like CDISC ODM.

Connecting clinical care and research: Single-source with x4T – Process design, architecture, and use cases

2015 ◽  
Vol 57 (1) ◽  
Author(s):  
Christian Forster ◽  
Philipp Bruland ◽  
Jens Lechtenbörger ◽  
Bernhard Breil ◽  
Gottfried Vossen
Keyword(s):  
Clinical Research ◽  
Real Time ◽  
Process Design ◽  
Clinical Care ◽  
Routine Care ◽  
Use Cases ◽  
Single Source ◽  
Dual Source ◽  
Documentation Systems ◽  
Cdisc Odm

AbstractClinical research and routine care documentation have traditionally been performed in a dual-source approach, where data is collected redundantly and maintained in separated systems. The generic x4T single-source process removes redundant steps and is adoptable to specific types of studies. In support of the various processes, we have designed and implemented an architecture called x4T based on CDISC ODM. The software has been deployed in three different use cases containing up to 3976 subjects until now and allows real-time access to live data from routine care documentation systems.

Fast Selective Encryption Methods for Bitmap Images

2015 ◽  
Vol 6 (3) ◽  
pp. 51-69 ◽  
Author(s):  
Han Qiu ◽  
Gerard Memmi
Keyword(s):  
Data Protection ◽  
General Purpose ◽  
Use Cases ◽  
Process Unit ◽  
Selective Encryption ◽  
Quality Of Data ◽  
Protection Method ◽  
Reconstruction Quality ◽  
Image Protection

The authors are interested in image protection within resource environments offered by commodity computers such as desktops, laptops, tablets, or even smartphones. Additionally, the authors have in mind use cases where a large amount images are to be protected. Traditional encryption is not fast enough for such environments and such use cases. The authors derived a new solution by parallelizing selective encryption and using available GPU (Graphic Process Unit) acceleration. Progress obtained in terms of performance allows considering selective encryption as a general purpose solution for the use cases considered. After presenting related works, a ‘first level' of protection is described and a new ‘strong level' of protection method is introduced. Different architecture designs and implementation choices are extensively discussed, considering various criteria: performance indeed, but also image reconstruction quality and quality of data protection.

Research on the Performance Optimization of Massive Data Query Based on the Index

2013 ◽  
Vol 765-767 ◽  
pp. 867-870 ◽  
Author(s):  
Peng Wang ◽  
Ai Xue Tian
Keyword(s):  
Response Time ◽  
Performance Optimization ◽  
System Response ◽  
Massive Data ◽  
Query Performance ◽  
Data Query ◽  
System Response Time ◽  
Tree Index ◽  
The Way

The explosive growth in data quantity today, massive data query performance is good or bad becomes important. In order to avoid system response time is long, wasted resources from happening. We are actively exploring methods and strategies to make massive data query performance optimization. Massive data query performance optimization is a systematic project, involving many aspects, in which the index plays an important role. This article obtains from the index, based on Oracle, the paper analyzes the structure of B-tree index, the way of data scanning, as well as how to correctly use the index and rely on the index attribute to optimize massive data query performance.

Key features to ensure sustainability of a tele-oncology program at a national cancer center.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13613-e13613
Author(s):  
Chevon Rariy ◽  
Lynn Truesdale ◽  
Jennifer Greenman ◽  
Julian C. Schink
Keyword(s):  
Patient Satisfaction ◽  
Hybrid Model ◽  
Symptom Management ◽  
Clinical Care ◽  
Effective Means ◽  
Use Cases ◽  
Cancer Center ◽  
Provider Education ◽  
Specialty Area ◽  
Satisfaction Rates

e13613 Background: Prior to COVID-19, there were few telehealth services offered in the oncology specialty area. During the pandemic, we at a national cancer center rapidly scaled our oncology telehealth program to meet the needs of our patients. At the peak of the pandemic, telehealth initially served as a risk-mitigation strategy providing continued care to our patients while socially distancing, yet additionally, we have embedded necessary processes in place to create a sustained a telehealth oncology program that encompasses a hybrid model including face to face visits augmented with telehealth visits, where appropriate. Here we describe the key telehealth program features that have enabled a national cancer center to evolve into a hybrid model of oncology care across its five geographically distinct hospitals. Methods: Transitioning into a sustainable hybrid telehealth model of care involves a foundation of clinical leadership and partnerships among multiple departments. The telehealth oncology program leaders collaborate with the operations, technology, finance, clinical care teams, and governance council to implement telehealth growth initiatives and nimbly troubleshoot and ameliorate issues. A concierge service provides telehealth readiness checks to ensure timely resolution of issues. Workflows are followed to standardize processes. Telehealth use-cases ensure patients who need on-site services keep their in-person appointments, allowing telehealth visits for symptom management to enhance patient outcomes. A provider education session includes training on telehealth technology and “webside manner” training to ensure we preserve the personal touch with our patients in each telehealth encounter. Program data is regularly collected and reviewed to track the program’s success and opportunities for improvement. Results: After the initial peak of telehealth visits driven by the COVID pandemic, we continue to see a sustained 10-fold increase in service volume versus Jan/Feb 2020. There were 25,328 total telehealth visits from Mar. 2020-Jan. 2021, 75 clinical trial visits between July-Dec. 2020, and 848 readiness check escalations from Nov. 2020-Dec. 2021. Service lines expanded from 2 to 33, including growing rural health partnerships and a home chemotherapy infusion model. Use-cases expanded to bridge to on-site care, rapid initial visits, preop/postop checks, symptom management, and surveillance. Press Ganey patient satisfaction rates are as high as 92% and 90% of providers reported overall satisfaction with the telehealth consultations. Conclusions: Our key program features have enabled the growth and success of our enterprise tele-oncology program. One of the most promising indicators of success is the positive provider and patient satisfaction rates. Telehealth provides an effective means to provide a bridge to onsite cancer care even for our complex oncology patients.

scholarly journals Biobanking between the EU and Third Countries — Can Data Sharing Be Facilitated via Soft Regulatory Tools?

2018 ◽  
Vol 25 (5) ◽  
pp. 517-536 ◽  
Author(s):  
Santa Slokenberga
Keyword(s):  
Data Sharing ◽  
Data Protection ◽  
Clinical Care ◽  
Research Capacity ◽  
Genomic Research ◽  
General Data Protection Regulation ◽  
Biobank Research ◽  
General Data ◽  
Legal Frameworks ◽  
The Eu

AbstractIn biobanking, collaboration and data sharing contribute to building genomic research capacity, and have the potential to further scientific advances that ultimately can result in advances in clinical care. However, in the absence of common applicable legal frameworks that enable collaboration, capacity building is hindered. With the applicability of the General Data Protection Regulation, the obstacles to data sharing which involve export of data from European Union Member States to third countries are expected to grow, rendering the collaboration between the EU and third countries even more challenging. This article examines how, if at all, data sharing in biobank research between the EU and third countries could be facilitated via the use of soft regulatory tools. It argues that although the existing soft tools might not in itself be suitable for meeting all the GDPR requirements, they could be the basis on which to raise the area-specific data protection bar globally.

scholarly journals PoBery: Possibly-complete Big Data Queries with Probabilistic Data Placement and Scanning

2021 ◽  
Vol 2 (3) ◽  
pp. 1-28
Author(s):  
Jie Song ◽  
Qiang He ◽  
Feifei Chen ◽  
Ye Yuan ◽  
Ge Yu
Keyword(s):  
Big Data ◽  
Query Processing ◽  
State Of The Art ◽  
Data Placement ◽  
Probabilistic Data ◽  
Trade Off ◽  
Query Performance ◽  
Data Query ◽  
Query Efficiency ◽  
The Given

In big data query processing, there is a trade-off between query accuracy and query efficiency, for example, sampling query approaches trade-off query completeness for efficiency. In this article, we argue that query performance can be significantly improved by slightly losing the possibility of query completeness, that is, the chance that a query is complete. To quantify the possibility, we define a new concept, Probability of query Completeness (hereinafter referred to as PC). For example, If a query is executed 100 times, PC = 0.95 guarantees that there are no more than 5 incomplete results among 100 results. Leveraging the probabilistic data placement and scanning, we trade off PC for query performance. In the article, we propose PoBery (POssibly-complete Big data quERY), a method that supports neither complete queries nor incomplete queries, but possibly-complete queries. The experimental results conducted on HiBench prove that PoBery can significantly accelerate queries while ensuring the PC. Specifically, it is guaranteed that the percentage of complete queries is larger than the given PC confidence. Through comparison with state-of-the-art key-value stores, we show that while Drill-based PoBery performs as fast as Drill on complete queries, it is 1.7 ×, 1.1 ×, and 1.5 × faster on average than Drill, Impala, and Hive, respectively, on possibly-complete queries.

scholarly journals Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study (Preprint)

2021 ◽  
Author(s):  
Nicole Martinez-Martin ◽  
Henry T Greely ◽  
Mildred K Cho
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Data Protection ◽  
Delphi Study ◽  
Ethical Issues ◽  
Clinical Care ◽  
Personal Data ◽  
Digital Phenotyping ◽  
Consensus Statements ◽  
Health Applications

BACKGROUND Digital phenotyping (also known as <i>personal sensing</i>, <i>intelligent sensing</i>, or <i>body computing</i>) involves the collection of biometric and personal data <i>in situ</i> from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person’s mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. OBJECTIVE This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. METHODS We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. RESULTS The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. CONCLUSIONS The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.

scholarly journals Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review

10.2196/22594 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e22594
Author(s):  
Felix Nikolaus Wirth ◽  
Marco Johns ◽  
Thierry Meurers ◽  
Fabian Prasser
Keyword(s):  
Infectious Disease ◽  
Disease Management ◽  
Data Protection ◽  
Spatial Data ◽  
Privacy Protection ◽  
Use Cases ◽  
Contact Tracing ◽  
Use Case ◽  
Protection Measures ◽  
Individual Level

Background The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. Objective Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented. Methods We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions. Results We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases. Conclusions We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

scholarly journals Data Integration for Future Medicine (DIFUTURE)

2018 ◽  
Vol 57 (S 01) ◽  
pp. e57-e65 ◽  
Author(s):  
Fabian Prasser ◽  
Oliver Kohlbacher ◽  
Ulrich Mansmann ◽  
Bernhard Bauer ◽  
Klaus Kuhn
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Data Sharing ◽  
Data Protection ◽  
State Of The Art ◽  
Data Access ◽  
Use Cases ◽  
Working Environment ◽  
Security And Privacy ◽  
Use Case

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios. Discussion: Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.

Optimizing data query performance of Bi-cluster for large-scale scientific data in supercomputers

2021 ◽  
Author(s):  
Xia Liao ◽  
Yixian Shen ◽  
Shengguo Li ◽  
Yutong Lu ◽  
Yufei Du ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Data ◽  
Query Performance ◽  
Data Query
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