Restoring Faith in HIPAA Privacy Protections while Enabling More Efficient Data Sharing (Preprint)

2020 ◽  
Author(s):  
James Jose ◽  
Rachele Hendricks-Sturrup
Keyword(s):  
Data Sharing ◽  
Control Process ◽  
Data Access ◽  
Quality Data ◽  
Patient Privacy ◽  
Multicenter Studies ◽  
High Quality Data ◽  
Privacy Law ◽  
Efficient Data ◽  
Item Data

UNSTRUCTURED A method of data sharing among healthcare researchers that could provide high levels of efficiency for data access while achieving advanced privacy protections could do much to enable multicenter studies based on data extracted from the electronic health record (EHR). We propose a specific use of a data enclave that fits within the “expert pathway” of the Health Insurance Portability and Accountability Act (HIPAA) rule, which would and that can allow sharing of protected healthcare information (PHI) with substantial reductions in burdens related to data use agreements (DUAs) and IRB reviews. Data is held by an infrastructure custodian acting under a business associate agreement. Researchers access the data while it is retained by the custodian, using remote desktop functionality with analytic tools on the custodian’s site, versus not on personal workstations or laptops. Because researchers cannot review line item data, but rather aggregate results, no IRB or DUA is needed. We anticipate that this data enclave method could be implemented as an effort to improve data access and provide high-quality data insights without weakening patient privacy protections under HIPAA. Lastly, we conclude that a comprehensive federal privacy law or standard is warranted alongside this proposed administrative control process for data enclaves to address important privacy loopholes.

scholarly journals Publishing descriptions of non-public clinical datasets: guidance for researchers, repositories, editors and funding organisations

2015 ◽  
Author(s):  
Iain Hrynaszkiewicz ◽  
Varsha Khodiyar ◽  
Andrew L Hufton ◽  
Susanna-Assunta Sansone
Keyword(s):  
Clinical Research ◽  
Data Sharing ◽  
Peer Review Process ◽  
Open Data ◽  
Data Access ◽  
Research Data ◽  
Future Research ◽  
Patient Privacy ◽  
Journal Articles ◽  
Data Repositories

AbstractSharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles – where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. We propose changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).

Het uitwisselen van onderzoeksgegevens: belangen en barrières

2021 ◽  
Author(s):  
T. DEVRIENDT ◽  
M. SHABANI ◽  
P. BORRY
Keyword(s):  
Data Sharing ◽  
Quality Data ◽  
High Quality Data ◽  
Collective Ownership ◽  
The Common ◽  
Recognition Systems ◽  
Technical Factors ◽  
Legal Restrictions ◽  
Access To Data ◽  
Common Vision

Data sharing: interests, impediments and restrictions. The sharing of data is of increasing importance. Data sharing platforms are currently built, aiming to make data more findable, accessible, interoperable and reusable. These platforms are, however, unable to address non-technical factors that may influence data sharing. Various factors, such as the desire to avoid reputational damage, ensuring a correct interpretation of data, loss of control, short-term grant cycles, the opportunity costs of data sharing, faulty recognition systems that do not reward the production of high-quality data itself, ethical and legal restrictions, can constitute impediments for data sharing. The role that platforms fulfill, will depend on the common vision on the fundamental rules surrounding data sharing and scientific competition. If a system of collective ownership is pursued, including guaranteed access to data under specific circumstances, an appropriate science policy should undergird data sharing platforms.

Constructing legal authority to facilitate multi-level interagency health data sharing in the United States

2015 ◽  
Vol 9 (2) ◽  
pp. 175-194 ◽  
Author(s):  
Beth Ann Fiedler
Keyword(s):  
Data Sharing ◽  
Data Access ◽  
The United States ◽  
Rapid Identification ◽  
Health Data ◽  
Patient Privacy ◽  
Content Type ◽  
Data Collection Process ◽  
Level Information ◽  
Multi Level

Purpose – The purpose of this paper is to forward specific policy proposals permitting greater sharing of health data across multi-level government agencies with the purpose of improving rapid identification of bioterrorist attack or disease epidemics while protecting patient privacy. Design/methodology/approach – A systematic literature review searched the following keyword phrases: knowledge sharing in the public sector, raw data sharing, interagency information systems, federal data sharing technology network and network theory on five primary databases. Findings – The volunteer nature of data sharing must evolve through public health policy to permit interagency data access agreements while minimizing privacy infringement. A multi-level information infrastructure network linking agencies tasked to develop medical countermeasures is recommended. Originality/value – This study optimizes the health data collection process to create a medical countermeasure network, demonstrates the utility of operationalizing data metrics for a US federal agency and advances meaningful use of electronic medical records.

scholarly journals Individual notions of fair data sharing from the perspectives of Swiss stakeholders

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lester Darryl Geneviève ◽  
Andrea Martani ◽  
Bernice Simone Elger ◽  
Tenzin Wangmo
Keyword(s):  
Qualitative Study ◽  
Distributive Justice ◽  
Data Sharing ◽  
Health Data ◽  
Quality Data ◽  
Snowball Sampling ◽  
Structured Interviews ◽  
High Quality Data ◽  
Negotiation Processes ◽  
Research Domains

Abstract Background The meaningful sharing of health data between different stakeholders is central to the advancement of science and to improve care offered to individual patients. However, it is important that the interests of individual stakeholders involved in this data sharing ecosystem are taken into account to ensure fair data sharing practices. In this regard, this qualitative study investigates such practices from the perspectives of a subset of relevant Swiss expert stakeholders, using a distributive justice lens. Methods Using purposive and snowball sampling methodologies, 48 expert stakeholders from the Swiss healthcare and research domains were recruited for semi-structured interviews. After the experts had consented, the interviews were audio-recorded and transcribed verbatim, but omitting identifying information to ensure confidentiality and anonymity. A thematic analysis using a deductive approach was conducted to identify fair data sharing practices for secondary research purposes. Themes and subthemes were then identified and developed during the analysis. Results Three distributive justice themes were identified in the data sharing negotiation processes, and these are: (i) effort, which was subcategorized into two subthemes (i.e. a claim to data reciprocity and other reciprocal advantages, and a claim to transparency on data re-use), (ii) compensation, which was subcategorized into two subthemes (i.e. a claim to an academic compensation and a claim to a financial compensation), and lastly, (iii) contribution, i.e. the significance of data contributions should be matched with a corresponding reward. Conclusions This qualitative study provides insights, which could inform policy-making on claims and incentives that encourage Swiss expert stakeholders to share their datasets. Importantly, several claims have been identified and justified under the basis of distributive justice principles, whilst some are more debatable and likely insufficient in justifying data sharing activities. Nonetheless, these claims should be taken seriously and discussed more broadly. Indeed, promoting health research while ensuring that healthcare systems guarantee better services, it is paramount to ensure that solutions developed are sustainable, provide fair criteria for academic careers and promote the sharing of high quality data to advance science.

scholarly journals An Efficient Data Sharing and Retrieval Method for Cloud Computing

2020 ◽  
Vol 9 (6) ◽  
pp. 458-460
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Sharing ◽  
Research Work ◽  
Data Access ◽  
Local Data ◽  
Sensitive Data ◽  
Retrieval Method ◽  
User Data ◽  
Efficient Data

now a days mobile devices can use cloud for data Access and manipulation without knowing overhead of local data management this may lead to leakage of sensitive data. Major disadvantage is to provide security for the user data , so this leads to concern for the user to access cloud computing. Lot of Research work carried out to provide security to user data over cloud computing, However these solutions are not resolve issues of mobile cloud computing due to constrained resource in mobile devices[2]. To overcome these issues, proposed methodology efficient data sharing and retrieval method, provide secure access control terminology using attribute based encryption in cloud platforms, as well as by using lazy-revocation technique will reduce user revocation cost

scholarly journals Considerations for an integrated population health databank in Africa: lessons from global best practices

2021 ◽  
Vol 6 ◽  
pp. 214
Author(s):  
Jude O. Igumbor ◽  
Edna N. Bosire ◽  
Marta Vicente-Crespo ◽  
Ehimario U. Igumbor ◽  
Uthman A. Olalekan ◽  
...  
Keyword(s):  
Best Practices ◽  
Population Health ◽  
Data Sharing ◽  
Critical Role ◽  
Data Access ◽  
Health Data ◽  
Research Data ◽  
Data Sources ◽  
Quality Data ◽  
Perceptions And Expectations

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Velocity Analysis Using Separated Diffractions for Lunar Penetrating Radar Obtained by Yutu-2 Rover

2021 ◽  
Vol 13 (7) ◽  
pp. 1387
Author(s):  
Chao Li ◽  
Jinhai Zhang
Keyword(s):  
Dielectric Permittivity ◽  
Lunar Regolith ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Quality Data ◽  
High Quality Data ◽  
Shallow Subsurface ◽  
Subsurface Structures ◽  
Ground Penetrating ◽  
Permittivity Model

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.

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