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Quarto GHA Workflow Runner committed Sep 13, 2024
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2 changes: 1 addition & 1 deletion .nojekyll
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30 changes: 28 additions & 2 deletions chapters/access.html
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<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>

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Expand Down Expand Up @@ -491,6 +491,19 @@ <h2 data-number="6.1" class="anchored" data-anchor-id="sec-access-background"><s
<p>Not all data in the N3C Data Enclave requires an approved DUR to access-mock datasets and publicly-available data (e.g., US census data) are accessible by everyone with Enclave access. These low-risk data are covered more in Chapters <a href="understanding.html" class="quarto-xref"><span>7</span></a> and <a href="publishing.html" class="quarto-xref"><span>10</span></a>.</p>
<p>The harmonized EHR data that <em>do</em> require an approved DUR to access are made available in two different “levels” (known as Level 2 and Level 3 for historical reasons–see below), each with different amounts of data obfuscation, and correspondingly different access requirements. Deciding which level of data is appropriate for your study is important, because accessing Level 3 data is more work and restrictive than accessing Level 2 data. On the other hand, some studies can be accomplished with only Level 3 data. Note that if you start with a lower level of data, it is possible to “upgrade” a project’s access level, though all participants in the project will need to complete another DUR for the new level.</p>
<p>In addition to the primary Level 2 and 3 datasets are “PPRL” data. PPRL data includes extra non-EHR sources of information such as obituary-based mortality records and viral variant sequencing information. These are available alongside only Level 3 data as an optional add-on; we’ll discuss PPRL in more detail below.</p>
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<p>In order to preserve the ethical use of unconsented data for public health benefit, every attempt to obscure identities of institutions, communities, or individuals contributing data should be taken as shown here. Access to EHR-derived data should minimize privacy and never provide more access to identifiers than is required for the research question.</p>
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<section id="sec-access-background-l3" class="level3" data-number="6.1.1">
<h3 data-number="6.1.1" class="anchored" data-anchor-id="sec-access-background-l3"><span class="header-section-number">6.1.1</span> Level 3, Limited Data Set (LDS)</h3>
<p>Level 3, or LDS data is the most complete and protected (the term “limited data set” is defined by HIPAA and may contain a limited set of potentially identifying information). This dataset contains two pieces of Protected Health Information (PHI) defined by HIPAA: full, 5-digit patient zip codes,<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a> and accurate dates of events and services (except for dates of birth which are limited to month and year).<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a> Level 3 data are in the OMOP common data model, with some N3C-specific additions and conveniences, and are versioned as releases as described above. (OMOP and N3C-specific additions are covered in <a href="understanding.html" class="quarto-xref"><span>Chapter 7</span></a>.)</p>
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<p>Workspaces are used for multiple purposes. Some store the Level 1/2/3 data; the “LDS Release” workspace for example stores the tables for the Level 3 LDS data. (The Data Catalog provides a more efficient way to access these tables however, more on that below.) Some are used to store “external” datasets such as publicly-available US Census data (see <a href="understanding.html#sec-understanding-public" class="quarto-xref"><span>Section 7.6</span></a>). The “N3C Training Area” workspace can be accessed by anyone as a place to practice on notional data (see <a href="support.html#sec-support-notional" class="quarto-xref"><span>Section 11.6</span></a>). Most, however, house research projects, and these are indicated with an <code>RP-XXXXXX</code> prefix.</p>
<p>Work in one project workspace can access only data or files from another project workspace if a “reference” has been added from the former to the latter. Researchers do not have permission to add such references. Thus, access from a research project workspace to protected datasets is possible only if the appropriate references have been added by administrators, which is done after a corresponding Data Use Request has been approved. Said another way, rather than providing researchers access to data, researchers are provided access to project workspaces, and these are in turn provided access to data.</p>
<p>This reference-based permission scheme supports a number of useful features for N3C. Naturally, restricting workspace access to a subset of data affords the possibility of different levels of data access with correspondingly different access requirements. A single researcher may be involved with multiple research projects of different levels but cannot share data or files across them, thus it is impossible for a researcher with access to Level 3 data to share it with their colleagues in another project with Level 2 data.</p>
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<p>Shared workspaces facilitating secure, collaborative data access are not unique to N3C amongst cloud-hosted, centralized RWD environments. These technologies, coupled with appropriate <a href="../chapters/governance.html">governance</a> and <a href="../chapters/onboarding.html">team-science</a> principles, can facilitate successful research projects in the face of data complexity.</p>
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<section id="sec-access-dur" class="level2" data-number="6.4">
<h2 data-number="6.4" class="anchored" data-anchor-id="sec-access-dur"><span class="header-section-number">6.4</span> The DUR - Data Use Request</h2>
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12 changes: 6 additions & 6 deletions chapters/colophon.html
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30 changes: 28 additions & 2 deletions chapters/cycle.html
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<h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-cycle-overview-domain"><span class="header-section-number">3.2.6</span> OMOP domain mapping</h3>
<p>The OMOP CDM is person-centric. OMOP organizes the patient’s temporal EHR records in multiple domains; condition, procedure, drug, device, and measurement. OMOP domain mapping is the process of aligning data from different sources to the OMOP framework, using OMOP standard terminology and concepts, and ensuring that the data are populated in the correct OMOP domain table. This process is important because it helps to ensure that the data are consistent and can be easily understood and analyzed. By mapping data to the OMOP framework, researchers and analysts are able to compare and combine data from different sources, enabling more comprehensive and accurate insights into healthcare outcomes and trends.</p>
<p>OMOP domain mapping typically involves the creation of mapping tables that translate the source code from different CDM data sources to the OMOP standard concept terminology and concept ids used in the OMOP vocabulary framework. The OMOP vocabulary dictates which source code should be placed in which target domain after it is translated into OMOP concepts. This vocabulary transformation using the OMOP concept relationships arranges the data into a well-organized and consistent format that can be easily analyzed and queried.</p>
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<p>Given the diversity of systems and medical vocabularies used in healthcare, vocabulary mapping is a crucial, but challenging and time-consuming part of RWD preparation for analysis. While technologies like Common Data Models (CDMs) and versioned medical vocabularies help, they are not universal solutions. The NIH and NCATS are currently engaged in a <a href="https://aspe.hhs.gov/code-map-services-interoperability-common-data-models-0">Code Map Services</a> project to address this need broadly.</p>
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<section id="sec-cycle-overview-globalid" class="level3" data-number="3.2.7">
<h3 data-number="3.2.7" class="anchored" data-anchor-id="sec-cycle-overview-globalid"><span class="header-section-number">3.2.7</span> N3C Global ID generation for all primary key fields</h3>
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<p>The scorecards allow sites to be directly involved in the data quality improvement process by highlighting areas for targeted improvement in their local source data, which would ultimately result in data quality improvements in their N3C payloads. Additionally, the scorecards allow the DI&amp;H team to monitor and maintain data quality across subsequent N3C data submissions and prevent any regression on those metrics. If the scorecards reveal that a released site is no longer passing key data quality metrics, then the site is unreleased until they are able to remediate their data quality issues.</p>
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<p>Unit harmonization, data quality checks, and site scorecards are all crucial ways to ensure data quality when working with RWD from multiple institutions. Having these elements in place provides quality insights, cross-site imputation of missing units of measurements, and the opportunity for local data administrators to observe, investigate, and communicate any anomalies that may otherwise go undetected without cross-site comparisons.</p>
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