National COVID Cohort Collaborative Data Enhancements: A Path for Expanding Common Data Models.

Introduction: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include: long COVID specialty clinic indicator; Admission, Discharge, and Transfer (ADT) transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation.

Methods: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.

Enhancing real world data interoperability in healthcare: A methodological approach to laboratory unit harmonization.

Objective: The primary aim of this study is to address the critical issue of non-standardized units in clinical laboratory data, which poses significant challenges to data interoperability and secondary usage. Despite UCUM (Unified Code for Units of Measure) offering a unique representation for laboratory test units, nearly 60% of laboratory codes in healthcare organizations use non-standard units. We sought to design, implement and test a methodology for the harmonization of units to the UCUM standards across a large research network.

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

Background: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).

Leveraging a global, federated, real-world data network to optimize investigator-initiated pediatric clinical trials: the TriNetX Pediatric Collaboratory Network.

Objective: Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.

Materials And Methods: The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA).

A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data.

Background: Pancreatic Duct Adenocarcinoma (PDAC) screening can enable early-stage disease detection and long-term survival. Current guidelines use inherited predisposition, with about 10% of PDAC cases eligible for screening. Using Electronic Health Record (EHR) data from a multi-institutional federated network, we developed and validated a PDAC RISk Model (Prism) for the general US population to extend early PDAC detection.

Exploring Breast Cancer Systemic Drug Therapy Patterns in Real-World Data.

Purpose: To explore medications and their administration patterns in real-world patients with breast cancer.

Methods: A retrospective study was performed using TriNetX, a federated network of deidentified, Health Insurance Portability and Accountability Act-compliant data from 21 health care organizations across North America. Patients diagnosed with breast cancer between January 1, 2013, and May 31, 2022, were included.

The Detection of Date Shifting in Real-World Data.

Objectives: Analysis of health care real-world data (RWD) provides an opportunity to observe the actual patient diagnostic, treatment, and outcome events. However, researchers should understand the possible limitations of RWD. In particular, the dates in these data may be shifted from their actual values, which might affect the validity of study conclusions.

Automatic Outlier Detection in Laboratory Result Distributions Within a Real World Data Network.

Laboratory data must be interoperable to be able to accurately compare the results of a lab test between healthcare organizations. To achieve this, terminologies like LOINC (Logical Observation Identifiers, Names and Codes) provide unique identification codes for laboratory tests. Once standardized, the numeric results of laboratory tests can be aggregated and represented in histograms.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research.

Materials And Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities.

Applying FHIR Genomics for Research - From Sequencing to Database.

The availability of next-generation sequencing (NGS) technologies and their continually declining costs have resulted in the accumulation of large genomic data sets. NGS results have traditionally been delivered in PDF format, and in some cases, structured data, e.g.

Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research.

Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening.

Building an i2b2-Based Population Repository for COVID-19 Research.

Reuse of Electronic Health Records (EHRs) for specific diseases such as COVID-19 requires data to be recorded and persisted according to international standards. Since the beginning of the COVID-19 pandemic, Hospital Universitario 12 de Octubre (H12O) evolved its EHRs: it identified, modeled and standardized the concepts related to this new disease in an agile, flexible and staged way. Thus, data from more than 200,000 COVID-19 cases were extracted, transformed, and loaded into an i2b2 repository.

Evolving Effect of the COVID-19 Pandemic on Cancer-Related Encounters.

Purpose: This is an update to a previously published report characterizing the impact that efforts to control the COVID-19 pandemic have had on the normal course of cancer-related encounters.

Methods: Data were analyzed from 22 US health care organizations (members of the TriNetX global network) having relevant, up-to-date encounter data. Although the original study compared encounter data pre-COVID-19 (January-April 2019) with the corresponding months in 2020, this update considers data through April 2021.

Ensuring a safe(r) harbor: Excising personally identifiable information from structured electronic health record data.

Recent findings have shown that the continued expansion of the scope and scale of data collected in electronic health records are making the protection of personally identifiable information (PII) more challenging and may inadvertently put our institutions and patients at risk if not addressed. As clinical terminologies expand to include new terms that may capture PII (e.g.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.

Assessing real-world medication data completeness.

Objective: Analysis of healthcare Real-World Data (RWD) provides an opportunity to observe actual patient diagnostic, treatment and outcomes events. However, researchers should understand the possible limitations of RWD. In particular, these data may be incomplete, which would affect the validity of study conclusions.

Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity.

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization.

Effects of the COVID-19 Pandemic on Cancer-Related Patient Encounters.

Purpose: While there are studies under way to characterize the direct effects of the COVID-19 pandemic on the care of patients with cancer, there have been few quantitative reports of the impact that efforts to control the pandemic have had on the normal course of cancer diagnosis and treatment encounters.

Methods: We used the TriNetX platform to analyze 20 health care institutions that have relevant, up-to-date encounter data. Using this COVID and Cancer Research Network (CCRN), we compared cancer cohorts identified by querying encounter data pre-COVID (January 2019-April 2019) and current (January 2020-April 2020).

Using a Federated Network of Real-World Data to Optimize Clinical Trials Operations.

Clinical trials, whether industry, cooperative group sponsored, or investigator initiated, have an unacceptable rate of failure as a result of the inability to recruit sufficient numbers of patients. Even those trials that are completed often require time-consuming protocol amendments to achieve accrual goals. These inefficiencies in clinical trial research result in increasing costs and prolong the time needed to bring improved treatments to cancer clinical practice.

tranSMART: An Open Source Knowledge Management and High Content Data Analytics Platform.

The tranSMART knowledge management and high-content analysis platform is a flexible software framework featuring novel research capabilities. It enables analysis of integrated data for the purposes of hypothesis generation, hypothesis validation, and cohort discovery in translational research. tranSMART bridges the prolific world of basic science and clinical practice data at the point of care by merging multiple types of data from disparate sources into a common environment.