Participant choices for return of genomic results in the eMERGE Network.

PURPOSE: Secondary findings are typically offered in an all or none fashion when sequencing is used for clinical purposes. This study aims to describe the process of offering categorical and granular choices for results in a large research consortium. METHODS: Within the third phase of the electronic MEdical Records and GEnomics (eMERGE) Network, several sites implemented studies that allowed participants to choose the type of results they wanted to receive from a multigene sequencing panel. Sites were surveyed to capture the details of the implementation protocols and results of these choices. RESULTS: Across the ten eMERGE sites, 4664 participants including adolescents and adults were offered some type of choice. Categories of choices offered and methods for selecting categories varied. Most participants (94.5%) chose to learn all genetic results, while 5.5% chose subsets of results. Several sites allowed participants to change their choices at various time points, and 0.5% of participants made changes. CONCLUSION: Offering choices that include learning some results is important and should be a dynamic process to allow for changes in scientific knowledge, participant age group, and individual preference.

The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

PURPOSE: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. METHODS: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. RESULTS: Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. CONCLUSIONS: The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Correction: The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Leveraging electronic health records for clinical research.

Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.

Implementation of a Regional Perinatal Data Repository from Clinical and Billing Records.

Objectives To describe the implementation of the first phase of a regional perinatal data repository and to provide a roadmap for others to navigate technical, privacy, and data governance concerns in implementing similar resources. Methods Our implementation integrated regional physician billing records with maternal and infant electronic health records from an academic delivery hospital. These records, representing births during 2013-2015, constituted a data core supporting linkage to additional ancillary data sets. Measures obtained from pediatric follow-up, urgent care, emergency, and inpatient encounters were linked at the individual level as were measures obtained by home visitors during pre- and postnatal encounters. Residential addresses were geocoded supporting linkage to area-level measures. Results Integrated data contained regional billing records for 69,290 newborns representing approximately 81% of all regional live births and nearly 95% of live births in the region's most populous county. Billing records linked to 7293 infant delivery hospital records and 7107 corresponding maternal hospital records. Manual review demonstrated 100% validity of matches among audited records. Additionally, 2430 home visiting records were linked to the data core as were pediatric primary care, urgent care, emergency department, and inpatient visits representing 42,541 children. More than 99% of the newborn billing records were geocoded and assigned a census tract identifier. Conclusions for Practice Our approach to methodological and regulatory challenges affords opportunities for expansion of systems to integrate electronic health records originating from additional medical centers as well as individual- and area-level linkage to additional data sets relevant to perinatal health.

Pediatric Eosinophilic Esophagitis Symptom Scores (PEESS v2.0) identify histologic and molecular correlates of the key clinical features of disease.

BACKGROUND: The Pediatric Eosinophilic Esophagitis Symptom Score (PEESS v2.0) measures patient-relevant outcomes. However, whether patient-identified domains (dysphagia, gastroesophageal reflux disease [GERD], nausea/vomiting, and pain) align with clinical symptomology and histopathologic and molecular features of eosinophilic esophagitis (EoE) is unclear. OBJECTIVE: The purpose of this study was to determine whether clinical features of EoE, measured through PEESS v2.0, associate with histopathologic and molecular features of EoE. This represents a novel approach for analysis of allergic diseases, given the availability of allergic tissue biopsy specimens. METHODS: We systematically recruited treated and untreated pediatric patients with EoE (aged 2-18 years) and examined parent proxy-reported symptoms using the PEESS v2.0. Clinical symptomology was collected by questionnaire. Esophageal biopsy samples were quantified for levels of eosinophils, eosinophil peroxidase (EPX) immunohistochemical staining, and mast cells. Molecular features were assessed by using the EoE Diagnostic Panel (94 EoE-related gene transcripts). Associations between domain scores and clinical symptoms and biological features were analyzed with Wilcoxon rank sum and Spearman correlation. RESULTS: The PEESS v2.0 domains correlated to specific parent-reported symptoms: dysphagia (P = .0012), GERD (P = .0001), and nausea/vomiting (P < .0001). Pain correlated with multiple symptoms (P < .0005). Dysphagia correlated most strongly with overall histopathology, particularly in the proximal esophagus (P ≤ .0049). Markers of esophageal activity (EPX) were significantly associated with dysphagia (strongest r = 0.37, P = .02). Eosinophil levels were more associated with pain (r = 0.27, P = .06) than dysphagia (r = 0.24, P = .13). The dysphagia domain correlated most with esophageal gene transcript levels, predominantly with mast cell-specific genes. CONCLUSION: We have (1) established a validated, parent proxy-reported measure for pediatric EoE, the PEESS v2.0; (2) verified that the parent proxy effectively captures symptoms; (3) determined that the dysphagia domain most closely aligns with symptoms and tissue-based molecular biomarkers; (4) established that symptoms correlate with EPX staining; and (5) observed association between mast cells and dysphagia.

Increased prevalence of eosinophilic gastrointestinal disorders in pediatric PTEN hamartoma tumor syndromes.

OBJECTIVES: The PTEN hamartoma tumor syndromes (PHTSs) are a collection of disorders caused by germline mutations of the tumor suppressor gene PTEN. Eosinophilic gastrointestinal disorders (EGIDs) are rare diseases characterized by food-induced, eosinophil-dominant inflammation in various segments of the gastrointestinal tract. On the basis of our clinical observations of several patients with EGID-PHTS, we investigated whether there is an association between these 2 disorders. METHODS: The Cincinnati Children's Hospital Medical Center (CCHMC) Informatics for Integrating Biology and the Bedside (i2b2) warehouse was queried for the years 2007 to 2012 using International Classification of Diseases-9 codes for PTEN-related diseases; the results were cross-referenced with participants enrolled in the Cincinnati Center for Eosinophilic Disorder's EGID database to identify patients with both disorders. In an effort to replicate our findings, the Cleveland Clinic Genomic Medicine Institute PTEN database was queried for cases between 2005 and 2012. Inclusion criteria were age ≤ 18 years, history of PHTS, and an esophagogastroduodenoscopy (EGD) and/or colonoscopy with at least 1 histologic EGID diagnosis confirmed by a CCHMC pathologist. The Pearson χ(2) test was used to determine the odds of EGID enrichment in PHTS. RESULTS: Of the 1,058,260 CCHMC distinct patients identified by the i2b2 search, 53 had clinical diagnoses suggestive of PHTS. Thirteen of the 53 had PTEN mutations, with 8 of 13 (62%) having had an EGD and/or colonoscopy. Five of the 8 had confirmed EGID. At the Cleveland Clinic, 3 of 75 patients (3/4 who had EGD and/or colonoscopy) with PHTS had confirmed EGID. CCHMC i2b2 query data showed a substantial enrichment of EGIDs in PHTSs (odds ratio  272; confidence interval 89-831, P < 0.0001). An EGID prevalence estimate from the i2b2 query supported a marked enrichment of EGIDs in PHTSs in the Cleveland Clinic database (P < 0.0001). Among the 8 subjects with EGIDs and PHTSs, the age at EGID and PHTS diagnosis was 7.6 ± 3.2 and 7.9 ± 5.8 years, respectively. Patients with EGID-PHTS had excess eosinophils in biopsies of the esophagus (75%), stomach (38%), and colon (13%), with a notable presence of eosinophil-rich gastrointestinal polyposis (88%). CONCLUSIONS: EGID is a previously unrecognized comorbid disease in pediatric patients with PHTS. These data suggest a potential role of PTEN in contributing to EGID susceptibility.

Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research.

OBJECTIVE: The current study aims to fill the gap in available healthcare de-identification resources by creating a new sharable dataset with realistic Protected Health Information (PHI) without reducing the value of the data for de-identification research. By releasing the annotated gold standard corpus with Data Use Agreement we would like to encourage other Computational Linguists to experiment with our data and develop new machine learning models for de-identification. This paper describes: (1) the modifications required by the Institutional Review Board before sharing the de-identification gold standard corpus; (2) our efforts to keep the PHI as realistic as possible; (3) and the tests to show the effectiveness of these efforts in preserving the value of the modified data set for machine learning model development. MATERIALS AND METHODS: In a previous study we built an original de-identification gold standard corpus annotated with true Protected Health Information (PHI) from 3503 randomly selected clinical notes for the 22 most frequent clinical note types of our institution. In the current study we modified the original gold standard corpus to make it suitable for external sharing by replacing HIPAA-specified PHI with newly generated realistic PHI. Finally, we evaluated the research value of this new dataset by comparing the performance of an existing published in-house de-identification system, when trained on the new de-identification gold standard corpus, with the performance of the same system, when trained on the original corpus. We assessed the potential benefits of using the new de-identification gold standard corpus to identify PHI in the i2b2 and PhysioNet datasets that were released by other groups for de-identification research. We also measured the effectiveness of the i2b2 and PhysioNet de-identification gold standard corpora in identifying PHI in our original clinical notes. RESULTS: Performance of the de-identification system using the new gold standard corpus as a training set was very close to training on the original corpus (92.56 vs. 93.48 overall F-measures). Best i2b2/PhysioNet/CCHMC cross-training performances were obtained when training on the new shared CCHMC gold standard corpus, although performances were still lower than corpus-specific trainings. DISCUSSION AND CONCLUSION: We successfully modified a de-identification dataset for external sharing while preserving the de-identification research value of the modified gold standard corpus with limited drop in machine learning de-identification performance.

High prevalence of eosinophilic esophagitis in patients with inherited connective tissue disorders.

BACKGROUND: Eosinophilic esophagitis (EoE) is an emerging chronic inflammatory disease mediated by immune hypersensitization to multiple foods and strongly associated with atopy and esophageal remodeling. OBJECTIVE: We provide clinical and molecular evidence indicating a high prevalence of EoE in patients with inherited connective tissue disorders (CTDs). METHODS: We examined the rate of EoE among patients with CTDs and subsequently analyzed esophageal mRNA transcript profiles in patients with EoE with or without CTD features. RESULTS: We report a cohort of 42 patients with EoE with a CTD-like syndrome, representing 0.8% of patients with CTDs and 1.3% of patients with EoE within our hospital-wide electronic medical record database and our EoE research registry, respectively. An 8-fold risk of EoE in patients with CTDs (relative risk, 8.1; 95% confidence limit, 5.1-12.9; χ(2)1 = 112.0; P < 10(-3)) was present compared with the general population. Esophageal transcript profiling identified a distinct subset of genes, including COL8A2, in patients with EoE and CTDs. CONCLUSION: There is a remarkable association of EoE with CTDs and evidence for a differential expression of genes involved in connective tissue repair in this cohort. Thus, we propose stratification of patients with EoE and CTDs into a subset referred to as EoE-CTD.

Applications of Business Analytics in Healthcare.

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and as they begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics, namely the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently for improving healthcare delivery, and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and how analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.

Kidney and Cardiovascular Effectiveness of Empagliflozin Compared With Dipeptidyl Peptidase-4 Inhibitors in Patients With Type 2 Diabetes.

Placebo-controlled trials of sodium-glucose co-transporter-2 inhibitors demonstrate kidney and cardiovascular benefits for patients with type 2 diabetes and chronic kidney disease (CKD). We used real-world data to compare the kidney and cardiovascular effectiveness of empagliflozin to dipeptidyl peptidase-4 inhibitors (DPP4is), a commonly prescribed antiglycemic medication, in a diverse population with and without CKD. Using electronic health record data from 20 large US health systems, we leveraged propensity overlap weighting to compare the outcomes for empagliflozin and DPP4i initiators with type 2 diabetes between 2016 and 2020. The primary composite kidney outcome included 40% estimated glomerular filtration rate decrease, incident end-stage kidney disease, or all-cause mortality through 2 years or censoring. We also assessed cardiovascular and safety outcomes. Of 62,197 new users, 20,279 initiated empagliflozin and 41,918 initiated DPP4i. Over a median follow-up of 1.1 years, empagliflozin prescription was associated with a lower risk of the primary outcome (hazard ratio [HR] 0.75, 95% confidence interval [CI] 0.65 to 0.87) than DPP4is. The risks for mortality (HR 0.76, 95% CI 0.62 to 0.92) and a cardiovascular composite of stroke, myocardial infarction, or all-cause mortality (HR 0.81, 95% CI 0.70 to 0.95) were also lower for empagliflozin initiators. No difference in heart failure hospitalization risk between groups was observed. Genital mycotic infections were more common in patients prescribed empagliflozin (HR 1.72, 95% CI 1.58 to 1.88). Empagliflozin was associated with a lower risk of the primary outcome in patients with CKD (HR 0.68, 95% CI 0.53 to 0.88) and those without CKD (HR 0.79, 95% CI 0.67 to 0.94). In conclusion, the initiation of empagliflozin was associated with a significantly lower risk of kidney and cardiovascular outcomes than DPP4is over a median of just over 1 year. The association with a lower risk for clinical outcomes was apparent even for patients without known CKD at baseline.

Collecting patient-reported outcome measures in the electronic health record: Lessons from the NIH pragmatic trials Collaboratory.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.

Concordance With Screening and Treatment Guidelines for Chronic Kidney Disease in Type 2 Diabetes.

Importance: Chronic kidney disease (CKD) is an often-asymptomatic complication of type 2 diabetes (T2D) that requires annual screening to diagnose. Patient-level factors linked to inadequate screening and treatment can inform implementation strategies to facilitate guideline-recommended CKD care. Objective: To identify risk factors for nonconcordance with guideline-recommended CKD screening and treatment in patients with T2D. Design, Setting, and Participants: This retrospective cohort study was performed at 20 health care systems contributing data to the US National Patient-Centered Clinical Research Network. To evaluate concordance with CKD screening guidelines, adults with an outpatient clinician visit linked to T2D diagnosis between January 1, 2015, and December 31, 2020, and without known CKD were included. A separate analysis reviewed prescription of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) and sodium-glucose cotransporter 2 (SGLT2) inhibitors in adults with CKD (estimated glomerular filtration rate [eGFR] of 30-90 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio [UACR] of 200-5000 mg/g) and an outpatient clinician visit for T2D between October 1, 2019, and December 31, 2020. Data were analyzed from July 8, 2022, through June 22, 2023. Exposures: Demographics, lifestyle factors, comorbidities, medications, and laboratory results. Main Outcomes and Measures: Screening required measurement of creatinine levels and UACR within 15 months of the index visit. Treatment reflected prescription of ACEIs or ARBs and SGLT2 inhibitors within 12 months before or 6 months following the index visit. Results: Concordance with CKD screening guidelines was assessed in 316 234 adults (median age, 59 [IQR, 50-67] years), of whom 51.5% were women; 21.7%, Black; 10.3%, Hispanic; and 67.6%, White. Only 24.9% received creatinine and UACR screening, 56.5% received 1 screening measurement, and 18.6% received neither. Hispanic ethnicity was associated with lack of screening (relative risk [RR], 1.16 [95% CI, 1.14-1.18]). In contrast, heart failure, peripheral arterial disease, and hypertension were associated with a lower risk of nonconcordance. In 4215 patients with CKD and albuminuria, 3288 (78.0%) received an ACEI or ARB; 194 (4.6%), an SGLT2 inhibitor; and 885 (21.0%), neither therapy. Peripheral arterial disease and lower eGFR were associated with lack of CKD treatment, while diuretic or statin prescription and hypertension were associated with treatment. Conclusions and Relevance: In this cohort study of patients with T2D, fewer than one-quarter received recommended CKD screening. In patients with CKD and albuminuria, 21.0% did not receive an SGLT2 inhibitor or an ACEI or an ARB, despite compelling indications. Patient-level factors may inform implementation strategies to improve CKD screening and treatment in people with T2D.

Clinical genomics in the world of the electronic health record.

The widespread adoption of electronic health records presents a number of benefits to the field of clinical genomics. They include the ability to return results to the practitioner, to use genetic findings in clinical decision support, and to have data collected in the electronic health record that serve as a source of phenotypic information for analysis purposes. Not all electronic health records are created equal, however. They differ in their features, capabilities, and ease of use. Therefore, to understand the potential of the electronic health record, it is first necessary to understand its capabilities and the impact that implementation strategy has on usability. Specifically, we focus on the following areas: (i) how the electronic health record is used to capture data in clinical practice settings; (ii) how the implementation and configuration of the electronic health record affect the quality and availability of data; (iii) the management of clinical genetic test results and the feasibility of electronic health record integration; and (iv) the challenges of implementing an electronic health record in a research-intensive environment. This is followed by a discussion of the minimum functional requirements that an electronic health record must meet to enable the satisfactory integration of genomic results as well as the open issues that remain.

PedsQL eosinophilic esophagitis module: feasibility, reliability, and validity.

OBJECTIVE: Eosinophilic esophagitis (EoE) is a chronic esophageal inflammatory condition with a paucity of information on health-related quality of life (HRQOL). The objective of the study was to report on the measurement properties of the PedsQL EoE Module. METHODS: The PedsQL EoE Module was completed in a multisite study by 196 pediatric patients with EoE and 262 parents of patients with EoE. RESULTS: The PedsQL EoE Module scales evidenced excellent feasibility (0.6%-3.1% missing), excellent group comparison reliability across total scale scores (patient α 0.93; parent proxy α 0.94), good reliability for the 7 individual scales (patient α 0.75-0.87; parent proxy α 0.81-0.92), excellent test-retest reliability (patient intraclass correlation coefficient 0.88; parent intraclass correlation coefficient 0.82), demonstrated no floor effects and low ceiling effects, and demonstrated a high percentage of scaling success for most scales. Intercorrelations with the PedsQL Generic Core Scales were in the medium (0.30) to large (0.50) range. PedsQL EoE Module scores were worse among patients with active histologic disease (≥ 5 eos/hpf) compared with those in remission (patient self-report: 63.3 vs 69.9 [P < 0.05]; parent proxy report: 65.1 vs 72.3 [P < 0.01]), and those treated with dietary restrictions compared with those with no restrictions (patient self-report: 61.6 vs 74.3 [P < 0.01]; parent proxy report: 65.5 vs 74.7 [P < 0.01]). CONCLUSIONS: The results demonstrate excellent measurement properties of the PedsQL EoE Module. Patients with active histologic disease and those treated with dietary restrictions demonstrated worse PedsQL scores. The PedsQL EoE Module may be used in the evaluation of pediatric EoE disease-specific HRQOL in clinical research and practice.

Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them.

OBJECTIVES: To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems. MATERIALS AND METHODS: We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term. RESULTS: Of included 179 537 986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100 000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name. DISCUSSIONS: Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further. CONCLUSIONS: Overall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%-40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.

Optimizing data integration in trials that use EHR data: lessons learned from a multi-center randomized clinical trial.

BACKGROUND: Despite great promise, trials that ascertain patient clinical data from electronic health records (EHR), referred to here as "EHR-sourced" trials, are limited by uncertainty about how existing trial sites and infrastructure can be best used to operationalize study goals. Evidence is needed to support the practical use of EHRs in contemporary clinical trial settings. MAIN TEXT: We describe a demonstration project that used EHR data to complement data collected for a contemporary multi-center pharmaceutical industry outcomes trial, and how a central coordinating center supported participating sites through the technical, governance, and operational aspects of this type of activity. We discuss operational considerations related to site selection, data extraction, site performance, and data transfer and quality review, and we outline challenges and lessons learned. We surveyed potential sites and used their responses to assess feasibility, determine the potential capabilities of sites and choose an appropriate data extraction strategy. We designed a flexible, multimodal approach for data extraction, enabling each site to either leverage an existing data source, create a new research datamart, or send all data to the central coordinating center to produce the requisite data elements. We evaluated site performance, as reflected by the speed of contracting and IRB approval, total patients enrolled, enrollment yield, data quality, and compared performance by data collection strategy. CONCLUSION: While broadening the type of sites able to participate in EHR-sourced trials may lead to greater generalizability and improved enrollment, sites with fewer technical resources may require additional support to participate. Central coordinating center support is essential to facilitate the execution of operational processes. Future work should focus on sharing lessons learned and creating reusable tools to facilitate participation of heterogeneous trial sites.

Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network.

OBJECTIVE: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network. MATERIAL AND METHODS: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population. RESULTS: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%. DISCUSSION: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population. CONCLUSIONS: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.