Distributed Research Networks Research Articles

Factors Influencing the Acceptance of Distributed Research Networks in Korea: Data Accessibility and Data Security Risk.

Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data; therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understand researchers' acceptance of these networks to ensure their effective application in multicenter research. In this study, we sought to identify the factors influencing the adoption of DRNs among researchers in Korea. We used snowball sampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate the hypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. We applied a structural equation model to identify relationships within the research model. Data accessibility and data security were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship between data accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, but it was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. This study highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findings can contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.

Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes

Objective:We developed and evaluated a novel one-shot distributed algorithm for evidence synthesis in distributed research networks with rare outcomes. Materials and Methods:Fed-Padé, motivated by a classic mathematical tool, Padé approximants, reconstructs the multi-site data likelihood via Padé approximant whose key parameters can be computed distributively. Thanks to the simplicity of [2,2] Padé approximant, Fed-Padé requests an extremely simple task and low communication cost for data partners. Specifically, each data partner only needs to compute and share the log-likelihood and its first 4 gradients evaluated at an initial estimator. We evaluated the performance of our algorithm with extensive simulation studies and four observational healthcare databases. Results:Our simulation studies revealed that a [2,2]-Padé approximant can well reconstruct the multi-site likelihood so that Fed-Padé produces nearly identical estimates to the pooled analysis. Across all simulation scenarios considered, the median of relative bias and rate of instability of our Fed-Padé are both <0.1%, whereas meta-analysis estimates have bias up to 50% and instability up to 75%. Furthermore, the confidence intervals derived from the Fed-Padé algorithm showed better coverage of the truth than confidence intervals based on the meta-analysis. In real data analysis, the Fed-Padé has a relative bias of <1% for all three comparisons for risks of acute liver injury and decreased libido, whereas the meta-analysis estimates have a substantially higher bias (around 10%). Conclusion:The Fed-Padé algorithm is nearly lossless, stable, communication-efficient, and easy to implement for models with rare outcomes. It provides an extremely suitable and convenient approach for synthesizing evidence in distributed research networks with rare outcomes.

An Investigation of Factors Influencing the Postponement of the Use of Distributed Research Networks in South Korea: Web-Based Users' Survey Study.

Distributed research networks (DRNs) offer researchers the advantage of using various big data sets that are difficult to access and use. In addition, since the data are not physically exposed to the outside, it is possible to conduct research using medical data safely without data exposure. However, researchers still have difficulties and are concerned about using DRNs. Few studies involving DRNs have been conducted from the user's viewpoint. Therefore, it is necessary to look at DRNs from the researcher's point of view and find ways to facilitate the active use of DRNs. This study aimed to identify the factors that made researchers hesitate to use DRNs and to derive a method to facilitate active DRN use. We conducted a web-based survey of people working in the medical fields, such as hospitals and universities. We used 131 respondents' data from a survey from December 6 to 17, 2021. We conducted multiple regression analyses to determine the factors affecting the postponement of using DRNs. In addition, 2 independent sample t tests were conducted to analyze the difference between the 2 groups according to the following factors: organization, gender, experience with DRNs, length of the research career, position, and age. Performance risk (t5=2.725, P=.007) and workload from DRNs (t5=3.543, P=.001) were significantly associated with users' postponement of DRN use. Researchers working at hospitals were found to feel more burdened by DRN use than researchers working at universities (t129=1.975, P=.05). It was also found that women perceived a higher privacy risk of DRNs than men (t129=-2.303, P=.02) and that those who had experience using DRNs delayed their use less than those without experience (t129=-4.215, P<.001). It is necessary to simplify the research and approval processes to reduce the performance risk and workload of research using DRNs. To optimize the process, DRN providers should develop a way to improve users' experiences. More user-friendly functionalities should be developed from the researcher's point of view. It is necessary to continuously promote effective functionalities for DRNs to reduce concerns about privacy risks. This study identified the concerns of DRN users in terms of DRN use and suggested ways to actively use DRNs. The derived results can be reflected in planning and developing DRNs. Our research will be helpful to prepare an activation plan for DRNs.

Imputing missing covariates in time-to-event analysis within distributed research networks: A simulation study.

In distributed research network (DRN) settings, multiple imputation cannot be directly implemented because pooling individual-level data are often not feasible. The performance of multiple imputation in combination with meta-analysis is not well understood within DRNs. To evaluate the performance of imputation for missing baseline covariate data in combination with meta-analysis for time-to-event analysis within DRNs, we compared two parametric algorithms including one approximated linear imputation model (Approx), and one nonlinear substantive model compatible imputation model (SMC), as well as two non-parametric machine learning algorithms including random forest (RF), and classification and regression trees (CART), through simulation studies motivated by a real-world data set. Under the setting with small effect sizes (i.e., log-Hazard ratios [logHR]) and homogeneous missingness mechanisms across sites, all imputation methods produced unbiased and more efficient estimates while the complete-case analysis could be biased and inefficient; and under heterogeneous missingness mechanisms, estimates with RF method could have higher efficiency. Estimates from the distributed imputation combined by meta-analysis were similar to those from the imputation using pooled data. When logHRs were large, the SMC imputation algorithm generally performed better than others. These findings suggest the validity and feasibility of imputation within DRNs in the presence of missing covariate data in time-to-event analysis under various settings. The performance of the four imputation algorithms varies with the effect sizes and level of missingness.

Establishing patient-partner priorities within the primary and integrated health care innovations network in Canada.

Background: Many experienced primary care researchers have seen the benefits of meaningful engagement with patients and community members in their research, elevating them to a research partner status. The research questions and agendas, however, are often initiated and determined by the researchers. Increasingly, research teams, organizations and networks actively engage with patient-partners to better understand what their research priorities would look like, bringing patient-partners into the research process much earlier. Among many other initiatives within the Strategy for Patient Oriented Research program, a Primary and Integrated Health Care Innovations Network (PIHCIN) was established in Canada, which includes 11 geographically distributed research networks. As part of PIHCIN, a Pan-Canadian Patient Council (the Council) was established with representatives from each provincial site. To ensure their voices were being heard, the Council decided to establish their own research priorities for consideration by the PIHCI network. Objective: To establish patient-partner priorities in the Canadian primary health care context Methods: The main goal of the Council was to submit research priorities to identify what was important from a patient-partner perspective. Through consensus building and prioritization voting, the Council developed and presented a draft of their research priorities to the PIHCI Network Leadership Council. In a second step, the Council decided that the process was missing from the document. Subsequently, with the use of the SMART goal framework, the Council engaged in a process to establish how research priorities could be accomplished. Results: The initial research priorities comprised 11 items, including priorities concerning patient's access to data, integrated health teams, patient initiated collaborative research, virtual care, broader definition of health, etc. The initial priorities eventually evolved into a set of principles, strategic operating goals, and finalized research priorities. Conclusion: Through an iterative process, the Council was able to lead, initiate and differentiate strategic operating goals from the research priorities. This process and results can provide insight for developing critical input from patient-partners for primary health care research.

Long-term ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance.

The period of disrupted human activity caused by the COVID‐19 pandemic, coined the “anthropause,” altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long‐term social‐ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic‐related delays in data availability and ecological response lags, we detail three case studies that show how long‐term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long‐term data in separating disturbance impacts from natural variation and long‐term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research.

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.

BackgroundCommon data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free text–based pathology reports into the CDM’s format. There are few use cases of representing cancer data in CDM.ObjectiveIn this study, we aimed to construct a CDM database of colon cancer–related pathology with natural language processing (NLP) for a research platform that can utilize both clinical and omics data. The essential text entities from the pathology reports are extracted, standardized, and converted to the OMOP CDM format in order to utilize the pathology data in cancer research.MethodsWe extracted clinical text entities, mapped them to the standard concepts in the Observational Health Data Sciences and Informatics vocabularies, and built databases and defined relations for the CDM tables. Major clinical entities were extracted through NLP on pathology reports of surgical specimens, immunohistochemical studies, and molecular studies of colon cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using regular expressions in Python. Unstructured data, such as text that does not have a pattern, were handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the Logical Observation Identifiers Names and Codes databases and the Systematized Nomenclature of Medicine (SNOMED) standard terminologies recommended by the OMOP CDM. The database-table relationships were newly defined through SNOMED standard terminology concepts. The standardized data were inserted into the CDM tables. For evaluation, 100 reports were randomly selected and independently annotated by a medical informatics expert and a nurse.ResultsWe examined and standardized 1848 immunohistochemical study reports, 3890 molecular study reports, and 12,352 pathology reports of surgical specimens (from 2017 to 2018). The constructed and updated database contained the following extracted colorectal entities: (1) NOTE_NLP, (2) MEASUREMENT, (3) CONDITION_OCCURRENCE, (4) SPECIMEN, and (5) FACT_RELATIONSHIP of specimen with condition and measurement.ConclusionsThis study aimed to prepare CDM data for a research platform to take advantage of all omics clinical and patient data at Seoul National University Bundang Hospital for colon cancer pathology. A more sophisticated preparation of the pathology data is needed for further research on cancer genomics, and various types of text narratives are the next target for additional research on the use of data in the CDM.

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.

ObjectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.Materials and MethodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.ResultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.ConclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.

Strategies for Incorporating Long-Term, Distributed-Network Research Projects into the Undergraduate Curriculum: Lessons from the Ecological Research as Education Network's Decomposition Project

Increasingly, undergraduate institutions are incorporating original research into the curriculum as a matter of best practice. However, while the practice of science has grown more collaborative, undergraduate research has remained largely confined to single-institution studies. Incorporating long-term, distributed research projects into the undergraduate research experience can better prepare students to interpret and engage in science of the future. The Decomposition in Aquatic and Terrestrial Invaded Systems (DATIS) project within the Ecological Research as Education Network (EREN) offers a good model for examining how to minimize challenges and maximize opportunities associated with classroom use of long-term, collaborative research projects. Eleven key challenges are identified, and practical solutions are provided for each. By modeling this scientific approach in primarily undergraduate institutions, we are preparing graduates who will have the tools and knowledge to work collaboratively and create their own distributed research networks. Our goal is that the decomposition project we describe here can inform and inspire others seeking to engage in research at the undergraduate level, either as potential research coordinators or as collaborators in an existing network.

Considerations for using distributed research networks to conduct aspects of randomized trials

Stakeholders in the clinical research enterprise are aligned around the need to make clinical research in general, and randomized controlled trials in particular, more meaningful and efficient. To that end, we have built distributed research networks (DRNs) for the Sentinel System, the National Institutes of Health (NIH) Collaboratory, and the National Patient-Centered Clinical Research Network (PCORnet). DRNs reuse electronic health record (EHR) and claims data for research. The design and use of health data DRNs is complicated by lack of uniformity in data collection, a fragmented healthcare system, and the imperative to protect research participants.We describe the key elements of successful DRNs, as well as methods, challenges, and solutions we have encountered in using DRNs to support different phases of randomized, multi-site, clinical research. This work supports “real-world” efforts to build a learning health system and will enable others to conduct randomized clinical trial research using a DRN.

A Generic Method and Implementation to Evaluate and Improve Data Quality in Distributed Research Networks

With the increasing personalization of clinical therapies, translational research is evermore dependent on multisite research cooperations to obtain sufficient data and biomaterial. Distributed research networks rely on the availability of high-quality data stored in local databases operated by their member institutions. However, reusing data documented by independent health providers for the purpose of care, rather than research ("secondary use"), reveal a high variability in terms of data formats, as well as poor data quality, across network sites. The aim of this work is the provision of a process for the assessment of data quality with regard to completeness and syntactic accuracy across independently operated data warehouses using common definitions stored in a central (network-wide) metadata repository (MDR). For assessment of data quality across multiple sites, we employ a framework of so-called bridgeheads. These are federated data warehouses, which allow the sites to participate in a research network. A central MDR is used to store the definitions of the commonly agreed data elements and their permissible values. We present the design for a generator of quality reports within a bridgehead, allowing the validation of data in the local data warehouse against a research network's central MDR. A standardized quality report can be produced at each network site, providing a means to compare data quality across sites, as well as to channel feedback to the local data source systems, and local documentation personnel. A reference implementation for this concept has been successfully utilized at 10 sites across the German Cancer Consortium. We have shown that comparable data quality assessment across different partners of a distributed research network is feasible when a central metadata repository is combined with locally installed assessment processes. To achieve this, we designed a quality report and the process for generating such a report. The final step was the implementation in a German research network.

Clinical Research Informatics: Contributions from 2018.

Summary Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results : Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions : Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

Genomic Common Data Model for Seamless Interoperation of Biomedical Data in Clinical Practice: Retrospective Study.

BackgroundClinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine.ObjectiveThe aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice.MethodsExisting genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations.ResultsThe G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the EGFR gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the KRAS gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation.ConclusionsWe developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

Cross-Network Directory Service: Infrastructure to enable collaborations across distributed research networks.

IntroductionExisting large‐scale distributed health data networks are disconnected even as they address related questions of healthcare research and public policy. This paper describes the design and implementation of a fully functional prototype open‐source tool, the Cross‐Network Directory Service (CNDS), which addresses much of what keeps distributed networks disconnected from each other.MethodsThe set of services needed to implement a Cross‐Directory Service was identified through engagement with stakeholders and workgroup members. CNDS was implemented using PCORnet and Sentinel network instances and tested by participating data partners.ResultsWeb services that enable the four major functional features of the service (registration, discovery, communication, and governance) were developed and placed into an open‐source repository. The services include a robust metadata model that is extensible to accommodate a virtually unlimited inventory of metadata fields, without requiring any further software development. The user interfaces are programmatically generated based on the contents of the metadata model.ConclusionThe CNDS pilot project gathered functional requirements from stakeholders and collaborating partners to build a software application to enable cross‐network data and resource sharing. The two partners—one from Sentinel and one from PCORnet—tested the software. They successfully entered metadata about their organizations and data sources and then used the Discovery and Communication functionality to find data sources of interest and send a cross‐network query. The CNDS software can help integrate disparate health data networks by providing a mechanism for data partners to participate in multiple networks, share resources, and seamlessly send queries across those networks.

Statistical methods for building better biomarkers of chronic kidney disease.

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).

Introduction:Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data.Methods:PCORnet’s Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals.Results:The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners.Discussion:The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

The Challenges and Opportunities of Using Large Administrative Claims Databases for Biosimilar Monitoring and Research in the United States

The number of marketed biosimilars in the USA is increasing and will require careful post-marketing monitoring. We address some of the challenges and opportunities of using administrative claims data both inside and outside the context of distributed research networks to monitor post-marketing biosimilar use, effectiveness, and safety in the USA. Three major challenges and opportunities of using administrative claims to monitor biosimilars in the post-marketing environment are presented. These include (1) having sufficient longitudinal follow-up of patients in the target population, (2) identifying biosimilar exposures at the specificity of interest, and (3) having sufficient and complete detailed information on study outcomes and potential confounders. As true for all safety surveillance and comparative effectiveness studies, biologic and biosimilar research requires a focused inquiry to determine whether administrative claims or distributed research networks are suitable data sources. Administrative data affords longitudinal follow-up on large numbers of patients and includes dispensed medications and refill information, affording exposure data based on adjudicated payment. However, data elements including biosimilar exposures, outcomes, and confounders may not be measured at the desired level of specificity. Opportunities to link multiple sources of data together and augment existing data sources to include additional information may increase the utility of using administrative claims data in the monitoring of biosimilars.

Enabling Open-Science Initiatives in Clinical Psychology and Psychiatry Without Sacrificing Patients’ Privacy: Current Practices and Future Challenges

The psychological and psychiatric communities are generating data on an ever-increasing scale. To ensure that society reaps the greatest utility in research and clinical care from such rich resources, there is significant interest in wide-scale, open data sharing to foster scientific endeavors. However, it is imperative that such open-science initiatives ensure that data-privacy concerns are adequately addressed. In this article, we focus on these issues in clinical research. We review the privacy risks and then discuss how they can be mitigated through appropriate governance mechanisms that are both social (e.g., the application of data-use agreements) and technological (e.g., de-identification of structured data and unstructured narratives). We also discuss the benefits and drawbacks of these mechanisms, particularly as regards data fidelity. Our focus is on de-identification methods that meet regulatory requirements, such as the Privacy Rule of the Health Insurance Portability and Accountability Act of 1996. To illustrate their potential, we show how the principles we discuss have been applied in a large-scale clinical database and distributed research networks. We close this article with a discussion of challenges in supporting data privacy as open-science initiatives grow in their scale and complexity.

Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports.

The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan‐McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan‐McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype–genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes—two heterogeneous, underutilized sources of knowledge about patient phenotypes—with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portal https://pmsdn.hms.harvard.edu. PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high‐quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients.