Distributed Data Network Research Articles

Extreme restriction design as a method for reducing confounding by indication in pharmacoepidemiologic research.

Confounding by indication is a concern in observational pharmacoepidemiologic studies, including those that use active comparator, new user (ACNU) designs. Here, we present a method of restriction to an indication, which we call "extreme restriction," to reduce confounding in such studies. As a case study, we evaluated the effect of proton pump inhibitors (PPIs) on hospitalization for community-acquired pneumonia (HCAP). PPI use has been associated with increased HCAP risk, but this association likely results from confounding by indication due to gastroesophageal reflux disease (GERD). Using the UK's Clinical Practice Research Datalink, we compared the risk of HCAP within 180days between PPI users and histamine-2 receptor antagonist (H2RA) users in an ACNU cohort using Cox proportional hazard models with a time-fixed exposure definition adjusted for high-dimensional propensity score deciles. We then performed the same analysis on an "extremely-restricted" cohort of incident nonsteroidal anti-inflammatory drug (NSAID) users, some of whom received PPIs for prophylaxis. Because PPIs were given as prophylaxis in this population, confounding due to GERD should be limited. We compared effect estimates between ACNU and restricted cohorts to evaluate confounding in both analyses. In the ACNU cohort, PPIs were associated with an increased risk of HCAP (hazard ratio [HR]: 1.25; 95% confidence interval [CI]: 1.05, 1.47), but this association was not present in the restricted cohort (HR: 1.06; 95% CI: 0.75, 1.49). Restriction to a single indication for treatment may reduce confounding by indication in studies conducted in distributed data networks and other large databases.

Validity of Privacy-Protecting Analytical Methods That Use Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks.

Distributed data networks enable large-scale epidemiologic studies, but protecting privacy while adequately adjusting for a large number of covariates continues to pose methodological challenges. Using 2 empirical examples within a 3-site distributed data network, we tested combinations of 3 aggregate-level data-sharing approaches (risk-set, summary-table, and effect-estimate), 4 confounding adjustment methods (matching, stratification, inverse probability weighting, and matching weighting), and 2 summary scores (propensity score and disease risk score) for binary and time-to-event outcomes. We assessed the performance of combinations of these data-sharing and adjustment methods by comparing their results with results from the corresponding pooled individual-level data analysis (reference analysis). For both types of outcomes, the method combinations examined yielded results identical or comparable to the reference results in most scenarios. Within each data-sharing approach, comparability between aggregate- and individual-level data analysis depended on adjustment method; for example, risk-set data-sharing with matched or stratified analysis of summary scores produced identical results, while weighted analysis showed some discrepancies. Across the adjustment methods examined, risk-set data-sharing generally performed better, while summary-table and effect-estimate data-sharing more often produced discrepancies in settings with rare outcomes and small sample sizes. Valid multivariable-adjusted analysis can be performed in distributed data networks without sharing of individual-level data.

Outpatient influenza antivirals in a distributed data network for influenza surveillance.

Electronic data collected from routine health care can be used for public health surveillance. To examine the Sentinel System, a distributed data network of health plans, as a source for influenza surveillance, we compared trends in outpatient prescription dispensings of influenza antivirals in Sentinel to trends in CDC's ILINet and NREVSS systems over five seasons. There were 2 102 885 dispensed prescriptions of oseltamivir capsules, 494 188 of oseltamivir powder, and 7955 of zanamivir. Across all seasons, the magnitude and timing of peaks in drug utilization were highly comparable to those in ILINet and NREVSS. Oseltamivir capsules and powder were well correlated with ILINet and NREVSS. This lays the foundation for further exploration of Sentinel's utility for influenza surveillance.

Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service.

Electronic health records (EHRs) provide an alternative to traditional public health surveillance surveys and administrative data for measuring the prevalence and impact of chronic health conditions in populations. As the infrastructure for secondary use of EHR data improves, many stakeholders are poised to benefit from data partnerships for regional access to information. Electronic health records can be transformed into a common data model that facilitates data sharing across multiple organizations and allows data to be used for surveillance. The Colorado Health Observation Regional Data Service, a regional distributed data network, has assembled diverse data partnerships, flexible infrastructure, and transparent governance practices to better understand the health of communities through EHR-based, public health surveillance. This article describes attributes of regional distributed data networks using EHR data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. Colorado Health Observation Regional Data Service and our experience may serve as a model for other regions interested in similar surveillance efforts. While benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.

A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks.

Introduction:Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs.Objective:We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform.Methods:We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners’ hardware configurations and software requirements.Results:We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation.Discussion:The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs.Conclusion:DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.

Network Coding for Backhaul Offloading in D2D Cooperative Fog Data Networks

Future distributed data networks are expected to be assisted by users cooperation and coding schemes. Given the explosive increase in the end‐users’ demand for download of the content from the servers, in this paper, the implementation of instantly decodable network coding (IDNC) is considered in full‐duplex device‐to‐device (D2D) cooperative fog data networks. In particular, this paper is concerned with designing efficient transmission schemes to offload traffic from the expensive backhaul of network servers by employing IDNC and users cooperation. The generalized framework where users send request for multiple packets and the transmissions are subject to erasure is considered. The optimal problem formulation is presented using the stochastic shortest path (SSP) technique over the IDNC graph with induced subgraphs. However, as the optimal solution suffers from the intractability of being NP‐hard, it is not suitable for real‐time communications. The complexity of the problem is addressed by presenting a greedy heuristic algorithm used over the proposed graph model. The paper shows that by implementing IDNC in a full‐duplex cooperative D2D network model significant reduction in the number of downloads required from the servers can be achieved, which will result in offloading of the backhaul servers and thus saving valuable servers’ resources. It is also shown that the performance of the proposed heuristic algorithm is very close to the optimal solution with much lower computational complexity.

Distributed Data Networks That Support Public Health Information Needs.

Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network.

An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative. These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size. Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.

Real-time surveillance for chronic conditions in Massachusetts using EHR data

ObjectiveTo assess the feasibility of tracking the prevalence of chronicconditions at the state and community level over time using MDPHnet,a distributed network for querying electronic health record systemsIntroductionPublic health agencies and researchers have traditionally reliedon the Behavioral Risk Factor Surveillance System (BRFSS) andsimilar tools for surveillance of non-reportable conditions. Thesetools are valuable but the data are delayed by more than a year,limited in scope, and based only on participant self-report. Thesecharacteristics limit the utility of traditional surveillance systems forprogram monitoring and impact assessments. Automated surveillanceusing electronic health record (EHR) data has the potential to increasethe efficiency, breadth, accuracy, and timeliness of surveillance. Wesought to assess the feasibility and utility of public health surveillancefor chronic diseases using EHR data using MDPHnet. MDPHnet isa distributed data network that allows the Massachusetts Departmentof Public Health to query participating practices’ EHR data for thepurposes of public health surveillance (www.esphealth.org). Practicesretain the ability to approve queries on a case-by-case basis and thenetwork is updated daily.MethodsWe queried the quarterly prevalence of pediatric asthma, smoking,type 2 diabetes, obesity, overweight, and hypertension statewideand in 9 Massachusetts communities between January 1, 2012 andJuly 1, 2016. We selected these 9 communities because they wereparticipating in a state-funded initiative to decrease the prevalenceof one or more of these conditions. Conditions were defined usingalgorithms based upon vital signs, diagnosis codes, laboratorymeasures, prescriptions, and self-reported smoking status. Eligiblepatients were those with at least 1 encounter of any kind within the2 years preceding the start of each quarter. Results were adjusted forage, sex, and race / ethnicity using the 2010 Massachusetts censusdata.ResultsSurveillance data were available for 1.2 million people overall,approximately 20% of the state population. Coverage varied bycommunity with >28% coverage for 7 of the communities and11% coverage in the eighth. The ninth community had only 2%coverage and was dropped from further analyses. The race / ethnicitydistribution in MDPHnet data was comparable to census datastatewide and in most communities. Queries for all six conditionssuccessfully executed across the network for all time periods ofinterest. The prevalence of asthma among children under 10 yrs rosefrom 12% in January 2012 to 13% in July 2016. Current smoking inadults age≥20 rose from 14% in 2013 to 16% in 2016 (we excludedresults from 2012 due to changes in documentation propelled by theintroduction of meaningful use criteria). This is comparable to the15% rate of smoking per BRFSS in 20141. Obesity among adultsincreased slightly from 22% to 24% during the study period, resultsnearly identical to the most recent BRFSS results for Massachusetts(23% in 2014 and 24% in 2015)2. The prevalence of each conditionvaried widely across the communities under study. For example, forthe third quarter of 2016, the prevalence of asthma among childrenunder 10 ranged from 5% to 23% depending on the community,the prevalence of smoking among adults ranged from 11% to 35%,and the prevalence of type 2 diabetes among adults ranged from7% to 14%. We also examined differences in disease estimates byrace / ethnicity. Substantial racial / ethnic differences were evidentfor type 2 diabetes among adults, with whites having the lowestprevalence at 7% and blacks having the highest at 12% in the thirdquarter of 2016; this trend was consistent over the study period.ConclusionsOur study demonstrates that MDPHnet can provide theMassachusetts Department of Public Health with timely population-level estimates of chronic diseases for numerous conditions at boththe state and community level. MDPHnet surveillance providesprevalence estimates that align well with BRFSS and other traditionalsurveillance sources but is able to make surveillance more timelyand more efficient with more geographical specificity compared totraditional surveillance systems. Our ability to generate real-timetime-series data supports the use of MDPHnet as a source for project/program evaluation.

The Nascent Pan-Canadian Real-world Health Data Network (PRHDN)

ABSTRACT
 ObjectiveCanada’s large single payer health systems have created provincial centres with rich, population-wide health and social data holdings that are linkable at the person level. Complementary federal data include a subset of standardized and enriched administrative healthcare data at the Canadian Institute for Health Information, Statistics Canada’s extensive array of survey- and census-based social and geography-based information, and large national datasets that are being created through pan-Canadian initiatives. Unfortunately, these data assets are rarely combined in multi-province or pan-Canadian studies, often because data are not directly comparable from one province to another, or cannot be shared due to legislative or other barriers. There is now growing interest in enabling multi-province studies — even when data are not directly comparable — and in sharing experiences to make more effective use of linked or linkable administrative data across Canada.
 ApproachNine provincial and national organizations have created a detailed Implementation Plan for the new PRHDN distributed data network. Without requiring that record-level data leave provincial boundaries, the PRHDN will create shared core research data infrastructure: (i) validated algorithms that implement case definitions applicable across provinces, (ii) harmonized common data and (iii) common analytic protocols. The PRHDN will also establish complementary infrastructure including dedicated personnel to assist researchers and decision makers, joined-up training and capacity building sessions, opportunities to share learning and experience related to linking new datasets such as electronic medical records and omic datasets, and forums for knowledge translation and exchange with decision makers.
 ResultsThe PRHDN is already bringing together expertise from across Canada as researchers, decision makers and data custodians begin to identify opportunities for enhanced use of health data in Canadian research, policy making and practice. A simple PRHDN website has been created (https://www.prhdn.ca/) and more than 200 researchers and policy/decision makers have become members of the PRHDN consortium. Surveys of consortium members are identifying priorities for the first algorithm validation work (to-date the top four priorities are mental health, cardiovascular disease, diabetes and respiratory disease) and the PRHDN Leads Team is functioning as a decision-making body, e.g., in discussions with Statistics Canada.
 ConclusionsWorking together, provincial and national organizations across Canada have identified concrete steps that can be taken to enable multi-province and pan-Canadian studies based on administrative data within one year of the start of funding. The PRHDN Leads Team is currently discussing the PRHDN vision and Implementation Plan with potential funders.

Distributed data networks: a blueprint for Big Data sharing and healthcare analytics.

This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources.

On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi

Given that next generation networks are expected to be populated by a large number of devices, there is a need for quick deployment and evaluation of alternative mechanisms to cope with the possible generated traffic in large-scale distributed data networks. In this sense, the Raspberry Pi has been a popular network node choice due to its reduced size, processing capabilities, low cost and its support by widely-used operating systems. For information transport, network coding is a new paradigm for fast and reliable data processing in networking and storage systems, which overcomes various limitations of state-of-the-art routing techniques. Therefore, in this work, we provide an in-depth performance evaluation of Random Linear Network Coding (RLNC)-based schemes for the Raspberry Pi Models 1 and 2, by showing the processing speed of the encoding and decoding operations and the corresponding energy consumption. Our results show that, in several scenarios, processing speeds of more than 80 Mbps in the Raspberry Pi Model 1 and 800 Mbps in the Raspberry Pi Model 2 are attainable. Moreover, we show that the processing energy per bit for network coding is below 1 nJ or even an order of magnitude less in these scenarios.

The Pioneering Role of the Vaccine Safety Datalink Project (VSD) to Advance Collaborative Research and Distributed Data Networks.

Introduction:Large-scale distributed data networks consisting of diverse stakeholders including providers, patients, and payers are changing health research in terms of methods, speed and efficiency. The Vaccine Safety Datalink (VSD) set the stage for expanded involvement of health plans in collaborative research.Expanding Surveillance Capacity and Progress Toward a Learning Health System:From an initial collaboration of four integrated health systems with fewer than 10 million covered lives to 16 diverse health plans with nearly 100 million lives now in the FDA Sentinel, the expanded engagement of health plan researchers has been essential to increase the value and impact of these efforts. The collaborative structure of the VSD established a pathway toward research efforts that successfully engage all stakeholders in a cohesive rather than competitive manner. The scientific expertise and methodology developed through the VSD such as rapid cycle analysis (RCA) to conduct near real-time safety surveillance allowed for the development of the expanded surveillance systems that now exist.Building on Success and Lessons Learned:These networks have learned from and built on the knowledge base and infrastructure created by the VSD investigators. This shared technical knowledge and experience expedited the development of systems like the FDA’s Mini-Sentinel and the Patient Centered Outcomes Research Institute (PCORI)’s PCORnetConclusion:This narrative reviews the evolution of the VSD, its contribution to other collaborative research networks, longer-term sustainability of this type of distributed research, and how knowledge gained from the earlier efforts can contribute to a continually learning health system.

Benefits of large healthcare databases for drug risk research

Large electronic healthcare databases have become an important worldwide data resource for drug safety research after approval. Signal generation methods and drug safety studies based on these data facilitate the prospective monitoring of drug safety after approval, as has been recently required by EU law and the German Medicines Act. Despite its large size, a single healthcare database may include insufficient patients for the study of a very small number of drug-exposed patients or the investigation of very rare drug risks. For that reason, in the United States, efforts have been made to work on models that provide the linkage of data from different electronic healthcare databases for monitoring the safety of medicines after authorization in (i) the Sentinel Initiative and (ii) the Observational Medical Outcomes Partnership (OMOP). In July 2014, the pilot project Mini-Sentinel included a total of 178million people from 18 different US databases. The merging of the data is based on a distributed data network with a common data model. In the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) there has been no comparable merging of data from different databases; however, first experiences have been gained in various EU drug safety projects. In Germany, the data of the statutory health insurance providers constitute the most important resource for establishing a large healthcare database. Their use for this purpose has so far been severely restricted by the Code of Social Law (Section 75, Book 10). Therefore, a reform of this section is absolutely necessary.

Transparent reporting of data quality in distributed data networks.

Introduction:Poor data quality can be a serious threat to the validity and generalizability of clinical research findings. The growing availability of electronic administrative and clinical data is accompanied by a growing concern about the quality of these data for observational research and other analytic purposes. Currently, there are no widely accepted guidelines for reporting quality results that would enable investigators and consumers to independently determine if a data source is fit for use to support analytic inferences and reliable evidence generation.Model and Methods:We developed a conceptual model that captures the flow of data from data originator across successive data stewards and finally to the data consumer. This “data lifecycle” model illustrates how data quality issues can result in data being returned back to previous data custodians. We highlight the potential risks of poor data quality on clinical practice and research results. Because of the need to ensure transparent reporting of a data quality issues, we created a unifying data-quality reporting framework and a complementary set of 20 data-quality reporting recommendations for studies that use observational clinical and administrative data for secondary data analysis. We obtained stakeholder input on the perceived value of each recommendation by soliciting public comments via two face-to-face meetings of informatics and comparative-effectiveness investigators, through multiple public webinars targeted to the health services research community, and with an open access online wiki.Recommendations:Our recommendations propose reporting on both general and analysis-specific data quality features. The goals of these recommendations are to improve the reporting of data quality measures for studies that use observational clinical and administrative data, to ensure transparency and consistency in computing data quality measures, and to facilitate best practices and trust in the new clinical discoveries based on secondary use of observational data.

Patients, consumers, and caregivers: the original data stewards.

Introduction:For patients and families, the issue of data quality is personal. When treatment decisions or research conclusions are based on incomplete or inaccurate information—a misdiagnosis, an incomplete medication list, the wrong demographic data—care safety, quality, and outcomes suffer—and patients and families suffer.Opportunity for Change:Thanks in part to the power of health information technology, individuals have new opportunities to be part of the solution when it comes to assessing and improving data quality. Building on a framework for data quality, there are numerous opportunities for patients to be part of ‘feedback loops’ to understand and improve data quality are presented.Foundation for the Future:As the source of patient-centered data, consumers have important roles to play in reviewing and confirming their own health information and should be engaged partners in efforts to understand and improve the quality of electronic health data.

Strategy support for the Post‐Market Monitoring (PMM) of GM plants: Review of existing PMM strategies developed for the safety assessment of human and animal health

Strategy support for the Post‐Market Monitoring (PMM) of GM plants: Review of existing PMM strategies developed for the safety assessment of human and animal health

Exploration of heterogeneity in distributed research network drug safety analyses.

Distributed data networks representing large diverse populations are an expanding focus of drug safety research. However, interpreting results is difficult when treatment effect estimates vary across datasets (i.e., heterogeneity). In a previous study, risk estimates were generated for selected drugs and potential adverse outcomes. Analyses were replicated across eight distributed data sources using an identical analytic structure. To evaluate heterogeneity of risk estimates across data sources, the estimates were combined with summary-level data characterizing the population of each data source. Meta-analysis, meta-regression, and plots of the influence on overall results versus contribution to heterogeneity were examined and used to illustrate an approach to heterogeneity assessment. Heterogeneity, as measured by the I-squared statistic, was high with variability across outcomes. Plots of the relationship between influence on overall results and contribution to heterogeneity suggest that certain datasets and characteristics were influential but there was variability dependent on the drug and outcome being assessed. Exploratory meta-regression identified many possible influential factors, but may be subject to ecological bias and false positive conclusions. Distributed data network drug safety analyses can produce heterogeneous risk estimates that may not be easily explained. Approaches illustrated here can be useful for research that is subject to similar problems with heterogeneity.

Predicting 90-day mortality after bariatric surgery: an independent, external validation of the OS-MRS prognostic risk score

BackgroundThe Obesity Surgery Mortality Risk Score (OS-MRS) was developed using data from 1995 to 2004; it has yet to be validated for more recent patients in integrated delivery system settings. The objective of this study was to validate the OS-MRS using data from electronic health records in a distributed data network. MethodsWe conducted a retrospective cohort study of 3,817 adults who underwent an open (21.4%) or laparoscopic (78.6%) gastric bypass surgery between 2005 and 2007 in the Scalable Partnering Network. Our main outcome was all-cause mortality during the 90 days after surgery. We scored patients’ risk of mortality by adding characteristics according to the OS-MRS (i.e., 1 point for each predictor). ResultsSixteen of 3,817 (0.42/100; 95% CI, .24–.68) patients died within 90 days. The OS-MRS discriminated low-risk and high-risk patients effectively: low-risk (2 of 1,654 patients; .12 deaths/100 patients), intermediate-risk (10 of 2,008 patients; .50 deaths/100 patients), and high-risk (4 of 155 patients; 2.58 deaths/100 patients). High-risk patients were 21.3 times more likely to die in the first 90 days after surgery than low-risk patients (risk ratio = 21.3; 95% CI, 3.9–115.6). ConclusionIn these 10 U.S. healthcare delivery systems, the OS-MRS appears valid—albeit with the caveat that we observed a small number of deaths. The OS-MRS appears useful for identifying the small fraction of patients at high risk for 90-day mortality after open and laparoscopic RYGB.

The BioCASe Monitor Service - A tool for monitoring progress and quality of data provision through distributed data networks.

The BioCASe Monitor Service (BMS) is a web-based tool for coordinators of distributed data networks that provide information to web-portals and data aggregators via the BioCASe Provider Software. Building on common standards and protocols, it has three main purposes: (1) monitoring provider’s progress in data provision, (2) facilitating checks of data mappings with a focus on the structure, plausibility and completeness, and (3) verifying compliance of provided data for transformation into other target schemas.Herein two use cases, GBIF-D and OpenUp!, are presented in which the BMS is being applied for monitoring the progress in data provision and performing quality checks on the ABCD (Access to Biological Collection Data) schema mapping.However, the BMS can potentially be used with any conceptual data schema and protocols for querying web services. Through flexible configuration options it is highly adaptable to specific requirements and needs. Thus, the BMS can be easily implemented into coordination workflows and reporting duties within other distributed data network projects.