Knowledge sharing in the health scenario

The understanding of certain data often requires the collection of similar data from different places to be analysed and interpreted. Interoperability standards and ontologies, are facilitating data interchange around the world. However, beyond the existing networks and advances for data transfer, data sharing protocols to support multilateral agreements are useful to exploit the knowledge of distributed Data Warehouses. The access to a certain data set in a federated Data Warehouse may be constrained by the requirement to deliver another specific data set. When bilateral agreements between two nodes of a network are not enough to solve the constraints for accessing to a certain data set, multilateral agreements for data exchange are needed.We present the implementation of a Multi-Agent System for multilateral exchange agreements of clinical data, and evaluate how those multilateral agreements increase the percentage of data collected by a single node from the total amount of data available in the network. Different strategies to reduce the number of messages needed to achieve an agreement are also considered. The results show that with this collaborative sharing scenario the percentage of data collected dramaticaly improve from bilateral agreements to multilateral ones, up to reach almost all data available in the network.     

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

PurposeClinicians 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.MethodsThree 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.ResultsHarmonized, 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.ConclusionsThe GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.     

Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network’s Common Measures Working Group

PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network’s efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.ResultsCMG identified 10 high-priority CFIR constructs as important for genomic medicine. Of those, eight did not have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model.ConclusionWe developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.     

A logic model for precision medicine implementation informed by stakeholder views and implementation science

PurposePrecision medicine promises to improve patient outcomes, but much is unknown about its adoption within health-care systems. A comprehensive implementation plan is needed to realize its benefits.MethodsWe convened 80 stakeholders for agenda setting to inform precision medicine policy, delivery, and research. Conference proceedings were audio-recorded, transcribed, and thematically analyzed. We mapped themes representing opportunities, challenges, and implementation strategies to a logic model, and two implementation science frameworks provided context.ResultsThe logic model components included inputs: precision medicine infrastructure (clinical, research, and information technology), big data (from data sources to analytics), and resources (e.g., workforce and funding); activities: precision medicine research, practice, and education; outputs: precision medicine diagnosis; outcomes: personal utility, clinical utility, and health-care utilization; and impacts: precision medicine value, equity and access, and economic indicators. Precision medicine implementation challenges include evidence gaps demonstrating precision medicine utility, an unprepared workforce, the need to improve precision medicine access and reduce variation, and uncertain impacts on health-care utilization. Opportunities include integrated health-care systems, partnerships, and data analytics to support clinical decisions. Examples of implementation strategies to promote precision medicine are: changing record systems, data warehousing techniques, centralized technical assistance, and engaging consumers.ConclusionWe developed a theory-based, context-specific logic model that can be used by health-care organizations to facilitate precision medicine implementation.     

Development of a consent resource for genomic data sharing in the clinical setting

PurposeData sharing between clinicians, laboratories, and patients is essential for improvements in genomic medicine, but obtaining consent for individual-level data sharing is often hindered by a lack of time and resources. To address this issue, the Clinical Genome Resource (ClinGen) developed tools to facilitate consent, including a one-page consent form and online supplemental video with information on key topics, such as risks and benefits of data sharing.MethodsTo determine whether the consent form and video accurately conveyed key data sharing concepts, we surveyed 5,162 members of the general public. We measured comprehension at baseline, after reading the form and watching the video. Additionally, we assessed participants’ attitudes toward genomic data sharing.ResultsParticipants’ performance on comprehension questions significantly improved over baseline after reading the form and continued to improve after watching the video.ConclusionResults suggest reading the form alone provided participants with important knowledge regarding broad data sharing, and watching the video allowed for broader comprehension. These materials are now available at http://www.clinicalgenome.org/share. These resources will provide patients a straightforward way to share their genetic and health information, and improve the scientific community’s access to data generated through routine healthcare.     

A comparison of methods for assessing penetrating trauma on retrospective multi-center data

ObjectiveTraumaSCAN-Web (TSW) is a computerized decision support system for assessing chest and abdominal penetrating trauma which utilizes 3D geometric reasoning and a Bayesian network with subjective probabilities obtained from an expert. The goal of the present study is to determine whether a trauma risk prediction approach using a Bayesian network with a predefined structure and probabilities learned from penetrating trauma data is comparable in diagnostic accuracy to TSW.MethodsParameters for two Bayesian networks with expert-defined structures were learned from 637 gunshot and stab wound cases from three hospitals, and diagnostic accuracy was assessed using 10-fold cross-validation. The first network included information on external wound locations, while the second network did not. Diagnostic accuracy of learned networks was compared to that of TSW on 194 previously evaluated cases.ResultsFor 23 of the 24 conditions modeled by TraumaSCAN-Web, 16 conditions had Areas Under the ROC Curve (AUCs) greater than 0.90 while 21 conditions had AUCs greater than 0.75 for the first network. For the second network, 16 and 20 conditions had AUCs greater than 0.90 and 0.75, respectively. AUC results for learned networks on 194 previously evaluated cases were better than or equal to AUC results for TSW for all diagnoses evaluated except diaphragm and heart injuries.ConclusionsFor 23 of the 24 penetrating trauma conditions studied, a trauma diagnosis approach using Bayesian networks with predefined structure and probabilities learned from penetrating trauma data was better than or equal in diagnostic accuracy to TSW. In many cases, information on wound location in the first network did not significantly add to predictive accuracy. The study suggests that a decision support approach that uses parameter-learned Bayesian networks may be sufficient for assessing some penetrating trauma conditions.     

Centers for Mendelian Genomics: A decade of facilitating gene discovery

PurposeMendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration.MethodsOver the past 10 years, the National Institutes of Health–supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution.ResultsWe highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher.ConclusionThe work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients.     

The influence of a professional physician network on clinical decision making

ObjectiveThe aim of this study was to examine the role of physicians’ professional networks in decision-making processes.MethodsA professional network was examined in three stages: content analysis and categorization of discussions concerning decision-making processes, in-depth interviews, and a questionnaire.ResultsThe RAMBAM network has professional as well as social roles. On a professional level, physicians seek approval of their initial line of reasoning regarding their clinical cases, but will consider other approaches if such are suggested by persons of professional repute or if answers are based on evidence-based medicine and include referral to a relevant source. On a social level, physicians want to be part of their professional community and share information and experiences.ConclusionPhysicians’ professional networks have a social role that is expressed by a feeling of belonging to a community, as well as a professional role of capturing and disseminating medical knowledge during physicians’ decision-making processes. Professional networks constitute a unique source of tacit knowledge that extends existing formal knowledge resources.Practice implicationsThe study can increase physicians’ awareness of professional networks as a unique source of tacit knowledge and can assist in the future design of medical professional networks as knowledge resources for medical decision making.     

Hypothesis generation using network structures on community health center cancer-screening performance

Research objectives: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. Methods: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. Results: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.     

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput,portable, and computational methods

ObjectiveThe combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale.MethodsBased on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review.ResultsThe practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources.ConclusionsMachine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.     

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

ObjectiveOne of the hardest technical tasks in employing Bayesian network models in practice is obtaining their numerical parameters. In the light of this difficulty, a pressing question, one that has immediate implications on the knowledge engineering effort, is whether precision of these parameters is important. In this paper, we address experimentally the question whether medical diagnostic systems based on Bayesian networks are sensitive to precision of their parameters.Methods and materialsThe test networks include Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders and six other medical diagnostic networks constructed from medical data sets available through the Irvine Machine Learning Repository. Assuming that the original model parameters are perfectly accurate, we lower systematically their precision by rounding them to progressively courser scales and check the impact of this rounding on the models’ accuracy.ResultsOur main result, consistent across all tested networks, is that imprecision in numerical parameters has minimal impact on the diagnostic accuracy of models, as long as we avoid zeroes among parameters.ConclusionThe experiments’ results provide evidence that as long as we avoid zeroes among model parameters, diagnostic accuracy of Bayesian network models does not suffer from decreased precision of their parameters.     

The analyses of human inherited disease and tissue-specific proteins in the interaction network

ObjectivesWith the announcement of human proteome and interaction data, it becomes possible to comprehensively investigate the tissue-expression and network properties of inherited disease proteins. In this study, our goal was to develop methods to map the disease and expression data and analyze the disorder-tissue associations.MethodsIn this paper, we manually classified the human disease proteins into 22 disorder classes and systematically analyzed the properties of disease proteins in different disorder classes. Then, we investigated the similarity of different disorder classes by computing the overlap of different disorder proteins and networks. We proposed two novel measures, Enrichment Ratio and P-value for comparative analysis of disease proteins across tissues and revealed the associations between disorder classes and tissues/cells.ResultsCompared with non-disease proteins, disease proteins tend to express in more tissues, have higher expression levels and interact with more other proteins in the network. The overlap percentages of networks are much higher than those of proteins, implying that different disorder classes usually influence each other by means of their interacting neighbors. The metabolic, muscular and hematologic proteins are related with most tissues/cells, and cancer proteins are closely associated with the disorders in immune cells.ConclusionThis paper provided novel methods to investigate proteome-wide disease proteins and their interacting networks in order to understand different disease’s associations.     

Optimizing annotation resources for natural language de-identification via a game theoretic framework

ObjectiveElectronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not hold true and HCOs may overinvest in de-identification technology. The goal of this study is to design a natural language de-identification framework, rooted in game theory, which enables an HCO to optimize their investments given the expected capabilities of an adversarial recipient.MethodsWe introduce a Stackelberg game to balance risk and utility in natural language de-identification. This game represents a cost-benefit model that enables an HCO with a fixed budget to minimize their investment in the de-identification process. We evaluate this model by assessing the overall payoff to the HCO and the adversary using 2100 clinical notes from Vanderbilt University Medical Center. We simulate several policy alternatives using a range of parameters, including the cost of training a de-identification model and the loss in data utility due to the removal of terms that are not identifiers. In addition, we compare policy options where, when an attacker is fined for misuse, a monetary penalty is paid to the publishing HCO as opposed to a third party (e.g., a federal regulator).ResultsOur results show that when an HCO is forced to exhaust a limited budget (set to $2000 in the study), the precision and recall of the de-identification of the HCO are 0.86 and 0.8, respectively. A game-based approach enables a more refined cost-benefit tradeoff, improving both privacy and utility for the HCO. For example, our investigation shows that it is possible for an HCO to release the data without spending all their budget on de-identification and still deter the attacker, with a precision of 0.77 and a recall of 0.61 for the de-identification. There also exist scenarios in which the model indicates an HCO should not release any data because the risk is too great. In addition, we find that the practice of paying fines back to a HCO (an artifact of suing for breach of contract), as opposed to a third party such as a federal regulator, can induce an elevated level of data sharing risk, where the HCO is incentivized to bait the attacker to elicit compensation.ConclusionsA game theoretic framework can be applied in leading HCO’s to optimized decision making in natural language de-identification investments before sharing EMR data.     

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

ObjectiveThe human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy.Methods and materialWe apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance.ResultsIn a first experiment, we compared the TNBN approach with other models such as static Bayesian networks, dynamic Bayesian networks and association rules. TNBN achieved a 64.2% sparser structure over the static network. In a second experiment, the TNBN model was applied to a dataset associating antiretroviral drugs with mutations developed under different antiretroviral regimes. The learned models captured previously described mutational pathways and associations between antiretroviral drugs and drug resistance mutations. Predictive accuracy reached 90.5%.ConclusionOur results suggest possible applications of TNBN for studying drug-mutation and mutation–mutation networks in the context of antiretroviral therapy, with direct impact on the clinical management of patients under antiretroviral therapy. This opens new horizons for predicting HIV mutational pathways in immune selection with relevance for antiretroviral drug development and therapy plan.     

The difficulties of broad data sharing in genomic medicine: Empirical evidence from diverse participants in prenatal and pediatric clinical genomics research

PurposeThis study aimed to understand broad data sharing decisions among predominantly underserved families participating in genomic research.MethodsDrawing on clinic observations, semistructured interviews, and survey data from prenatal and pediatric families enrolled in a genomic medicine study focused on historically underserved and underrepresented populations, this paper expands empirical evidence regarding genomic data sharing communication and decision-making.ResultsOne-third of parents declined to share family data, and pediatric participants were significantly more likely to decline than prenatal participants. The pediatric population was significantly more socioeconomically disadvantaged and more likely to require interpreters. Opt-in was tied to altruism and participants’ perception that data sharing was inherent to research participation. Opt-out was associated with privacy concerns and influenced by clinical staff’s presentation of data handling procedures. The ability of participants to make informed choices during enrollment about data sharing was weakened by suboptimal circumstances, which was revealed by poor understanding of data sharing in follow-up interviews as well as discrepancies between expressed participant desires and official recorded choices.ConclusionThese empirical data suggest that the context within which informed consent process is conducted in clinical genomics may be inadequate for respecting participants’ values and preferences and does not support informed decision-making processes.     

Method of regulatory network that can explore protein regulations for disease classification

ObjectiveTo develop regulatory network to explore and model the regulatory relationships of protein biomarkers and classify different disease groups.MethodsRegulatory network is constructed to be a hopfield-like network with nodes representing biomarkers and directional connections to be regulations in between. The input to the network is the measured expression levels of biomarkers, and the output is the summation of regulatory strengths from other biomarkers. The network is optimized towards minimizing the energy function that is defined as the measure of the disagreement between the input and output of the network. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network.ResultsTwo datasets have been used as test beds, one dataset includes patients of nasopharyngeal carcinoma with different responses to chemotherapy drug, and the other consists of patients of severe acute respiratory syndrome, influenza, and control normals. The regulatory networks among protein biomarkers were reconstructed for different disease conditions in each dataset. We demonstrated our methods have better classification capability when comparing with conventional methods including Fisher linear discriminant (FLD), K-nearest neighborhood (KNN), linear support vector machines (linSVM) and radial basis function based support vector machines (rbfSVM).ConclusionThe derived networks can effectively capture the unique regulatory patterns of protein markers associated with different patient groups and hence can be used for disease classification. The discovered regulation relationships can potentially provide insights to revealing the molecular signaling pathways.In this paper, a novel technique of regulatory network is proposed on purpose of modeling biomarker regulations and classifying different disease groups. The network is composed of a certain number of nodes that are directionally connected in between in which nodes denote predictors and connections to be the regulation relationship. The network is optimized towards minimizing its energy function with biomarker expression data acquired from a specific patient group, thus the optimized network can model the regulatory relationship of biomarkers under the same circumstance. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. The regulatory network can extract unique features of each disease condition, thus one immediate application of regulatory network is to classifying different diseases. We demonstrated that regulatory network is capable of performing disease classification through comparing with conventional methods including FLD, KNN, linSVM and rbfSVM on two protein datasets. We believe our method is promising in mining knowledge of protein regulations and be powerful for disease classification.     

Empowering citizens with access control mechanisms to their personal health resources

BackgroundAdvancements in information and communication technologies have allowed the development of new approaches to the management and use of healthcare resources. Nowadays it is possible to address complex issues such as meaningful access to distributed data or communication and understanding among heterogeneous systems. As a consequence, the discussion focuses on the administration of the whole set of resources providing knowledge about a single subject of care (SoC). New trends make the SoC administrator and responsible for all these elements (related to his/her demographic data, health, well-being, social conditions, etc.) and s/he is granted the ability of controlling access to them by third parties. The subject of care exchanges his/her passive role without any decision capacity for an active one allowing to control who accesses what.PurposeWe study the necessary access control infrastructure to support this approach and develop mechanisms based on semantic tools to assist the subject of care with the specification of access control policies. This infrastructure is a building block of a wider scenario, the Person-Oriented Virtual Organization (POVO), aiming at integrating all the resources related to each citizen's health-related data. The POVO covers the wide range and heterogeneity of available healthcare resources (e.g., information sources, monitoring devices, or software simulation tools) and grants each SoC the access control to them.MethodsSeveral methodological issues are crucial for the design of the targeted infrastructure. The distributed system concept and focus are reviewed from the service oriented architecture (SOA) perspective. The main frameworks for the formalization of distributed system architectures (Reference Model-Open Distributed Processing, RM-ODP; and Model Driven Architecture, MDA) are introduced, as well as how the use of the Unified Modelling Language (UML) is standardized. The specification of access control policies and decision making mechanisms are essential keys for this approach and they are accomplished by using semantic technologies (i.e., ontologies, rule languages, and inference engines).ResultsThe results are mainly focused on the security and access control of the proposed scenario. An ontology has been designed and developed for the POVO covering the terminology of the scenario and easing the automation of administration tasks. Over that ontology, an access control mechanism based on rule languages allows specifying access control policies, and an inference engine performs the decision making process automatically. The usability of solutions to ease administration tasks to the SoC is improved by the Me-As-An-Admin (M3A) application. This guides the SoC through the specification of personal access control policies to his/her distributed resources by using semantic technologies (e.g., metamodeling, model-to-text transformations, etc.). All results are developed as services and included in an architecture in accordance with standards and principles of openness and interoperability.ConclusionsCurrent technology can bring health, social and well-being care actually centered on citizens, and granting each person the management of his/her health information. However, the application of technology without adopting methodologies or normalized guidelines will reduce the interoperability of solutions developed, failing in the development of advanced services and improved scenarios for health delivery. Standards and reference architectures can be cornerstones for future-proof and powerful developments. Finally, not only technology must follow citizen-centric approaches, but also the gaps needing legislative efforts that support these new paradigms of healthcare delivery must be identified and addressed.     

To share or not to share: A randomized trial of consent for data sharing in genome research

PurposeDespite growing concerns toward maintaining participants' privacy, individual investigators collecting tissue and other biological specimens for genomic analysis are encouraged to obtain informed consent for broad data sharing. Our purpose was to assess the effect on research enrollment and data sharing decisions of three different consent types (traditional, binary, or tiered) with varying levels of control and choices regarding data sharing.MethodsA single-blinded, randomized controlled trial was conducted with 323 eligible adult participants being recruited into one of six genome studies at Baylor College of Medicine in Houston, Texas, between January 2008 and August 2009. Participants were randomly assigned to one of three experimental consent documents (traditional, n = 110; binary, n = 103; and tiered, n = 110). Debriefing in follow-up visits provided participants a detailed review of all consent types and the chance to change data sharing choices or decline genome study participation.ResultsBefore debriefing, 83.9% of participants chose public data release. After debriefing, 53.1% chose public data release, 33.1% chose restricted (controlled access database) release, and 13.7% opted out of data sharing. Only one participant declined genome study participation due to data sharing concerns.ConclusionOur findings indicate that most participants are willing to publicly release their genomic data; however, a significant portion prefers restricted release. These results suggest discordance between existing data sharing policies and participants' judgments and desires.     

Using network projections to explore co-incidence and context in large clinical datasets: Application to homelessness among U.S. Veterans

IntroductionNetwork projections of data can provide an efficient format for data exploration of co-incidence in large clinical datasets. We present and explore the utility of a network projection approach to finding patterns in health care data that could be exploited to prevent homelessness among U.S. Veterans.MethodWe divided Veteran ICD-9-CM (ICD9) data into two time periods (0–59 and 60–364 days prior to the first evidence of homelessness) and then used Pajek social network analysis software to visualize these data as three different networks. A multi-relational network simultaneously displayed the magnitude of ties between the most frequent ICD9 pairings. A new association network visualized ICD9 pairings that greatly increased or decreased. A signed, subtraction network visualized the presence, absence, and magnitude difference between ICD9 associations by time period.ResultA cohort of 9468 U.S. Veterans was identified as having administrative evidence of homelessness and visits in both time periods. They were seen in 222,599 outpatient visits that generated 484,339 ICD9 codes (average of 11.4 (range 1–23) visits and 2.2 (range 1–60) ICD9 codes per visit). Using the three network projection methods, we were able to show distinct differences in the pattern of co-morbidities in the two time periods. In the more distant time period preceding homelessness, the network was dominated by routine health maintenance visits and physical ailment diagnoses. In the 59 days immediately prior to the homelessness identification, alcohol related diagnoses along with economic circumstances such as unemployment, legal circumstances, along with housing instability were noted.ConclusionNetwork visualizations of large clinical datasets traditionally treated as tabular and difficult to manipulate reveal rich, previously hidden connections between data variables related to homelessness. A key feature is the ability to visualize changes in variables with temporality and in proximity to the event of interest. These visualizations lend support to cognitive tasks such as exploration of large clinical datasets as a prelude to hypothesis generation.