Comparative effectiveness research and medical informatics

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

Demonstration of an Analytic Process using Home Health Care Electronic Health Records: A Case Example Exploring the Prevalence of Patients with a Substance Use History and a Venous Access Device

Electronic health records (EHR) are an important, but underutilized source for home health care research and practice improvement. Although the use of EHR is more efficient than prospective data collection, an analysis of EHR data can be complex and time-consuming. To demonstrate the overall process, we describe a secondary analysis of EHR data that explored the prevalence of home health care patients with a substance use history (SUH) and a venous access device (VAD). We detail our process of EHR data extraction, management, and analysis to assist researchers and clinicians interested in similar work. The example analysis showed that that 10.6% of adult home health care patients had a SUH, 8.8% had a long-term VAD, and 1.3% had both. EHRs can be a valuable data source for home health care research and quality improvement projects, but a systematic and thoughtful strategy is needed to fully leverage their potential.

Constructing Epidemiologic Cohorts from Electronic Health Record Data

In the United States, electronic health records (EHR) are increasingly being incorporated into healthcare organizations to document patient health and services rendered. EHRs serve as a vast repository of demographic, diagnostic, procedural, therapeutic, and laboratory test data generated during the routine provision of health care. The appeal of using EHR data for epidemiologic research is clear: EHRs generate large datasets on real-world patient populations in an easily retrievable form permitting the cost-efficient execution of epidemiologic studies on a wide array of topics. Constructing epidemiologic cohorts from EHR data involves as a defining feature the development of data machinery, which transforms raw EHR data into an epidemiologic dataset from which appropriate inference can be drawn. Though data machinery includes many features, the current report focuses on three aspects of machinery development of high salience to EHR-based epidemiology: (1) selecting study participants; (2) defining “baseline” and assembly of baseline characteristics; and (3) follow-up for future outcomes. For each, the defining features and unique challenges with respect to EHR-based epidemiology are discussed. An ongoing example illustrates key points. EHR-based epidemiology will become more prominent as EHR data sources continue to proliferate. Epidemiologists must continue to improve the methods of EHR-based epidemiology given the relevance of EHRs in today’s healthcare ecosystem.

Measuring the Interactions Between Health Demand, Informatics Supply, and Technological Applications in Digital Medical Innovation for China: Content Mapping and Analysis

BACKGROUND

There were 2 major incentives introduced by the Chinese government to promote medical informatics in 2009 and 2016. As new drugs are the major source of medical innovation, informatics-related concepts and techniques are a major source of digital medical innovation. However, it is unclear whether the research efforts of medical informatics in China have met the health needs, such as disease management and population health.

OBJECTIVE

We proposed an approach to mapping the interplay between different knowledge entities by using the tree structure of Medical Subject Headings (MeSH) to gain insights into the interactions between informatics supply, health demand, and technological applications in digital medical innovation in China.

METHODS

All terms under the MeSH tree parent node "Diseases [C]" or node "Health [N01.400]" or "Public Health [N06.850]" were labelled as H. All terms under the node "Information Science [L]" were labelled as I, and all terms under node "Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]" were labelled as T. The H-I-T interactions can be measured by using their co-occurrences in a given publication.

RESULTS

The H-I-T interactions in China are showing significant growth and a more concentrated interplay were observed. Computing methodologies, informatics, and communications media (such as social media and the internet) constitute the majority of I-related concepts and techniques used for resolving the health promotion and diseases management problems in China. Generally there is a positive correlation between the burden and informatics research efforts for diseases in China. We think it is not contradictory that informatics research should be focused on the greatest burden of diseases or where it can have the most impact. Artificial intelligence is a competing field of medical informatics research in China, with a notable focus on diagnostic deep learning algorithms for medical imaging.

CONCLUSIONS

It is suggested that technological transfers, namely the functionality to be realized by medical/health informatics (eg, diagnosis, therapeutics, surgical procedures, laboratory testing techniques, and equipment and supplies) should be strengthened. Research on natural language processing and electronic health records should also be strengthened to improve the real-world applications of health information technologies and big data in the future.

How much can we trust electronic health record data?

Trust in EHR data is becoming increasingly important as a greater share of clinical and health services research use EHR data. We discuss reasons for distrust and acknowledge limitations. Researchers continue to use EHR data because of strengths including greater clinical detail than sources like administrative billing claims. Further, many limitations are addressable with existing methods including data quality checks and common data frameworks. We discuss how to build greater trust in the use of EHR data for research, including additional transparency and research priority areas that will both enhance existing strengths of the EHR and mitigate its limitations.

Point-of-Care Clinical Trials in Sports Medicine Research: Identifying Effective Treatment Interventions Through Comparative Effectiveness Research

CONTEXT

Recently, calls to conduct comparative effectiveness research (CER) in athletic training to better support patient care decisions have been circulated. Traditional research methods (eg, randomized controlled trials [RCTs], observational studies) may be ill suited for CER. Thus, innovative research methods are needed to support CER efforts.

OBJECTIVES

To discuss the limitations of traditional research designs in CER studies, describe a novel methodologic approach called the point-of-care clinical trial (POC-CT), and highlight components of the POC-CT (eg, incorporation of an electronic medical record [EMR], Bayesian adaptive feature) that allow investigators to conduct scientifically rigorous studies at the point of care.

DESCRIPTION

Practical concerns (eg, high costs and limited generalizability of RCTs, the inability to control for bias in observational studies) may stall CER efforts in athletic training. In short, the aim of the POC-CT is to embed a randomized pragmatic trial into routine care; thus, patients are randomized to minimize potential bias, but the study is conducted at the point of care to limit cost and improve the generalizability of the findings. Furthermore, the POC-CT uses an EMR to replace much of the infrastructure associated with a traditional RCT (eg, research team, patient and clinician reminders) and a Bayesian adaptive feature to help limit the number of patients needed for the study. Together, the EMR and Bayesian adaptive feature can improve the overall feasibility of the study and preserve the typical clinical experiences of the patient and clinician.

CLINICAL ADVANTAGES

The POC-CT includes the basic tenets of practice-based research because studies are conducted at the point of care, in real-life settings, and during routine clinical practice. If implemented effectively, the POC-CT can be seamlessly integrated into daily clinical practice, allowing investigators to establish patient-reported evidence that may be quickly applied to patient care decisions. This design appears to be a promising approach for CER investigations and may help establish a "learning health care system" in the sports medicine community.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Using primary care electronic health record data for comparative effectiveness research: experience of data quality assessment and preprocessing in The Netherlands

AIM

Details of data quality and how quality issues were solved have not been reported in published comparative effectiveness studies using electronic health record data.

METHODS

We developed a conceptual framework of data quality assessment and preprocessing and apply it to a study comparing angiotensin-converting enzyme inhibitors with angiotensin receptor blockerss on renal function decline in diabetes patients.

RESULTS

The framework establishes a line of thought to identify and act on data issues. The core concept is to evaluate whether data are fit-for-use for research tasks. Possible quality problems are listed through specific signal detections, and verified whether they are true problems. Optimal solutions are selected for the identified problems.

CONCLUSION

This framework can be used in observational studies to improve validity of results.

Leveraging dialog systems research to assist biomedical researchers' interrogation of Big Clinical Data

The worldwide adoption of electronic health records (EHR) promises to accelerate clinical research, which lies at the heart of medical advances. However, the interrogation of such Big Data by clinical researchers can be laborious and error-prone, involving iterative and ineffective communication of data requests to data analysts. Research on this communication process is rare. There also exists no contemporary system that offers intelligent solutions to assist clinical researchers in their quest for clinical data. In this article, we first provide a detailed characterization of the challenges encountered in this communication space. Second, we identify promising synergies between fields studying human-to-human and human-machine communication that can shed light on biomedical data query mediation. We propose a mixed-initiative dialog-based approach to support autonomous clinical data access and recommend needed technology development and communication study for accelerating clinical research.

Facilitating biomedical researchers' interrogation of electronic health record data: Ideas from outside of biomedical informatics

Electronic health records (EHR) are a vital data resource for research uses, including cohort identification, phenotyping, pharmacovigilance, and public health surveillance. To realize the promise of EHR data for accelerating clinical research, it is imperative to enable efficient and autonomous EHR data interrogation by end users such as biomedical researchers. This paper surveys state-of-art approaches and key methodological considerations to this purpose. We adapted a previously published conceptual framework for interactive information retrieval, which defines three entities: user, channel, and source, by elaborating on channels for query formulation in the context of facilitating end users to interrogate EHR data. We show the current progress in biomedical informatics mainly lies in support for query execution and information modeling, primarily due to emphases on infrastructure development for data integration and data access via self-service query tools, but has neglected user support needed during iteratively query formulation processes, which can be costly and error-prone. In contrast, the information science literature has offered elaborate theories and methods for user modeling and query formulation support. The two bodies of literature are complementary, implying opportunities for cross-disciplinary idea exchange. On this basis, we outline the directions for future informatics research to improve our understanding of user needs and requirements for facilitating autonomous interrogation of EHR data by biomedical researchers. We suggest that cross-disciplinary translational research between biomedical informatics and information science can benefit our research in facilitating efficient data access in life sciences.

Integration of Genomics in Primary Care

Primary care is changing rapidly. The wide-scale expansion of electronic medical records is redefining the way we approach chronic disease management, and automated decision support is increasingly being leveraged to reduce risk and optimize quality. Many of these interventions are now beginning to integrate genomic data. We explore the convergence of these 2 forces (expansion of clinical informatics and integration of translational genomics), and we highlight several applications where these forces are helping our patients avoid potentially preventable events. Because gene-environment interactions are dynamic, the utility of gene-based decision support varies over time. Primary care providers will serve a key role as our patients navigate these changes.

Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research

OBJECTIVE

To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development.

MATERIALS AND METHODS

Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features.

RESULTS

Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs.

DISCUSSION

Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software.

CONCLUSIONS

Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.

CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data

OBJECTIVES

Comparative effectiveness research (CER) requires the capture and analysis of data from disparate sources, often from a variety of institutions with diverse electronic health record (EHR) implementations. In this paper we describe the CER Hub, a web-based informatics platform for developing and conducting research studies that combine comprehensive electronic clinical data from multiple health care organizations.

METHODS

The CER Hub platform implements a data processing pipeline that employs informatics standards for data representation and web-based tools for developing study-specific data processing applications, providing standardized access to the patient-centric electronic health record (EHR) across organizations.

RESULTS

The CER Hub is being used to conduct two CER studies utilizing data from six geographically distributed and demographically diverse health systems. These foundational studies address the effectiveness of medications for controlling asthma and the effectiveness of smoking cessation services delivered in primary care.

DISCUSSION

The CER Hub includes four key capabilities: the ability to process and analyze both free-text and coded clinical data in the EHR; a data processing environment supported by distributed data and study governance processes; a clinical data-interchange format for facilitating standardized extraction of clinical data from EHRs; and a library of shareable clinical data processing applications.

CONCLUSION

CER requires coordinated and scalable methods for extracting, aggregating, and analyzing complex, multi-institutional clinical data. By offering a range of informatics tools integrated into a framework for conducting studies using EHR data, the CER Hub provides a solution to the challenges of multi-institutional research using electronic medical record data.

Assessment of psychological status in 100 elderly patients with functional dyspepsia

AIM: To assess the psychological status in elderly patients with functional dyspepsia, and to analyze their nursing interventions.

METHODS: One hundred elderly patients with functional dyspepsia treated at the Fifth Affiliated Hospital of Zhengzhou University from January 2011 to December 2013 were included in a study group, and 100 healthy volunteers comprised a control group. The self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were administered to assess the psychological status.

RESULTS: The SAS and SDS scores were significantly higher in the study group than in the control group (53.1 points ± 5.4 points vs 32.7 points ± 3.4 points, 57.8 points ± 4.3 points vs 33.5 points ± 3.2 points, P < 0.05). The somatization, depression and anxiety scores were also significantly higher in the study group (P < 0.05). After intervention, the SAS and SDS scores decreased significantly in the study group (P < 0.05).

CONCLUSION: Elderly patients with functional dyspepsia show varying degrees of anxiety, depression and other negative emotions, and targeted nursing interventions can help improve the patient's negative emotions.

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study

Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the 'emrAdapter' tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.

Using large clinical corpora for query expansion in text-based cohort identification

In light of the heightened problems of polysemy, synonymy, and hyponymy in clinical text, we hypothesize that patient cohort identification can be improved by using a large, in-domain clinical corpus for query expansion. We evaluate the utility of four auxiliary collections for the Text REtrieval Conference task of IR-based cohort retrieval, considering the effects of collection size, the inherent difficulty of a query, and the interaction between the collections. Each collection was applied to aid in cohort retrieval from the Pittsburgh NLP Repository by using a mixture of relevance models. Measured by mean average precision, performance using any auxiliary resource (MAP=0.386 and above) is shown to improve over the baseline query likelihood model (MAP=0.373). Considering subsets of the Mayo Clinic collection, we found that after including 2.5 billion term instances, retrieval is not improved by adding more instances. However, adding the Mayo Clinic collection did improve performance significantly over any existing setup, with a system using all four auxiliary collections obtaining the best results (MAP=0.4223). Because optimal results in the mixture of relevance models would require selective sampling of the collections, the common sense approach of "use all available data" is inappropriate. However, we found that it was still beneficial to add the Mayo corpus to any mixture of relevance models. On the task of IR-based cohort identification, query expansion with the Mayo Clinic corpus resulted in consistent and significant improvements. As such, any IR query expansion with access to a large clinical corpus could benefit from the additional resource. Additionally, we have shown that more data is not necessarily better, implying that there is value in collection curation.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project

Background:

The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.

Objective:

Investigators from Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.

Methods:

SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm’s Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill ‘go-live’ at each site.

Progress to Date:

A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15–18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted—25% from structured data; 25% from text mining.

Conclusion:

In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.

A review of covariate selection for non-experimental comparative effectiveness research

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias.

Building the informatics infrastructure for comparative effectiveness research (CER): a review of the literature

BACKGROUND

Technological advances in clinical informatics have made large amounts of data accessible and potentially useful for research. As a result, a burgeoning literature addresses efforts to bridge the fields of health services research and biomedical informatics. The Electronic Data Methods Forum review examines peer-reviewed literature at the intersection of comparative effectiveness research and clinical informatics. The authors are specifically interested in characterizing this literature and identifying cross-cutting themes and gaps in the literature.

METHODS

A 3-step systematic literature search was conducted, including a structured search of PubMed, manual reviews of articles from selected publication lists, and manual reviews of research activities based on prospective electronic clinical data. Two thousand four hundred thirty-five citations were identified as potentially relevant. Ultimately, a full-text review was performed for 147 peer-reviewed papers.

RESULTS

One hundred thirty-two articles were selected for inclusion in the review. Of these, 88 articles are the focus of the discussion in this paper. Three types of articles were identified, including papers that: (1) provide historical context or frameworks for using clinical informatics for research, (2) describe platforms and projects, and (3) discuss issues, challenges, and applications of natural language processing. In addition, 2 cross-cutting themes emerged: the challenges of conducting research in the absence of standardized ontologies and data collection; and unique data governance concerns related to the transfer, storage, deidentification, and access to electronic clinical data. Finally, the authors identified several current gaps on important topics such as the use of clinical informatics for cohort identification, cloud computing, and single point access to research data.

Comparative effectiveness research for prostate cancer radiation therapy: current status and future directions

Comparative effectiveness research aims to help clinicians, patients and policymakers make informed treatment decisions under real-world conditions. Prostate cancer patients have multiple treatment options, including active surveillance, androgen deprivation therapy, surgery and multiple modalities of radiation therapy. Technological innovations in radiation therapy for prostate cancer have been rapidly adopted into clinical practice despite relatively limited evidence for effectiveness showing the benefit for one modality over another. Comparative effectiveness research has become an essential component of prostate cancer research to help define the benefits, risks and effectiveness of the different radiation therapy modalities currently in use for prostate cancer treatment.

Journals publishing bio-medicolegal research in Europe

Fragmentation of bio-medicolegal knowledge has led to a proliferation of ultra-specialised sub-disciplines and branches, often published in 'field-oriented' scientific journals.The aim of this work is to provide an in-depth analytical picture of bio-medicolegal sources of publication, within and outside the traditional conception of legal medicine. An extensive search of bio-medicolegal articles published in the last five and a half years was performed on the MEDLINE database according to MeSH terms combined with free-text protocols. We performed a systematic analysis of targeted journals after merging, selecting and categorising all retrieved records, taking into account data from the 2009 JCR Science Edition (released on June 2010); 1,037 different journals were identified, of which only 48 (4.6%) focus specifically on bio-medicolegal matters, and of which only seven (14.6%) have an impact factor (IF). Despite this apparent dispersion, 47% of articles were published in bio-medicolegal journals (BML), of which 70.2% were in journals with IF (BML-IF). Articles published in BML-IF journals (33% of total papers) reach almost 50%, mainly in "Forensic Science International", "International Journal of Legal Medicine" and "Journal of Forensic Sciences". Instead, publications in not specifically bio-medicolegal journals (Not BML-IF) are greatly scattered and even fragmented in about 650 journals.The sub-disciplines that appear most frequently in Not BML-IF rather than BML-IF journals are Forensic Psychiatry (48.2% vs. 5.1%), Criminology (37.1% vs. 8.3%), Malpractice (50.7% vs. 4.0%), Medical Law and Ethics (46.4% vs. 6.9%) and Clinical Forensic Medicine (39.5% vs. 21.3%). The proposed bibliometric analysis revealed the preference of Forensic Pathology, Criminalistics (Biological), Forensic Genetics, Forensic Anthropology and Forensic Entomology for journals traditionally considered pertinent to the medico-legal discipline, with a considerable dispersion involving Toxicology, Psychiatry, Criminology and Malpractice, which were published in less well-known journals. This dispersion could be reduced adapting specialised forensic sections and increasing the IF of forensic journals, in order to respond suitably to the present demand for visibility by bio-medicolegal scientists, clearly oriented towards enhancing the objective impact of their curricula and attempting to attract funding to their research projects.

Informatics and standards for nanomedicine technology

There are several issues to be addressed concerning the management and effective use of information (or data), generated from nanotechnology studies in biomedical research and medicine. These data are large in volume, diverse in content, and are beset with gaps and ambiguities in the description and characterization of nanomaterials. In this work, we have reviewed three areas of nanomedicine informatics: information resources; taxonomies, controlled vocabularies, and ontologies; and information standards. Informatics methods and standards in each of these areas are critical for enabling collaboration; data sharing; unambiguous representation and interpretation of data; semantic (meaningful) search and integration of data; and for ensuring data quality, reliability, and reproducibility. In particular, we have considered four types of information standards in this article, which are standard characterization protocols, common terminology standards, minimum information standards, and standard data communication (exchange) formats. Currently, because of gaps and ambiguities in the data, it is also difficult to apply computational methods and machine learning techniques to analyze, interpret, and recognize patterns in data that are high dimensional in nature, and also to relate variations in nanomaterial properties to variations in their chemical composition, synthesis, characterization protocols, and so on. Progress toward resolving the issues of information management in nanomedicine using informatics methods and standards discussed in this article will be essential to the rapidly growing field of nanomedicine informatics.

Scientific and organizational collaboration in comparative effectiveness research: the VA cooperative studies program model

Comparative effectiveness research (CER) has the ability to improve health and inform patients, clinicians, and decision makers. However, calls for more devoted efforts with regard to CER have been countered by methodological, resource, and translational challenges related to conducting these studies. The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) is a clinical research infrastructure that has contributed much evidence to support clinical practice for several decades. Although the CSP does not exclusively focus on CER, it employs strategies that lend themselves toward the planning and execution of studies that seek to compare interventions and/or strategies for treating disease. Consequently, the CSP provides a model for addressing important scientific, structural, and operational factors for clinical research, including large, national and multinational comparative effectiveness studies. Exploration of the difficulties the CSP has encountered can help to elucidate barriers that face CER. This article discusses factors and approaches for collaboratively developing and conducting definitive studies that produce outcomes aimed at influencing clinical practice, lessons that have resulted from such efforts, and ongoing challenges. Future program directions are also presented to highlight areas of emphasis and implications for CER within the VA and nationally.