Effect of the Tool to Reduce Inappropriate Medications on Medication Communication and Deprescribing.

OBJECTIVES: To examine the effect of the Tool to Reduce Inappropriate Medications (TRIM), a web tool linking an electronic health record (EHR) to a clinical decision support system, on medication communication and prescribing. DESIGN: Randomized clinical trial. SETTING: Primary care clinics at a Veterans Affairs Medical Center. PARTICIPANTS: Veterans aged 65 and older prescribed seven or more medications randomized to receipt of TRIM or usual care (N = 128). INTERVENTION: TRIM extracts information on medications and chronic conditions from the EHR and contains data entry screens for information obtained from brief chart review and telephonic patient assessment. These data serve as input for automated algorithms identifying medication reconciliation discrepancies, potentially inappropriate medications (PIMs), and potentially inappropriate regimens. Clinician feedback reports summarize discrepancies and provide recommendations for deprescribing. Patient feedback reports summarize discrepancies and self-reported medication problems. MEASUREMENTS: Primary: subscales of the Patient Assessment of Care for Chronic Conditions (PACIC) related to shared decision-making; clinician and patient communication. Secondary: changes in medications. RESULTS: 29.7% of TRIM participants and 15.6% of control participants provided the highest PACIC ratings; this difference was not significant. Adjusting for covariates and clustering of patients within clinicians, TRIM was associated with significantly more-active patient communication and facilitative clinician communication and with more medication-related communication among patients and clinicians. TRIM was significantly associated with correction of medication discrepancies but had no effect on number of medications or reduction in PIMs. CONCLUSION: TRIM improved communication about medications and accuracy of documentation. Although there was no association with prescribing, the small sample size provided limited power to examine medication-related outcomes.

Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains.

BACKGROUND: The US Veterans Administration (VA) has developed a robust and mature computational infrastructure in support of its electronic health record (EHR). Web technology offers a powerful set of tools for structuring clinical decision support (CDS) around clinical care. This paper describes informatics challenges and design issues that were confronted in the process of building three Web-based CDS systems in the context of the VA EHR. METHODS: Over the course of several years, we implemented three Web-based CDS systems that extract patient data from the VA EHR environment to provide patient-specific CDS. These were 1) the VACS (Veterans Aging Cohort Study) Index Calculator which estimates prognosis for HIV+ patients, 2) Neuropath/CDS which assists in the medical management of patients with neuropathic pain, and 3) TRIM (Tool to Reduce Inappropriate Medications) which identifies potentially inappropriate medications in older adults and provides recommendations for improving the medication regimen. RESULTS: The paper provides an overview of the VA EHR environment and discusses specific informatics issues/challenges that arose in the context of each of the three Web-based CDS systems. We discuss specific informatics methods and provide details of approaches that may be useful within this setting. CONCLUSIONS: Informatics issues and challenges relating to data access and data availability arose because of the particular architecture of the national VA infrastructure and the need to link to that infrastructure from local Web-based CDS systems. Idiosyncrasies of VA patient data, especially the medication data, also posed challenges. Other issues related to specific functional needs of individual CDS systems. The goal of this paper is to describe these issues so that our experience may serve as a useful foundation to assist others who wish to build such systems in the future.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

Development of the Tool to Reduce Inappropriate Medications (TRIM): A Clinical Decision Support System to Improve Medication Prescribing for Older Adults.

STUDY OBJECTIVE: To create a clinical decision support system (CDSS) for evaluating problems with medications among older outpatients based on a broad set of criteria. DESIGN: Web-based CDSS development. SETTING: Primary care clinics at a Veterans Affairs medical center. PARTICIPANTS: Forty veterans 65 years and older who were prescribed seven or more medications that included those for treatment of diabetes mellitus and hypertension. MEASUREMENTS AND MAIN RESULTS: The Tool to Reduce Inappropriate Medications (TRIM) uses a program to extract age, medications, and chronic conditions from the electronic health record to identify high-risk patients and as input for evaluating the medication regimen. Additional health variables obtained through chart review and direct patient assessment are entered into a Web-based program. Based on a series of algorithms, TRIM generates feedback reports for clinicians. TRIM identified medication reconciliation discrepancies in 98% (39/40) of veterans, potentially inappropriate medications in 58% (23/40), potential problems with feasibility (based on poor adherence and/or cognitive impairment) in 25% (10/40), potential overtreatment of hypertension in 50% (20/40), potential overtreatment of diabetes in 43% (17/40), inappropriate dosing of renally excreted medications in 5% (2/40), and patient-reported adverse reactions in 5% (2/40). CONCLUSION: This evaluation of TRIM demonstrated that data elements can be extracted from the electronic health record to identify older primary care patients at risk for potentially problematic medication regimens. Supplemented with chart review and direct patient assessment, these data can be processed through clinical algorithms that identify potential problems and generate patient-specific feedback reports. Additional work is necessary to assess the effects of TRIM on medication deprescribing.

YPED: an integrated bioinformatics suite and database for mass spectrometry-based proteomics research.

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography-tandem mass spectrometry (LC-MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Extending the NIF DISCO framework to automate complex workflow: coordinating the harvest and integration of data from diverse neuroscience information resources.

This paper describes how DISCO, the data aggregator that supports the Neuroscience Information Framework (NIF), has been extended to play a central role in automating the complex workflow required to support and coordinate the NIF's data integration capabilities. The NIF is an NIH Neuroscience Blueprint initiative designed to help researchers access the wealth of data related to the neurosciences available via the Internet. A central component is the NIF Federation, a searchable database that currently contains data from 231 data and information resources regularly harvested, updated, and warehoused in the DISCO system. In the past several years, DISCO has greatly extended its functionality and has evolved to play a central role in automating the complex, ongoing process of harvesting, validating, integrating, and displaying neuroscience data from a growing set of participating resources. This paper provides an overview of DISCO's current capabilities and discusses a number of the challenges and future directions related to the process of coordinating the integration of neuroscience data within the NIF Federation.

Databases in SenseLab for the genomics, proteomics, and function of olfactory receptors.

We present here, the salient aspects of three databases: Olfactory Receptor Database (ORDB) is a repository of genomics and proteomics information of ORs; OdorDB stores information related to odorous compounds, specifically identifying those that have been shown to interact with olfactory rectors; and OdorModelDB disseminates information related to computational models of olfactory receptors (ORs). The data stored among these databases is integrated. Presented in this chapter are descriptions of these resources, which are part of the SenseLab suite of databases, a discussion of the computational infrastructure that enhances the efficacy of information storage, retrieval, dissemination, and automated data population from external sources.

The NIF DISCO Framework: facilitating automated integration of neuroscience content on the web.

This paper describes the capabilities of DISCO, an extensible approach that supports integrative Web-based information dissemination. DISCO is a component of the Neuroscience Information Framework (NIF), an NIH Neuroscience Blueprint initiative that facilitates integrated access to diverse neuroscience resources via the Internet. DISCO facilitates the automated maintenance of several distinct capabilities using a collection of files 1) that are maintained locally by the developers of participating neuroscience resources and 2) that are "harvested" on a regular basis by a central DISCO server. This approach allows central NIF capabilities to be updated as each resource's content changes over time. DISCO currently supports the following capabilities: 1) resource descriptions, 2) "LinkOut" to a resource's data items from NCBI Entrez resources such as PubMed, 3) Web-based interoperation with a resource, 4) sharing a resource's lexicon and ontology, 5) sharing a resource's database schema, and 6) participation by the resource in neuroscience-related RSS news dissemination. The developers of a resource are free to choose which DISCO capabilities their resource will participate in. Although DISCO is used by NIF to facilitate neuroscience data integration, its capabilities have general applicability to other areas of research.

Approaches to neuroscience data integration.

As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

The NIF LinkOut broker: a web resource to facilitate federated data integration using NCBI identifiers.

This paper describes the NIF LinkOut Broker (NLB) that has been built as part of the Neuroscience Information Framework (NIF) project. The NLB is designed to coordinate the assembly of links to neuroscience information items (e.g., experimental data, knowledge bases, and software tools) that are (1) accessible via the Web, and (2) related to entries in the National Center for Biotechnology Information's (NCBI's) Entrez system. The NLB collects these links from each resource and passes them to the NCBI which incorporates them into its Entrez LinkOut service. In this way, an Entrez user looking at a specific Entrez entry can LinkOut directly to related neuroscience information. The information stored in the NLB can also be utilized in other ways. A second approach, which is operational on a pilot basis, is for the NLB Web server to create dynamically its own Web page of LinkOut links for each NCBI identifier in the NLB database. This approach can allow other resources (in addition to the NCBI Entrez) to LinkOut to related neuroscience information. The paper describes the current NLB system and discusses certain design issues that arose during its implementation.

Federated access to heterogeneous information resources in the Neuroscience Information Framework (NIF).

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

Issues in the design of a pilot concept-based query interface for the neuroinformatics information framework.

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface.

The neuroscience information framework: a data and knowledge environment for neuroscience.

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.

X!!Tandem, an improved method for running X!tandem in parallel on collections of commodity computers.

The widespread use of mass spectrometry for protein identification has created a demand for computationally efficient methods of matching mass spectrometry data to protein databases. A search using X!Tandem, a popular and representative program, can require hours or days to complete, particularly when missed cleavages and post-translational modifications are considered. Existing techniques for accelerating X!Tandem by employing parallelism are unsatisfactory for a variety of reasons. The paper describes a parallelization of X!Tandem, called X!!Tandem, that shows excellent speedups on commodity hardware and produces the same results as the original program. Furthermore, the parallelization technique used is unusual and potentially useful for parallelizing other complex programs.

OdorMapComparer: an application for quantitative analyses and comparisons of fMRI brain odor maps.

Brain odor maps are reconstructed flat images that describe the spatial activity patterns in the glomerular layer of the olfactory bulbs in animals exposed to different odor stimuli. We have developed a software application, OdorMapComparer, to carry out quantitative analyses and comparisons of the fMRI odor maps. This application is an open-source window program that first loads two odor map images being compared. It allows image transformations including scaling, flipping, rotating, and warping so that the two images can be appropriately aligned to each other. It performs simple subtraction, addition, and average of signals in the two images. It also provides comparative statistics including the normalized correlation (NC) and spatial correlation coefficient. Experimental studies showed that the rodent fMRI odor maps for aliphatic aldehydes displayed spatial activity patterns that are similar in gross outlines but somewhat different in specific subregions. Analyses with OdorMapComparer indicate that the similarity between odor maps decreases with increasing difference in the length of carbon chains. For example, the map of butanal is more closely related to that of pentanal (with a NC = 0.617) than to that of octanal (NC = 0.082), which is consistent with animal behavioral studies. The study also indicates that fMRI odor maps are statistically odor-specific and repeatable across both the intra- and intersubject trials. OdorMapComparer thus provides a tool for quantitative, statistical analyses and comparisons of fMRI odor maps in a fashion that is integrated with the overall odor mapping techniques.

YPED: a web-accessible database system for protein expression analysis.

We have developed an integrated web-accessible software system called the Yale Protein Expression Database (YPED) to address the need for storage, retrieval, and integrated analysis of large amounts of data from high throughput proteomic technologies. YPED is an open source system which integrates gel analysis results with protein identifications from DIGE experiments. The system associates the DIGE gel spots and image, analyzed with DeCyder, with mass spectrometric protein identifications from selected gel spots. Following in gel trypsin digestion, proteins in spots of interest are analyzed using MALDI-TOF/TOF on an AB 4700 or, more recently, on an AB 4800 with protein identifications performed by Mascot in conjunction with the AB GPS Explorer system. In addition to DIGE, YPED currently handles protein identifications from MudPIT, iTRAQ, and ICAT experiments. Sample descriptions are compatible with the evolving MIAPE standards. Tandem MS/MS results from MudPIT, and ICAT analyses are validated with the Trans-Proteomic Pipeline and then stored in the database for viewing and linking to the identified proteins. Researchers can view, subset, and download their data through a secure Web interface that includes a table containing proteins identified, a sample summary, the sample description, and a clickable gel image for DIGE samples. Tools are available to facilitate sample comparison and the viewing of phosphoproteins. A summary report with PANTHER Classification System annotations is also available to aid in biological interpretation of the results. The source code is open-source and is available from http://yped.med.yale.edu/yped_dist.

Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis.

Genome-wide association studies can help identify multi-gene contributions to disease. As the number of high-density genomic markers tested increases, however, so does the number of loci associated with disease by chance. Performing a brute-force test for the interaction of four or more high-density genomic loci is unfeasible given the current computational limitations. Heuristics must be employed to limit the number of statistical tests performed. In this paper we explore the use of biological domain knowledge to supplement statistical analysis and data mining methods to identify genes and pathways associated with disease. We describe Pathway/SNP, a software application designed to help evaluate the association between pathways and disease. Pathway/SNP integrates domain knowledge--SNP, gene and pathway annotation from multiple sources--with statistical and data mining algorithms into a tool that can be used to explore the etiology of complex diseases.

SenseLab: new developments in disseminating neuroscience information.

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.

NeuroExtract: facilitating neuroscience-oriented retrieval from broadly-focused bioscience databases using text-based query mediation.

This paper describes NeuroExtract, a pilot system which facilitates the integrated retrieval of Internet-based information relevant to the neurosciences. The approach involved extracting descriptive metadata from the sources using domain-specific queries; retrieving, processing, and organizing the data into structured text files; searching the data files using text-based queries; and, providing the results in a Web page along with descriptions to entries and URL links to the original sources. NeuroExtract has been implemented for three bioscience resources, SWISSPROT, GEO, and PDB, which provide neuroscience-related information as sub-topics. We discuss several issues that arose in the course of NeuroExtract's implementation. This project is a first step in exploring how this general approach might be used, in conjunction with other query mediation approaches, to facilitate the integration of many Internet-accessible resources relevant to the neurosciences.

Evidence for association between multiple complement pathway genes and AMD.

In this paper we explore the use of biological knowledge to supplement statistical analysis in identifying genes associated with disease. It has been previously found that the 402H variant in complement factor H (CFH) is associated with risk for developing age related macular degeneration (AMD). By focusing on the single nucleotide polymorphisms (SNPs) in the complement pathway, we were able to use the genotype data from a recently published AMD genome wide association study to identify two additional genes, C7 and MBL2, as potentially associated with subtypes of AMD. Two SNPs situated in introns of C7 and MBL2 could help differentiate between two forms of AMD: wet (more severe form of AMD) and dry (milder form of AMD). We identified a C7 haplotype associated with protection against developing wet AMD among individuals with homozygous CFH risk allele 402H (p-value 0.001 for wet AMD versus dry AMD, odds ratio (OR) 0.16, OR 95% CI 0.05-0.49) as well as among individuals with at least one CFH risk allele (p-value 0.007 for wet AMD versus dry AMD, OR 0.35, OR 95% CI 0.16-0.77). The fact that the statistical scores for the C7 and MBL2 SNPs were significant (low false discovery rate) at the pathway level, but not significant at the genome level suggests that focusing at the pathway level can be beneficial for identifying SNP signals that would be lost at the genome-wide level.