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Power system’s operational flexibility represents its ability to respond to predicted or unexpected changes in generation and demand. Traditional policy and planning models usually do not consider the technical operating constraints directly responsible for its operational flexibility. Nevertheless, this capability becomes increasingly important with the integration of significant shares of renewables. Incorporating flexibility can significantly change optimal generation strategies, lower the total system costs and improve policy impact estimates. The goal of this research is to prove that, for computational efficiency reasons, it is useful to cluster some of the original units into larger ones. This process reduces the number of continuous and binary variables and can, in certain conditions, be performed without significant loss of accuracy. To this purpose the Dispa-SET unit commitment and power dispatch model which focuses on balancing and flexibility problems in the European grids has been applied to the Western Balkans power system. Various clustering methods are implemented and tested on the same dataset and validated against the “No clustering” formulation. “Per unit” aggregates very small or very flexible units into larger ones with averaged characteristics, ”Per typical unit” considers one typical power plant per technology; and ”Per technology” additionally simplifies the mathematical formulation by completely neglecting units flexibility capabilities. The results have shown that the difference between disaggregated and clustered approaches remains acceptable and for certain accuracy metrics falls within a 2% margin. This is especially true in case of highly interconnected regional systems with relatively high shares of hydro energy. ispartof: Applied Energy vol:252 status: published

This paper deals with intermodal transportation of goods from Europe to the United States. The main objective is the development of a mathematical model called Transatlantic multi-mode container routing problem. It provides a tool for network optimization in transportation science. The developed model enables users to determine the optimal collaborative routing in a network system that achieves the minimization of costs for a company that ships a single commodity from multiple origins to multiple destinations. The model consists of network of maritime shipping lines between the ports and then networks of road and railway transportation on both sides of the Atlantic Ocean. In order to develop the model it was necessary to make assumptions, define sets, parameters, decision variables, constraints and objective function. Branch and cut algorithm used by GAMS modelling software provides the solution of the model. Short analysis of trends and statistics of road and rail transportation as complements to maritime transportation on both continents is also presented.

Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems.Clinical condition-syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions.Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created.The consensus definitions have not yet been validated through implementation.The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.

Objective The nationwide health information network (NHIN) has been proposed to securely link community and state health information exchange (HIE) entities to create a national, interoperable network for sharing healthcare data in the USA. This paper describes a framework for evaluating the costs, effort, and value of nationwide data exchange as the NHIN moves toward a production state. The paper further presents the results of an initial assessment of the framework by those engaged in HIE activities. Design Using a literature review and knowledge gained from active NHIN technology and policy development, the authors constructed a framework for evaluating the costs, effort, and value of data exchange between an HIE entity and the NHIN. Measurement An online survey was used to assess the perceived usefulness of the metrics in the framework among HIE professionals and researchers. Results The framework is organized into five broad categories: implementation; technology; policy; data; and value. Each category enumerates a variety of measures and measure types. Survey respondents generally indicated the framework contained useful measures for current and future use in HIE and NHIN evaluation. Answers varied slightly based on a respondent's participation in active development of NHIN components. Conclusion The proposed framework supports efforts to measure the costs, effort, and value associated with nationwide data exchange. Collecting longitudinal data along the NHIN's path to production should help with the development of an evidence base that will drive adoption, create value, and stimulate further investment in nationwide data exchange.

Objective To study the effects of using a population-based informatics system for osteoporosis screening and treatment in women aged 65 years or older. Design A population-based informatics system (PRECARES: PREventive CAre REminder System) was implemented to meet the needs of the workflow of a primary care practice. Patients treated in either of two sections of a primary care internal medicine department were selected for the intervention, and patients of a comparable third section served as the control group. PRECARES identified women in the intervention group who were due for osteoporosis screening on the basis of age and who had no record of previous screening in our clinical system. If these eligible patients did not have an upcoming outpatient appointment, appointment secretaries sent a letter requesting that they call to make an appointment for a dual-energy x-ray absorptiometry scan. Measurements At baseline and 3 months after the letters were sent, a database was used to determine the rate of osteoporosis screening in the intervention and control groups. Results A total of 689 patients in the intervention group were sent the letter. Three months after the letters were sent, the rate of osteoporosis screening was 76.4% (2409/3152) in the intervention group vs 69% (928/1344) in the control group (p

Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases (“data marts”) can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software.

Transfer learning is the ability to apply previously learned knowledge to new problems or domains. In qualitative reasoning, model formulation is the process of moving from the unruly, broad set of concepts used in everyday life to a concise, formal vocabulary of abstractions, assumptions, causal relationships, and models that support problem-solving. Approaching transfer learning from a model formulation perspective, we found that analogy with examples can be used to learn how to solve AP Physics style problems. We call this process analogical model formulation and implement it in the Companion cognitive architecture. A Companion begins with some basic mathematical skills, a broad common sense ontology, and some qualitative mechanics, but no equations. The Companion uses worked solutions, explanations of example problems at the level of detail appearing in textbooks, to learn what equations are relevant, how to use them, and the assumptions necessary to solve physics problems. We present an experiment, conducted by the Educational Testing Service, demonstrating that analogical model formulation enables a Companion to learn to solve AP Physics style problems. Across six different variations of relationships between base and target problems, or transfer levels, a Companion exhibited a 63% improvement in initial performance. While already a significant result, we describe an in-depth analysis of this experiment to pinpoint the causes of failures. Interestingly, the sources of failures were primarily due to errors in the externally generated problem and worked solution representations as well as some domain-specific problem-solving strategies, not analogical model formulation. To verify this, we describe a second experiment which was performed after fixing these problems. In this second experiment, a Companion achieved a 95.8% improvement in initial performance due to transfer, which is nearly perfect. We know of no other problem-solving experiments which demonstrate performance of analogical learning over systematic variations of relationships between problems at this scale.

Objectives: Chronic disease prevalence and burden is growing, as is the need for applicable large community-based clinical trials of potential interventions. To support the development of clinical trial management systems for such trials, a community-based primary care research information model is needed. We analyzed the requirements of trials in this environment, and constructed an information model to drive development of systems supporting trial design, execution, and analysis. We anticipate that this model will contribute to a deeper understanding of all the dimensions of clinical research and that it will be integrated with other clinical research modeling efforts, such as the Biomedical Research Integrated Domain Group (BRIDG) model, to complement and expand on current domain models. Design: We used unified modeling language modeling to develop use cases, activity diagrams, and a class (object) model to capture components of research in this setting. The initial primary care research object model (PCROM) scope was the performance of a randomized clinical trial (RCT). It was validated by domain experts worldwide, and underwent a detailed comparison with the BRIDG clinical research reference model. Results: We present a class diagram and associated definitions that capture the components of a primary care RCT. Forty-five percent of PCROM objects were mapped to BRIDG, 37% differed in class and/or subclass assignment, and 18% did not map. Conclusion: The PCROM represents an important link between existing research reference models and the real-world design and implementation of systems for managing practice-based primary care clinical trials. Although the high degree of correspondence between PCROM and existing research reference models provides evidence for validity and comprehensiveness, existing models require object extensions and modifications to serve primary care research.

Objective: All electronic health (e-health) interventions require validation as health information technologies, ideally in randomized controlled trial settings. However, as with other types of complex interventions involving various active components and multiple targets, health informatics trials often experience problems of design, methodology, or analysis that can influence the results and acceptance of the research. Our objective was to review selected key methodologic issues in conducting and reporting randomized controlled trials in health informatics, provide examples from a recent study, and present practical recommendations. Design: For illustration, we use the COMPETE III study, a large randomized controlled clinical trial investigating the impact of a shared decision-support system on the quality of vascular disease management in Ontario, Canada. Results: We describe a set of methodologic, logistic, and statistical issues that should be considered when planning and implementing trials of complex e-health interventions, and provide practical recommendations for health informatics trialists. Conclusions: Our recommendations emphasize validity and pragmatic considerations and would be useful for health informaticians conducting or evaluating e-health studies.

Objectives: To develop a security infrastructure to support controlled and secure access to data and analytical resources in a biomedical research Grid environment, while facilitating resource sharing among collaborators. Design: A Grid security infrastructure, called Grid Authentication and Authorization with Reliably Distributed Services (GAARDS), is developed as a key architecture component of the NCI-funded cancer Biomedical Informatics Grid (caBIG™). The GAARDS is designed to support in a distributed environment 1) efficient provisioning and federation of user identities and credentials; 2) group-based access control support with which resource providers can enforce policies based on community accepted groups and local groups; and 3) management of a trust fabric so that policies can be enforced based on required levels of assurance. Measurements: GAARDS is implemented as a suite of Grid services and administrative tools. It provides three core services: Dorian for management and federation of user identities, Grid Trust Service for maintaining and provisioning a federated trust fabric within the Grid environment, and Grid Grouper for enforcing authorization policies based on both local and Grid-level groups. Results: The GAARDS infrastructure is available as a stand-alone system and as a component of the caGrid infrastructure. More information about GAARDS can be accessed at . Conclusions: GAARDS provides a comprehensive system to address the security challenges associated with environments in which resources may be located at different sites, requests to access the resources may cross institutional boundaries, and user credentials are created, managed, revoked dynamically in a de-centralized manner.

Objectives: As patient care becomes more collaborative in nature, there is a need for information technology that supports interdisciplinary practices of care. This study developed and performed usability testing of a standalone computer-based information tool to support the interdisciplinary practice of palliative severe pain management (SPM). Design: A grounded theory-participatory design (GT-PD) approach was used with three distinct palliative data sources to obtain and understand user requirements for SPM practice and how a computer-based information tool could be designed to support those requirements. Results: The GT-PD concepts and categories provided a rich perspective of palliative SPM and the process and information support required for different SPM tasks. A conceptual framework consisting of an ontology and a set of three problem-solving methods was developed to reconcile the requirements of different interdisciplinary team members. The conceptual framework was then implemented as a prototype computer-based information tool that has different modes of use to support both day-to-day case management and education of palliative SPM. Usability testing of the computer tool was performed, and the tool tested favorably in a laboratory setting. Conclusion: An interdisciplinary computer-based information tool can be developed to support the different work practices and information needs of interdisciplinary team members, but explicit requirements must be sought from all prospective users of such a tool. Qualitative methods such as the hybrid GT-PD approach used in this research are particularly helpful for articulating computer tool design requirements.

Objective: To develop software infrastructure that will provide support for discovery, characterization, integrated access, and management of diverse and disparate collections of information sources, analysis methods, and applications in biomedical research. Design: An enterprise Grid software infrastructure, called caGrid version 1.0 (caGrid 1.0), has been developed as the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG™) program. It is designed to support a wide range of use cases in basic, translational, and clinical research, including 1) discovery, 2) integrated and large-scale data analysis, and 3) coordinated study. Measurements: The caGrid is built as a Grid software infrastructure and leverages Grid computing technologies and the Web Services Resource Framework standards. It provides a set of core services, toolkits for the development and deployment of new community provided services, and application programming interfaces for building client applications. Results: The caGrid 1.0 was released to the caBIG community in December 2006. It is built on open source components and caGrid source code is publicly and freely available under a liberal open source license. The core software, associated tools, and documentation can be downloaded from the following URL: https://cabig.nci.nih. gov/workspaces/Architecture/caGrid. Conclusions: While caGrid 1.0 is designed to address use cases in cancer research, the requirements associated with discovery, analysis and integration of large scale data, and coordinated studies are common in other biomedical fields. In this respect, caGrid 1.0 is the realization of a framework that can benefit the entire biomedical community.

Objective: The European INFOBIOMED Network of Excellence[1][1] recognized that a successful education program in biomedical informatics should include not only traditional teaching activities in the basic sciences but also the development of skills for working in multidisciplinary teams. Design: A carefully developed 3-year training program for biomedical informatics students addressed these educational aspects through the following four activities: (1) an internet course database containing an overview of all Medical Informatics and BioInformatics courses, (2) a BioMedical Informatics Summer School, (3) a mobility program based on a ‘brokerage service’ which published demands and offers, including funding for research exchange projects, and (4) training challenges aimed at the development of multi-disciplinary skills. Measurements: This paper focuses on experiences gained in the development of novel educational activities addressing work in multidisciplinary teams. The training challenges described here were evaluated by asking participants to fill out forms with Likert scale based questions. For the mobility program a needs assessment was carried out. Results: The mobility program supported 20 exchanges which fostered new BMI research, resulted in a number of peer-reviewed publications and demonstrated the feasibility of this multidisciplinary BMI approach within the European Union. Students unanimously indicated that the training challenge experience had contributed to their understanding and appreciation of multidisciplinary teamwork. Conclusion: The training activities undertaken in INFOBIOMED have contributed to a multi-disciplinary BMI approach. It is our hope that this work might provide an impetus for training efforts in Europe, and yield a new generation of biomedical informaticians. [1]: #ref-1

Objectives: The Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research. Design: The BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams. Measurements: The BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG™; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases. Results: The first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG™ program. Conclusions: The BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG™, and CDISC and HL7 are using the BRIDG model to support standards development.

Objective: This paper presents a model designed to enable rapid detection and assessment of biological threats that may require swift intervention by the international public health community. Design: We utilized Strauss' grounded theory to develop an expanded model of social disruption due to biological events based on retrospective and prospective case studies. We then applied this model to the temporal domain and propose a heuristic staging model, the Wilson-Collmann Scale for assessing biological event evolution. Measurements: We retrospectively and manually examined hard copy archival local media reports in the native vernacular for three biological events associated with substantial social disruption. The model was then tested prospectively through media harvesting based on keywords corresponding to the model parameters. Results: Our heuristic staging model provides valuable information about the features of a biological event that can be used to determine the level of concern warranted, such as whether the pathogen in question is responding to established public health disease control measures, including the use of antimicrobials or vaccines; whether the public health and medical infrastructure of the country involved is adequate to mount the necessary response; whether the country's officials are providing an appropriate level of information to international public health authorities; and whether the event poses a international threat. The approach is applicable for monitoring open-source (public-domain) media for indications and warnings of such events, and specifically for markers of the social disruption that commonly occur as these events unfold. These indications and warnings can then be used as the basis for staging the biological threat in the same manner that the United States National Weather Service currently uses storm warning models (such as the Saffir-Simpson Hurricane Scale) to detect and assess threatening weather conditions. Conclusion: Used as a complement to current epidemiological surveillance methods, our approach could aid global public health officials and national political leaders in responding to biological threats of international public health significance.

Objective: Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks. Design: This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans. Measurements: We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage. Results: As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure. Conclusion: HealthMap is a useful free and open resource employing text-processing algorithms to identif y important disease outbreak information through a user-friendly interface. J Am Med Inform Assoc. 2008;15:150 -157. DOI 10.1197/jamia.M2544 .

Objective: A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. Design: A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster. Measurements: Several possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as "important" those articles selected by the Surgical Oncology Society. Results: Our results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. Conclusions: The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.

Objective: To specify and identify disease and patient care processes represented by temporal patterns in clinical events and observations, and retrieve patient populations containing those patterns from clinical data repositories, in support of clinical research, outcomes studies, and quality assurance. Design: A data processing method called PROTEMPA (Process-oriented Temporal Analysis) was developed for defining and detecting clinically relevant temporal and mathematical patterns in retrospective data. PROTEMPA provides for portability across data sources, "pluggable" data processing environments, and the creation of libraries of pattern definitions and data processing algorithms. Measurements: A proof-of-concept implementation of PROTEMPA in Java was evaluated against standard SQL queries for its ability to identify patients from a large clinical data repository who show the features of HELLP syndrome, and categorize those patients by disease severity and progression based on time sequence characteristics in their clinical laboratory test results. Results were verified by manual case review. Results: The proof-of-concept implementation was more accurate than SQL in identifying patients with HELLP and correctly assigned severity and disease progression categories, which was not possible using SQL only. Conclusions: PROTEMPA supports the identification and categorization of patients with complex disease based on the characteristics of and relationships between time sequences in multiple data types. Identifying patient populations who share these types of patterns may be useful when patient features of interest do not have standard codes, are poorly-expressed in coding schemes, may be inaccurately or incompletely coded, or are not represented explicitly as data values.

This paper deals with the measurement of current distribution across the rails of rail launchers. The proposed approach is based on inductive method by magnetically coupling an array of search loops with the rail-armature circuit. The procedure was already successfully employed for current distribution measurements among the brushes of multi-brush armatures and its extension to the current distribution reconstruction across the rails is here discussed. The method sensitivity and the measurement model uncertainty in a simple case study are firstly analyzed based on an analytical model formulation, to obtain information about the applicability of this approach. Then, a finite element analysis and experimental results are presented, discussing various rails-armature configurations.

Constructing an appropriate model is a crucial step in performing the reasoning required to successfully answer a query about the behavior of a physical situation. In the compositional modeling approach of Falkenhainer and Forbus (1991), a system is provided with a library of composable pieces of knowledge about the physical world called model fragments. The model construction problem involves selecting appropriate model fragments to describe the situation. Model construction can be considered either for static analysis of a single state or for simulation of dynamic behavior over a sequence of states. The latter is significantly more difficult than the former since one must select model fragments without knowing exactly what will happen in the future states.The model construction problem in general can advantageously be formulated as a problem of reasoning about relevance of knowledge that is available to the system using a general framework for reasoning about relevance described by Levy (1993) and Levy and Sagiv (1993). In this paper, we present a model formulation procedure based on that framework for selecting model fragments efficiently for the case of simulation. For such an algorithm to be useful, the generated model must be adequate for answering the given query and, at the same time, as simple as possible. We define formally the concepts of adequacy and simplicity and show that the algorithm in fact generates an adequate and simplest model.

Many of the aerospace components are characterized by having pocket-shaped thin-walled structures. During milling, the varying dynamics of the workpiece due to the change of thickness affects the final part quality. Available dynamic models rely on computationally prohibitive techniques that limit their use in the aerospace industry. In this paper, a new dynamic model was developed to predict the vibrations of thin-walled pocket structures during milling while taking into account the continuous change of thickness. The model is based on representing the change of thickness of a pocket-structure with a two-directional multispan plate. For the model formulation, the Rayleigh-Ritz method is used together with multispan beam models for the trial functions in both the x- and y-directions. An extensive finite element (FE) validation of the developed model was performed for different aspect ratios of rectangular and nonrectangular pockets and various change of thickness schemes. It was shown that the proposed model can accurately capture the dynamic effect of the change of thickness with prediction errors of less than 5 and at least 20 times reduction in the computation time. Experimental validation of the models was performed through the machining of thin-walled components. The predictions of the developed models were found to be in excellent agreement with the measured dynamic responses. © 2011 American Society of Mechanical Engineers.

Objective To formulate a model for translating manual infection control surveillance methods to automated, algorithmic approaches. Design We propose a model for creating electronic surveillance algorithms by translating existing manual surveillance practices into automated electronic methods. Our model suggests that three dimensions of expert knowledge be consulted: clinical, surveillance, and informatics. Once collected, knowledge should be applied through a process of conceptualization, synthesis, programming, and testing. Results We applied our framework to central vascular catheter associated bloodstream infection surveillance, a major healthcare performance outcome measure. We found that despite major barriers such as differences in availability of structured data, in types of databases used and in semantic representation of clinical terms, bloodstream infection detection algorithms could be deployed at four very diverse medical centers. Conclusions We present a framework that translates existing practice—manual infection detection—to an automated process for surveillance. Our experience details barriers and solutions discovered during development of electronic surveillance for central vascular catheter associated bloodstream infections at four hospitals in a variety of data environments. Moving electronic surveillance to the next level—availability at a majority of acute care hospitals nationwide—would be hastened by the incorporation of necessary data elements, vocabularies and standards into commercially available electronic health records.

Objective Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines. Design The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects. Measurements The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction. Results Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results. Conclusion A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.

Mayo Clinic's Enterprise Data Trust is a collection of data from patient care, education, research, and administrative transactional systems, organized to support information retrieval, business intelligence, and high-level decision making. Structurally it is a top-down, subject-oriented, integrated, time-variant, and non-volatile collection of data in support of Mayo Clinic's analytic and decision-making processes. It is an interconnected piece of Mayo Clinic's Enterprise Information Management initiative, which also includes Data Governance, Enterprise Data Modeling, the Enterprise Vocabulary System, and Metadata Management. These resources enable unprecedented organization of enterprise information about patient, genomic, and research data. While facile access for cohort definition or aggregate retrieval is supported, a high level of security, retrieval audit, and user authentication ensures privacy, confidentiality, and respect for the trust imparted by our patients for the respectful use of information about their conditions.

Objective: Innovative approaches can strengthen patient-caregiver-clinician information exchange and more effectively address the physical and psychosocial challenges of advanced disease. This study reports initial findings from implementation of the Clinician Report (CR)—a patient and caregiver status report tool accessible by the oncology clinic team. Design: The CR tracks and communicates essential information from cancer patients and caregivers to the oncology team. The CR conveys patient symptoms, emotional strain, and key concerns. Measurements: Authors used a model developed to explain acceptance, implementation, and sustainability of Interactive Health Communication Systems (IHCS) to evaluate implementation of the new CR system. The study carried out qualitative analyses of interviews with clinicians regarding their experience utilizing the Clinician Report. Results: Primary CR benefits included enhancement of patients' clinic visit experiences, greater caregiver involvement, and facilitation of earlier interventions. Challenges included CR functional issues, users' desire for greater depth of information, user privacy concerns, and limited patient use. These findings are discussed using parameters of the implementation model. Limitations of this study include its small clinician sample size, which represented only a portion of existing organizational settings in which CR systems might be implemented. Conclusion: Though in its early implementation stages, the CR demonstrates the potential to positively impact care delivery in the cancer clinic setting, particularly by facilitating earlier interventions and improving patient- caregiver-clinician communication both during and between clinic visits. J Am Med Inform Assoc. 2008;15:679 - 686. DOI 10.1197/jamia.M2532.

Objective To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse. Design We propose a design in which unstructured text and coded data are fused into a single model called structured narrative . Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. Validation The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. Discussion The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. Conclusion Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

Objective: Existing neuroanatomical ontologies, databases and information systems, such as the Foundational Model of Anatomy (FMA), represent outgoing connections from brain structures, but cannot represent the "internal wiring" of structures and as such, cannot distinguish between different independent connections from the same structure. Thus, a fundamental aspect of Neuroanatomy, the functional pathways and functional systems of the brain such as the pupillary light reflex system, is not adequately represented. This article identifies underlying anatomical objects which are the source of independent connections (collections of neurons) and uses these as basic building blocks to construct a model of functional neuroanatomy and its functional pathways. Design: The basic representational elements of the model are unnamed groups of neurons or groups of neuron segments. These groups, their relations to each other, and the relations to the objects of macroscopic anatomy are defined. The resulting model can be incorporated into the FMA. Measurements: The capabilities of the presented model are compared to the FMA and the Brain Architecture Management System (BAMS). Results: Internal wiring as well as functional pathways can correctly be represented and tracked. Conclusion: This model bridges the gap between representations of single neurons and their parts on the one hand and representations of spatial brain structures and areas on the other hand. It is capable of drawing correct inferences on pathways in a nervous system. The object and relation definitions are related to the Open Biomedical Ontology effort and its relation ontology, so that this model can be further developed into an ontology of neuronal functional systems.

Objective Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative. Design We developed and evaluated a unified model of aberrancy-detection algorithms and a software infrastructure that uses this model to conduct studies to evaluate detection performance. We used a task-analytic methodology to identify the common features and meaningful distinctions among different algorithms and to provide an extensible framework for gathering evidence about the relative performance of these algorithms using a number of evaluation metrics. We implemented our model as part of a modular software infrastructure (Biological Space-Time Outbreak Reasoning Module, or BioSTORM) that allows configuration, deployment, and evaluation of aberrancy-detection algorithms in a systematic manner. Measurement We assessed the ability of our model to encode the commonly used EARS algorithms and the ability of the BioSTORM software to reproduce an existing evaluation study of these algorithms. Results Using our unified model of aberrancy-detection algorithms, we successfully encoded the EARS algorithms, deployed these algorithms using BioSTORM, and were able to reproduce and extend previously published evaluation results. Conclusion The validated model of aberrancy-detection algorithms and its software implementation will enable principled comparison of algorithms, synthesis of results from evaluation studies, and identification of surveillance algorithms for use in specific public health settings.

Objective: This paper presents a multiple perspectives model of clinical information system implementation, the Contextual Implementation Model (CIM) . Although other implementation models have been developed, few are grounded in data and others fail to take adequate account of the clinical environment and users' requirements. Design: The CIM arose from qualitative data collected from four clinical units in two large Australian teaching hospitals. The aim of the study was to explore physicians' test management work practices associated with the compulsory use of a hospital-wide, mandatory computerized provider order entry (CPOE) system.1 The dataset consisted of non-participatory observations of physicians using CPOE (n=55 sessions) and interviews with health professionals (n=28) about test management work practices. Data were analyzed by two researchers independently using an iterative grounded approach. Results: A core underlying theme of ‘contextual differences’ emerged which explained physicians' use of the CPOE system in the sites. The CIM focuses attention on diversity at three contextual levels: the organizational level; the clinical or departmental level, and the individual level. Within each of these levels there are dimensions for consideration (for example, organizational culture, leadership and diverse ways of working) which affect physicians' attitudes to, and use of, CPOE. Conclusion: The CIM provides a contextual differences perspective which can be used to facilitate the implementation of clinical information systems. Developing a clinical information system implementation model serves as a framework to guide future implementations to ensure their safe and efficient use and also improve the likelihood of uptake by physicians.

Objective: Few research designs look at the deep structure of complex social systems. We report the design and implementation of a multimethod evaluation model to assess the impact of computerized order entry systems on both the technical and social systems within a health care organization. Design: We designed a multimethod evaluation model informed by sociotechnical theory and an appreciation of the nature of wicked problems. We mobilized this model to assess the impact of an electronic medication management system via a three-year program of research at a major academic hospital. Measurements: Model components include measurements relating to three dimensions of system impact: safety and quality, organizational culture, and work and communication patterns. Results: Application of the evaluation model required the development and testing of purpose-built measurement tools such as software to collect multidimensional work measurement data. The model applied established research methods including medication error audits and social network analysis. Design features of these tools and techniques are described, along with the practical challenges of their implementation. The distinctiveness of doing research within a unique paradigm of complex systems, explicating the wickedness and the dimensionality of sociotechnical theory, is articulated. Conclusion: Designing an effective evaluation model requires a deep understanding of the nature and complexity of the problems that information technology interventions in health care are trying to address. Adopting a sociotechnical perspective for model generation improves our ability to develop evaluation models that are adaptive and sensitive to the characteristics of wicked problems and provides a strong theoretical basis from which to analyze and interpret findings. © 2007 J Am Med Inform Assoc.

Objective: This study sought to define a scalable architecture to support the National Health Information Network (NHIN). This architecture must concurrently support a wide range of public health, research, and clinical care activities. Study Design: The architecture fulfils five desiderata: (1) adopt a distributed approach to data storage to protect privacy, (2) enable strong institutional autonomy to engender participation, (3) provide oversight and transparency to ensure patient trust, (4) allow variable levels of access according to investigator needs and institutional policies, (5) define a self-scaling architecture that encourages voluntary regional collaborations that coalesce to form a nationwide network. Results: Our model has been validated by a large-scale, multi-institution study involving seven medical centers for cancer research. It is the basis of one of four open architectures developed under funding from the Office of the National Coordinator of Health Information Technology, fulfilling the biosurveillance use case defined by the American Health Information Community. The model supports broad applicability for regional and national clinical information exchanges. Conclusions: This model shows the feasibility of an architecture wherein the requirements of care providers, investigators, and public health authorities are served by a distributed model that grants autonomy, protects privacy, and promotes participation.

Objective Inefficiencies in clinical trial data collection cause delays, increase costs, and may reduce clinician participation in medical research. In this proof-of-concept study, we examine the feasibility of using point-of-care data capture for both the medical record and clinical research in the setting of a working clinical trial. We hypothesized that by doing so, we could increase reuse of patient data, eliminate redundant data entry, and minimize disruption to clinic workflow. Design We developed and used a point-of-care electronic data capture system to record data during patient visits. The standards-based system was used for clinical research and to generate the clinic note for the medical record. The system worked in parallel with data collection procedures already in place for an ongoing multicenter clinical trial. Our system was iteratively designed after analyzing case report forms and clinic notes, and observing clinic workflow patterns and business procedures. Existing data standards from CDISC and HL7 were used for database insertion and clinical document exchange. Results Our system was successfully integrated into the clinic environment and used in two live test cases without disrupting existing workflow. Analyses performed during system design yielded detailed information on practical issues affecting implementation of systems that automatically extract, store, and reuse healthcare data. Conclusion Although subject to the limitations of a small feasibility study, our study demonstrates that electronic patient data can be reused for prospective multicenter clinical research and patient care, and demonstrates a need for further development of therapeutic area standards that can facilitate researcher use of healthcare data.

Organizations today face intense competitive pressure to maximize their performance over time as customer expectations increase. The literature suggests that there is a need to develop a tool or techniques that practitioners in the industry can apply to help in support system design for operating assets as a long-term service solution in order to maintain optimized performance and obtain the best return on investments. This technique should integrate the industry domain knowledge to create and deliver support solutions for in service assets. There is no generic architecture for system support engineering available for practitioners to use. This will lead to the following question “Can industrial practitioners have a generic architecture to simplify the development of such a system”? If the answer is yes; then how possible is it? Therefore, this paper will present an empirical approach to model the formulation for a system support engineering (SSE) generic framework. The development of the SSE Framework combines both literature analysis and empirical work. Also, it will provide a possible answer to the research question and suggest further recommendations and opportunities for future research.

Decision support systems developers face difficulties in formulating decision models that represent decision makers’ subjective problem-solving knowledge. Decision makers expect that formulated models are easy to understand and that those models incorporate their subjectivity in decision making. Although it is understood that knowledge discovery techniques can be used to support the processes of model formulation, there is little empirical evidence on the effectiveness of such approaches. In this paper, we investigate the effectiveness of a knowledge discovery technique in automated modeling of subjective classification problem-solving knowledge. We identify two measures of agreement and similarity to evaluate the effectiveness of a neuro-fuzzy classifier in this task. Findings from the experiment show that the neuro-fuzzy classifier can effectively model subjective classification problem-solving knowledge from a small set of examples, and it can represent the formulated models as a concise and intuitive set of fuzzy rules.