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Clinical & Medical informatics glossary & taxonomy
Evolving Terminology for Emerging Technologies
Comments? Questions? Revisions?  Mary Chitty 
mchitty@healthtech.com
Last revised October 17, 2014
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Related informatics glossaries include Algorithms & data analysis,   Bioinformatics,   Cheminformatics  Drug discovery informatics   Information management & interpretationIT infrastructure  Regulatory  Research 
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Clinical trials  Molecular Medicine  Molecular Diagnostics  Therapeutic areas Cancer  Cardiovascular CNS & Neurology Immunology Infectious Diseases Inflammation   

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adaptive clinical trials:  The demand to accelerate drug development while reducing health care costs encourages companies to seek innovative, more efficacious solutions. There is a realization that adaptive designs for clinical trials have the potential to accelerate every phase of drug development. An adaptive design means that the dosing, eligibility criteria, sample size, or treatment settings can be adjusted during the course of the trial as evidence accumulates. The final goal of adaptive clinical trials is to bring technological advances to patients in the most efficient manner. Nevertheless, an adaptive clinical trial should not become an end in itself. An array of questions should be addressed during the planning stage of a trial. Is an adaptive design appropriate for a particular study? What are the regulatory requirements for adaptive design? How to build in and then manage an adaption? How will it impact data analysis. Adaptive Clinical Trials 

A process for improving the efficiency of clinical trials based on interim analyses of clinical data, potentially leading to reductions in overall sample size, shorter project duration, improved quality of results, and reduced costs. Tufts Center for the Study of Drug Development, Glossary of terms, 2007  http://csdd.tufts.edu/InfoServices/Glossary.asp 

The pharma industry is gradually coming to realize that the classically structured clinical trial does not offer enough flexibility to make use of continuously emerging knowledge that is generated as the trial progresses. Unacceptable levels of attrition in the clinical stage of development are driving profound changes in the architecture, design, and analysis of clinical trials. The majority of respondents to our survey said that reduction in patient numbers, less exposure to study drug, and drops in overall trial duration were key points in favor of adaptive designs; however, a majority also had specific concerns with adaptive trials―concerns that involved methodological, logistical, and regulatory uncertainties:   Herman Mucke, Adaptive Clinical Trials: Innovations in clinical trial design, management and analysis, Insight Pharma Reports

Adaptive Clinical trials webcast Jerald Schindler, VP Biostatistics and Research Decision Sciences Late Stage Clinical Statistics, Merck Research Laboratories http://www.bio-itworld.com/webcasts/lsw/schindler.aspx  See related pivotal clinical trials.  

Bayesian clinical trials:  In recent years, there has been an explosion in predictive technologies to help researchers select only the most promising candidates for clinical development. The need for such tools is driven by the disastrous economic consequences of late-stage failures, which account for over 60% of all drug terminations. Insight Pharma Reports, Bayesian Forecasting of Phase III Outcomes: The Next Wave in Predictive Tools,  2007

Bayesian network: Wikipedia  http://en.wikipedia.org/wiki/Bayesian_network 
Bayesian networks: 
A quick intro, Karen Sachs, Biomedical Computation Review, Summer 2005 http://www.biomedicalcomputationreview.org/1/1/9.pdf A computational analysis approach, machine learning tool. 

Bayesian statistics: Bayesian statistics is an approach for learning from evidence as it accumulates. In clinical trials, traditional (frequentist) statistical methods may use information from previous studies only at the design stage. Then, at the data analysis stage, the information from these studies is considered as a complement to, but not part of, the formal analysis. In contrast, the Bayesian approach uses Bayes’ Theorem to formally combine prior information with current information on a quantity of interest. The Bayesian idea is to consider the prior information and the trial results as part of a continual data stream, in which inferences are being updated each time new data become available.

Throughout this document we will use the terms “prior distribution”, “prior probabilities”, or simply “prior” to refer to the mathematical entity (the probability distribution) that is used in these Bayesian calculations. The term “prior information” refers to the set of all information that may be used to construct the prior distribution. FDA, CBER, CDHR Guidance for the use of Bayesian Statistics in Medical Device Clinical Trials Feb. 2010 http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm071072.htm#1

biomathematics: The application of mathematics to problems in biology and medicine. An essential tool in fields such as population genetics, cellular neurobiology, comparative genetics, biomedical imaging, pharmacokinetics, and epidemiology. It plays an increasingly vital role in the effort to understand diseases and disorders, and to improve therapies.  Collection Development Manual, National Library of Medicine, US 2004  http://www.nlm.nih.gov/tsd/acquisitions/cdm/subjects14.html Related terms: bioinformatics, computational biology

biomedical informatics:  "the field that is concerned with the optimal use of information, often aided by the use of technology and people, to improve individual health, health care, public health, and biomedical research"1. William Hersh 2010 , Biomedical Informatics, Ohio State University http://medicine.osu.edu/bmi/Pages/index.aspx
http://en.wikipedia.org/wiki/Biomedical_informatics
    Related terms: medical informatics

biomedical ontologies: Open Biomedical Ontologies is an umbrella web address for well-structured controlled vocabularies for shared use across different biological and medical domains.  http://obo.sourceforge.net/          Biomedical Ontologies: Overview   

biomedical ontology recommender web services: http://www.bioontology.org/wiki/index.php/Ontology_Recommender_Web_service 
Mark A Musen, Clement Jonquet and Nigam H. Shah, Journal of Biomedical Semantics 2010, 1(Suppl 1):S1doi:10.1186/2041-1480-1-S1-S1 http://www.jbiomedsem.com/content/1/S1/S1  

BIRN Biomedical Informatics Research Network: A national initiative to advance biomedical research through data sharing and online collaboration. .. focuses directly on the biomedical research community’s unique, data-intensive sharing and analysis needs, which are particularly evident in fields such as biomedical imaging and genetics.   a user-driven, software-based framework for research teams to share significant quantities of data – rapidly, securely and privately – across geographic distance and/or incompatible computing systems. ... We also offer data-sharing software tools specific to biomedical research, best practices references, expert advice and other resources.  http://www.nbirn.net/

case definition : Optimal case definition is important in epidemiological research, but can be problematic when no satisfactory gold standard is available. In particular, difficulties arise where the pathology underlying a disorder is unknown or cannot be reliably diagnosed. This problem can be overcome if diagnoses are viewed not necessarily as labels for disease processes, but more generally as a useful method for classifying people for the purpose of preventing or managing illness. With this perspective, the value of a case definition lies in its practical utility in distinguishing groups of people whose illnesses share the same causes or determinants of outcome (including response to treatment). A corollary is that the best-case definition for a disorder may vary according to the purpose for which it is being applied.  Assessing case definitions in the absence of a diagnostic gold standard, D Coggon, C Martyn, KT Palmer, B Evanoff, Intl J Epidemiol 34 (4): 949-952 

CDISC Clinical Data Interchange Standards Consortium: An open, multidisciplinary, non- profit organization committed to the development of industry standards to support the electronic acquisition, exchange, submission and archiving of clinical trials data and metadata for medical and biopharmaceutical product development. http://www.cdisc.org/  

clinical bioinformatics: Different from other informatics, clinical bioinformatics should focus more on clinical informatics, including patient complaints, history, therapies, clinical symptoms and signs, physician's examinations, biochemical analyses, imaging profiles, pathologies and other measurements. It was emphasized that the simultaneous evaluation of clinical and basic research could improve medical care, care provision data, and data exploitation methods in disease therapy and algorithms for the analysis of such heterogeneous data sets.  Clinical bioinformatics: a new emerging science Xiangdong Wang and Lance Liotta, Journal of Clinical Bioinformatics 1:1 2011 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3097094/

The concept of clinical bioinformatics was recently defined in the Open Editorial of Journal of Clinical Bioinformatics (JCBi) by Drs Wang and Liotta. Concept of clinical bioinformatics was proposed to be “a new emerging science combining clinical informatics, bioinformatics, medical informatics, information technology, mathematics, and omics science together”. In order to emphasize their understanding, they further defined the term "Clinical bioinformatics" as "clinical application of bioinformatics-associated sciences and technologies to understand molecular mechanisms and potential therapies for human diseases", a new and important concept for the development of disease-specific biomarkers, mechanism-oriented understanding and individualized medicine.  About the Journal of Clinical Bioinformatics http://www.jclinbioinformatics.com/about

clinical data repositories, shared: Agency for Healthcare Research & Quality http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5554&mode=2&holderDisplayURL=http://prodportallb.ahrq.gov:7087/publis

clinical forecasting: It is clear that late-stage clinical failures account for a large proportion of the expenses. This can be as a result of both the large out-of-pocket investments in Phase III clinical trials and because unsuccessful trials tie up capital resources during their conduct, and potentially also for the time spent during any attempted recovery following regulatory rejection. So, there is an interest in strategies that could halt, as early as possible, the development of drugs that eventually fail. Clinical forecasting in drug development, Asher D. Schachter and Marco F. Ramoni, Nature Reviews Drug Discovery 6, 107-108 (February 2007) | doi:10.1038/nrd2246 http://www.nature.com/nrd/journal/v6/n2/full/nrd2246.html  Narrower term: Bayesian clinical forecasting, Bayesian clinical trials  

clinical informatics: Integration of clinical workflow and business strategies of any healthcare organization will spell success for the providers of the future. Efficient exchange of data and information is essential for this merger, and information technology is the tool with which to accomplish the consolidation. Clinical Informatics is the practice evolving from this need in healthcare. HIMSS Clinical informatics http://www.himss.org/ASP/topics_clinicalInformatics.asp 

is the application of informatics and information technology to deliver healthcare services. It is also referred to as applied clinical informatics and operational informatics. AMIA considers informatics when used for healthcare delivery to be essentially the same regardless of the health professional group involved (whether dentist, pharmacist, physician, nurse, or other health professional). Clinical Informatics is concerned with information use in health care by clinicians. Clinical informatics includes a wide range of topics ranging from clinical decision support to visual images (e.g. radiological, pathological, dermatological, ophthalmological, etc); from clinical documentation to provider order entry systems; and from system design to system implementation and adoption issues. AMIA Clinical informatics http://www.amia.org/applications-informatics/clinical-informatics

The application of informatics approaches to the clinical- evaluation phase of drug development. These approaches can include clinical- trial simulations to improve trial design and patient selection, as well as electronic capturing and storing of clinical data and protocols. The goal is to reduce expenses and time to market   
Clinical Informatics News
http://www.clinicalinformaticsnews.com/

clinical ontologies:  
Open Clinical Ontologies http://www.openclinical.org/ontologies.html  support from Cancer Research UK   

clinical trial data model: Phase Forward Submits XML-based Clinical Trial Data Model to Worldwide Standards Organizations, 1999 http://www.oasis-open.org/cover/phaseCDISC19990621.html

Clinical Trial Design Task Force: The Clinical Trial Design Task Force (CTD-TF) of the National Cancer Institute (NCI) Investigational Drug Steering Committee (IDSC) has published a series of discussion papers on phase II trial design in Clinical Cancer Research. The IDSC has developed formal recommendations about aspects of phase II trial design that are the subject of frequent debate, such as endpoints (response versus progression-free survival), randomization (single-arm designs versus randomization), inclusion of biomarkers, biomarker-based patient enrichment strategies, and statistical design (e.g., two-stage designs versus multiple-group adaptive designs). Although these recommendations in general encourage the use of progression-free survival as the primary endpoint, randomization, inclusion of biomarkers, and incorporation of newer designs, we acknowledge that objective response as an endpoint and single-arm designs remain relevant in certain situations. The design of any clinical trial should always be carefully evaluated and justified based on characteristic specific to the situation. The Design of Phase II Clinical Trials Testing Cancer Therapeutics: Consensus Recommendations from the Clinica l Trial Design Task Force of the National Cancer Institute Investigational Drug Steering Committee. Seymour L, et. al,   Clin Cancer Res. 2010 Mar 9. [Epub ahead of print] http://www.ncbi.nlm.nih.gov/pubmed/20215557

 
clinical trial simulation:
A relatively new effort to devise in silico simulations of human physiology and genetic variation to help identify which compounds will eventually fail in the drug development process.   

clinomics: The application of oncogenomic research. Daniel von Hoff, Univ. of Arizona "All hands on deck at dawn" Nature Genetics 27 (4): 347-349, April 2001 

cluster analysis: The clustering, or grouping, of  large data sets (e.g., chemical and/ or pharmacological data sets) on the basis of similarity criteria for appropriately scaled  variables that represent the data of interest. Similarity criteria (distance based, associative, correlative, probabilistic) among the several clusters facilitate the recognition of patterns and reveal otherwise hidden structures (Rouvray, 1990; Willett, 1987, 1991). IUPAC Computational

A set of statistical methods used to group variables or observations into strongly inter- related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health- related phenomenon with well- defined distribution patterns in relation to time or place or both. MeSH, 1990

Has been used in medicine to create taxonomies of diseases and diagnosis and in archaeology to establish taxonomies of stone tools and funereal objects. Cluster analysis can be supervised, unsupervised or partially supervised  Related terms: clustering analysis, dendogram, heat map, pattern recognition, profile chart. Narrower terms: hierarchical clustering, k-means clustering, self- organizing maps

clustering analysis: This is a general type of analysis that involves grouping gene or array expression profiles based on similarity. Clustering is a major subfield within the broad world of numerical analysis, and many specific clustering methods are known. 

cohort studies large scale: In the past 20 years, birth cohort studies to assess the risks to developing children from harmful chemicals in air, water and food have been undertaken in many countries. These birth cohort studies usually started during pregnancy and followed children through adolescence or beyond. Even the largest of these birth cohort studies, however, were not big enough to study rare outcomes such as childhood cancer or sudden infant death syndrome. To increase the sample size, investigators working with these older cohort studies are now making an effort to pool their data. Their efforts are hampered by the fact that the older studies did not usually agree upon disease outcome definitions, time periods of measurement, or methods for measuring biomarkers and chemical contaminants in air, water and food. This makes pooling data extremely difficult. 

To avoid such problems in the next generation of large-scale birth cohort studies, it is worthwhile for investigators from various countries to invest time up front to agree on how to assess disease outcomes, measure biomarkers, and measure environmental exposures. Pooling of data, should that be desirable, with then be much more straightforward.  Coordination of new large scale birth cohort studies, Children’s Environmental Health, WHO 2014 http://www.who.int/ceh/cohorts/en/ 

communications standards: It is clear that shared understanding of the basic data elements within pharmacogenomics is a critical building block upon which to build an information infrastructure. Methods for communicating these data are therefore equally as important. The two main areas that require progress are the definition of shared syntax (how information is structured in a data file) and semantics (how the information should be interpreted by others). Russ Altman "Challenges for Biomedical Informatics and Pharmacogenomics, Stanford Medical Informatics, 2002 http://www.annualreviews.org/doi/abs/10.1146/annurev.pharmtox.42.082401.140850  Related terms: Information management & interpretation  controlled vocabularies, syntax, semantics

comparative data mining: Algorithms & data management Useful for clinical trial meta-analyses  

comparative effectiveness research CER: is the conduct and synthesis of systematic research comparing different interventions and strategies to prevent, diagnose, treat and monitor health conditions. The purpose of this research is to inform patients, providers, and decision-makers, responding to their expressed needs, about which interventions are most effective for which patients under specific circumstances. To provide this information, comparative effectiveness research must assess a comprehensive array of health-related outcomes for diverse patient populations… [Internet.] Federal Coordinating Council for Comparative Effectiveness Research [cited 28 July 2010].  Comparative Effectiveness Research, Health Services Research Information Central, National Library of Medicine, NIH http://www.nlm.nih.gov/hsrinfo/cer.html

complex: It has become common to use complicated and complex interchangeably … The essence of ‘complicated’ is hard to figure out. ..Complex, on the other hand is a term reserved for systems that display properties that are not predictable from a complete description of their components, and that are generally considered to be qualitatively different from the sum of their parts. [Editorial, "Complicated is not complex" Nature Biotechnology 17: 511 June 1999] Would it be fair to say that Mendelian genetics is linear, while genomics and polygenic diseases/traits are nonlinear?

According to the Oxford English Dictionary one of the meanings of complicated is complex, though it also means not easy to unravel or separate. Both complex and complicated are contrasted with simple. Whatever the original senses of these two words, the above distinction seems a useful one now.  Related term: complexity; Narrower terms: biocomplexity, complex diseases, complex genomes; complex phenotypes, complex traits  

complex diseases: Diseases characterized by risk to relatives of an affected individual which is greater than the incidence of the disorder in the population. [NHLBI] 

The research activities in the Department of Genetics and Complex Diseases and its pre and postdoctoral training programs concentrate on the molecular, cellular, and organismic adaptations and responses to nutrients, toxins, and radiation stress and explore the genetic basis controlling the heterogeneity of these interactions in experimental systems. The integrated interdisciplinary opportunities also aim to apply this knowledge to human populations to understand, prevent, and treat complex human diseases. Dept of Genetics and Complex Diseases, Harvard School of Public Health  2011 http://www.hsph.harvard.edu/departments/genetics-and-complex-diseases/ 

How are complex diseases related to polygenic diseases?  Related terms: SNPs & genetic variations;   Omes & omics phenome, phenomics

computational pharmacology: Pharmacogenomics

computational physiology: The International Union of Physiological Sciences (IUPS) Physiome Project is an internationally collaborative open- source project to provide a public domain framework for computational physiology, including the development of modeling standards, computational tools and web-accessible databases of models of structure and function at all spatial scales [1,2,3]. It aims to develop an infrastructure for linking models of biological structure and function across multiple levels of spatial organization and multiple time scales. The levels of biological organisation, from genes to the whole organism, includes gene regulatory networks, protein- protein and protein- ligand interactions, protein pathways, integrative cell function, tissue and whole heart structure- function relations. The whole heart models include the spatial distribution of protein expression. Keynote: Peter J. Hunter, Univ of Auckland, International Society of Computational Biology, Detroit, MI, 2005 http://www.iscb.org/ismb2005/keynotes.html 

computational therapeutics:  An emerging biomedical field. It is concerned with the development of techniques for using software to collect, manipulate and link biological and medical data from diverse sources.   It is also concerned with the use of such information in simulation models to make predictions or therapeutically relevant discoveries or advances. (Referred to by some as in silico pharmacology) C. Anthony Hunt Lab, Biosystems at Univ. of California, San Francisco, http://biosystems.ucsf.edu/   

CONSORT  Consolidated Standards of Reporting Trials,    http://www.consort-statement.org/    consumer health informatics: is the field devoted to informatics from multiple consumer or patient views. These include patient-focused informatics, health literacy and consumer education. The focus is on information structures and processes that empower consumers to manage their own health--for example health information literacy, consumer-friendly language, personal health records, and Internet-based strategies and resources. The shift in this view of informatics analyses consumers' needs for information; studies and implements methods for making information accessible to consumers; and models and integrates consumers' preferences into health information systems. Consumer informatics stands at the crossroads of other disciplines, such as nursing informatics, public health, health promotion, health education, library science, and communication science. AMIA Consumer Health Informatics  http://www.amia.org/applications-informatics/consumer-health-informatics

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digital health: Digital health is not a new concept. Seth Frank, writing 13 years ago, penned “Digital Health Care—The convergence of health care and the Internet” in The Journal of Ambulatory Care Management. Today, technology is rapidly transforming healthcare. Eric Topol’s The Creative Destruction of Medicine enumerates how these digital technologies, social networking, mobile connectivity and bandwidth, increasing computing power and the data universe will converge with wireless sensors, genomics, imaging, and health information systems to creatively destroy medicine as we know it. He refers to this as digital medicine, or the digitization of human beings.  At Rock Health, we support entrepreneurs working in the space Topol describes—at the intersection of healthcare and technology; and not solely in medicine, but across healthcare, including wellness and administration. As part of our research, we track companies and catalog venture funding in the digital health space.  Defining digital health and choosing which companies to include is complex, so here is some transparency as to how we catalog deals: What Digital Health is Is [and Isn’t) Rock Health http://rockhealth.com/2013/04/what-digital-health-is-and-isnt/ 

Digital health is the convergence of the digital and genetics revolutions with health and healthcare. As we are seeing and experiencing, digital health is empowering us to better track, manage, and improve our own and our family’s health. It’s also helping to reduce inefficiencies in healthcare delivery, improve access, reduce costs, increase quality, and make medicine more personalized and precise.The essential elements of the digital health revolution include wireless devices, hardware sensors and software sensing technologies, microprocessors and integrated circuits, the Internet, social networking, mobile and body area networks, health information technology, genomics, and personal genetic information. The lexicon of Digital Health is extensive and includes all or elements of mHealth (aka Mobile Health), Wireless Health, Health 2.0, eHealth, Health IT, Big Data, Health Data, Cloud Computing, e-Patients, Quantified Self and Self-tracking, Wearable Computing, Gamification, Telehealth & Telemedicine, Precision and Personalized Medicine, plus Connected Health. Story of Digital Health, Paul Sonnier, 2014 http://storyofdigitalhealth.com/definition/

drug utilization: The utilization of drugs as reported in individual hospital studies, FDA studies, marketing, or consumption, etc. This includes drug stockpiling, and patient drug profiles. MeSH, 1973

drug utilization review:  Formal programs for assessing drug prescription against some standard. Drug utilization review may consider clinical appropriateness, cost effectiveness, and, in some cases, outcomes. Review is usually retrospective, but some analysis may be done before drugs are dispensed (as in computer systems which advise physicians when prescriptions are entered). Drug utilization review is mandated for Medicaid programs beginning in 1993. MeSH, 1994  Related terms: ATC/DDD, EPhMRA, PBIRG

eCommon Technical Document: The Electronic Common Technical Document (eCTD) is CDER/CBER’s standard format for electronic regulatory submissions. The FDA would like to work closely with people who plan to provide a submission using the eCTD specifications  FDA, Common Technical Document http://www.fda.gov/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/ElectronicSubmissions/ucm153574.htm 

Electronic Common Technical Document, Wikipedia http://en.wikipedia.org/wiki/Electronic_Common_Technical_Document

EDC electronic data capture: Wikipedia http://en.wikipedia.org/wiki/Electronic_Data_Capture 

effectiveness research: See comparative effectiveness research, see also under outcomes research

e-health: http://en.wikipedia.org/wiki/EHealth 

Electronic Health Records EHRs:  Electronic Health Records (EHRs) are safe and confidential records that your doctor, other health care provider, medical office staff, or a hospital keeps on a computer about your health care or treatments. If your providers use electronic health records, they can join a network to securely share your records with each other. EHRs can help lower the chances of medical errors, eliminate duplicate tests, and may improve your overall quality of care.  EHRs can help your providers have the same up-to-date information about your conditions, treatments, tests, and prescriptions. Electronic Health Records, Medicare.gov http://www.medicare.gov/manage-your-health/electronic-health-records/electronic-health-records.html

The healthcare environment will be profoundly changed by the convergence of technology, and ready access to updated patient information. The program will cover the use of combinatorial device technology to integrate healthcare systems, and the novel connectivity of global electronic medical record efforts. Clinical management of disease will be addressed through the use of handheld and point-of-care devices. The value of real time patient information to the clinical management team and the pharmaceutical researcher will be leveraged while addressing the ethical and legal implications. 

Emerging lessons: Electronic Health Records, Agency for Health Care Research and Quality, 
http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5562&mode=2&holderDisplayURL=http://prodportallb.ahrq.

electronic prescribing: Agency for Healthcare Research & Quality, 
http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5554&mode=2&holderDisplayURL=http://prodportal

electronic records -- FDA:  http://www.fda.gov/ora/compliance_ref/part11/  

Electronic standards for the transfer of regulatory information, Glossary of Abbreviations and Terms ICH m2 2005 http://estri.org/recommendations/Glossary.pdf   

evidence based medicine:  Evidence-based medicine is defined in the Roundtable’s charter to mean that: to the greatest extent possible, the decisions that shape the health and health care of Americans– by patients, providers, payers and policymakers alike—will be grounded on a reliable evidence base, will account appropriately for individual variation in patient needs, and will support the generation of new insights on clinical effectiveness.  Institute of Medicine, Round Table on Evidence Based Medicine  http://www.iom.edu/CMS/AboutIOM/28189.aspx  

genomic epidemiology: An emerging discipline involving population studies and microarray/ expression studies.  Related terms: environmental factors, public health; molecular epidemiology, human genome epidemiology, phenotypic prevention

GIS Geographic Information Systems and: GIS link data and geography digitally for the purpose of making maps. This technology often provides a useful way to reveal spatial and temporal relationships among data.  Researchers, public health professionals, policy makers, and others use GIS to better understand geographic relationships that affect health outcomes, public health risks, disease transmission, access to health care, and other public health concerns.  GIS and Public Health, National Center for Health Statistics, 2007 http://www.cdc.gov/nchs/gis.htm   

health informatics: "the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning." Procter, R. Dr. (Editor, Health Informatics Journal, Edinburgh, United Kingdom). Definition of health informatics [Internet]. Message to: Virginia Van Horne (Content Manager, HSR Information Central, Bethesda, MD). 2009 Aug 16 [cited 2009 Sept 21]. National Library of Medicine http://www.nlm.nih.gov/hsrinfo/informatics.html   Related terms: biomedical informatics, healthcare informatics, medical informatics     Wikipedia http://en.wikipedia.org/wiki/Health_informatics  

health IT tools: Agency for Healthcare Research & Quality, http://healthit.ahrq.gov/portal/server.pt?open=512&objID=919&parentname=CommunityPage&parentid=9&mode=2&in_h

health record: Historically, the definition of a legal medical or health record seemed straightforward. The contents of the paper chart formed the provider of care’s legal business record. Patients had limited interest in or access to the information contained in their record. With the advent of various electronic media, the Internet, and the consumer’s enhanced role in compiling health records, the definition of the legal health record became more complex. The need remains to ensure information is accessible for its ultimate purposes regardless of the technologies employed or users involved. The definition of the legal health record (LHR) must therefore be reassessed in light of such new technologies, users, and uses.  Amatayakul, Margret et al. "Definition of the Health Record for Legal Purposes (AHIMA Practice Brief)." Journal of AHIMA 72, no.9 (2001): 88A-H. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_009223.hcsp?dDocName=bok1_009223 

Healthcare Informatics: For years, the [IBM] Almaden Research Center has been at the vanguard of research into healthcare informatics. Our contributions to the Nationwide Health Information Network (NHIN), developed under contract to the U.S. Department of Health and Human Services, have paved the way for many other health IT research advances. The NHIN prototype pioneered new standards-based technology for secure access and real time sharing and exchanging of health care data among all concerned parties—patients, physicians, hospitals, laboratories, and pharmacies. Other groundbreaking work in fields as diverse as privacy protection and interoperability has cemented IBM's status as a forerunner in the field. Today, we continue to push forward in a number of new areas, including Information Integration, Multimodal Analytics, Healthcare Standards, and Public Health.  http://www.almaden.ibm.com/cs/disciplines/hc/ 

Wikipedia http://en.wikipedia.org/wiki/Healthcare_informatics 

HL7: Health Level Seven is one of several American National Standards Institute (ANSI) -accredited Standards Developing Organizations (SDOs) operating in the healthcare arena. … Health Level Seven’s domain is clinical and administrative data. "Level Seven" refers to the highest level of the International Organization for Standardization (ISO) communications model for Open Systems Interconnection (OSI) - the application level. http://www.hl7.org/  

human factors: Human factors/usability engineering focuses on the interactions between people and devices. The critical element in these interactions is the device user interface ... Human factors/usability engineering is used to design the machine-human (device-user) interface. The user interface includes all components with which users interact while preparing the device for use (e.g., unpacking, set up, calibration), using the device, or performing maintenance (e.g., cleaning, replacing a battery, making repairs). FDA, CDHR Medical Devices General Human Factors Information and Resources http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/HumanFactors/ucm124829.htm

immunoinformatics: or computational immunology, is an emerging area that provides fundamental methodologies in the study of immunomics, that is, immune-related genomics and proteomics. The integration of immunoinformatics with systems biology approaches may lead to a better understanding of immune-related diseases at various systems levels. Such methods can contribute to translational studies that bring scientific discoveries of the immune system into better clinical practice. One of the most intensely studied areas of the immune system is immune epitopes. Epitopes are important for disease understanding, host-pathogen interaction analyses, antimicrobial target discovery, and vaccine design. The information about genetic diversity of the immune system may help define patient subgroups for individualized vaccine or drug development. Cellular pathways and host immune-pathogen interactions have a crucial impact on disease pathogenesis and immunogen design. Epigenetic studies may help understand how environmental changes influence complex immune diseases such as allergy. High-throughput technologies enable the measurements and catalogs of genes, proteins, interactions, and behavior. Such perception may contribute to the understanding of the interaction network among humans, vaccines, and drugs, to enable new insights of diseases and therapeutic responses. The integration of immunomics information may ultimately lead to the development of optimized vaccines and drugs tailored to personalized prevention and treatment. Methods Mol Biol. 2010;662:203-20. doi: 10.1007/978-1-60761-800-3_10. Immunoinformatics and systems biology methods for personalized medicine. Yan Q. http://www.ncbi.nlm.nih.gov/pubmed/20824473    See also Therapeutic areas: immunogenomics

in silico clinical trials: See computer trials simulations  

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laboratory informatics: the specialized application of information technology aimed at optimizing laboratory operations. It is a collection of informatics tools utilized within laboratory environments to collect, store, process, analyze, report, and archive data and information from the laboratory and supporting processes. Laboratory informatics includes the integration of systems, the electronic delivery of results to customers, and the supporting systems including training and policies. Examples of laboratory informatics include: Laboratory Information Management Systems (LIMS), Electronic Laboratory Notebooks (ELNs), Chromatography Data Systems (CDS), and Scientific Data Management Systems (SDMS). ASTM E1578 Standard Guide for Laboratory Informatics 2013  http://www.astm.org/Standards/E1578.htm    Wikipedia http://en.wikipedia.org/wiki/Laboratory_informatics   Related term?: Drug discovery & development  LIMS  

LOINC Logical Observation Identifiers Names and Codes: The purpose of the LOINC database is to facilitate the exchange and pooling of results, such as blood hemoglobin, serum potassium, or vital signs, for clinical care, outcomes management, and research. Regenstrief Institute Inc. Germany http://www.loinc.org/  

meaningful use: is using certified electronic health record (EHR) technology to: Improve quality, safety, efficiency, and reduce health disparities, Engage patients and family, Improve care coordination, and population and public health, Maintain privacy and security of patient health information, Ultimately, it is hoped that the meaningful use compliance will result in: Better clinical outcomes, Improved population health outcomes, Increased transparency and efficiency, Empowered individuals, More robust research data on health systems HealthIT.gov HER Incentives and Certification http://www.healthit.gov/providers-professionals/meaningful-use-definition-objectives

medical bioinformatics: Linking clinical data to patient gene profiling. Covers haplotyping, genotyping, population genomics, gene expression profiling, particularly for use in diagnosis, prognosis and therapeutic stratification of patients.  Related terms: BiomarkersExpression,   Microarrays and protein chips

medical informatics: The field of information science concerned with the analysis and dissemination of medical data through the application of computers to various aspects of health care and medicine. MeSH, 1987

An emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information. The end objective of biomedical informatics is the coalescing of data, knowledge, and the tools necessary to apply that data and knowledge in the decision- making process, at the time and place that a decision needs to be made. The focus on the structures and algorithms necessary to manipulate the information separates Biomedical Informatics from other medical disciplines where information content is the focus. Medical Informatics FAQ, 1999 http://www.faqs.org/faqs/medical-informatics-faq/  

MedDRA Medical Dictionary for Regulatory Activities: Developed by the International Conference on Harmonisation (ICH) and is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) acting as trustee for the ICH steering committee. http://www.meddramsso.com/   

meta-analysis: The use of statistical techniques in a systematic review to integrate the results of included studies. Sometimes misused as a synonym for systematic reviews, where the review includes a meta- analysis.  Cochrane Collaboration "Glossary of terms in the Cochrane Collaboration, 2014  http://www.cochrane.org/glossary/5   

A quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc., with application chiefly in the areas of research and medicine. MeSH, 1989

meta-regression:  Meta-regression is an extension to subgroup analyses that allows the effect of continuous, as well as categorical, characteristics to be investigated, and in principle allows the effects of multiple factors to be investigated simultaneously (although this is rarely possible due to inadequate numbers of studies) (Thompson 2002). Meta-regression should generally not be considered when there are fewer than ten studies in a meta-analysis. General Methods for Cochran Reviews, Cochran Collaborative http://handbook.cochrane.org/chapter_9/9_6_4_meta_regression.htm

mHealth: Mobile Health Wikipedia http://en.wikipedia.org/wiki/MHealth  Uses mobile devices.

National Center for Integrative Biomedical Informatics: http://portal.ncibi.org/gateway/   One of seven National Centers for Biomedical Computing (NCBC) within the NIH Roadmap. The NCBC program is focused on building a universal computing infrastructure designed to speed progress in biomedical research.  

National Electronics Clinical Trials and Research (NECTAR): An enriched pipeline of biomedical discoveries, an infrastructure to facilitate the translation of these discoveries from the laboratory to the clinic, and a robust force of clinical investigators will make it possible to test new therapeutic and preventive strategies in larger numbers of patients far sooner than currently possible. These large studies are often best conducted through networks of investigators who are equipped with tools to facilitate collaboration and information sharing. Because of the vast number of therapies, diagnostics, and treatments that must be evaluated through clinical trials, many clinical research networks operate simultaneously, but independently, of each other. As a result, researchers must sometimes duplicate data that already exists because they are unaware of the data or do not have access to the data. Standardizing data reporting would enable seamless data- and sample-sharing across studies. By enhancing the efficiency of clinical research networks through informatics and other technologies, researchers will be better able to broaden the scope of their research. Reduced duplication of studies will leave more time and funds to address additional research questions.  NECTAR, NIH Common Fund http://commonfund.nih.gov/clinicalresearch/overview-networks.aspx

neuroimaging: Neuroimaging informatics tools and resources http://www.nitrc.org/ 

neuroinformatics:  Neuroinformatics publishes original articles and reviews in the new field of neuroinformatics. The emphasis is on data structure and software tools related to analysis, modeling, integration, and sharing, in all areas of neuroscience research. In particular, we invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanied by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies. The journal also publishes independent "tests and evaluations" of available neuroscience databases and software tools and fosters a commitment to the principles of tool and data sharing.  Aims and Scope, Neuroinformatics, Humana Press

Neuroinformatics: The Human Brain Project, National Institute of Mental Health, NIH, US, 2008 http://wwwapps.nimh.nih.gov/research-funding/scientific-meetings/recurring-meetings/human-brain-project/index.shtml 
Neuroinformatics Site,  http://www.neuroinf.org/
NIH Blueprint for Neuroscience Research http://neuroscienceblueprint.nih.gov/neuroscience_resources/neuroinformatics.htm 

Nuclear Morphometric Descriptors NMD: Today's imaging technology uses sophisticated hardware platforms coupled with powerful and user-friendly software packages that are commercially available as complete image analysis systems. There are many different mathematically derived nuclear morphometric descriptors (NMD's) (i.e. texture features) that can be calculated by these image analysis systems, but for the most part, these NMD's quantify nuclear size, shape, DNA content (ploidy), and chromatin organization (i.e. texture, both Markovian and non-Markovian) parameters. We have utilized commercially available image analysis systems and the NMD's calculated by these systems to create a mathematical solution, termed quantitative nuclear grade (QNG), for making clinical, diagnostic, and prognostic outcome predictions in both prostate and bladder cancer. "Quantitative nuclear grade (QNG): a new image analysis- based biomarker of clinically relevant nuclear structure alterations" Veltri RW, Partin AW, Miller MC, Journal of Cell Biochemistry Suppl 35: 151-157, 2000 

Office of the National Coordinator for Health Information Technology (ONC): is at the forefront of the administration’s health IT efforts and is a resource to the entire health system to support the adoption of health information technology and the promotion of nationwide health information exchange to improve health care. . http://www.aids.gov/federal-resources/federal-agencies/hhs/office-of-the-national-coordinator-for-health-information-technology/

outcomes research:  The terms "outcomes research" and "effectiveness research" have been used to refer to a wide range of studies, and there is no single definition for either that has gained widespread acceptance. As these fields evolved, it appears that "outcomes research" emerged from a new emphasis on measuring a greater variety of impacts on patients and patient care (function, quality of life, satisfaction, readmissions, costs, etc). The term "effectiveness research" was used to emphasize the contrast with efficacy studies, and highlighted the goal of learning how medical interventions affected real patients in "typical" practice settings (OTA, 1994). Effectiveness studies sought to understand the impact of health care on patients with diverse characteristics, rather than highly homogeneous study populations. While the terms may have different initial roots, there does not appear to be much value in distinguishing these activities, and the field is generally referred to as OER. .. OER evaluates the impact of health care (including discrete interventions such as particular drugs, medical devices, and procedures as well as broader programmatic or system interventions) on the health outcomes of patients and populations. OER may include evaluation of economic impacts linked to health outcomes, such as cost- effectiveness and cost utility. OER emphasizes health problem- (or disease-) oriented evaluations of care delivered in general, real- world settings; multidisciplinary teams; and a wide range of outcomes, including mortality, morbidity, functional status, mental well- being, and other aspects of health-related quality of life.   Outcome of Outcomes Research at AHCPR: Final Report, Agency for Health Care Policy and Research, AHCPR Publication No. 99-R044  http://www.ahrq.gov/clinic/out2res/outcom1.htm  Related term: comparative effectiveness research


pathology informatics:
involves collecting, examining, reporting, and storing large complex sets of data derived from tests performed in clinical laboratories, anatomic pathology laboratories, or research laboratories in order to improve patient care and enhance our understanding of disease-related processes. Pathology Informaticians seek to continuously improve existing laboratory information technology and enhance the value of existing laboratory test data, and develop computational algorithms and models aimed at deriving clinical value from new data sources. Association for Pathology Informatics About API http://www.pathologyinformatics.org/content/about-api

patient engagement: Surgeon General C Everett Koop once said “Drugs don’t work in patients who don’t take them.” I’ll offer a corollary of my own: Patients who aren’t engaged don’t comply with therapies or report complications.  Enabling Patient Engagement and Healthcare Innovation, FDA Testimony Healthcare Innovation DDMAC Public Hearings on Internet & Social Media #FDASM Zen Chu, 2009 http://www.slideshare.net/MedicalVentures/zen-chu-healthcare-innovation-fda-testimony-ddmac-public-hearings-on-internet-social-media 

patient reported outcomes: the PROMIS (Patient-Reported Outcomes Measurement Information System) initiative is developing new ways to measure patient-reported outcomes (PROs), such as pain, fatigue, physical functioning, emotional distress, and social role participation that have a major impact on quality-of-life across a variety of chronic diseases. Clinical measures of health outcomes, such as x-rays and lab tests, may have minimal relevance to the day-to-day functioning of patients with chronic diseases. Often, the best way patients can judge the effectiveness of treatments is by changes in symptoms. The goal of PROMIS is to improve the reporting and quantification of changes in PROs. PROMIS Patient Reported Outcomes, NIH Common Fund  http://commonfund.nih.gov/promis/overview.aspx 

Personal Health Record PHR: The PHR is a tool that you can use to collect, track and share past and current information about your health or the health of someone in your care. Sometimes this information can save you the money and inconvenience of repeating routine medical tests. Even when routine procedures do need to be repeated, your PHR can give medical care providers more insight into your personal health story. AHIMA, MyPHR - http://www.myphr.com/startaphr/what_is_a_phr.aspx#sthash.gyUVxDmV.dpuf

personalized medicine: how to leverage technology to optimize speed, quality and cost of clinical trials.  Themes covered include best practices in data collection and analysis, systems integration, improving trial monitoring, recruiting and engaging patient communities using Web 2.0 technologies, adaptive clinical trials, pharmacovigilance, and utilization of EHR data for drug development.  Track 8: eHealth and HIT Solutions for Personalized Medicine Bio-IT World Conference & Expo April 12-14, 2011 • Boston, MA Program | Register | Download Brochure 

pharmacoepidemiology:
The study of the utilization and effects of drugs in large numbers of people. To accomplish this study, pharmacoepidemiology borrows from both pharmacology and epidemiology. Thus, pharmacoepidemiology can be called a bridge science spanning both pharmacology and epidemiology. About Pharmacoepidemiology, International Society Pharmacoepidemiology http://www.pharmacoepi.org/about/index.cfm 

Pharmacoepidemiology focuses heavily on questions of pharmacodynamics, concentrating on clinical patient outcomes and on therapeutics (i.e., appropriate use of drugs), and to a lesser extent on pharmacokinetics.  Penn Medicine University of Pennsylvania  http://www.cceb.upenn.edu/research/pharmaco.php 

phase II clinical trials design: The optimal design of phase II studies continues to be the subject of vigorous debate, especially studies of newer molecularly targeted agents. The observations that many new therapeutics "fail" in definitive phase III studies, coupled with the numbers of new agents to be tested as well as the increasing costs and complexity of clinical trials, further emphasize the critical importance of robust and efficient phase II design. The Design of Phase II Clinical Trials Testing Cancer Therapeutics: Consensus Recommendations from the Clinica l Trial Design Task Force of the National Cancer Institute Investigational Drug Steering Committee. Seymour L, et. al,   Clin Cancer Res. 2010 Mar 9. [Epub ahead of print] http://www.ncbi.nlm.nih.gov/pubmed/20215557
Related term: Clinical trials Phase II 

pivotal clinical trials: The intermediate-sized clinical trials supported through this RFA are a pivotal decision point in the NCI chemoprevention drug development program. The consensus view of a Working Group from the NCI and the FDA acknowledges that "the interim analysis of a validated surrogate endpoint of cancer incidence may facilitate the timely and cost-effective marketing of efficacious drugs (Kelloff et al., Cancer Epidemiol. Biomark. Prev. 4: 1-10, 1995)." Thus, the efficacy and safety data from these studies potentially supports FDA marketing approval (NDA applications) for chemoprevention indications, and certainly facilitates decisions regarding the most appropriate recommendations for subsequent large, community-based efficacy studies. Pivotal clinical trials for chemoprevention clinical development  National Cancer Institute, NIH RFA: CA-98-001 1997 http://grants.nih.gov/grants/guide/rfa-files/rfa-ca-98-001.html  Related term: adaptive clinical trials  

Population Health Management and Quality Improvement April 28-29, 2014 • Medical Informatics World Boston, MA Program | Register | Download Brochure

predictive biomedicine: Predictive Biomedicine (PB) will cover the development and use of informatics and computational tools to manage, present, and interpret experimental data as well as those used in modeling and bio-simulation. Companies and thought-leaders; products and technologies; relevant research programs and their results will be covered. From data management challenges to systems biology initiatives, PB will report on industry’s efforts to reduce dependence on trial and error and adopt more data-driven predictive methods to drive drug discovery and development and even health care delivery. John Russell, editor Predictive Biomedicine eNewsletter, Sept 2008 http://www.bio-itworld.com/issues/2008/sept/russell-transcript-predictive-biomedicine.html?terms=GNS%3A+Building+a+SNPs-to-Outcomes+Engine 

predictive genomics: Wayne D. Hall1,+, Katherine I. Morley1,2 and Jayne C. Lucke1, The prediction of disease risk in genomic medicine: Scientific prospects and implications for public policy and ethics EMBO reports vol. 5 | Suppl 1 | pp S22-S26 | 2004 DOI: 10.1038/sj.embor.7400224  See also Pharmacogenomics  predictive pharmacogenomics 

prognosis: The probable outcome or course of a disease; the chance of  recovery. [ORD]

Not a major emphasis in clinical medicine today. Nicholas Christakis' Death Foretold is an eloquent book about the delicate balance between medical reality and optimism, and how seldom this is discussed in either classrooms or hospital rooms today.

public health informatics: The systematic application of information and computer sciences to public health practice, research, and learning. It is the discipline that integrates public health with information technology. The development of this field and dissemination of informatics knowledge and expertise to public health professionals is the key to unlocking the potential of information systems to improve the health of the nation. www.nlm.nih.gov/pubs/cbm/phi2001.html  MeSH 2003

is the application of informatics in areas of public health, including surveillance, prevention, preparedness, and health promotion. Public health informatics and the related population informatics, work on information and technology issues from the perspective of groups of individuals. Public health is extremely broad and can even touch on the environment, work and living places and more. Generally, AMIA focuses on those aspects of public health that enable the development and use of interoperable information systems for public health functions such as biosurveillance, outbreak management, electronic laboratory reporting and prevention. AMIA Public Health Informatics http://www.amia.org/applications-informatics/public-health-informatics

real world data: Data collected in real-world health environments (as opposed to controlled clinical trials) provides valuable insights into the usage and comparative effectiveness of various treatments. Real-world data can include: De-identified health data collected as a by-product of patient care  Collection and analysis of observational data Electronic medical record (EMR) system data used for cohort discovery  Condition-specific data marts based on de-identified inpatient data. Cerner, Real world data http://www.cerner.com/solutions/Research/Real-World_Data/

SNOMED Systematized Nomenclature of Medicine: Terminology and implementation support products and services. College of American Pathologists http://www.snomed.org/  

systems medicine: http://en.wikipedia.org/wiki/Systems_medicine
 

syndromics, syndromic systems:
Systems of information for the detected of occurrences of syndromes. Edilson Damasio, Systems of information and surveillance of occurrences in bioterrorism, 9th World Congress on Health Information and Libraries, Brazil, Sept. 20-23, 2005 http://www.icml9.org/program/track3/activity.php?lang=en&id=20  

telehealth:  Agency for healthcare research & quality http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5554&mode=2&holderDisplayURL=http://prodportall
Wikipedia http://en.wikipedia.org/wiki/Telehealth 

telemedicine: http://en.wikipedia.org/wiki/Telemedicine

translational bioinformatics: is the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health. Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders, including biomedical scientists, clinicians, and patients. AMIA Translational bioinformatics http://www.amia.org/applications-informatics/translational-bioinformatics

PLOS Computational Biology Translational Bioinformatics http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11

UMLS Unified Medical Language System In 1986, the National Library of Medicine (NLM), began a long term research and development project to build a Unified Medical Language System ® (UMLS ® ). The purpose of the UMLS is to aid the development of systems that help health professionals and researchers retrieve and integrate electronic biomedical information from a variety of sources and to make it easy for users to link disparate information systems, including computer- based patient records, bibliographic databases, factual databases, and expert systems. The UMLS project develops "Knowledge Sources" that can be used by a wide variety of applications programs to overcome retrieval problems caused by differences in terminology and the scattering of relevant information across many databases.  UMLS FactSheet, National Library of Medicine, NIH, US http://www.nlm.nih.gov/pubs/factsheets/umls.html

uncertainty factor: Mathematical adjustments for reasons of safety when knowledge is incomplete. For example, factors used in the calculation of doses that are not harmful (adverse) to people. These factors are applied to the lowest-observed-adverse-effect-level (LOAEL) or the no-observed-adverse-effect-level (NOAEL) to derive a minimal risk level (MRL). Uncertainty factors are used to account for variations in people's sensitivity, for differences between animals and humans, and for differences between a LOAEL and a NOAEL. Scientists use uncertainty factors when they have some, but not all, the information from animal or human studies to decide whether an exposure will cause harm to people [also sometimes called a safety factor]. ATSDR Glossary, Agency for Toxic Substances & Disease Registry, http://www.atsdr.cdc.gov/glossary.html 2009

virtual cancer patient: Cancer

women's health - statistical modeling: Gender differences in prevalence, risk and course of a variety of health outcomes depend upon a complex interplay of factors, including biological, social and psychological factors. The multivariate nature of our research hypotheses poses significant problems for the design and interpretation of studies in women's health. The statistical modeling core is committed to the application and development of multivariate techniques that are vital to the testing of these hypotheses. Women's Health Research at Yale, Statistical Modeling, http://info.med.yale.edu/womenshealth//research/statistic.html  

Bibliography
Coiera, Enrico, Health Informatics Glossary http://coiera.com/textbook-resources/glossary/
International Health Terminology Standards Development Organisation, SNOMED CT® IHTSDO Glossary - (DRAFT VERSION) July 2013 International Release http://ihtsdo.org/fileadmin/user_upload/doc/download/doc_IhtsdoGlossary_Current-en-US_INT_20130731.pdf  

MedDRA Medical Dictionary for Regulatory Activities, Maintenance and Support Services Organization. An international medical terminology designed to support the classification, retrieval, presentation, and communication of medical information throughout the medical product regulatory cycle. http://www.meddra.org/ 
Informatics Conferences http://www.chicorporate.com/Conferences/Search.aspx?k=&r=&s=NFO
BioIT World Expo http://www.bio-itworldexpo.com/
Molecular Medicine Tri Conference http://www.triconference.com/

Informatics CDs, DVDs http://www.chicorporate.com/Conferences/CompactDiscs.aspx?s=NFO  
Informatics Short courses http://www.healthtech.com/Conferences_Upcoming_ShortCourses.aspx?s=NFO

Insight Pharma Reports Informatics series http://www.insightpharmareports.com/Reports/All.aspx?s=NFO 
Insight Pharma Reports, Bayesian Forecasting of Phase III Outcomes: The Next Wave in Predictive Tools,  2007    

BioIT World magazine http://www.bio-itworld.com/   
   BioIT World archives http://www.bio-itworld.com/BioIT/BioITArchive.aspx
Clinical Informatics News http://www.clinicalinformaticsnews.com/

Barnett publications informatics http://www.barnettinternational.com/EducationalServices/Publications.aspx?j=Biostatistics
http://www.barnettinternational.com/EducationalServices/Publications.aspx?j=Data%20Management
http://www.barnettinternational.com/EducationalServices/Publications.aspx?t=Research%20&%20Statistics
Barnett Live Seminars informatics http://www.barnettinternational.com/EducationalServices/Seminars.aspx?j=Data%20Management
Barnett Web Seminars informatics http://www.barnettinternational.com/EducationalServices/Webinars.aspx?t=Research%20&%20Statistics

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