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You are here > Biopharmaceutical Glossary Homepage/Search > Informatics > Clinical & Medical Informatics Clinical
& Medical informatics glossary & taxonomy
Drug
discovery term index Informatics
term index Technologies term
index Biology term index
Finding guide to terms in these glossaries Site
Map 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 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 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 statistics: The fundamental idea in Bayesian statistics is that one’s uncertainty about an unknown quantity of interest is represented by probabilities for possible values of that quantity.... The Bayesian paradigm states that probability is the only measure of one’s uncertainty about an unknown quantity. In a Bayesian clinical trial, uncertainty about an endpoint (also called parameter) is quantified according to probabilities, which are updated as information is gathered from the trial. Center for Devices & Radiological Health, FDA, Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials - Draft Guidance for Industry and FDA Staff , This guidance document is being distributed for comments purposes only. Draft released for comment on May 23, 2006 docket number 2006D-0191. http://www.fda.gov/cdrh/osb/guidance/1601.html#4 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: Biomedical and health informatics is an emerging, interdisciplinary and diverse field that: Combines health sciences (such as medicine, dentistry, nursing, pharmacy and allied health) with computer science, management and decision science, biostatistics, engineering and information technology. Solves problems in health care delivery, pharmaceutical, biomedical and health sciences research, health education and clinical/medical decision making. Is essential in all aspects of health care and biomedicine. American Medical Informatics Association About AMIA https://www.amia.org/inside http://en.wikipedia.org/wiki/Biomedical_informatics Google = about 14,900 May 8, 2003; about 37,400 Apr. 28, 2004; about 670,000 Nov 10, 2006, about 372,000 Jan 2, 2008 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 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 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 clinical healthcare informatics: Within the domain of clinical healthcare informatics, AMIA seeks to transform healthcare and enhance human health through a creative and innovative use of informatics with respect to applications of communications and information technology. This will be accomplished through a well educated and properly trained informatics workforce, an enhanced performance of health care processes and systems, relevant public policy, and a relevant research agenda. Strategic Plan, American Medical Informatics Association, 2007 http://www.amia.org/inside/stratplan/ 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
Clinical informatics contains
two major divisions. The first relates to all those aspects of clinical
informatics whose objective is the application of informatics and information
technology to deliver healthcare services. At times, this has also been referred
to as applied clinical informatics. Despite some variations, 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. The other branch relates to clinical research informatics. Its primary objective is the use of informatics in the discovery and management of new knowledge relating to health and disease. This includes the management of the relevant knowledge base. Clinical research informatics could be thought to encompass translational bioinformatics. However, for the present at least, AMIA has chosen to consider it a separate division since the communities of practitioners tend to be separate and since the field is still in its infancy. Strategic Plan, American Medical Informatics Association, 2007 http://www.amia.org/inside/stratplan/ 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 Google = about 6530 May 8, 2003; about 15,400 June 10, 2004; about 216,000 Nov 10, 2006 clinical ontologies:
Ontologies are correctly defined as hierarchies of concepts but are frequently
applied to mean controlled syntax, database schema, semantic networks or
thesaurus. In using an ontological approach to extract knowledge about disease
progression and disease presentation, including co-morbidities, we have extended
the approach of ontology construction to incorporate critical temporal domains.
Towards this goal, we have applied LexiMine (SPSS) as a method for syntactical
analysis of free text to establish the value in the analysis of full articles
versus abstracts in knowledge extraction. Ontologies
in Breast Cancer: Concepts vs. Words, Dr. Michael Liebman, Director,
Computational Biology and Biomedical Informatics, Professor, Cancer Biology,
Abramson Cancer Center of the University of Pennsylvania Data
Integration for the Pharmaceutical Industry, Sept. 24-25, 2003,
Baltimore MD Google = about 50 May
29, 2003; about 74 June 10, 2004; about 332 Nov 10, 2006, about 4,850 Nov 18,
2009 clinical
protocols:
The basis and success of any drug or device development
program is the clinical trial protocol. As the protocol is used to
directly inform, instruct, guide or to provide a rationale for nearly
all study start-up activities and their work products -- including
everything from site identification, feasibility and trial registry
filing -- it is critical that protocol development is a well thought out
and seamlessly executed process. Knowing how to effectively optimize a
clinical trial protocol is essential to a compound achieving IRB
approval, ensuring the success of the study and ultimately achieving
market approval, and there is much variability between companies and
individuals on how to optimally approach the development and
optimization of this critical document. Clinical Protocol Optimization
October 3-4, 2011 •
Cambridge, MA Program
| Register
| Download Brochure clinical
training:
case studies, lessons learned and presentations focused on
the challenges associated with role-based training in the clinical
research environment. Included are strategies for linking training
initiatives to study outcomes, key regulatory considerations and
findings, and examples of how training deficiencies can put clinical
research activities at risk. Leading industry training professionals
will share their approaches to managing training challenges, as well as
how they leverage resources across their companies to optimize training
and compliance. Clinical Training Forum October 5-6,
2011 • Cambridge, MA Program | Register | Download Brochure 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 informatics: 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
7: eClinical Solutions for Clinical Trials and Clinical Operations Bio-IT
World Conference & Expo April 12-14, 2011 • Boston, MA Program | Register
| Download Brochure 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 Google = about 198 May 8, 2003; about 587 June 10, 2004, about 664 Aug. 22, 2005 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: Large prospective cohorts and biobanks are ideal models for defining disease burden in a population and for launching studies to examine the many genetic and environmental factors that contribute to disease, paving the way for personalized medicine. Advantages of large-scale approaches over smaller scale or retrospective study designs include greater generalizability of the research findings and efficiencies in time and resources because a single large, well defined cohort can be built to address multiple research questions within a single research framework. The U.K. Biobank is a large-scale national resource initiated in 2004 to assess trends in disease burden and examine genetic and environmental risk factors for specific diseases. The approach used by the Biobank has enabled its leaders to achieve exceptional efficiencies in recruitment, assessment and record linkage. http://commonfund.nih.gov/newmodels/ 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, c.2001 http://bmir.stanford.edu/file_asset/index.php/91/BMIR-2001-0898.pdf 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: A rigorous evaluation of the impact of different options that are available for treating a given medical condition for a particular set of patients. Such a study may compare similar treatments, such as competing drugs, or it may analyze very different approaches, such as surgery and drug therapy.” Such research may include the development and use of clinical registries, clinical data networks, and other forms of electronic health data that can be used to generate or obtain outcomes data as they apply to CER. Recovery Act Limited Competition: NIH Challenge Grants in Health and Science Research (RC1), 2009 http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.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/ Google = about 310 June 10, 2004, about 1,700 Aug. 22, 2005 CONSORT
Consolidated Standards of Reporting Trials,
http://www.consort-statement.org/
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
eClinical
Solutions April
25-26, 2012 • Boston, MA Program
| Register | Download
Brochure 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:
eHealth
and HIT Solutions for Personalized Medicine
April 25-26, 2012 • Boston, MA Program | Register | Download
Brochure http://en.wikipedia.org/wiki/EHealth electronic data: New technology is available and being created
everyday to make the collection, correction, and assessment of data from
clinical trials more efficient. The goal is to better integrate systems and data
across departments and regions in order to optimize the speed and cost of trials
and drug development.
Electronic Data in Clinical Trials
February 7-8, 2011 • Coral Gables,
FL Program | Register
| Download Brochure Electronic Health Records EHR: A real- time patient health record with access to evidence- based decision support tools that can be used to aid clinicians in decision- making. The EHR can automate and streamline a clinician's workflow, ensuring that all clinical information is communicated. It can also prevent delays in response that result in gaps in care. The EHR can also support the collection of data for uses other than clinical care, such as billing, quality management, outcome reporting, and public health disease surveillance and reporting. US Dept. of Health & Human Services, Health IT Strategic Framework, Glossary, 2004, http://www.hhs.gov/onchit/framework/hitframework/glossary.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. electronic
prescribing:
Agency for Healthcare
Research & Quality, 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/ HMO
Collaboratory:
In the context of health care reform activities, the
NIH is eager to step up the production of comparative effectiveness
research (CER) and health systems analyses to develop faster, more
personalized and cost-effective data regarding which interventions work
best for whom. ... One
approach to speed efficiency, generate faster evidence, take advantage of
high-throughput technologies and leverage known economies of scale in this
research effort is to facilitate new collaborative research activities
across HMOs. The HMORN research organizations, because of their history of
public sector research and their affiliation with leading-edge integrated
healthcare delivery systems, are ideally positioned to lead new research
efforts in a number of cross-cutting NIH interest areas, including:
Mega-Epidemiology Studies , Clinical Trial Enterprise , Health Care
Delivery human factors: Human factors is the science and the methods used to make devices easier and safer to use. The Human Factors team advances the FDA’s patient safety mission by distributing information about the design, testing, and selection of usable medical devices for clinical and home settings. Human Factors FDA http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/PostmarketRequirements/HumanFactors/default.htm in
silico
clinical trials: See computer trials simulations laboratory informatics: A relatively new field that aims to expedite the exchange of laboratory data via electronic data exchange. Laboratory informatics specialists design standards and systems to support the acquisition, retrieval and communication of test results and other laboratory data. Information systems are as critical to public health laboratories as instrumentation and reagents. Association of Public Health Laboratories, 2008 http://www.aphl.org/aphlprograms/informatics/Pages/defofinformatics.aspx Related term?: Drug discovery & development LIMS Google = about 1,250 Dec. 31, 2002; about 3,000 Oct. 22, 2004; about 31,900 Nov 18, 2009 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/ 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. Google = about 512, Oct. 15, 2003 Related terms: Biomarkers, Expression, 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/ Google = about 163,000 July 19, 2002; about 479,000 Oct. 22, 2004, about 696, 000 Oct. 3, 2005; about 1,690,000 Nov 18, 2009 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, 2005 http://www2.cochrane.org/resources/handbook/glossary.pdf 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: Can formally test whether there is evidence of different effects in different subgroups of trials. For example, you can use meta-regression to test whether treatment effects are bigger in low quality studies than in high quality studies. Cochran Collaborative, Diversity and Heterogeneity, 2002 http://www.cochrane-net.org/openlearning/HTML/mod13-5.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 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): Provides leadership for the development and nationwide implementation of an interoperable health information technology infrastructure to improve the quality and efficiency of health care and the ability of consumers to manage their care and safety. http://www.hhs.gov/healthit/ 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 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, Mission Statement, 2010 http://www.pathologyinformatics.org/mission.htm 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 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 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
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 Public health informatics, and its corollary, population informatics, is concerned with informatics focused on groups rather than individuals. This parallels the field of public health. Public health is potentially extremely broad and might even reflect an interest in information technology with regard to ecology, architecture, climate, agriculture, and such. AMIA will focus on those aspects of public health that are considered to be in the purview of the Centers for Disease Control including security with respect to biosurveillance and bioterrorism. At this time it does not concern itself with informatics relating to the broadest reaches of public health. Strategic Plan, American Medical Informatics Association, 2007 http://www.amia.org/inside/stratplan/ SNOMED
Systematized Nomenclature of Medicine:
Terminology and
implementation support products and services. College of American
Pathologists http://www.snomed.org/ systems
medicine:
in-depth modeling approaches from in silico
to in vivo with an emphasis on drug discovery, ADME predictions, and systems
medicine leading to effective translation into the clinic. Track
6: Systems & Predictive Medicine Bio-IT
World Conference & Expo April 12-14, 2011 • Boston, MA Program | Register
| Download Brochure 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 Google = about 76, Nov 5, 2005, about 92 Oct. 25, 2006 telehealth: Agency for healthcare research & quality http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5554&mode=2&holderDisplayURL=http://prodportall telemedicine: http://en.wikipedia.org/wiki/Telemedicine translational bioinformatics: AMIA refers to translational bioinformatics as the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, 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. Issues relating to database management, administration, or policy will be coordinated through the Clinical Research Informatics domain. American Medical Informatics Association, AMIA Strategic Plan, 2007 http://www.amia.org/inside/stratplan/ 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 virtual medicinal product: A SNOMED concept http://www.snomed.org/snomedct/documents/snomed_ct_user_guide.pdf 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
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.meddramsso.com/
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