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Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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Pharmaceutical Poisoning: Potential Scenarios01:26

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Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
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Allergic Drug Reactions01:27

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Allergic reactions related to drugs are hypersensitivity responses driven by the immune system and bear no connection to the drug's therapeutic action. While drugs in isolation do not trigger an immune response, they can interact with endogenous proteins to form antigens. These antigens stimulate lymphocytes to produce antibodies. IgE-type antibodies attach themselves to mast cells. Upon subsequent exposure to the same stimulus, the antigen-antibody interaction is initiated, unleashing...
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Drug Toxicity: Risk factors01:24

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Adverse Drug Reactions (ADRs) are potential complications that arise during pharmacotherapy, influenced by multiple risk factors. Age plays a significant role; both neonates and the elderly are at heightened risk due to their respective immature and diminished metabolic and elimination processes. Gender also impacts ADRs, with females experiencing a 1.5 to 1.7-fold greater risk than males, which may be linked to pharmacokinetic, pharmacodynamic, and hormonal differences. Notably, neonates, the...
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Drug Toxicity: Overview01:00

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Drug toxicity quantifies the harm a compound causes to an organism, varying by dose and potentially impacting whole systems or specific organs like the liver. Toxic reactions may arise from venomous insect or spider bites, with effects ranging from mild symptoms to severe outcomes such as brain damage or death. Common forms of acute poisoning include ethanol intoxication and overdose of pain or fever medications, with substances like GHB and heroin being particularly lethal at doses close to...
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Drug-Receptor Interaction: Antagonist01:28

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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
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Related Experiment Video

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents.

M Topaz1, D L Seger, F Goss

  • 1Maxim Topaz PhD, RN, MA, 93 Worcester St., Wellesley Gateway, Suite 2030I, Wellesley, MA, 02481, USA, E mail: mtopaz80@gmail.com.

Methods of Information in Medicine
|February 25, 2016
PubMed
Summary
This summary is machine-generated.

Common adverse sensitivity models cover most clinical allergy data but have alignment issues. Reconciling these standards is crucial for improving data interoperability in electronic health records.

Keywords:
AllergyHealth Level 7electronic health recordshealth information systemshypersensitivity

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Area of Science:

  • Health Informatics
  • Clinical Data Standards
  • Electronic Health Records

Background:

  • Adverse sensitivity information is vital for electronic health records.
  • Clinicians often record this critical data as unstructured free text.
  • Existing standards for structured adverse sensitivity data entry are underutilized.

Purpose of the Study:

  • To identify and compare prevalent adverse sensitivity information models.
  • To evaluate the coverage of these models for allergy information in clinical notes.
  • To assess the suitability of current models for representing real-world patient data.

Main Methods:

  • Comparative analysis of four major adverse sensitivity information models: HL7-DAM, FHIR, C-CDA, and OpenEHR.
  • Evaluation of model coverage using a corpus of 120 inpatient and outpatient clinical notes.
  • Assessment of attribute representation, including value-sets and exceptions.

Main Results:

  • Allergy specialist notes contained the most adverse sensitivity attributes; emergency department notes contained the fewest.
  • The evaluated models demonstrated significant overlap, covering 75%-95% of central adverse sensitivity attributes.
  • Key challenges identified include misaligned value-sets and poor representation of adverse sensitivity exceptions, hindering interoperability.

Conclusions:

  • Current adverse sensitivity information models capture a substantial amount of data from clinical notes.
  • Significant discrepancies exist in attribute representation, particularly value-sets and exceptions.
  • Further reconciliation between standards is necessary to achieve seamless data interoperability.