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Related Concept Videos

Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Collection III01:05

Data Collection III

The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the patient.
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Data quality assessment for comparative effectiveness research in distributed data networks.

Jeffrey S Brown1, Michael Kahn, Sengwee Toh

  • 1*Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. jeff_brown@hphc.org

Medical Care
|June 25, 2013
PubMed
Summary
This summary is machine-generated.

Electronic health data in distributed networks offer real-world evidence but require robust data quality checks. Establishing standards for data quality is crucial for reliable comparative effectiveness research (CER).

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09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Area of Science:

  • Health Informatics
  • Comparative Effectiveness Research
  • Data Science

Background:

  • Electronic health data (EHD) collected during routine care are valuable for real-world evidence (RWE) generation.
  • Distributed data networks (DDNs) are often necessary to aggregate EHD from multiple sources for RWE.
  • Assessing the effectiveness, safety, and quality of medical care relies on comprehensive RWE.

Purpose of the Study:

  • To establish best practices and recommendations for data quality checking in DDNs for comparative effectiveness research (CER).
  • To address the need for standardized methods to evaluate the quality of EHD used in DDNs.
  • To enhance the reliability and validity of RWE generated from distributed health data networks.

Main Methods:

  • Exploration of data quality checking requirements within DDNs.
  • Description of data quality approaches employed by existing multi-site networks.
  • Review of methods for assessing EHD quality for CER.

Main Results:

  • No universally established standards exist for evaluating EHD quality for CER in DDNs.
  • Data quality checks range from basic syntactic rules to complex semantic and cross-site consistency evaluations.
  • Common checks include temporal trend analysis, data refresh validation, and event/exposure rate analysis by demographics and time.

Conclusions:

  • Secondary use of EHD in DDNs for CER is promising but complex, especially with periodic data updates.
  • A functional learning health system depends on understanding the quality and validity of EHD within DDNs.
  • Implementing robust data quality checking is essential to increase confidence in findings derived from DDNs.