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Related Concept Videos

Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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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.
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Introduction to Epidemiology01:26

Introduction to Epidemiology

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Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Updated: Apr 23, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Does process flow make a difference to mortality and cost? An observational study.

Kate Silvester, Paul Harriman, Paul Walley

    International Journal of Health Care Quality Assurance
    |September 26, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Patient mortality rises when emergency departments face admission challenges. Understanding whole-system delays is crucial for improving patient flow and reducing deaths.

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    Last Updated: Apr 23, 2026

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    14.3K

    Area of Science:

    • Healthcare Management
    • Public Health
    • Health Services Research

    Background:

    • Emergency care systems are complex, with patient flow disruptions impacting outcomes.
    • Previous research often links emergency department overcrowding to increased patient mortality.

    Purpose of the Study:

    • To investigate the relationship between emergency care patient flow, mortality rates, and healthcare costs.
    • To utilize a patient-flow model to analyze these interconnected factors.

    Main Methods:

    • Analysis of three years of performance data from a UK National Health Service (NHS) trust.
    • Identification of compromised patient flow periods and investigation of root causes for delays.
    • Comparison of event timelines disrupting patient flow with patient mortality statistics.

    Main Results:

    • Patient mortality rates were observed to increase during periods when accident and emergency (A&E) departments experienced admission difficulties.
    • Four key delay types were identified as influencing mortality: increased volume/mixed admissions, process delays, unplanned capacity adjustments, and long-term capacity restructuring.

    Conclusions:

    • This observational study highlights the correlation between specific system events and increased mortality, offering insights into whole-system issues.
    • Managers must prioritize system-wide flow and cost considerations over localized, cost-focused decisions to prevent adverse effects on patient care.
    • Current data presentation methods need improvement to enable correlation analysis with mortality reports.