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Related Concept Videos

Data Collection I01:30

Data Collection I

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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...
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Data Collection III01:05

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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...
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Data Validation01:03

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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.
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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Health Information Technology and Healthcare Information System01:30

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Statistical Methods for Analyzing Epidemiological Data01:25

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Related Experiment Video

Updated: May 1, 2026

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System
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Algorithms to Identify Major Congenital Malformations in Routinely Collected Healthcare Data: A Systematic Review.

Melanie H Jacobson1, Meritxell Sabidó2, Ana Sofia Afonso3

  • 1Global Epidemiology, Johnson & Johnson, 1125 Trenton-Harbourton Road, Titusville, NJ, 08560, USA. Mjacob17@its.jnj.com.

Drug Safety
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This systematic review identified algorithms for major congenital malformations (MCMs) in healthcare data. Findings aid researchers in pregnancy safety studies using routinely collected data.

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Area of Science:

  • Pharmacoepidemiology
  • Reproductive Health
  • Public Health

Background:

  • Major congenital malformations (MCMs) are critical outcomes in pregnancy safety research.
  • Accurate identification of MCMs is essential for reliable evidence generation.

Purpose of the Study:

  • To systematically review and summarize algorithms for identifying MCMs in routinely collected healthcare data.
  • To cover data sources from the USA, Canada, and Europe.

Main Methods:

  • Conducted a systematic literature review from January 1, 2010, to April 11, 2025.
  • Included search terms for MCMs, healthcare data, and pregnant individuals/infants.
  • Performed duplicate study review and data extraction.

Main Results:

  • 289 studies were included, with over half from Europe, primarily using national register data.
  • Algorithms varied by data source, geography, coding systems, and record utilization.
  • Most validation studies (70.4%) used claims/EHR data, with positive predictive values often exceeding 70%.

Conclusions:

  • This is the first comprehensive review of MCM identification algorithms in routine healthcare data.
  • The findings support researchers in generating robust evidence for pregnancy safety pharmacoepidemiology.