Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

187
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
187
Modeling in Therapy01:26

Modeling in Therapy

138
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
138
Data Validation01:03

Data Validation

5.2K
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...
5.2K
Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

117
The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
117
Learning Disabilities01:25

Learning Disabilities

255
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
255
Pedigree Analysis01:35

Pedigree Analysis

84.7K
Overview
84.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Educating current and future healthcare professionals on evidence-based sustainable medicine.

Clinical medicine (London, England)·2026
Same author

Demonstrating trends for early colorectal cancer incidence: a retrospective analysis of patients in Northwest Ohio.

Frontiers in oncology·2026
Same author

Surgical Tray Set Rationalization at an Elective Surgical Hub in England: Methodology, Feasibility, Implementation and Impact on Financial Costs and Carbon Emissions.

World journal of surgery·2026
Same author

The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach.

Journal of intellectual disability research : JIDR·2026
Same author

Molnupiravir is not a selective CES2 probe substrate: in vitro evidence for CES1 involvement.

Drug metabolism and disposition: the biological fate of chemicals·2026
Same author

Changing trends and variation in service delivery for inpatient diabetes care in England: A national survey.

Diabetic medicine : a journal of the British Diabetic Association·2026

Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests
08:30

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

Published on: September 6, 2024

1.8K

Data quality and autism: Issues and potential impacts.

Johannes Heyl1, Flavien Hardy2, Katie Tucker3

  • 1Getting It Right First Time, NHS England and NHS Improvement, London, UK; Department of Physics and Astronomy, University College London, London, UK.

International Journal of Medical Informatics
|December 1, 2022
PubMed
Summary

Data inconsistencies are common in healthcare records for autistic patients. These data errors can impact patient outcome analysis and understanding of service use in this population.

Keywords:
AutismData accuracyData consistencyHealthcare data

More Related Videos

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
06:15

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

7.8K
A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research
08:42

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research

Published on: July 31, 2017

8.3K

Related Experiment Videos

Last Updated: Aug 19, 2025

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests
08:30

Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

Published on: September 6, 2024

1.8K
Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
06:15

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

7.8K
A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research
08:42

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research

Published on: July 31, 2017

8.3K

Area of Science:

  • Healthcare Informatics
  • Autism Spectrum Disorder Research
  • Data Quality Assessment

Background:

  • Large healthcare datasets offer potential for improving patient outcomes.
  • Understanding data limitations is crucial for accurate insights.
  • Autism diagnosis recording in healthcare databases requires scrutiny.

Purpose of the Study:

  • To identify data inconsistencies in autism diagnosis recording within the Hospital Episodes Statistics (HES) dataset.
  • To assess potential biases introduced by these inconsistencies.
  • To evaluate the impact of data inconsistencies on patient outcomes for autistic individuals.

Main Methods:

  • Extracted data from HES for autistic patients (April 2013 - March 2021).
  • Linked initial hospital spells to subsequent ones to identify recording inconsistencies.
  • Utilized random forest classifiers and regression modeling to identify features associated with inconsistencies.

Main Results:

  • Data inconsistencies were found in 43.7% of subsequent hospital spells for autistic patients.
  • Factors associated with inconsistencies include age, deprivation, time since first spell, and patient demographics.
  • A random forest model achieved an AUC of 0.864 in predicting data inconsistencies.

Conclusions:

  • Data inconsistencies are prevalent in HES records for autistic patients.
  • These inconsistencies are linked to various patient and hospital admission characteristics.
  • Such data quality issues can potentially distort the understanding of service utilization among key demographic groups.