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

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Encoding01:19

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Review and Preview01:13

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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...

You might also read

Related Articles

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

Sort by
Same author

Assessing Executive Nurse Leaders' Financial Literacy Level: A Mixed-Methods Study.

The Journal of nursing administration·2019
Same author

The Language of Scholarship: How to Rapidly Locate and Avoid Common APA Errors.

Journal of continuing education in nursing·2015
Same author

Delivering team training to medical home staff to impact perceptions of collaboration.

Professional case management·2015
Same author

Evaluation of a standardized hourly rounding process (SHaRP).

Journal for healthcare quality : official publication of the National Association for Healthcare Quality·2012
Same author

Managing cardiac devices near the end of life: a survey of hospice and palliative care providers.

The American journal of hospice & palliative care·2010
Same author

Bridging the gap: academic and practitioner perspectives to identify early career competencies needed in healthcare management.

The Journal of health administration education·2007
Same journal

The Effect of Moral Sensitivity of Nurses on Their Disaster Response Self-Efficacy: A Descriptive, Cross-Sectional and Correlational Study.

Hospital topics·2026
Same journal

Correlation Between Medication Safety and Work Dynamics with Nursing Medication Error.

Hospital topics·2026
Same journal

Exploring the Link Between Abusive Supervision and Medical Errors in Nursing Interns: The Role of Emotional Exhaustion and Emotional Intelligence.

Hospital topics·2026
Same journal

Job Satisfaction Among Midwives in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis.

Hospital topics·2026
Same journal

Assessment of the Pharmacovigilance System in One of the Largest Hospitals in Indonesia.

Hospital topics·2026
Same journal

Implementation of a Medical Emergency Team to Improve the Quality of the Acute Emergency Management System in the Accident Service of National Hospital, Sri Lanka: Impact on Staff Perception in in-Ward Emergency Care.

Hospital topics·2026
See all related articles

Related Experiment Video

Updated: May 24, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Converting data into information.

Beryl C Vallejo1, Rebecca Krepper, Hope Nora

  • 1St. Luke's Episcopal Health System, Houston, TX, USA.

Hospital Topics
|March 13, 2012
PubMed
Summary
This summary is machine-generated.

Harnessing healthcare data requires integrating information from multiple databases. This complex process involves cultural and technological changes to support clinical decisions.

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Related Experiment Videos

Last Updated: May 24, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

  • Health Informatics
  • Data Science in Healthcare
  • Clinical Decision Support Systems

Background:

  • 21st-century healthcare improvement relies on effective data mining.
  • Integrating data from diverse hospital and clinical databases is challenging.
  • Data conversion for clinical decision support requires significant effort.

Purpose of the Study:

  • To describe the process of transforming a hospital into a data-driven organization.
  • To highlight the complexities in converting data for clinical decision support.
  • To share lessons learned from a long-term organizational transformation.

Main Methods:

  • Qualitative description of a hospital's journey.
  • Focus on cultural and technological integration steps.
  • Longitudinal case study approach.

Main Results:

  • Becoming data-driven is a complex, resource-intensive undertaking.
  • Successful data integration requires addressing both cultural and technological barriers.
  • The journey involves significant time and dedicated resources.

Conclusions:

  • Healthcare organizations must invest in data infrastructure and cultural change.
  • Overcoming data silos is crucial for advancing clinical decision support.
  • A strategic, long-term approach is necessary for data-driven transformation.