Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Documentation VII: EMR

Methods of Documentation VII: EMR

Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...

Methods of Documentation VI: Case Management Model

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...

Documentation of Nursing Diagnosis

Documentation of Nursing Diagnosis

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.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...

Methods of Documentation V: CBE

Methods of Documentation V: CBE

Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

Errors occurring during blood pressure monitoring

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...

You might also read

Related Articles

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

Sort by

Same author

Applying Persuasive Design Techniques to Influence Data-Entry Behaviors in Primary Care: Repeated Measures Evaluation Using Statistical Process Control.

JMIR human factors·2018

Same author

Designer germanium quantum dot phototransistor for near infrared optical detection and amplification.

Nanotechnology·2015

Same author

Size tunable Ge quantum dots for near-ultraviolet to near-infrared photosensing with high figures of merit.

Nanoscale·2014

Same author

Moving mobile: using an open-sourced framework to enable a web-based health application on touch devices.

Studies in health technology and informatics·2013

Same author

Application of the Apriori algorithm for adverse drug reaction detection.

Studies in health technology and informatics·2009

Same author

Free anterolateral thigh flap for extremity reconstruction: clinical experience and functional assessment of donor site.

Plastic and reconstructive surgery·2001

Search research articles

Related Experiment Video

Updated: May 19, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Analyzing primary care data to characterize inappropriate emergency room use.

Justin St-Maurice¹, M H Kuo

¹School of Health Information Science, University of Victoria, BC, Canada.

Studies in Health Technology and Informatics

|August 10, 2012

Summary

Primary care data analysis revealed significant links between pain, mental health, and inappropriate emergency room use. This innovative approach offers insights into healthcare utilization and patient well-being.

More Related Videos

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

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

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

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 19, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

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

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

Published on: January 8, 2020

Area of Science:

Health Services Research
Biomedical Informatics
Public Health

Background:

Primary care data in Ontario offers a comprehensive biopsychosocial patient profile.
Secondary analysis of this data is a recent development.
Understanding factors influencing emergency room (ER) use is crucial for healthcare system optimization.

Purpose of the Study:

To investigate the relationship between biopsychosocial concepts from primary care data and inappropriate ER use.
To demonstrate a novel method for analyzing de-identified primary care records.
To identify specific patient factors associated with high ER utilization.

Main Methods:

Extraction of de-identified primary care data from Ontario.
Application of natural language processing (NLP) to extract Unified Medical Language System (UMLS) codes.
Statistical analysis using logistic regression to correlate UMLS codes with ER use patterns.

Main Results:

Pain and mental health concepts were statistically significant predictors of inappropriate ER use.
The NLP and statistical approach successfully identified key biopsychosocial factors.
The study highlights the potential of primary care data for health services research.

Conclusions:

Primary care data, when analyzed with NLP, can reveal significant associations with healthcare utilization patterns.
Pain and mental health are key areas to address to potentially reduce inappropriate ER visits.
This methodology can be adapted for analyzing system use in diverse healthcare contexts.