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Related Concept Videos

Methods of Documentation VII: EMR01:30

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,...
Obesity01:24

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in adipocytes...
Purpose of Health Records II01:19

Purpose of Health Records II

Health records serve various essential purposes in the healthcare system. Here are some key purposes:
Purpose of Health Records I01:11

Purpose of Health Records I

The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Drug Dosing: Obese Patients01:21

Drug Dosing: Obese Patients

In the United States, obesity is a prominent concern. It is linked to heightened mortality rates due to increased occurrences of conditions such as hypertension, atherosclerosis, coronary artery disease, and diabetes compared to nonobese individuals. A patient is classified as obese if their actual body weight surpasses the ideal or desirable body weight by 20%, based on Metropolitan Life Insurance Company data. Ideal body weights consider average weights and heights for males and females...

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Related Experiment Video

Updated: May 22, 2026

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

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Published on: September 18, 2018

An electronic health record-enabled obesity database.

G Craig Wood1, Xin Chu, Christina Manney

  • 1Geisinger Obesity Research Institute, Geisinger Clinic, Danville, PA 17822, USA.

BMC Medical Informatics and Decision Making
|May 30, 2012
PubMed
Summary
This summary is machine-generated.

Electronic health records (EHRs) can efficiently provide valuable data for studying weight loss outcomes after Roux-en-Y gastric bypass (RYGB) surgery. This approach facilitates research on obesity treatments using real-world patient information.

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Area of Science:

  • Bariatric Surgery Research
  • Obesity Treatment Outcomes
  • Health Informatics

Background:

  • Weight loss therapy effectiveness is typically measured by body mass index and other obesity metrics.
  • Electronic health records (EHRs) contain accessible data for obesity research, but few studies utilize them.
  • This study focused on developing methods to extract EHR data for patients undergoing Roux-en-Y gastric bypass (RYGB) surgery.

Purpose of the Study:

  • To develop and implement processes for extracting data from electronic health records (EHRs).
  • To construct a comprehensive database of patients who have undergone Roux-en-Y gastric bypass (RYGB) surgery.
  • To demonstrate the utility of EHR-derived data for studying post-operative outcomes.

Main Methods:

  • Clinical data were extracted from the EHR as part of standard care in a bariatric surgery program.
  • Data were deposited into a data warehouse, then extracted, cleaned, and stored in research datasets.
  • Kaplan-Meier analysis was employed to estimate post-operative follow-up duration.

Main Results:

  • Data from 2028 RYGB patients, including demographics, labs, medications, co-morbidities, and surveys, were obtained.
  • Pre- and post-operative diagnostic and prescribing data were available for all patients.
  • Kaplan-Meier estimates indicated that over 74% of patients had post-operative weight data available at 4 years.

Conclusions:

  • Various EHR-derived data relevant to obesity can be efficiently acquired.
  • These data are suitable for studying significant outcomes following RYGB surgery.
  • The study highlights the potential of EHRs in advancing obesity and bariatric surgery research.