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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,...
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Purpose of Health Records I01:11

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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:

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Exploiting time in electronic health record correlations.

George Hripcsak1, David J Albers, Adler Perotte

  • 1Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA. hripcsak@columbia.edu

Journal of the American Medical Informatics Association : JAMIA
|November 26, 2011
PubMed
Summary
This summary is machine-generated.

Analyzing large electronic health record databases reveals temporal patterns in clinical associations. Incorporating time into analysis enhances understanding of healthcare processes and patient states.

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Area of Science:

  • Clinical informatics
  • Health data science
  • Temporal data analysis

Background:

  • Large clinical databases offer potential for uncovering complex relationships.
  • Understanding temporal dynamics in healthcare is crucial for accurate interpretation.

Purpose of the Study:

  • Demonstrate temporal pattern discovery in clinical associations using large databases.
  • Illustrate various types of clinical associations and the value of temporal analysis.
  • Ascertain the utility of time-series analysis in electronic health records.

Main Methods:

  • Lagged linear correlation applied to clinical laboratory values and concepts from electronic health records.
  • Utilized a 22-year, 3-million-patient database.
  • Interpolated time points and normalized patients to mitigate inter-patient variability.

Main Results:

  • Identified detailed temporal patterns in clinical associations, including definitional, intentional, and physiological.
  • Observed that diseases may appear to follow their effects due to healthcare workflow.
  • Time interpolation reduced noise and improved the clarity of results.

Conclusions:

  • Electronic health records reflect healthcare processes, not just patient states.
  • Interpretable clinical associations can be derived from large databases by properly incorporating temporal data.
  • Careful interpretation is necessary when analyzing temporal clinical associations.