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Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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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...
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Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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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...
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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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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...
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Purpose of Health Records I01:11

Purpose of Health Records I

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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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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Related Experiment Video

Updated: Oct 27, 2025

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

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Using Routinely Collected Electronic Health Record Data to Predict Readmission and Target Care Coordination.

Courtney Omary, Phyllis Wright, Mathu A Kumarasamy

    Journal for Healthcare Quality : Official Publication of the National Association for Healthcare Quality
    |July 23, 2021
    PubMed
    Summary

    Electronic health records (EHR) data can predict hospital readmissions for chronic renal failure (CRF) patients. Using routinely collected EHR variables improves readmission prediction accuracy by 30% compared to traditional methods.

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    Area of Science:

    • Nephrology
    • Health Informatics
    • Predictive Analytics

    Background:

    • Patients with chronic renal failure (CRF) face high 30-day hospital readmission rates.
    • Routinely collected electronic health record (EHR) data offers potential for predicting CRF readmissions.
    • Current generic risk tools may not capture the specific needs of CRF populations.

    Purpose of the Study:

    • To compare the predictive accuracy of EHR-derived variables versus manually extracted data for CRF readmissions.
    • To assess the utility of routinely collected EHR data for stratifying readmission risk in CRF patients.
    • To evaluate the potential of EHR data for targeted interventions to reduce readmissions.

    Main Methods:

    • Multivariate logistic regression analysis was applied to one year of admission data from an academic medical center.
    • Three routinely collected EHR variables (creatinine, B-type natriuretic peptide, length of stay) were categorized.
    • Comparison of predictive performance was made against paper-based methods using the C-statistic (AUC).

    Main Results:

    • Categorizing specific EHR variables improved readmission prediction by 30% compared to paper-based methods (AUC).
    • Marginal effects analysis yielded patient-specific risk scores ranging from 0% to 44.3%.
    • EHR-based prediction demonstrated superior accuracy for CRF readmission risk.

    Conclusions:

    • Routinely collected EHR data is an effective and efficient strategy for stratifying readmission risk in CRF patients.
    • Utilizing EHR data allows for more precise identification of high-risk individuals.
    • This approach supports targeted care interventions and may reduce overall hospital readmissions.