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Related Concept Videos

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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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Planning Nursing Care I01:21

Planning Nursing Care I

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The planning phase of the nursing process helps nurses set priorities, outline patient-centered goals and expected outcomes, and tailor nursing interventions to align with the aligned care plan. Through the planning phase, the nurse applies critical thinking skills to align and develop interventions according to the patient's needs. It provides continuity of care allowing patients to receive the maximum benefit from treatment. It serves as a pilot plan for allocating individual staff to a...
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Nursing Diagnosis01:22

Nursing Diagnosis

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Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
The nursing diagnosis focuses on evidence-based...
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Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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Related Experiment Video

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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Predicting Negative Events: Using Post-discharge Data to Detect High-Risk Patients.

Lina Sulieman1, Daniel Fabbri1, Fei Wang2

  • 1Vanderbilt University, Nashville, TN.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 9, 2017
PubMed
Summary

Including post-discharge data significantly improves prediction of negative patient outcomes. This new model enhances risk detection for conditions like hip fracture and congestive heart failure (CHF), outperforming existing methods.

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Area of Science:

  • Medical Informatics
  • Clinical Prediction Models
  • Health Outcomes Research

Background:

  • Predicting patient readmission and mortality is crucial but challenging in medical informatics.
  • Current models primarily use pre-discharge data, limiting predictive accuracy for future negative outcomes.

Purpose of the Study:

  • To evaluate the impact of incorporating post-discharge data on predicting negative patient outcomes.
  • To assess the performance of a new predictive model against standard methods for hip fracture and congestive heart failure (CHF) patients.

Main Methods:

  • Utilized data from 704 hip fracture and 5250 congestive heart failure (CHF) patients admitted to Vanderbilt University Medical Center (2010-2013).
  • Developed and compared a post-discharge data model against standard prediction models and the LACE index.

Main Results:

  • The post-discharge model improved the Area Under the Curve (AUC) of the LACE index by 20-30%.
  • Achieved higher AUCs compared to standard models: 15-27% for hip fracture and 10-12% for CHF patients.

Conclusions:

  • Incorporating post-discharge data enhances the accuracy of predicting negative patient outcomes.
  • The developed model shows superior performance in identifying high-risk patients for both acute (hip fracture) and chronic (CHF) conditions.