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Related Concept Videos

Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

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Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
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Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
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Data Collection II01:29

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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Stitching Meaning: Practices of Data Textile Creators.

Sydney Purdue, Eduardo Puerta, Enrico Bertini

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    This summary is machine-generated.

    Data textiles, like temperature blankets, visually represent information through fiber arts. This study surveyed creators, revealing diverse methods and personal benefits, suggesting new visualization opportunities.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Textile Arts

    Background:

    • Tens of thousands create data-encoding textile pieces, such as temperature blankets, mapping weather data through color.
    • Despite this widespread practice, data visualization and physicalization research has largely overlooked this community.
    • Data textiles represent a significant, yet underexplored, area of information physicalization.

    Purpose of the Study:

    • To explore the domain of data textiles (fiber arts encoding information).
    • To survey data fiber artists regarding their projects and creative processes.
    • To identify opportunities for enhancing data visualization through insights from data textiles.

    Main Methods:

    • Conducted a survey of data fiber artists.
    • Created a corpus of 159 examples of data textiles.
    • Developed a schema to characterize data encoding methods in these projects.
    • Gathered insights into creators' data workflows, motivations, and discoveries.

    Main Results:

    • Data textile creators employ distinct processes, mapping data through fabric structures and material properties like color and texture.
    • Creators engage with data in varied ways, stemming from diverse data-tracking procedures.
    • The creation process fosters personal growth and enhances creators' understanding of their data.

    Conclusions:

    • Data textiles offer novel approaches to data physicalization and visualization.
    • Opportunities exist to develop tools supporting data fiber artists.
    • Concepts from data textiles, such as texture and structural layouts, can inform future visualization designs.