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Related Concept Videos

Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:03

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Data Collection I01:30

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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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Methods of Documentation VI: Case Management Model01:15

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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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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Data Collection III01:05

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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.
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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Model-driven approach to data collection and reporting for quality improvement.

Vasa Curcin1, Thomas Woodcock2, Alan J Poots2

  • 1Department of Primary Care and Public Health, Imperial College London, Reynolds Building, St. Dunstan's Road, London W6 8RP, United Kingdom.

Journal of Biomedical Informatics
|May 31, 2014
PubMed
Summary
This summary is machine-generated.

Healthcare improvement teams can now rapidly model data needs and generate collection tools using a novel software approach. This empowers non-experts to create custom Electronic Health Record (EHR) data instruments and reports for local initiatives.

Keywords:
D2.1 (Software engineering) requirements/specification J.3 (life and medical sciences): Health model-driven architecturesData collectionHealthcare analyticsMetricsPerformance analyticsQuality improvement

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Area of Science:

  • Health Informatics
  • Software Engineering
  • Healthcare Improvement Science

Background:

  • Continuous data collection and analysis are crucial for healthcare improvement.
  • Hospital Electronic Health Record (EHR) systems often lack readily available data for local initiatives.
  • Improvement teams require inexpensive and rapid tools due to time and funding constraints.

Purpose of the Study:

  • To investigate a model-driven software approach for rapid local domain modeling in healthcare improvement.
  • To enable non-informatics experts to define performance metrics and data collection instruments.
  • To address the informatics challenge of providing accessible tools for healthcare improvement initiatives.

Main Methods:

  • Designed a generic Improvement Data Model (IDM) for capturing project-specific data and quality measures.
  • Developed Web Improvement Support in Healthcare (WISH), a prototype tool.
  • WISH automatically generates data schemas, web interfaces, and Statistical Process Control (SPC) reports from user-generated IDM models.

Main Results:

  • The model-driven software approach was successfully implemented in over 50 improvement projects.
  • More than 700 users utilized the WISH tool.
  • Detailed experiences from a Chronic Obstructive Pulmonary Disease (COPD) project in Northwest London hospitals are presented.

Conclusions:

  • The model-driven software approach provides a feasible mechanism for rapid data modeling and tool generation in healthcare improvement.
  • The WISH tool empowers improvement teams by creating custom data collection and reporting instruments.
  • The approach offers benefits in terms of speed, cost-effectiveness, and accessibility for local healthcare improvement initiatives.