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Related Concept Videos

Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

1.9K
Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
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Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

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The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
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Formats for Nursing Documentation01:28

Formats for Nursing Documentation

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Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
Nursing Assessment Form:
• A nursing assessment form is a foundational document that captures detailed patient data from physical assessments and nursing histories.
• It includes patient demographics, medical history,...
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Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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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.
For example, a patient with a chronic...
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Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
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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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Related Experiment Video

Updated: Apr 7, 2026

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients
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Extraction and processing of intensive care chart data from a patient data management system.

Nikolas B Schrader1, Burkhard Meißner2, Paul Fischer1

  • 1Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Würzburg, Germany.

Frontiers in Digital Health
|April 6, 2026
PubMed
Summary

This study introduces a Python-based ETL framework for extracting and standardizing intensive care data from Patient Data Management Systems (PDMS). The system ensures reproducible, GDPR-compliant research datasets by automating data processing and de-identification.

Keywords:
Python (programming language)SQL (structured query language)anaesthesiaextract transform and load (ETL)intensive care medicinepatient data management system (PDMS)

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Clinical Research Technology

Background:

  • Patient Data Management Systems (PDMS) in intensive care and perioperative settings contain valuable clinical research data.
  • Proprietary and fragmented PDMS architectures hinder data accessibility and necessitate extensive Extract, Transform, and Load (ETL) processing for secondary use.

Purpose of the Study:

  • To develop a modular, Python-based ETL framework to overcome barriers in secondary use of PDMS data.
  • To enable flexible, domain-specific extraction and standardization of high-frequency, multimodal PDMS data for clinical research.

Main Methods:

  • Developed a modular Python ETL framework with reusable components for data retrieval, preprocessing, harmonization, and de-identification.
  • Utilized Pydantic models for domain-specific data representation, enforcing schemas, type constraints, and plausibility checks.
  • Employed SQLAlchemy for database abstraction and structured preprocessing logic to standardize heterogeneous PDMS entries.

Main Results:

  • The framework produces reproducible, analysis-ready datasets via a transparent, auditable workflow with integrated logging for traceability.
  • Implemented salted, irreversible pseudonymization for GDPR and BayKrG compliance.
  • Replaced complex ad hoc queries with standardized, maintainable, and research-ready processes through modular extraction units.

Conclusions:

  • The governance-first ETL pipeline overcomes technical and regulatory barriers to secondary PDMS data use.
  • Modular architecture allows for reusable validation, pseudonymization, and audit logging across domains and installations.
  • Provides a pragmatic foundation for reproducible, governance-compliant access to high-frequency intensive care data, enabling incremental interoperability.