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Related Concept Videos

Data Validation01:03

Data Validation

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...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains for...
Pharmacovigilance01:19

Pharmacovigilance

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...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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 assessment...

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Related Experiment Video

Updated: Jun 18, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

Development of a validation algorithm for 'present on admission' flagging.

Terri J Jackson1, Jude L Michel, Rosemary Roberts

  • 1Australian Centre for Economic Research on Health, School of Medicine, University of Queensland, Brisbane, Australia. t.jackson@uq.edu.au

BMC Medical Informatics and Decision Making
|December 3, 2009
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately validates hospital-acquired diagnoses using

Related Experiment Videos

Last Updated: Jun 18, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

Area of Science:

  • Health Informatics
  • Medical Coding
  • Quality Improvement

Background:

  • Routine hospital data use for adverse outcome analysis is limited by indistinguishable pre-existing and acquired conditions.
  • A 'Present on Admission' (POA) indicator distinguishes conditions, aiding quality assurance and risk adjustment.
  • Identifying hospital-acquired diagnoses (not-POA) is crucial for quality improvement.

Purpose of the Study:

  • To develop and validate a computer algorithm for assessing the accuracy of 'not-POA' (hospital-acquired) diagnosis flagging.
  • To improve the reliability of routinely coded hospital data for quality assurance.

Main Methods:

  • Expert review of the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM) to identify non-hospital-acquired conditions.
  • Development of a computer algorithm based on expert review findings.
  • Testing the algorithm against Victorian Admitted Episodes Dataset (2005/06) diagnoses flagged as complications.

Main Results:

  • High inter-reviewer agreement (93.4%) on diagnosis flagging status.
  • The algorithm validated 96.14% of flagged hospital-acquired diagnoses in the Victorian dataset.
  • Lower individual code flagging acceptability (76.2%) linked to low-frequency codes.

Conclusions:

  • An indicator for diagnosis timing significantly enhances routine data use for hospital quality improvement.
  • The developed data-cleaning algorithm can guide coding practices for improved data validity.
  • The algorithm supports the development of coding standards and coder education for better data quality.