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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 Reporting and Recording01:24

Data Reporting and Recording

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...
Purpose of Health Records I01:11

Purpose of Health Records I

The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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:

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Healthcare Data Quality Language (HDQL): Streamlining the Translation of Healthcare System Requirements into Software

Tia Haddad1, Pushpa Kumarapeli1, Simon de Lusignan2

  • 1School of Computer Science and Mathematics, Kingston University London.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
Summary

Healthcare Information Systems (HIS) require better software quality models. The new Healthcare Data Quality Language (HDQL) framework helps professionals define HIS features for improved acceptance and performance.

Keywords:
Healthcare RequirementsHealthcare Software SystemsSoftware Quality Attribute ModelsSoftware Quality Attributes

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Area of Science:

  • Health Informatics
  • Software Engineering
  • Data Quality Management

Background:

  • Current software quality models inadequately address the evolving needs of Healthcare Information Systems (HIS).
  • HIS face unique regulatory and infrastructural challenges impacting software quality.
  • A gap exists between HIS requirements and established software quality attributes.

Purpose of the Study:

  • Introduce the Healthcare Data Quality Language (HDQL) as a novel framework.
  • Bridge the gap between HIS requirements and software quality attributes.
  • Enable intuitive definition of software features by healthcare professionals.

Main Methods:

  • Development of the Healthcare Data Quality Language (HDQL) framework.
  • Focus on intuitive feature definition for healthcare professionals.
  • Tailoring software features to specific healthcare needs.

Main Results:

  • HDQL provides a novel framework for HIS software quality.
  • Facilitates intuitive definition of software features by healthcare professionals.
  • Aims to improve system acceptance, performance, and evolution.

Conclusions:

  • HDQL addresses limitations in existing software quality models for HIS.
  • Empowers healthcare professionals to define system requirements effectively.
  • Promotes enhanced HIS acceptance, performance, and adaptability.