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Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
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Nursing Clinical Information System (NCIS)
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Purpose of Health Records I

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Here's a breakdown of how health records serve these purposes:

Purpose of Health Records II

Purpose of Health Records II

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Data Validation

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.
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Using Existing Clinical Information Models for Dutch Quality Registries to Reuse Data and Follow COUMT Paradigm.

Maike H J Schepens^1,2, Annemarie C Trompert³, Miranda L van Hooff^4,5

¹Cirka BV, Healthcare Strategy and Innovation, Zeist, The Netherlands.

Applied Clinical Informatics

|May 3, 2023

Summary

Dutch Clinical Information Models (DCIMs) can standardize health care data for national quality registries (NQRs). This study shows DCIMs effectively map to NQR data elements, supporting efficient data collection and reuse.

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Last Updated: Jul 31, 2025

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Area of Science:

Health Informatics
Clinical Data Management
Health Services Research

Background:

The increasing importance of reusing health care data for quality measurement, research, and finance necessitates standardized data collection.
Electronic health records (EHRs) are a preferred source for National Quality Registries (NQRs), but data collection often involves manual entry or batch processing.
Clinical Information Models (CIMs) offer a solution for standardizing data content, enabling efficient data extraction from EHRs.

Purpose of the Study:

To assess the data element coverage of Dutch CIMs (DCIMs) within existing Dutch NQRs.
To identify the most prevalent DCIMs based on data element coverage and frequency across NQRs.

Main Methods:

A six-step mapping methodology was employed to evaluate data element coverage, starting from clinical pathway descriptions to detailed data element mapping.
The prevalence of DCIMs was determined by counting data elements mapped to each DCIM and dividing by the total evaluated data elements.

Main Results:

An average of 83.0% of data elements in the studied NQRs could be mapped to existing DCIMs.
A subset of five DCIMs was sufficient to map 48.6% of the total data elements analyzed.

Conclusions:

Existing DCIMs demonstrate significant potential for streamlining data collection in Dutch NQRs, aligning with the 'Collect Once Use Many Times' (COUMT) principle.
Implementation of DCIMs in NQRs should prioritize the five most prevalent models, supported by a national agreement on COUMT principles and standardized code lists.