Existing Data Sources in Clinical Epidemiology: The Danish Prehospital Medical Record System.

IF 3.2 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2025-08-30 eCollection Date: 2025-01-01 DOI:10.2147/CLEP.S524197

Oscar Rosenkrantz, Christian S Benson, Tim Alex Lindskou, Cecilia H Fuglsang, Lars Pedersen, Søren Mikkelsen, Helle Collatz Christensen, Erika Frischknecht Christensen, Jacob Steinmetz, Henrik Toft Sørensen

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引用次数: 0

Abstract

Background: The Danish Prehospital Medical Record (DPMR) represents a pioneering nationwide electronic prehospital medical record system. While routinely collected data from the DPMR are increasingly used for research, a comprehensive description of its system and content is needed.

Objective: To provide an overview of the DPMR as a tool for research, including its structure, variables, and current volume of records.

Methods: We examined the DPMR's history, data structure, content, and data usage. We also analyzed aggregated DPMR data from 2016 to 2023 for selected key variables. Further, we searched MEDLINE to identify studies utilizing this data source in the past decade.

Results: Since 2016, the DPMR system has grown to include 1.8 million unique prehospital patients with over 6 million associated patient contacts. For each patient contact, the DPMR compiles information on the emergency medical call (dispatch criteria, level of urgency, and pre-arrival treatment), characteristics of the incident (patient examination, treatment, response time, on-scene time, and transport time), emergency medical services units (ambulances, rapid response vehicles with paramedics, anesthesiologists in ground-based mobile emergency care units and/or helicopters, or patient transports without treatment capability), and extensive patient-related data. The system currently encompasses 528 variables, standardized across all emergency medical services units. There are a limited number of studies on the data quality of the system and the proportion of patients with missing civil registration numbers has varied between approximately 5% and 9%, which should be taken into account when using it for research.

Conclusion: The DPMR is growing in importance as a research tool in Denmark. It provides nationwide patient-related and logistical prehospital data going back to 2016, enabling linkage with national registries for outcome research.

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临床流行病学的现有数据来源：丹麦院前医疗记录系统。

背景：丹麦院前医疗记录（DPMR）代表了一个开创性的全国院前电子医疗记录系统。虽然从DPMR常规收集的数据越来越多地用于研究，但需要对其系统和内容进行全面描述。目的：概述DPMR作为一种研究工具，包括其结构、变量和当前记录量。方法：我们检查了DPMR的历史、数据结构、内容和数据使用情况。我们还针对选定的关键变量分析了2016年至2023年的DPMR汇总数据。此外，我们检索MEDLINE以确定在过去十年中使用该数据源的研究。结果：自2016年以来，DPMR系统已发展到包括180万独特的院前患者和600多万相关患者接触者。对于每一次病人接触，DPMR汇编紧急医疗呼叫（调度标准、紧急程度和到达前治疗）、事件特征（病人检查、治疗、反应时间、现场时间和运输时间）、紧急医疗服务单位（救护车、配备护理人员的快速反应车、地面流动急救单位的麻醉师和/或直升机，或没有治疗能力的病人运输）的信息。以及大量与患者相关的数据。该系统目前包含528个变量，在所有紧急医疗服务单位标准化。关于该系统数据质量的研究数量有限，缺少民事登记号码的患者比例约在5%至9%之间，在使用该系统进行研究时应考虑到这一点。结论：DPMR作为一种研究工具在丹麦越来越重要。它提供可追溯到2016年的全国患者相关和后勤院前数据，从而与国家结果研究登记处建立联系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.