A2 Review article in a scientific journal
Utilization of Large Data Sets in Maternal Health in Finland A Case for Global Health Research

List of Authors: Lamminpää Reeta, Gissler Mika, Vehviläinen-Julkunen Katri
Publisher: Wolters Kluwer Health, Inc.
Publication year: 2017
Journal: Journal of Perinatal and Neonatal Nursing
Journal acronym: JPNN
Volume number: 31
Issue number: 3
Number of pages: 8
ISSN: 0893-2190
eISSN: 1550-5073


In recent years, the use of large data sets, such as electronic health records, has increased. These large data sets are often referred to as “Big Data,” which have various definitions. The purpose of this article was to summarize and review the utilization, strengths, and challenges of register data, which means a written record containing regular entries of items or details, and Big Data, especially in maternal nursing, using 4 examples of studies from the Finnish Medical Birth Register data and relate these to other international databases and data sets. Using large health register data is crucial when studying and understanding outcomes of maternity care. This type of data enables comparisons on a population level and can be utilized in research related to maternal health, with important issues and implications for future research and clinical practice. Although there are challenges connected with register data and Big Data, these large data sets offer the opportunity for timely insight into population-based information on relevant research topics in maternal health. Nurse researchers need to understand the possibilities and limitations of using existing register data in maternity research. Maternal child nurse researchers can be leaders of the movement to utilize Big Data to improve global maternal health.

Optimal maternal, infant, and family health is critical to parenting, societal well-being, and the economy in every society. Finland is a European country and a member of the European Union, with a population of approximately 5.5 million. It is among the countries with the lowest perinatal and maternal mortality rates in the world.

In reviewing maternal health outcomes, Big Data, such as electronic health records, could be used in assessing issues related to maternity care and maternal health on a population level. The criteria for a work to be considered as Big Data include the following components referred to as the 4Vs: volume, variety, velocity, and veracity.1 Volume indicates the large amount of data. Variety is the diversity of the collected data such as structured or unstructured texts and numerical values and the heterogeneity of the information. Velocity is related to the rate at which the data are generated and the speed of analytic processing, for example, the data from smartphones and other devices. Veracity is the uncertainty, accuracy, or appropriateness of the data for secondary use such as scientific purposes.1–3 These dimensions of Big Data are not independent of each other because a change in one dimension increases the probability that another dimension will also change.1 Register data based on routinely collected administrative, statistical, and epidemiological data can be considered “Big Data,” as they include at least 3 characteristics of the Big Data definition: volume, variety, and veracity. It is necessary to recognize, understand, and use Big Data in research.4

The purpose of this article was to review and summarize the uses and challenges of register data and Big Data using 4 examples from the Finnish Medical Birth Register (MBR) from 2012 to 2017 and relate these examples to other international databases and data sets.

Internal Authors/Editors

Last updated on 2019-21-08 at 21:52