Data Integration Protocol In Ten-steps (DIPIT) : a new standard for medical researchers


Autoria(s): Dipnall, Joanna Frith; Berk, Michael; Jacka, Felice; Williams, Lana; Dodd, Seetal; Pasco, Julie
Data(s)

01/10/2014

Identificador

http://hdl.handle.net/10536/DRO/DU:30067185

Idioma(s)

eng

Publicador

Elsevier

Relação

http://dro.deakin.edu.au/eserv/DU:30067185/dipnall-dataintegration-2014.pdf

http://www.dx.doi.org/10.1016/j.ymeth.2014.07.001

http://www.ncbi.nlm.nih.gov/pubmed/25025851

Direitos

2014, Elsevier

Palavras-Chave #data aggregation #data integration #data linkage #data mining #merging #standard #science & technology #life sciences & biomedicine #biochemical research methods #biochemistry & molecular biology #biomedical data integration #multiple data sources #missing data #record linkage #health data #information #services #outcomes #trials #claims
Tipo

Journal Article

Resumo

The exponential increase in data, computing power and the availability of readily accessible analytical software has allowed organisations around the world to leverage the benefits of integrating multiple heterogeneous data files for enterprise-level planning and decision making. Benefits from effective data integration to the health and medical research community include more trustworthy research, higher service quality, improved personnel efficiency, reduction of redundant tasks, facilitation of auditing and more timely, relevant and specific information. The costs of poor quality processes elevate the risk of erroneous outcomes, an erosion of confidence in the data and the organisations using these data. To date there are no documented set of standards for best practice integration of heterogeneous data files for research purposes. Therefore, the aim of this paper is to describe a set of clear protocol for data file integration (Data Integration Protocol In Ten-steps; DIPIT) translational to any field of research.