Abstract
The use of data cataloging tools allows keeping different records of both qualitative and quantitative information. However, the large amount of data is not always synonymous with quality, in the medical field this argument becomes even more critical if it is considered the consequences that the lack of a systematic and rigorous process can have for patients. The analysis was conducted through a systematic review includes several general cases and practical methodologies of data quality analysis in the health context. The search for results was made using the keywords "data quality" and "health." The study considers publications made from 2014 to 2018, topic related to Business, Management and Accounting, exclusively in the case of the Tutto platform peer-review journals were chosen, English language of publication. Efficient use of information requires databases that can collect and order health information. However, this is the first step, data quality attempts to go further through the creation of qualitative or statistical control processes and indicators able to ascertain the lack of data or identify potential anomalies. The conducted analysis sets the stage for future quality implementation in the clinical pathway and patient management.
Highlights
The availability of data and their quantity is today one of the issues most addressed by different sectors
The reading of the title and abstracts allowed to validate for the systematic review 16 sources equal to 1.90%; the rejection rate was 98.10% for a total of 824 items
19.17% of results are involved in analysing new IT systems to increase the quality of healthcare and the use of algorithms for managing medical records and medical needs; in 8.50% of cases, the articles that were presented by the database were not relevant to any of the keywords chosen for the systematic review
Summary
The availability of data and their quantity is today one of the issues most addressed by different sectors. In recent years there has been more significant interest in the correct management of qualitative and quantitative data, considered vital to improving service and health outcomes (Frost & Sullivan, 2012). According to the distinction made by Davenport (1998) for "data", we mean a discrete and objective fact on events, by "information" we mean in the same way a data but transformed by the processes of adding value, contextualization, categorization, calculation, correction, and condensation. The term data quality means the ability of data and information to respond optimally to the intended purpose; in particular, we often refer to a process characterized above all by a precise knowledge of the elements, and secondly by their management and analysis (Davenport, 1998). The main phase of data quality assessment, is the verification of all data management phases, the ultimate goal is to identify any deficiencies and increase their quality by reducing the costs of non-quality (Batini & Scannapieco, 2006; Wills, 2014; Biancone, Secinaro & Brescia; 2018)
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