Protocol for Analysis of Root Causes of Problems Affecting the Quality of the Diagnosis Related Group-Based Hospital Data: A Rapid Review and Delphi Process

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Lobo, MF (Author) FMUP; Oliveira, M (Author) Other; Oliveira, AR (Author) Other; Santos, JV (Author) Other; Alonso, V (Author) Other; Lopes, F (Author) Other; Ramalho, A (Author) Other; Souza, J (Author) Other; Viana, J (Author) Other; Caballero, I (Author) Other; Freitas A (Author) FMUP

Conference proceedings International

Title: WorldCIST (1)

Pages: 93-103

8th World Conference on Information Systems and Technologies, WorldCIST 2020

7 April 2020 through 10 April 2020

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00S-402

DOI: 10.1007/978-3-030-45688-7_10

Abstract (EN): The Diagnosis Related Group-based administrative hospital database is an important tool for hospital financing in several health systems. It is also an important data source for clinical, epidemiological and health services research. Therefore, the data quality of these databases is of utmost importance for the exploitation of the data. To prevent or to solve these problems, it is paramount to identify the root causes of the lack of data quality. In order to do this process easier, data quality analysts could benefit from having a catalog of potential root causes to explore. Unfortunately, literature has not covered this concern extensively, what motivated us to research. This paper presents a protocol for the analysis of root causes that may affect the quality of these data. The proposed protocol includes two stages: (1) a systematic review to extract root causes from the scientific literature, and (2) a Delphi technique to analyze the relevance of root causes and to map them into data quality dimensions. Moreover, we provide a pilot study (rapid review) based on a systematic review covering papers published during 2018 and 2019. This aimed to establish proof of concept of the proposed methodology and to examine its feasibility. Eighteen studies were included for data charting, rendering 70 root causes. The Ishikawa framework provided a meaningful way to represent the root causes. Most of these causes were associated with professional knowledge, education and processes performed in the data collection, analysis or treatment. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific