Data quality in healthcare: A report of practical experience with the Canadian Primary Care Sentinel Surveillance Network data
| Autor: | Ken Martin, John A. Queenan, Behrouz Ehsani-Moghaddam |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Canada
Databases Factual Leadership and Management Computer science media_common.quotation_subject Big data 030209 endocrinology & metabolism Context (language use) User requirements document 03 medical and health sciences Consistency (database systems) 0302 clinical medicine Public health surveillance Health care Quality (business) Public Health Surveillance 030212 general & internal medicine media_common Primary Health Care business.industry Health Policy Quality Improvement Data Accuracy Risk analysis (engineering) Data quality business Sentinel Surveillance |
| Zdroj: | Health information management : journal of the Health Information Management Association of Australia. 50(1-2) |
| ISSN: | 1833-3575 |
| Popis: | Data quality (DQ) is the degree to which a given dataset meets a user’s requirements. In the primary healthcare setting, poor quality data can lead to poor patient care, negatively affect the validity and reproducibility of research results and limit the value that such data may have for public health surveillance. To extract reliable and useful information from a large quantity of data and to make more effective and informed decisions, data should be as clean and free of errors as possible. Moreover, because DQ is defined within the context of different user requirements that often change, DQ should be considered to be an emergent construct. As such, we cannot expect that a sufficient level of DQ will last forever. Therefore, the quality of clinical data should be constantly assessed and reassessed in an iterative fashion to ensure that appropriate levels of quality are sustained in an acceptable and transparent manner. This document is based on our hands-on experiences dealing with DQ improvement for the Canadian Primary Care Sentinel Surveillance Network database. The DQ dimensions that are discussed here are accuracy and precision, completeness and comprehensiveness, consistency, timeliness, uniqueness, data cleaning and coherence. |
| Databáze: | OpenAIRE |
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