摘要：Background: The cornerstone of the public health function is to identify healthcare needs, to influence policy development, and to inform change in practice. Current data management practices with paper-based recording systems are prone to data quality defects. Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records using a data quality assessment framework. Methodology: We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled tuberculosis (TB) clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings: A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n = 59; 59 out of 117 TB01 cards) were digitized. There were 1239 comparable data fields, out of which 65% (n = 803) were correctly matched between paper based and digital records. However, 35% of the data fields (n = 436) had anomalies, either in paper-based records or in digital records. The calculated number of data quality issues per digital patient record was 1.9, whereas it was 2.1 issues per record for paper-based records. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n = 123) than in digital records (n = 110). Conclusion: There were fewer data quality issues in digital records as compared with the corresponding paper-based records of tuberculosis patients. Greater use of mobile data capture and continued data quality assessment can deliver more meaningful information for decision making.
关键词：mHealth; mobile data collection; data quality; data quality assessment framework; Tuberculosis Control; developing countries mHealth ; mobile data collection ; data quality ; data quality assessment framework ; Tuberculosis Control ; developing countries
