Table 4.

The data quality control measures in the COVID-19 registry system.

The general approach to data quality control	Measures are taken to control the quality of data
Preventive actions	•Developing a registry guideline for all staff •Training registrars and data entry staff •Using structured (coded) data in the registry software •Using the patient's national ID as the unique identifier in the registry software •Developing a data dictionary for the registry software •Developing data validation rules and checks in software (including Data-type check, Simple range and constraint check, Cross-reference, and data consistency check) •In the registry software, essential data were considered mandatory. •Integrating patient data from different sources in the registry software •Recruiting registrars from the staff of the same healthcare center participating in the registry •Selecting one of the staff of each healthcare center as the supervisor of the registration program in the same center •Establishing the data quality assessment and analysis committee for developing data quality guidelines
Detective actions	•Comparing data obtained from a variety of sources and systems to identify and correct inconsistencies, missing data, and duplicate cases •Continuous monitoring of data to identify abnormal and suspicious data, for example, unusual trend and frequency of registered cases from each participating centers
Corrective actions	•Continuous correcting errors based on the results of detective controls, (such as completing missing data, editing inconsistencies, removing duplicates, etc.) •Regular feedback to the relevant hospitals to correct the data