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Abstract High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment.
We reviewed current data quality assessment methods.
The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed.
Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods were descriptive surveys and data audits, whereas the common qualitative assessment methods were interview and documentation review.
This review study is limited by the coverage of the databases and the breadth of public health information systems. Further research could develop consistent data quality definitions and attributes. More research efforts should be given to assess the quality of data use and the quality of data collection process.
The ultimate goal of public health is to improve health at the population level, and this is achieved through the collective mechanisms and actions of public health authorities within the government context [ 12 ]. Three functions of public health agencies have been defined: Since data, information and knowledge underpin these three functions, public health is inherently a data-intensive domain [ 34 ].
High quality data are the prerequisite for better information, better decision-making and better population health [ 5 ]. Public health data represent and reflect the health and wellbeing of the population, the determinants of health, public health interventions and system resources [ 6 ].
The data on health and wellbeing comprise measures of mortality, ill health, and disability. The levels and distribution of the determinants of health are measured in terms of biomedical, behavioral, socioeconomic and environmental risk factors.
Data on public health interventions include prevention and health promotion activities, while those on system resources encompass material, funding, workforce, and other information [ 6 ]. Public health data are used to monitor trends in the health and wellbeing of the community and of health determinants.
Also, they are used to assess the risks of adverse health effects associated with certain determinants, and the positive effects associated with protective factors.
The data inform the development of public health policy and the establishment of priorities for investment in interventions aimed at modifying health determinants.
They are also used to monitor and evaluate the implementation, cost and outcomes of public health interventions, and to implement surveillance of emerging health issues [ 6 ]. Thus, public health data can help public health agencies to make appropriate decisions, take effective and efficient action, and evaluate the outcomes [ 78 ].
For example, health indicators set up the goals for the relevant government-funded public health agencies [ 5 ].
Public health data are generated from public health practice, with data sources being population-based and institution-based [ 56 ]. Population-based data are collected through censuses, civil registrations, and population surveys.guidance, based on the best available evidence and practice, for assessing data quality in PCTs conducted through the National Institutes of Health (NIH) Health Care Systems Research Collaboratory.
Feb 13, · The chief data officer (CDO) is a crucial role for organizations looking to embrace digital transformation. While the role is relatively new to many organizations, and the value a CDO adds is widely acknowledged by business leadership, disparities between organizations mean that not all CDOs are set up for monstermanfilm.comon: 53 State Street, 20th Floor, Boston, MA, Later, with the rapid development of information technology, research turned to the study of the data quality.
Research on data quality started abroad in the s, and many scholars proposed different definitions of data quality . Data Results and Analysis.
Week 6: Data Results and Analysis. After the data are collected, it is time to analyze the results! Discuss one of the four basic rules for understanding results in a research study.
Quality Improvement. In health care quality QI is a quality management program continuously addresses quality with a focus on processes (Bradley & Thompson, ).For this paper, data quality program identifies the quality process used to facilitate quality management programs for research.
May 14, · Data extracted from the publications included author, year of publication, aim of data quality assessment, country and context of the study, function and scope of the PHIS, definition of data quality, methods for data quality assessment, study design, data collection methods, data collected, research procedure, methods for data .