Data Cleaning in the Evaluation of a Multi-Site Intervention Project

Abstract

Context:The High Value Healthcare Collaborative (HVHC) sepsis project was a two-year multi-site project where Member health care delivery systems worked on improving sepsis care using a dissemination & implementation framework designed by HVHC. As part of the project evaluation, participating Members provided 5 data submissions over the project period. Members created data files using a uniform specification, but the data sources and methods used to create the data sets differed. Extensive data cleaning was necessary to get a data set usable for the evaluation analysis.Case Description:HVHC was the coordinating center for the project and received and cleaned all data submissions. Submissions received 3 sequentially more detailed levels of checking by HVHC. The most detailed level evaluated validity by comparing values within-Member over time and between Member. For a subset of episodes Member-submitted data were compared to matched Medicare claims data.Findings:Inconsistencies in data submissions, particularly for length-of-stay variables were common in early submissions and decreased with subsequent submissions. Multiple resubmissions were sometimes required to get clean data. Data checking also uncovered a systematic difference in the way Medicare and some members defined intensive care unit stay.Conclusions:Data checking is a critical for ensuring valid analytic results for projects using electronic health record data. It is important to budget sufficient resources for data checking. Interim data submissions and checks help find anomalies early. Data resubmissions should be checked as fixes can introduce new errors. Communicating with those responsible for creating the data set provides critical information.

A method for interoperable knowledge-based data quality assessment

“…We assume DQassessment is sensible at different stages with different perspectives, e.g. a data integration specialist validates data integration locally during implementation, a quality manager continuously monitors DQ in a data integration center and a researcher assesses DQ in a research data network specifically for the research question [8,[15][16][17][18][19]. For this purpose, the presented method is applicable at all stages on a compatible data repository ( Fig.…”

“…However, these recommendations are not specific enough to ensure comparability if implemented independently. Furthermore, which MMs provide sensible information and assessment of their results may depend on the planned data usage [14] and the role of the person assessing the DQ [8,[15][16][17][18][19]. Stausberg et al [20] suggest in their review that research should take into account proposals for formal definitions of DQ-indicators as well as standards for data definitions.…”

Common and Unique Barriers to the Exchange of Administrative Healthcare Data in Environmental Public Health Tracking Program

“…Data validation is critical for ensuring valid analytic results for any projects using health records and environmental monitored data [ 33 , 34 ]. Challenges can result from the initial delay from the responders, staff turnover, high caseloads, lack of resources, and competing prioritization in the health department [ 34 ]. It is critical to learn if data cleaning and duplicate resolution processes are inconsistent across the tracking state programs.…”

Research skills and the data spreadsheet: A research primer for low- and middle-income countries

“…Once all data entry is complete and the quality assurance exercise has been undertaken, the data needs to be ‘cleaned’. Data cleaning refers to identifying incomplete, inaccurate or irrelevant data and then replacing coarse data with clean entries in a methodical way [ 13 ]. In most cases, this involves identifying missing or incorrect data in the spreadsheet.…”