Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions

Abstract

Objective Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States. Materials and Methods There were more than 2.23 million rows and 32 million cells in the Medicaid data examined. The taxonomy was developed through document review, descriptive data analysis, and literature review. A software program was created to automatically detect defects by using a set of constraints whose development was facilitated by the taxonomy. Results Five major categories and seventeen subcategories of defects were identified. The major categories are missingness, incorrectness, syntax violation, semantic violation, and duplicity. More than 3 million defects were detected indicating substantial problems with data quality. Defect density exceeded 10% in five tables. The majority of the data defects belonged to format mismatch, invalid code, dependency-contract violation, and implausible value types. Such contextual knowledge can support prioritized quality improvement initiatives for the Medicaid data studied. Conclusions This research took the initial steps to understand the types of data defects and detect defects in large healthcare datasets. The results generally suggest that healthcare organizations can potentially benefit from focusing on data quality improvement. For those purposes, the taxonomy developed and the approach followed in this study can be adopted.

An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems

“…In recent years, software applications are playing a vital role in every organization and business. Companies rely on software applications for handling their daily operations [2]. These software applications and systems hold critical importance in the healthcare domain due to severe consequences associated with their malfunction.…”

“…These software applications and systems hold critical importance in the healthcare domain due to severe consequences associated with their malfunction. Therefore, healthcare applications are based on design rules and best practices for high-quality applications [1,2].…”

“…Software Development Life Cycle (SDLC) (abbreviations and acronyms given in Table 1) provides a basic set of rules that are used in design, development, and testing of different software applications [2]. However, with increase in size and complexity of software, ensuring quality via testing results in increased cost [1][2][3]. In recent years, the healthcare industry is growing in both high-and low-level income countries [4][5][6].…”

Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives

“…Although the trial-error approach has traditionally been used (Belciug and Gorunescu, 2020), there is a need for a more clinical and honed system. Generally, staff working at hospitals and schools often lack knowledge and competencies (Zhang and Koru, 2020) and may unintentionally be working at cross purposes. Therefore, individuals with ID/DD may be more vulnerable and at greater risk for their safety.…”

“…There is a clear need to develop a system that facilitates communication and access to health information for people with ID/DD, one that provides information to caregivers and education providers, limits errors, and, therefore, improves these individuals' education and quality of life. However, caregivers or teachers dealing with this group are generally not trained to understand this health information (Zhang and Koru, 2020). Therefore, to achieve better results and counter health inequalities, caregivers and teachers who work with students with ID/DD should receive special training to enhance their health information literacy (the ability to understand health information).…”