Discovering the most important data quality dimensions in health big data using latent semantic analysis

Big Data quality is a field which is emerging. Many authors nowadays agree that data quality is still very relevant, even for Big Data uses. However, there is a lack of frameworks or guidelines focusing on how to carry out big data quality initiatives. The starting point of any data quality work is to determine the properties of data quality, termed ‘data quality dimensions’ (DQDs). Even these dimensions lack precise rigour in terms of definition in existing literature. This current research aims to contribute towards identifying the most important DQDs for big data in the health industry. It is a continuation of previous work, which, using relevant literature, identified five DQDs (accuracy, completeness, consistency, reliability and timeliness) as being the most important DQDs in health datasets. The previous work used a human judgement based research method known as an inner hermeneutic cycle (IHC). To remove the potential bias coming from the human judgement aspect, this research study used the same set of literature but applied a statistical research method (used to extract knowledge from a set of documents) known as latent semantic analysis (LSA). Use of LSA concluded that accuracy and completeness were the only similar DQDs classed as the most important in health Big Data for both IHC and LSA

Discovering the most important data quality dimensions in health big data using latent semantic analysis

Big Data quality is a field which is emerging. Many authors nowadays agree that data quality is still very relevant, even for Big Data uses. However, there is a lack of frameworks or guidelines focusing on how to carry out big data quality initiatives. The starting point of any data quality work is to determine the properties of data quality, termed ‘data quality dimensions’ (DQDs). Even these dimensions lack precise rigour in terms of definition in existing literature. This current research aims to contribute towards identifying the most important DQDs for big data in the health industry. It is a continuation of previous work, which, using relevant literature, identified five DQDs (accuracy, completeness, consistency, reliability and timeliness) as being the most important DQDs in health datasets. The previous work used a human judgement based research method known as an inner hermeneutic cycle (IHC). To remove the potential bias coming from the human judgement aspect, this research study used the same set of literature but applied a statistical research method (used to extract knowledge from a set of documents) known as latent semantic analysis (LSA). Use of LSA concluded that accuracy and completeness were the only similar DQDs classed as the most important in health Big Data for both IHC and LSA

Perceptions and Challenges of EHR Clinical Data Quality

Despite the premise of better data, Electronic Health Record (EHR) data quality remains problematic. Traditional approaches for improving data quality through semantic and syntactic controls have not resolved the problems. To use the medical vernacular – “we have addressed the symptoms but not the cause.” This paper reports on an exploratory study undertaken in a large maternity hospital with an aim to expose detractors from high-quality data in EHRs. The study involved a perceptions survey that was completed by Nursing and Midwifery staff; chosen because of known data quality challenges in their area of practice. The study results indicate social, cultural and environmental aspects of information systems (IS) use are equally as problematic as the IS itself. A lack of agreement amongst healthcare practitioners surrounding what data quality means is also evident, with time, culture and lacking formal education on data quality being contributors to lower data quality outcome

The Impact of Information and Communication Technology (ICT) on the efficiency of healthcare delivery at Radiology department of Inkosi Albert Luthuli Hospital.

Masters Degree. University of KwaZulu-Natal, Durban.Healthcare service provision is undoubtedly a major priority for any governmental policy makers and society at large. Access to quality health care is declared a basic human right globally, yet there are many factors that still make it hard for countries to make this a reality. Issues such as shortage of skilled healthcare workers, high costs of healthcare provision and poor economic outlooks are some of the major contributors to gaps in provision of equitable healthcare services. Information and Communication Technology (ICT) has become an integral part of our daily life. The study aimed to investigate the role that ICT can play in improving the efficiency of healthcare delivery processes and spreading access to communities that are left behind in the provision of this basic human need. A quantitative methodology was used to evaluate the perception of professionals with regards to the adoption of ICT and its impact on healthcare services delivery at the radiology department. The target population was made up of administrators, radiographers and radiologists at the radiology department of Inkosi Albert Luthuli Central Hospital. Data was collected through questionnaires which were physically administered on site. A convenience sampling technique was used to identify and recruit study participants. The results revealed that 70% of respondents agree that ICT adoption does indeed increase efficiency of healthcare service delivery. The study did not find significant relationship between users’ attitude towards ICT adoption and the ability of ICT to improve efficiency in a healthcare facility. It is recommended that healthcare facilities adopting ICT should invest more time and resources in training and offering technical support to end users. The study can benefit healthcare facilities who seek to improve the quality, speed, accuracy of healthcare services by using ICT systems

Investigating the attainment of optimum data quality for EHR Big Data: proposing a new methodological approach

The value derivable from the use of data is continuously increasing since some years. Both commercial and non-commercial organisations have realised the immense benefits that might be derived if all data at their disposal could be analysed and form the basis of decision taking. The technological tools required to produce, capture, store, transmit and analyse huge amounts of data form the background to the development of the phenomenon of Big Data. With Big Data, the aim is to be able to generate value from huge amounts of data, often in non-structured format and produced extremely frequently. However, the potential value derivable depends on general level of governance of data, more precisely on the quality of the data. The field of data quality is well researched for traditional data uses but is still in its infancy for the Big Data context. This dissertation focused on investigating effective methods to enhance data quality for Big Data. The principal deliverable of this research is in the form of a methodological approach which can be used to optimize the level of data quality in the Big Data context. Since data quality is contextual, (that is a non-generalizable field), this research study focuses on applying the methodological approach in one use case, in terms of the Electronic Health Records (EHR). The first main contribution to knowledge of this study systematically investigates which data quality dimensions (DQDs) are most important for EHR Big Data. The two most important dimensions ascertained by the research methods applied in this study are accuracy and completeness. These are two well-known dimensions, and this study confirms that they are also very important for EHR Big Data. The second important contribution to knowledge is an investigation into whether Artificial Intelligence with a special focus upon machine learning could be used in improving the detection of dirty data, focusing on the two data quality dimensions of accuracy and completeness. Regression and clustering algorithms proved to be more adequate for accuracy and completeness related issues respectively, based on the experiments carried out. However, the limits of implementing and using machine learning algorithms for detecting data quality issues for Big Data were also revealed and discussed in this research study. It can safely be deduced from the knowledge derived from this part of the research study that use of machine learning for enhancing data quality issues detection is a promising area but not yet a panacea which automates this entire process. The third important contribution is a proposed guideline to undertake data repairs most efficiently for Big Data; this involved surveying and comparing existing data cleansing algorithms against a prototype developed for data reparation. Weaknesses of existing algorithms are highlighted and are considered as areas of practice which efficient data reparation algorithms must focus upon. Those three important contributions form the nucleus for a new data quality methodological approach which could be used to optimize Big Data quality, as applied in the context of EHR. Some of the activities and techniques discussed through the proposed methodological approach can be transposed to other industries and use cases to a large extent. The proposed data quality methodological approach can be used by practitioners of Big Data Quality who follow a data-driven strategy. As opposed to existing Big Data quality frameworks, the proposed data quality methodological approach has the advantage of being more precise and specific. It gives clear and proven methods to undertake the main identified stages of a Big Data quality lifecycle and therefore can be applied by practitioners in the area. This research study provides some promising results and deliverables. It also paves the way for further research in the area. Technical and technological changes in Big Data is rapidly evolving and future research should be focusing on new representations of Big Data, the real-time streaming aspect, and replicating same research methods used in this current research study but on new technologies to validate current results

A Self-Guided Educational Program Based on Informatics Competency Self-Assessment

The informatics competency gap for nurses has existed since the first use of technology in healthcare. Although numerous informatics competencies for nurses have been identified over the past 20 years, there is a lack of standardized educational content to help address the informatics competency gap. In a digital healthcare environment, the ability for nurses to understand and use informatics competencies is essential and the lack of informatics competency has a far-reaching impact. Based on the identified gaps, an informatics competency self-assessment tool with associated microeducation was developed to provide a standardized learning approach that would be supported by stakeholders in healthcare organizations. The electronic self-assessment tool embodies Knowles principles of adult learning. A review of current literature was performed using scholarly databases and peer-reviewed sources and a current nursing informatics competency self-assessment tool was identified to be used as the foundation of this project. Self-guided microeducation modules were developed, and a Delphi method was used to validate the content through the feedback of five subject-matter experts with informatics expertise. When completed, the electronic microeducation was linked to each question in the competency self-assessment tool. A final Delphi review of the educational project demonstrated that an informatics competency self-assessment tool with associated microeducation could provide a standardized learning approach that would be supported by stakeholders in healthcare organizations. This project impacts social change by providing a mechanism for to improve nursing informatics competencies that will reduce technology-related nursing burnout and improve patient outcomes

Does use of computer technology for perinatal data collection influence data quality?

Population health data, collected worldwide in an effort to monitor mortality and morbidity of mothers and babies, namely, perinatal data, are mandated at a federal level within Australia. The data are used to monitor patterns in midwifery, obstetric and neonatal practice, health outcomes, used for research purposes, funding allocation and education. Accuracy in perinatal data is most often reported via quantitative validation studies of perinatal data collections both internationally and in Australia. These studies report varying levels of accuracy and suggest researchers need to be more aware of the quality of data they use. This article presents findings regarding issues of concern identified by midwives relating to their perceptions of how technology affects the accuracy of perinatal data records. Perinatal data records are perceived to be more complete when completed electronically. However, issues regarding system functionality, the inconsistent use of terminology, lack of data standards and the absence of clear, written records contribute to midwives\u27 perceptions of the negative influence of technology on the quality of perinatal data