Toward a framework for data quality in cloud-based health information system

This Cloud computing is a promising platform for health information systems in order to reduce costs and improve accessibility. Cloud computing represents a shift away from computing being purchased as a product to be a service delivered over the Internet to customers. Cloud computing paradigm is becoming one of the popular IT infrastructures for facilitating Electronic Health Record (EHR) integration and sharing. EHR is defined as a repository of patient data in digital form. This record is stored and exchanged securely and accessible by different levels of authorized users. Its key purpose is to support the continuity of care, and allow the exchange and integration of medical information for a patient. However, this would not be achieved without ensuring the quality of data populated in the healthcare clouds as the data quality can have a great impact on the overall effectiveness of any system. The assurance of the quality of data used in healthcare systems is a pressing need to help the continuity and quality of care. Identification of data quality dimensions in healthcare clouds is a challenging issue as data quality of cloud-based health information systems arise some issues such as the appropriateness of use, and provenance. Some research proposed frameworks of the data quality dimensions without taking into consideration the nature of cloud-based healthcare systems. In this paper, we proposed an initial framework that fits the data quality attributes. This framework reflects the main elements of the cloud-based healthcare systems and the functionality of EHR

Towards a framework for data quality in Electronic health records

Electronic Health Record (EHR) refers to the digital form of a patient’s medical record. It is defined as a repository of patient data in digital form. This record is stored and exchanged securely and accessible by different levels of authorized users. Its key purpose is to support the continuity of care, and allow the exchange and integration of medical information for a patient. However, this would not be achieved without ensuring the quality of data populated in the EHR as the data quality can have a great impact on the overall effectiveness of EHR. The assurance of the quality of data used in healthcare systems is a pressing need to help the continuity and quality of care. Identification of data quality dimensions is a challenging issue as EHR data quality often focus only on data validation and verification, and overlook, for example,the appropriateness of use. Some research proposed frameworks of the data quality dimensions without taking into consideration the nature of e-healthcare systems. In this paper, we proposed an initial framework that fits the data quality attributes. This framework reflects the main elements of the healthcare systems and the functionality of EHR

Data quality assessment instrument for electronic health record systems in Saudi Arabia

The provision of high quality data is of considerable importance to both business and government; poor data may lead to poor decisions, so quality plays a crucial role. With the proliferation of electronic data collection by businesses and governments, there has arisen a pressing need to assure this quality. This has been recognized by both the private and public sectors, and many initiatives such as the Data Quality Initiative Framework by the Welsh government, passed in 2004, and the Data Quality Act by the United States government, passed in 2002, have been launched to improve it in those countries.At the same time, healthcare is a domain in which the timely provision of accurate, current and complete patient data is one of the most important objectives. Instigation of a so-called Electronic Health Record (EHR), defined as a repository of patient data in digital form that is stored and exchanged securely and is accessible by different levels of authorized users, has been attracting the attention of both research and industry. EHRs allow information regarding a patient’s health to be distributed among heterogeneous information systems. This evolution has added a layer of complexity in data quality, making data quality assurance a challenging issue, as the key barriers to optimal use of EHR data are the increasing quantity of data and their poor quality.Many data quality frameworks have been developed to measure the quality of data in information systems. However, there is no consensus on a rigorously defined set of data quality dimensions. Existing dimensions are usually based on literature reviews, industrial experiences or intuitive understanding and do not take into consideration the nature of e-healthcare systems. Moreover, definitions of these dimensions vary from one data quality framework to another. The aim of this research is to develop a data quality framework consisting of health-relevant dimensions, and data quality measures that help health organisations to enhance the quality of their data. The study provides both subjective and objective measures for assessing the quality of data.An 11-dimensional data quality framework has been developed and confirmed by EHR stakeholders and a group of experts and data consumers. With each dimension, several associated measures have been developed to help an organisation to measure the quality of the data populating their EHR systems. Some issues linked with the measures associated with security-related dimensions have arisen during the confirmation stage. Therefore, these issues were further discussed and reviewed with security experts in order to revise the proposed framework and its measures.Subsequently, a case study was conducted in a large hospital to examine the practicality of the proposed instrument. The instrument was used to help hospitals to assess their data. After that, the usefulness and practicality of the instrument were examined through an evaluation questionnaire distributed to quality assessment team members. Follow-up interviews with senior managers were carried out to discuss the output of the assessment and its practicality.The contribution of this research is the development of a proper data quality framework for EHRs in the context of Saudi Arabia which resulted in 11 health-relevant data quality dimensions. An instrument was also introduced to represent all developed and confirmed measures that assess data population in EHRs

A dimension-oriented taxonomy of data quality problems in electronic health records

The provision of high quality data is of considerable importance to health sector. Healthcare is a domain in which the timely provision of accurate, current and complete patient data is one of most important objectives. The quality of Electronic Health Record (EHR) data concerns health professionals and researchers for secondary use. To ensure high quality data in health sector, health-related organisations need to have appropriate methodologies and measurement processes to assess and analyse the quality of their data. Yet, no adequate attention has been paid to the existing data quality problems (dirty data) in health-related research. In practice, anomalies detection and cleansing is time-consuming and labour-intensive which makes it unrealistic to most health-related organisations. This paper proposes a dimension-oriented taxonomy of data quality problems. The mechanism of the data quality assessment relates the business impacts into data quality dimensions. As a case study, the new taxonomy-based data quality assessment was used to assess the quality of data populating an EHR system in a large Saudi Arabian hospital. The assessment results were discussed and reviewed with the top management of the hospital as well as the assessment team who participated in the data quality assessment process. Then, the assessment team evaluated this new approach

DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes in the objective function due to time-varying parameters. A response strategy consisting in reevaluating all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), hypervolume difference (HVD), Robust IGD (RIGD), and Robust General Distance (RGD) metrics are used to assess the DPb-MOPSO performance. Quantitative results are analyzed using Friedman’s analysis of variance, and the Wilcoxon sum ranks test, while the stability is analyzed using Lyapunov’s theorem. The DPb-MOPSO is more robust than several dynamic multi-objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 8/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO can resolve 7/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 6/8 with moderate changes. DPb-MOPSO assumes 7/8, 6/8, and 5/8 for solving FDA, and dMOP functions on IGD and 6/8, 5/8, and 5/8 on HVD metrics considering severe, moderate, and slight environmental changes respectively. Also, it is the winner for solving 8 DMOPs based on RIGD, and RGD metrics