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

DESIGN AND ANALYSIS QUALITY SYSTEM OF STUDENT INFORMATION PROCESSING DATA NEW STUDENT SMK MUHAMMADIYAH 3 YOGYAKARTA

This research aims to design and analyze websites new Student Information System Acceptance SMK Muhammadiyah 3 Yogyakarta with PHP and MySQL that can manage data on the implementation of the prospective student Admission. This research is Research and Development. This website development method using modified waterfall. Tests carried out to test the quality of the website System Information by Olsina (1998), namely correctness, functionality, reliability, efficiency, maintainability, and usability. Correctness quality obtained with white-box testing (testing Looping) and black box (Graph-based testing). The quality of the obtained functionality testing State Transition Testing. Reliability Testing Load testing obtained from 30 students to enter data, and editing data and deletion of data. Efficiency assessment obtained by recording the time it takes to open a page devoted. Maintainability obtained from Cross-browser testing. Obtained from Alpa Usability testing by a team of expert website and beta testing by admin, user1, and guest. Data analysis techniques used in this research is descriptive statistical analysis. The results mennunjukkan that Admission Information System SMK Muhammadiyah 3 Yogyakarta can be developed and be able to perform data processing of new students of SMK Muhammadiyah 3 Yogyakarta. Assessment obtained information systems Admission SMK Muhammadiyah 3 Yogyakarta meet quality testing correctness Graph-Based and Looping testing. Admission Information Systems SMK Muhammadiyah 3 Yogyakarta assessed for each criterion usability. Assessment of quality functionality Admission Information Systems SMK Muhammadiyah 3 Yogyakarta run well on testing State transition testing. Response time performance testing conducted by Information Systems and response pages less than 2 seconds so that said system meets the quality efficiency by Shneiderman. Maintainability testing done with cross browser testing and obtained system is capable of running on Mozilla Firefox, Google Chrome, and Internet Explorer. Keywords: Information Systems, correctness, Usability, Functionality, Reliability, Efficiency, Maintainabilit

Health information systems

Healthcare is an information intensive industry in which quality and timely information is a critical resource. There are a wide range of information systems in health that perform different functions but all are involved in the management of data and information. This chapter provides an overview of Health Information Systems and their use in supporting healthcare

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Making stillbirths count, making numbers talk - issues in data collection for stillbirths.

BACKGROUND: Stillbirths need to count. They constitute the majority of the world's perinatal deaths and yet, they are largely invisible. Simply counting stillbirths is only the first step in analysis and prevention. From a public health perspective, there is a need for information on timing and circumstances of death, associated conditions and underlying causes, and availability and quality of care. This information will guide efforts to prevent stillbirths and improve quality of care. DISCUSSION: In this report, we assess how different definitions and limits in registration affect data capture, and we discuss the specific challenges of stillbirth registration, with emphasis on implementation. We identify what data need to be captured, we suggest a dataset to cover core needs in registration and analysis of the different categories of stillbirths with causes and quality indicators, and we illustrate the experience in stillbirth registration from different cultural settings. Finally, we point out gaps that need attention in the International Classification of Diseases and review the qualities of alternative systems that have been tested in low- and middle-income settings. SUMMARY: Obtaining high-quality data will require consistent definitions for stillbirths, systematic population-based registration, better tools for surveys and verbal autopsies, capacity building and training in procedures to identify causes of death, locally adapted quality indicators, improved classification systems, and effective registration and reporting systems