Omar F. El-Gayar

Mobile Applications for Diabetes Self-Management: Status and Potential

Background: Advancements in smartphone technology coupled with the proliferation of data connectivity has resulted in increased interest and unprecedented growth in mobile applications for diabetes self-management. The objective of this article is to determine, in a systematic review, whether diabetes applications have been helping patients with type 1 or type 2 diabetes self-manage their condition and to identify issues necessary for large-scale adoption of such interventions. Methods: The review covers commercial applications available on the Apple App Store (as a representative of commercially available applications) and articles published in relevant databases covering a period from January 1995 to August 2012. The review included all applications supporting any diabetes self-management task where the patient is the primary actor. Results: Available applications support self-management tasks such as physical exercise, insulin dosage or medication, blood glucose testing, and diet. Other support tasks considered include decision support, notification/alert, tagging of input data, and integration with social media. The review points to the potential for mobile applications to have a positive impact on diabetes self-management. Analysis indicates that application usage is associated with improved attitudes favorable to diabetes self-management. Limitations of the applications include lack of personalized feedback; usability issues, particularly the ease of data entry; and integration with patients and electronic health records. Conclusions: Research into the adoption and use of user-centered and sociotechnical design principles is needed to improve usability, perceived usefulness, and, ultimately, adoption of the technology. Proliferation and efficacy of interventions involving mobile applications will benefit from a holistic approach that takes into account patients expectations and providers needs.

A systematic review of IT for diabetes self-management: are we there yet?

BACKGROUNDnRecent advances in information technology (IT) coupled with the increased ubiquitous nature of information technology (IT) present unique opportunities for improving diabetes self-management. The objective of this paper is to determine, in a systematic review, how IT has been used to improve self-management for adults with Type 1 and Type 2 diabetes.nnnMETHODSnThe review covers articles extracted from relevant databases using search terms related information technology and diabetes self-management published after 1970 until August 2012. Additional articles were extracted using the citation map in Web of Science. Articles representing original research describing the use of IT as an enabler for self-management tasks performed by the patient are included in the final analysis.nnnRESULTSnOverall, 74% of studies showed some form of added benefit, 13% articles showed no-significant value provided by IT, and 13% of articles did not clearly define the added benefit due to IT. Information technologies used included the Internet (47%), cellular phones (32%), telemedicine (12%), and decision support techniques (9%). Limitations and research gaps identified include usability, real-time feedback, integration with provider electronic medical record (EMR), as well as analytics and decision support capabilities.nnnCONCLUSIONnThere is a distinct need for more comprehensive interventions, in which several technologies are integrated in order to be able to manage chronic conditions such as diabetes. Such IT interventions should be theoretically founded and should rely on principles of user-centered and socio-technical design in its planning, design and implementation. Moreover, the effectiveness of self-management systems should be assessed along multiple dimensions: motivation for self-management, long-term adherence, cost, adoption, satisfaction and outcomes as a final result.

Opportunities for Business Intelligence and Big Data Analytics in Evidence Based Medicine

Evidence based medicine (EBM) is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Each year, a significant number of research studies (potentially serving as evidence) are reported in the literature at an ever-increasing rate outpacing the translation of research findings into practice. Coupled with the proliferation of electronic health records, and consumer health information, researchers and practitioners are challenged to leverage the full potential of EBM. In this paper we present a research agenda for leveraging business intelligence and big data analytics in evidence based medicine, and illustrate how analytics can be used to support EBM.

Advanced analytics for the automation of medical systematic reviews

While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic reviews. Specifically, we used soft-margin polynomial Support Vector Machine (SVM) as a classifier, exploited Unified Medical Language Systems (UMLS) for medical terms extraction, and examined various techniques to resolve the class imbalance issue. Through an empirical study, we demonstrated that soft-margin polynomial SVM achieves better classification performance than the existing algorithms used in current research, and the performance of the classifier can be further improved by using UMLS to identify medical terms in articles and applying re-sampling methods to resolve the class imbalance issue.

Towards a Business Intelligence Maturity Model for Healthcare

Healthcare is a very complex, knowledge-driven industry. Electronic health record implementations have created massive amounts of clinical and financial data. The accumulation of data is outpacing the ability of organizations to leverage the data for improving financial and clinical efficiencies and quality of care. It is believed that careful and attentive use of business intelligence (BI) in healthcare can transform data into knowledge that can improve patient outcomes and operational efficiency. BI maturity models are a way of identifying strengths and weaknesses of the information maturity of a business. This paper presents a comprehensive review of existing BI maturity models to determine their adequacy for use in healthcare. The review identifies gaps in existing BI maturity models and presents requirements for a healthcare-specific maturity model. The results of this study will be used to develop a BI maturity model that addresses the complex characteristics and needs of healthcare organizations.

An Ontology-Based Information Extraction (OBIE) Framework for Analyzing Initial Public Offering (IPO) Prospectus

With the large amounts of information associated with the Initial Public Offering (IPO) process, an intelligent tool is needed for assisting the decision-making activities for both the investors and the underwriters. Even though a large body of related studies exists in extant literature, minimum attention has been devoted to the aspect of understanding hidden semantics within the informative contents of IPO prospectus. In this paper, we present a framework for processing the textual content of IPO prospectus based on an emerging technique named Ontology Based Information Extraction (OBIE). Preliminary results indicates that the framework is capable of meeting the design requirements identified. Moreover, lessons learned during the design and implementation span technical and organizational considerations and can serve as guidance for future research and development in related areas.

Information Security Policy Compliance: An Empirical Study of Ethical Ideology

Information security policy compliance (ISP) is one of the key concerns that face organizations today. Although technical and procedural measures help improve information security, there is an increased need to accommodate human, social and organizational factors. Despite the plethora of studies that attempt to identify the factors that motivate compliance behavior or discourage abuse and misuse behaviors, there is a lack of studies that investigate the role of ethical ideology per se in explaining compliance behavior. The purpose of this research is to investigate the role of ethics in explaining Information Security Policy (ISP) compliance. In that regard, a model that integrates behavioral and ethical theoretical perspectives is developed and tested. Overall, analyses indicate strong support for the validation of the proposed theoretical model.

A semantic service-oriented architecture for distributed model management systems

Decision models are organizational resources that need to be managed to facilitate sharing and reuse. In todays networked economy, the ubiquity of the Internet and distributed computing environments further amplifies the need and the potential for distributed model management system (DMMS) that manages decision models throughout the modeling lifecycle and throughout the extended enterprise. This paper presents a service-oriented architecture for DMMS. The proposed architecture leverages service-oriented design principles and recent developments in semantic web services to enable model sharing and reuse in a distributed setting. The paper describes a prototype implementation, case study scenarios, and a discussion highlighting lessons learned and implications for research and practice.

A comparative analysis of semi-supervised learning: The case of article selection for medical systematic reviews

While systematic reviews are positioned as an essential element of modern evidence-based medical practice, the creation of these reviews is resource intensive. To mitigate this problem, there have been some attempts to leverage supervised machine learning to automate the article triage procedure. This approach has been proved to be helpful for updating existing systematic reviews. However, this technique holds very little promise for creating new reviews because training data is rarely available when it comes to systematic creation. In this research we assess and compare the applicability of semi-supervised learning to overcome this labeling bottleneck and support the creation of systematic reviews. The results indicated that semi-supervised learning could significantly reduce the human effort and is a viable technique for automating medical systematic review creation with a small-sized training dataset.

On the Design of IT-Enabled Self-Care Systems: A Socio-technical Perspective

Advances in information technology (IT) have resulted in a proliferation of IT-based solution to support the self-care and management for healthy individuals as well as patients with chronic conditions. Despite these advances, the adoption and diffusion of these solutions into practice is limited. The objective of this paper is to enhance adoption and diffusion by providing actionable recommendations for the design of IT systems for self-care. The recommendations are grounded in socio-technical design theory and in an extensive review of self-care literature. The findings indicate that despite the diversity of disease conditions, users, technologies, and implementation environments, IT-solutions for self-care often fail to encompass a holistic socio-technical view. The design of such systems will need to account for the intrinsic and interrelated characteristics of the underlying tasks, actors, technologies, and environment.