John O’Donoghue

Data Management within mHealth Environments: Patient Sensors, Mobile Devices, and Databases

Pervasive environments generate large quantities of data, originating from backend servers, portable devices, and wireless mobile sensors. Pervasive sensing devices that monitor properties of the environment (including human beings) can be a large data source. Unprocessed datasets may include data that is faulty and irrelevant, and data that is important and useful. If not managed correctly the large amount of data from a data-rich pervasive environment may result in information overload or delivery of incorrect information. Context-sensitive quality data management aims to gather, verify, process, and manage the multiple data sources in a pervasive environment in order to deliver high quality, relevant information to the end-user. Managing the quality of data from different sources, correlating related data, and making use of context, are all essential in providing end users with accurate and meaningful data in real time. This requirement is especially true for critical applications such as in a medical environment. This article presents the Data Management System (DMS) architecture. It is designed to deliver quality data service to its users. The DMS architecture employs an agent-based middleware to intelligently and effectively manage all pervasive data sources, and to make use of context to deliver relevant information to the end-user. Two of the DMS components are presented: (1) data validation and (2) data consistency. The DMS components have been rigorously evaluated using various medical-based test cases. This article demonstrates a careful, precise approach to data based on the quality of the data and the context of its use. It emphasises the DMS architecture and the role of software agents in providing quality data management.

Towards Improved Healthcare Performance: Examining Technological Possibilities and Patient Satisfaction with Wireless Body Area Networks

This paper investigates the benefits of using less intrusive wireless technologies for heart monitoring. By replacing well established heart monitoring devices (i.e. Holter) with wireless ECG based Body Area Networks (BAN), improved healthcare performance can be achieved, reflected in (1) high quality ECG recordings during physical activities and (2) increased patient satisfaction. A small scale clinical trial was conducted to compare both technologies and the results illustrate that the wireless ECG monitor was able to detect ECG signals intended for arrhythmia diagnostics. Furthermore, from a patient’s perspective, both technologies were evaluated using three dimensions, namely; hygienic aspects, physical activity, and skin reactions. Results demonstrate that the wireless ECG BAN showed better performance, especially regarding the hygienic aspects. It was also favourable for use during physical activities, and the signal quality of the wireless sensor system demonstrated good performance regarding signal noise and artefact disturbances. This paper concludes that wireless cardiac monitoring systems have significant benefits from a patient’s perspective, and further clinical trials should be conducted to further evaluate the new ECG based BAN system, to identify the possibility of widespread adoption and utilisation of wireless technology for arrhythmia diagnostics.

Feasibility of extracting data from electronic medical records for research: an international comparative study.

BackgroundElectronic medical records (EMR) offer a major potential for secondary use of data for research which can improve the safety, quality and efficiency of healthcare. They also enable the measurement of disease burden at the population level. However, the extent to which this is feasible in different countries is not well known. This study aimed to: 1) assess information governance procedures for extracting data from EMR in 16 countries; and 2) explore the extent of EMR adoption and the quality and consistency of EMR data in 7 countries, using management of diabetes type 2 patients as an exemplar.MethodsWe included 16 countries from Australia, Asia, the Middle East, and Europe to the Americas. We undertook a multi-method approach including both an online literature review and structured interviews with 59 stakeholders, including 25 physicians, 23 academics, 7 EMR providers, and 4 information commissioners. Data were analysed and synthesised thematically considering the most relevant issues.ResultsWe found that procedures for information governance, levels of adoption and data quality varied across the countries studied. The required time and ease of obtaining approval also varies widely. While some countries seem ready for secondary uses of data from EMR, in other countries several barriers were found, including limited experience with using EMR data for research, lack of standard policies and procedures, bureaucracy, confidentiality, data security concerns, technical issues and costs.ConclusionsThis is the first international comparative study to shed light on the feasibility of extracting EMR data across a number of countries. The study will inform future discussions and development of policies that aim to accelerate the adoption of EMR systems in high and middle income countries and seize the rich potential for secondary use of data arising from the use of EMR solutions.

Managing immune diseases in the smartphone era: how have apps impacted disease management and their future?

Immunology, similar to other areas of clinical science, is a data-rich discipline that involves a great deal of interaction between healthcare professionals and their patients. The focus of this editorial is to review the challenges and opportunities for mobile healthcare applications within immunology. It is clear that further research is required to fully maximize the potential of mobile apps (e.g., regulations and guidelines, electronic health). However, it is equally clear that mobile healthcare applications have had a positive impact on patient outcomes (better response rates, more efficient usage of time and more accurate diagnosis). Overall, healthcare applications have a fundamental role to play in the future management of diseases as they will help to ensure that we deliver more effective patient care.

Evaluating the effectiveness of clinical decision support systems: the case of multimorbidity care

General Practitioners (GPs) and healthcare systems, worldwide, are overwhelmed by the growing number of patients with multimorbidity, particularly in light of the additional complexity and costs involved in treating these patients. While it has been proven that clinical decision support systems (CDSS) play a key role in supporting healthcare decisions, there is little research into their role in the case of multimorbidity. This study examines practice systems currently used in Ireland and evaluates their effectiveness in such circumstances. The findings uncover a number of deficiencies, including: (1) the lack of provision of integrated medical guidelines for multiple chronic diseases within the CDSS, (2) the inability to centralise the patient rather than the disease, (3) the difficulty in seamlessly integrating CDSS into the patient consultation, and (4) the lack of adequate training of GPs on how best to use CDSS in multimorbidity decision making. The study underlines the need for further research into CDSS and multimorbidity, and highlights some of the key issues that must be addressed in order to improve how CDSS support the care of multimorbid patients.

Introduction to the Special Issue on Information Quality: The Challenges and Opportunities in Healthcare Systems and Services

The challenges facing the information quality community within a medical context are immense as the tools which collect, process, and use the healthcare-related data are in a constant state of flux. If one considers the drivers and challenges surrounding the technological (e.g., continuous advancements in digital utilization), the commercial (a hodgepodge of individual providers), and finally information and communication standards (with a lack of or maturity of), then the realization of an effective quality-based information system for healthcare seems very far away indeed. For example, it was reported that between 44,000 and 98,000 deaths occur annually as a consequence of medical errors within American hospitals alone [Kohn et al. 1999]. “A window of opportunity remains for health care to follow other high risk industries in establishing basic safety” [Stelfox et al. 2006] to help improve overall patient care. However, if one takes into account the unique operational practices within each healthcare institute and the archetypal resistance to change in adopting new and emerging medical devices and associated practices [Roback et al. 2007], then the challenge for all parties concerned is enormous. Many will agree with Grossman’s recent statement [Grossman 2008]: “The struggle to improve the quality of care is, at best, at a standstill”. Over the years, advancements have been made on a number of fronts, for example, through wireless sensor networks [Baker et al. 2007], telehome care [Johnston et al. 2000], and clinical decision-support systems [Johnston et al. 1994], all stimulated by the digital revolution with the goal of creating better healthcare services [Topol 2012]. Four high-quality articles are presented in this ACM JDIQ special issue on Information Quality: the Challenges and Opportunities in Healthcare Systems and Services. Each of the articles deals with various types of information quality across a number of areas. Article 1 focuses on primary healthcare research and its associated epidemiological databases in Ireland. The overall steps outlined in this article provide a practical template for the implementation of such databases in the future. Article 2 deals with data quality issues across databases which store drug-related information. The proposed solution aims to improve the data quality of relational drug databases and uses an ontology-based data access approach with the representation of conditional dependencies via an epistemic query language. Article 3 examines ambient assistant living and the role of contextual knowledge in supporting the data quality validation process. Article 4 presents a context-sensitive quality data management system for pervasive computing in healthcare. It looks at the important data quality aspects associated with pervasive healthcare devices with a primary focus on patient body area networks. This article is based on the PhD work of Dr. John O’Donoghue, Health Information Systems Research Centre, Ireland, which received the 2008 Ballou/Pazer DQ/IQ research award, which recognizes a PhD dissertation that demonstrates a significant contribution to the field of Information Quality (IQ).

Towards Improved Information Quality: The Integration of Body Area Network Data within Electronic Health Records

An Electronic Health Record (EHR) is internationally recognised as the primary digital format to communicate and store patient clinical information. The vast majority of patient vital sign monitoring solutions provide limited if any opportunities to seamlessly integrate real-time patient vital sign readings e.g. ECG in a coherent or unified approach. In this paper, we highlight the data quality benefits of integrating remote patient monitoring solutions i.e. a Body Area Network (BAN) datasets within patient EHR solutions. The presented Data Management System-Tripartite Ontology Medical Reasoning Model (TOMRM) solution demonstrates how patient care may be improved through the reduction of false alarm generations.

First impressions are lasting impressions: intention to participate in mobile health projects within developing countries

Abstract With healthcare infrastructure and health services remaining inadequate in many countries, numerous mobile health (mHealth) development studies have been reported, especially in low- and middle-income countries. This reflects a broad recognition that mobile technologies can play an important role in the provision of healthcare services at the regional, community and individual levels. However, implementing large-scale mHealth programmes has proven difficult; hence the use of mHealth pilots or feasibility studies as precursors to full rollouts. Existing literature reports that social, cultural, economic, political, technological and institutional factors influence the successful implementation of mHealth artefacts, yet there remains a dearth of research investigating individuals’ intention to participate in mHealth feasibility projects. Typically, mHealth projects involve a number of key stakeholders, ranging from direct stakeholders (such as healthcare workers who use the applications) to indirect stakeholders (such as the patients themselves who are not direct users of the technologies). Through the lens of impression management and self-determination theories, this study examines the extent to which an indirect user’s first impression, i.e. their initial encounter with the project and the project team on the ground, influences their decision to engage with an mHealth pilot project. Findings indicate that initial impressions formulated by indirect users impact their intention to participate in larger scale mHealth implementations. Through these early impressions participants formulate their perceptions about the project, and this directly influences their decision to engage. Furthermore, an individual’s first impressions can influence other potential participants’ decision to participate, especially in the context of the healthcare worker–patient relationship. The paper concludes that understanding indirect users’ expectations regarding the role of mHealth in addressing societal problems is essential to the success of wider mHealth projects in developing countries.

Sociocultural and Technological Barriers Across all Phases of Implementation for mobile Health in Developing Countries

Abstract In recent years, scholars are realizing the importance of examining mobile health (mHealth) implementation in developing countries. A vast array of research exists that focuses on barriers of mHealth adoption in such domains. However, the majority of these papers embrace the concept of adoption to cover the entire process of implementation. This chapter acknowledges that various phases of implementation exist. As a result, the researchers identify potential barriers for each phase of mHealth implementation in developing countries. By examining extant literature, this study reveals that various sociocultural and technological factors across individuals and organizations collectively can hinder mHealth implementation in developing regions. Extant research indicates that the focus of mHealth in these constituencies, a nascent area of research, places too much emphasis on the benefits associated with mHealth implementation. Subsequently, this chapter endeavors to outline the barriers that should assist with overcoming common obstacles in the successful implementation of mHealth initiatives in developing countries.

Supporting LIFE: Mobile Health Application for Classifying, Treating and Monitoring Disease Outbreaks of Sick Children in Developing Countries

This paper presents the Supporting LIFE L ow cost I ntervention F or dis E ase control project. Supporting LIFE applies a novel combination of Android based smartphone technology, patient vital sign sensors and expert decision support systems to assist Community Health Workers in resource-poor settings in their assessment, classification and treatment of seriously ill children, more specifically children from 2 months to 5 years of age. The application digitises widely accepted WHO/UNICEF paper based guidelines known as Community Case Management. The project also facilitates for disease monitoring and surveillance via a reporting website.