Canadian Patient Perceptions of Electronic Personal Health Records: An Empirical Investigation

Electronic personal health records (PHRs) have significant promise in helping to empower patients and consumers in general to take more responsibility for managing their own health, with lower costs for the healthcare system. However, few empirical studies have been undertaken to understand patient perspectives on the benefits of PHRs. This article describes an empirical study that proposes a theoretical model on PHR adoption and validates that model using the views of 389 Canadian patients. We found that perceived usefulness, security, privacy, and trust in PHRs, together with personal information technology innovativeness, are significant motivators of adoption, while computer anxiety may be an important deterrent. Overall, this study is a step toward understanding patient views that are key to the success of electronic PHRs. Growing adoption of this novel e-health approach is of importance as it may improve benefits for both patients and society

Automatic Message Triage: Decision Support from Patient-Provider Messages

Email communication between patients and healthcare providers is gaining popularity. However, healthcare providers are concerned about being inundated with patient messages and their inability to respond to messages in a timely manner. This work provides automated text mining decision support to overcome some of the challenges presented by email communication between patients and healthcare providers

An Empirical Investigation of Mobile Health Adoption in Preventive Interventions

Innovative applications of mobile information and communication technology (ICT) include the recent use of mobile services for preventive health interventions. We report on a one-month empirical study of such an intervention to evaluate a model that includes positive user adoption factors, together with user perceptions impeding adoption. Findings revealed intrinsic motivation to be a sufficient reason for adoption, and a multi-faceted perceived overall risk as the main reason for resisting the new mobile health service

Early Investigation of New Information Technology Acceptance: A Perceived Risk - Motivation Model

Early investigation of the acceptance of new information technology applications will help to improve the chances of later success. In incipient stages of product deployment, before people develop a full understanding of the aspects of a new technology, users may display particular reasons for or against its use. Accordingly, this study proposes and tests empirically, through structural equation modeling techniques, a perceived risk-motivation model for the early study of new information technology acceptance. The research framework is a Web scenario describing the possible use of wireless text messaging on cell phones to improve user adherence to healthy behavior. While the underlying personal motivation may favor adoption, perceived risks may be an obstacle. This study integrates a multi-faceted perceived risk concept with a motivational model in an unbiased measure of the initial development phase of new information technology, to estimate its acceptance

A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

This chapter reports on results from a systematic review of peer-reviewed studies related to big data knowledge visualization for clinical decision support (CDS). The aims were to identify and synthesize sources of big data in knowledge visualization, identify visualization interactivity approaches for CDS, and summarize outcomes. Searches were conducted via PubMed, Embase, Ebscohost, CINAHL, Medline, Web of Science, and IEEE Xplore in April 2019, using search terms representing concepts of: big data, knowledge visualization, and clinical decision support. A Google Scholar gray literature search was also conducted. All references were screened for eligibility. Our review returned 3252 references, with 17 studies remaining after screening. Data were extracted and coded from these studies and analyzed using a PICOS framework. The most common audience intended for the studies was healthcare providers (n = 16); the most common source of big data was electronic health records (EHRs) (n = 12), followed by microbiology/pathology laboratory data (n = 8). The most common intervention type was some form of analysis platform/tool (n = 7). We identified and classified studies by visualization type, user intent, big data platforms and tools used, big data analytics methods, and outcomes from big data knowledge visualization of CDS applications

A framework for mobile healthcare answers to chronically ill outpatient non-adherence

Non-adherence (also known as 'non-compliance') is a major barrier undermining healing efforts within out-of-hospital self-management programmes, resulting in waste of human and social resources. This study suggests a theoretical framework of activities through which mobile patient solutions might address non-adherence determinants in a broader context of clinical interventions. The goal of the paper is to explore a dilemma associated with such interventions: the uncertainty regarding the level of patient involvement and technology support. We follow a critical orientation approach in discussing this multi-faceted conundrum: we summarise the latest vision on adherence factors, we suggest several types of interventions through which mobile healthcare solutions could address them, and we explore in detail the dilemma of patient and technology roles. We conclude that there is no universally optimal solution, and practical conditions depending on patient, disease, treatment and healthcare system are determining factors in prescribing the level of patient involvement and technology support. Our work is intended to stimulate further research into the nature of mobile solutions in health care and, especially, into patient acceptance aspects, in an endeavour to contribute to improving adherence with minimum obtrusiveness