Consumer PHIM Going Beyond Paper and Computer Anxiety

Personal health information management (PHIM) refers to an individual’s use of various tools (i.e., email, paper, sticky notes, calendars, health portals) to manage their healthcare information (Jones 2008). With advances in technology, it becomes even more imperative that the healthcare community understand the factors that may influence consumers’ intentions to use various PHIM tools to manage his/her healthcare information. The Theory of Planned Behavior (TPB) and constructs from the Technology Acceptance Model (TAM), and the Computer Anxiety Rating Scale (CARS) guide this investigation into how consumers might use patient health portals to manage their healthcare information

A Vision for the Systematic Monitoring and Improvement of the Quality of Electronic Health Data

In parallel with the implementation of information and communications systems, health care organizations are beginning to amass large-scale repositories of clinical and administrative data. Many nations seek to leverage so-called Big Data repositories to support improvements in health outcomes, drug safety, health surveillance, and care delivery processes. An unsupported assumption is that electronic health care data are of sufficient quality to enable the varied use cases envisioned by health ministries. The reality is that many electronic health data sources are of suboptimal quality and unfit for particular uses. To more systematically define, characterize and improve electronic health data quality, we propose a novel framework for health data stewardship. The framework is adapted from prior data quality research outside of health, but it has been reshaped to apply a systems approach to data quality with an emphasis on health outcomes. The proposed framework is a beginning, not an end. We invite the biomedical informatics community to use and adapt the framework to improve health data quality and outcomes for populations in nations around the world

The Role of Information Quality in Healthcare Organizations: A Multi-Disciplinary Literature Review

The volume of data in healthcare repositories is growing exponentially, giving increased concerns on its organizational implications. The quality of data and information represents a considerable risk for organizations, particularly in healthcare, where consequences of poor quality may be fatal for patients. This research seeks to investigate the role of information quality in organizations, by reviewing multi-disciplinary research literature and provide a framework of the relations between IQ and its organizational implications. Findings suggest that research on information quality has focused on different aspects of organizational impact: organizational performance, process performance, process improvement, and decision-making. However, since the research is fragmented and scarce, this paper suggests a shift in research focus from defining, measuring and improving information quality, to understanding the implications and applications of information quality towards better and safer health services

A Delphi Study Analysis of Best Practices for Data Quality and Management in Healthcare Information Systems

Healthcare in the US continues to suffer from the poor data quality practices processes that would ensure accuracy of patient health care records and information. A lack of current scholarly research on best practices in data quality and records management has failed to identify potential flaws within the relatively new electronic health records environment that affect not only patient safety but also cost, reimbursements, services, and most importantly, patient safety. The focus of this study was to current best practices using a panel of 25 health care industry data quality experts. The conceptual lens was developed from the International Monetary Fund\u27s Data Quality Management model. The key research question asked how practices contribute to identifying improvements healthcare data, data quality, and integrity. The study consisted of 3 Delphi rounds. Each round was analyzed to identify consensus on proposed data quality strategies from previous rounds that met or exceeded the acceptance threshold to construct subsequent round questions. The 2 best practices identified to improve data collection were user training and clear processes. One significant and unanticipated finding was that the previous gold standard practices have become outdated with technological advances, leading to a higher potential for flawed or inaccurate patient healthcare data. There is an urgent need for health care leaders to maintain heightened awareness of the need to continually evaluate data collection and management policies, particularly as technology advances such as artificial intelligence matures. Developing national standards to address accurate and timely management of patient care data is critical for appropriate health care delivery decisions by health care providers

The impact of information quality awareness on users\u27 behaviors toward information quality practices

Healthcare organization rely more on electronic information to optimize most of their processes. Additional information sources and more diverse information increase the relevance and importance of information quality (IQ). The quality of information needs to be improved to support a more efficient and reliable utilization of information systems (IS). This improvement can only be achieved through the implementation of initiatives followed by most users across the organization. The purpose of this study is to develop a model related to how awareness of IS users about IQ issues would affect their actual practices toward IQ initiatives. It is posited that users’ motivation is influenced by their awareness on beneficial and problematic situations generated by IQ. The motivation that users may have regarding IQ impact, will influence their behavior regarding IQ practices. Social influences and facilitating conditions are considered as moderators of the interaction between intention and actual users’ behavior

Exploring data quality monitoring procedures in the clinical research setting: Insights from clinical studies

To learn about human health, clinical research studies are conducted. A substantial concern for all clinical research studies is the failure to collect, process and present good quality data. Poor data quality may stem from error. International guidelines have identified that it is an essential need to monitor study activity to ensure that the rights, safety and wellbeing of participants are protected. However, the guidelines provide limited insight on how to perform monitoring procedures including the nature and extent of monitoring needed to ensure quality. Without clear guidance, this leaves clinical researchers confused about the most appropriate quality assurance and control procedures. The central hypothesis of this thesis is that despite the wide variations, exploration and evaluation of appropriate data quality monitoring procedures in clinical research studies will provide guidance toward developing a “fit-for-use” data quality monitoring framework (DQMF). This hypothesis was tested in five key studies using an explanatory sequential design guided by the Data- Information-Knowledge-Wisdom (DIKW) model as the theoretical framework

Developing an intervention model for data quality management and health information use at community and district levels in Rwanda

Doctor of Philosophy in Nursing. University of KwaZulu-Natal, Howard College 2014.The purpose of this study was to develop an intervention model for health data quality management (DQM) and health information use at community and district levels in Rwanda and similar settings, based on a situation analysis of current practices and performance in Rwanda and existing evidence found in similar settings. This thesis is by publication and comprises three research papers based on the findings of three evaluation studies conducted, and reports on the study four which describes the model developed. Methods The study was initiated based on a systematic review of health DQM and best practices at community and district levels in low-and middle-income countries (LMIC). A retrospective design was used to evaluate the quality of clinical and community health data, and a survey of health information users was conducted. The mixed methods approach was adopted to collect quantitative and qualitative data, and the teamwork in “Group Model Building” (GMB) process through a workshop was used to develop the model.Findings Poor health DQM and health information use at community and District levels in Rwanda and other LMIC was found, particularly at the sources of data. Best practices were also found, but several issues hindering the quality of health data and utilization namely poor management of District Health Information System, lack of institutional support to all stakeholders involved in DQM, and lack of information culture. Variables that influenced the quality of health data and use included the training of the staff and community health workers (CHWs), regular formative supervision and monitoring and evaluation, involvement of all stakeholders, Data Quality Audit (DQA), feedback initiatives, understanding and perception of data usefulness, use of electronic and computerized systems, and proper leadership and coordination. Those variables were included in the model developed. Conclusion Based on the identified barriers to high quality data systems, an intervention model for health DQM and health information use at community and District levels in Rwanda was developed as the main achievement of this study

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Améliorer la surveillance passive des infections associées aux soins de santé au Canada

Les infections associées aux soins de santé (IASS), ou infections nosocomiales, sont les effets indésirables les plus fréquemment signalés dans le monde, affectant environ 1 patient hospitalisé sur 31 chaque jour selon le Centre pour le contrôle et la prévention des maladies (CDC). La surveillance permet de mesurer, prévenir et contrôler ces infections. Toutefois, elle se doit d’être renforcée au Canada. L’objectif principal de ce projet est d’améliorer la surveillance passive des IASS au Canada en élaborant une stratégie visant à réduire l’écart entre les données de surveillance active et les données administratives, en plus de comprendre les facteurs qui expliquent cet écart. Ce mémoire présente trois articles et les résultats d’un groupe de travail qui ont chacun des objectifs rattachés au but global du mémoire. En premier lieu, une revue de la littérature vise à évaluer la validité des données administratives en comparaison aux données de surveillance active, qui sont la référence pour la surveillance des IASS. Cet article identifie les divergences entre les deux types de surveillance et démontre que la surveillance passive n’offre pas de données assez précises pour être comparables à celles de la surveillance active. Le deuxième article a pour objectif d’identifier les barrières et les facilitateurs de l’utilisation des données administratives pour la surveillance, avec un focus sur les IASS au Canada. Il analyse et met en évidence 120 barrières et facilitateurs, dont la majorité concerne la qualité des données, et identifie diverses solutions pour contrer ces barrières. Un troisième article et un groupe de travail examinent une solution pour améliorer la qualité des données codées par le département des archives médicales, soit d’utiliser les données de surveillance active en combinaison avec les dossiers médicaux pour coder les IASS. L’article évalue la validité de cette solution sur deux types d’IASS et démontre l’efficacité de cette solution. Le groupe de travail évalue les barrières et facilitateurs de cette solution et travaille sur des recommandations pour la mettre en place. Ces trois études, en plus du groupe de travail, permettent de constater qu’il y a de nombreuses barrières à l’utilisation des données administratives pour la surveillance des IASS, en particulier concernant la qualité des données, mais qu’il existe des solutions prometteuses.Healthcare-associated infections (HAIs) are the most commonly reported adverse events worldwide, affecting approximately 1 in 31 hospitalized patients each day according to the Centers for Disease Control and Prevention (CDC). Surveillance helps to measure, prevent and control these infections, but needs to be strengthened in Canada. The main objective of this project is to improve passive surveillance of HAIs in Canada by developing a strategy to reduce the discrepancy between active surveillance data and administrative data, in addition to understanding the factors that explain this discrepancy. This work presents three articles and a working group, each of which has objectives related to the overall goal of the project. First, a review of the literature assesses the validity of administrative data compared to active surveillance data, which is the reference for HAI surveillance. This article identifies the discrepancies between the two types of surveillance and demonstrates that passive surveillance does not provide sufficiently accurate data to be comparable to that of active surveillance. The second article aims to identify the barriers and facilitators to the use of administrative data for surveillance, with a focus on HAIs in Canada. It analyzes and highlights 120 barriers and enablers, the majority of which relate to data quality, and identifies various solutions to counter the barriers. A third article and a working group examine a potential solution to improve the quality of data coded by the medical records department; to use active surveillance data in combination with medical records to code HAIs. The article evaluates the validity of this type of data for two types of IASS and demonstrates the effectiveness of this solution. The working group assesses the barriers and facilitators of this solution and works on recommendations to implement it. These three studies, in addition to the working group, show that there are many barriers to the use of administrative data for the surveillance of HAIs, in particular related to the quality of the data, but that promising solutions are available