Pilot implementation Driven by Effects Specifications and Formative Usability Evaluation

This chapter reports on the usability-engineering work performed throughout the pilot implementation of an Electronic Healthcare Record (EHR). The case describes and analyzes the use of pilot implementations to formatively evaluate whether the usability of the EHR meets the effects specified for its use. The project was initiated during the autumn of 2010 and concluded in the spring of 2012. The project configured and implemented an EHR at a Maternity ward at one hospital located in a European region and then transferred this system to another ward at another hospital in the same region. DOI: 10.4018/978-1-4666-4046-7.ch010 Copyright ©2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Pilot Implementation Driven by Effects Specification

We Need Numbers! - Heuristic Evaluation during Demonstrations (HED) for Measuring Usability in IT System Procurement

We introduce a new usability inspection method called HED (heuristic evaluation during demonstrations) for measuring and comparing usability of competing complex IT systems in public procurement. The method presented enhances traditional heuristic evaluation to include the use context, comprehensive view of the system, and reveals missing functionality by using user scenarios and demonstrations. HED also quantifies the results in a comparable way. We present findings from a real-life validation of the method in a large-scale procurement project of a healthcare and social welfare information system. We analyze and compare the performance of HED to other usability evaluation methods used in procurement. Based on the analysis HED can be used to evaluate the level of usability of an IT system during procurement correctly, comprehensively and efficiently.Peer reviewe

Holistic System Design for Distributed National eHealth Services

publishedVersio

mHealth through quantified-self : a user study

This work was partly supported by the IU-ATC project, funded by grant EP/J016756/1 from the Engineering and Physical Sciences Research Council (EPSRC). Chonlatee Khorakhun is funded by the Scottish Informatics and Computer Science Alliance (SICSA).We describe a user study of a mHealth prototype system based on a wellbeing scenario, exploiting the quantified-self approach to measurement and monitoring. We have used off-the-shelf equipment, with opensource, web-based, software, and exploiting the increasing popularity of smartphones and self-measurement devices in a user study. We emulate a mHealth scenario as a pre-clinical experiment, as a realistic alternative to a clinical scenario, with reduced risk to sensitive patient medical data. We discuss the efficacy of this approach for future mHealth systems for remote monitoring. Our system used the popular Fitbit device for monitoring personal wellbeing data, the Diaspora online social media platform (OSMP), and a simple Android/iOS remote notification application. We implemented remote monitoring, asynchronous user interaction, multiple actors, and user-controlled security and privacy mechanisms. We propose that the use of a quantified-self approach to mHealth is particularly valuable to undertake research and systems development.Postprin

A Novel Conceptual Architecture for Person-Centered Health Records

Personal health records available to patients today suffer from multiple limitations, such as information fragmentation, a one-size-fits-all approach and a focus on data gathered over time and by institution rather than health conditions. This makes it difficult for patients to effectively manage their health, for these data to be enriched with relevant information from external sources and for clinicians to support them in that endeavor. We propose a novel conceptual architecture for person-centered health record information systems that transcends many of these limitations and capitalizes on the emerging trend of socially-driven information systems. Our proposed personal health record system is personalized on demand to the conditions of each individual patient; organized to facilitate the tracking and review of the patient's conditions; and able to support patient-community interactions, thereby promoting community engagement in scientific studies, facilitating preventive medicine, and accelerating the translation of research findings

HCI for health and wellbeing: challenges and opportunities

In terms of Human–Computer Interaction, healthcare presents paradoxes: on the one hand, there is substantial investment in innovative health technologies, particularly around “big data” analytics and personal health technologies; on the other hand, most interactive health technologies that are currently deployed at scale are difficult to use and few innovative technologies have achieved significant market penetration. We live in a time of change, with a shift from care being delivered by professionals towards people being expected to be actively engaged and involved in shared decision making. Technically, this shift is supported by novel health technologies and information resources; culturally, the pace of change varies across contexts. In this paper, I present a “space” of interactive health technologies, users and uses, and interdependencies between them. Based on a review of the past and present, I highlight opportunities for and challenges to the application of HCI methods in the design and deployment of digital health technologies. These include threats to privacy, patient trust and experience, and opportunities to deliver healthcare and empower people to manage their health and wellbeing in ways that better fit their lives and values

Adaptive dashboard for IoT environments: application for senior residences

Les tableaux de bord sont de puissants outils électroniques qui peuvent fournir des informations exploitables et utiles pour une intervention rapide et une prise de décision éclairée. Ils peuvent être particulièrement bénéfiques pour favoriser un vieillissement en bonne santé en fournissant aux professionnels de la santé un aperçu en un coup d'œil des conditions du patient (par exemple, les personnes âgées). Alors que la population des personnes âgées augmente dans plusieurs pays, dont le Canada, un grand nombre d'entre eux seront forcés de déménager dans des résidences pour personnes âgées pour des raisons telles que la fragilité, la démence ou le sentiment de solitude. Cette population importante de personnes âgées augmentera la charge de travail des infirmières et des professionnels de la santé travaillant dans ces lieux, en raison du fait que les personnes âgées ont besoin de visites fréquentes et d'une surveillance en raison de leur état de santé. Ce problème a le potentiel de mettre plus de pression sur le système de santé déjà tendu dans les prochaines années. La pénurie d'infirmières et de main-d'œuvre rend la situation plus grave, en particulier dans les pays développés. Il faudrait donc prendre des initiatives pour soutenir les soignants de ces résidences. Le tableau de bord peut jouer un rôle clé pour aider les professionnels de la santé dans leurs tâches car il peut fournir des informations en un coup d'œil et en temps réel sur la situation actuelle. De nos jours, avec les progrès technologiques dans les dispositifs de détection et l'infrastructure IoT ainsi qu'un accès Internet élargi, la surveillance des patients à distance est devenue une option réalisable. Par ailleurs, en utilisant un tableau de bord, les professionnels de la santé peuvent visualiser les informations collectées à distance pour surveiller les personnes âgées vivant dans des résidences, ce qui fera gagner un temps considérable aux professionnels de la santé et les aidera à servir plus de patients. Cependant, il est important de considérer que les résidences pour personnes âgées accueillent généralement un grand nombre de résidents et les professionnels de la santé qui les desservent. Chaque professionnel de la santé est motivé par certains objectifs et exécute des tâches précises selon des priorités différentes. Cette différence change la façon dont chaque fournisseur de soins de santé utilisera le tableau de bord, car ils ont besoin d'informations qui les aident dans leurs tâches principales. Les informations qu'un groupe de professionnels de la santé trouve bénéfiques peuvent ne pas être utiles pour un autre groupe. Ainsi, la méthode de visualisation utilisée pour un individu peut ne pas être significative pour un autre. Par conséquence, les informations doivent être présentées de manière personnalisée et adaptée à un utilisateur ciblé. Il est important de souligner que la visualisation appropriée des informations dans les tableaux de bord est un facteur clé pour offrir une valeur réelle aux utilisateurs. Cette diversité de besoins, de préférences et de priorités doit être prise en compte tout au long de l'élaboration du tableau de bord. En raison de la diversité des rôles et des intérêts existant dans les résidences pour personnes âgées, et compte tenu du coût élevé du développement du tableau de bord, il est très difficile de développer des tableaux de bord séparés pour chaque partie. Cependant, les solutions existantes dans la littérature sont développées à l'aide de méthodes statiques et se concentrent sur la satisfaction des besoins d'un groupe particulier. Ces approches limitent les capacités des tableaux de bord existants à s'adapter aux besoins des différentes personnes. Dans cette étude, nous présentons AMI-Dash comme une tentative de réalisation d'une solution de tableau de bord qui permet une conception dynamique et une visualisation appropriée des informations pour plusieurs groupes. Notre solution vise à fournir les bonnes informations aux bonnes personnes en minimisant le temps nécessaire pour fournir un tableau de bord aux professionnels la santé, afin de les aider dans l'exercice de leurs fonctions en accédant à des informations exploitables. Nous avons également évalué notre solution sous deux aspects : l'évaluation de l'interaction homme-machine et l'évaluation technique. Le résultat de notre évaluation montre que la solution proposée peut satisfaire à la fois les exigences de l'utilisateur final et les exigences techniques tout en maintenant un haut niveau de satisfaction.Abstract: Dashboards are powerful electronic tools that can provide actionable insights for timely intervention and wise decision-making. They can be particularly beneficial to support healthy aging by providing healthcare professionals with at-a-glance overview of health conditions of patients (e.g., older adults). As the population of older adults is increasing in several countries including Canada, a large number of them will be forced to move to Senior Residences due to reasons like frailty, dementia or loneliness. This swelled senior population will increase the workload of nurses and health professionals working in these places, due to the fact that older adults need frequent visits and monitoring because of their health condition. This issue has the potential to put more pressure on the already stretched healthcare system in the next years. The situation is aggravated when it is coincided with the shortage of nurses and workforce especially in developed countries. Therefore, initiative should be taken to support healthcare professionals in these residences. Dashboard can play a key role to support healthcare professionals in their tasks as it can provide real-time information about the current situation in more helpful visualization form. Nowadays, with technological advancements in sensing devices and IoT infrastructure along with broadened internet access, remote patient monitoring has become a feasible option. By utilizing a dashboard, healthcare professionals can visualize information collected remotely to monitor patients/ older adults living in senior residences, which will save a considerable time of healthcare professionals and support them to serve more patients. However, it is important to consider that senior residences usually host a large number of older adults and healthcare professionals that serve them. Each healthcare professional is driven with certain goals, and they have different tasks and priorities. This difference, change how each healthcare professional will utilize the dashboard, as they need information that helps them in their main tasks. The information that a group of healthcare professionals find beneficial might not be useful for another group, and the visualization method used for an individual might not be meaningful for another. Therefore, information should be presented in a personalized way to the targeted user. It is important to emphasize that appropriate visualization of interesting information, in dashboards is a key factor to deliver real value to dashboard users. Due to the variety of roles and interests that exists in senior residences, and considering high development cost of a dashboard, developing separate dashboards for each party is not only difficult but also time consuming. Still, existing solutions in the literature are developed using static methods and they focused on satisfying the needs of a particular group in their domain. These approaches limited the capabilities of existing dashboards to adapt to the needs of different people. We argue that dashboard has to be tailored in order to address the diversity in needs, preferences and priorities of healthcare professionals. In this study we introduce AMI-Dash as an attempt to achieve a dashboard solution that allows dynamic design and information visualization. Our solution focused on providing the right information to the right people while minimizing the time required to deliver a dashboard to health professionals, so that supporting them in performing their duties by accessing timely and actionable information. We also evaluated our proposed solution from two aspects: Human-Computer Interaction Evaluation and Technical Evaluation. The result of our evaluation shows that proposed solution can satisfy both end-user and technical requirements while maintaining a high-level of satisfaction among users

Elektronický systém pro podporu provádění klinických studií s možností zpracování dat pomocí umělé inteligence

An increasing amount of data are collected through wearable devices during ambulatory, and long-term monitoring of biological signals, adoption of persuasive technology and dynamics of clinical trials information sharing - all of that changes the possible clinical intervention. Moreover, more and more smartphone apps are hitting the market as they become a tool in daily life for many people around the globe. All of these applications are generating a tremendous amount of data, that is difficult to process using traditional methods, and asks for engagement of advanced methods of data processing. For recruiting patients, this calls for a shift from traditional methods of engaging patients to modern communication platforms such as social media, that are providing easy access to up- to-date information on an everyday basis. These factors make the clinical study progression demanding, in terms of unified participant management and processing of connected digital resources. Some clinical trials put a strong accent on remote sensing data and patient engagement through their smartphones. To facilitate this, a direct participant messaging, where the researchers give support, guidance and troubleshooting on a personal level using already adopted communication channels, needs to be implemented. Since the...Objem dat, který je generován nositelnými zařízeními v průběhu ambulatorního i dlouhodobého snímání biologických signálů, adopce pervazivních technologií a dynamika předávání informací v rámci klinických studií - to vše mění způsoby, kterým mohou prováděny klinické studie. Více a více aplikací, které přicházejí na trh se stávají pomůckou v denním životě lidí po celém světě. Všechny tyto aplikace produkují obrovské množství dat, jež je obtížné zpracovat tradičními metodami, a vyvstává tak nutnost využití pokročilých metod. Je také možné sledovat odvrat od tradičních metod náboru pacientů, k moderním komunikačním platformám jako sociální sítě, které usnadňují přístup k aktuálním informacím. Tyto faktory činí postup v klinické studii náročným s ohledem na management účastníků studie a zpracování informací ze zdrojů dat. Některé klinické studie kladou velký důraz na sběr dat ze senzorů a zapojení pacientů do studie prostřednictvím jejich mobilních telefonů. Pro usnadnění tohoto přístupu, je nutné využít přímou komunikací s pacientem, kdy administrátoři studie poskytují podporu a pomáhají řešit problémy, které se mohou v průběhu studie vyskytnout, a to za pomocí moderních komunikačních platforem a elektronických zpráv vedených přímo s účastníkem studie. Celý tento postup je nicméně časově náročný, a je...Centre for Practical Applications Support and Spin-off Companies of the 1st Faculty of Medicine Charles UniversityCentrum podpory aplikačních výstupů a spin-off firem 1. LF UK1. lékařská fakultaFirst Faculty of Medicin

MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data and a lack of standardized data processing frameworks. We present MIMIC-Extract, an open-source pipeline for transforming raw electronic health record (EHR) data for critical care patients contained in the publicly-available MIMIC-III database into dataframes that are directly usable in common machine learning pipelines. MIMIC-Extract addresses three primary challenges in making complex health records data accessible to the broader machine learning community. First, it provides standardized data processing functions, including unit conversion, outlier detection, and aggregating semantically equivalent features, thus accounting for duplication and reducing missingness. Second, it preserves the time series nature of clinical data and can be easily integrated into clinically actionable prediction tasks in machine learning for health. Finally, it is highly extensible so that other researchers with related questions can easily use the same pipeline. We demonstrate the utility of this pipeline by showcasing several benchmark tasks and baseline results