Clinical Practice Implementation to Address ASCVD Risk: A Practice Change in Primary Care

Practice Problem: Heart disease stands as the leading cause of mortality in the United States. While healthcare providers strive to identify and optimize prevention strategies, particularly in high-risk patient populations, notable gaps in care persist, notably in the management of modifiable risk factors such as low-density lipoprotein cholesterol (LDL). By harnessing the power of artificial intelligence (AI) integrated software within clinical settings, we can revolutionize the landscape of this devastating chronic disease. PICOT: The PICOT question that guided this project was: In Primary Care Advanced Practice Providers (APP) caring for high-risk and/or very high-risk patients with atherosclerotic cardiovascular disease (ASCVD) (P), how do automated electronic alerts with guideline-based recommendations (I) compare to standard notification practice (C) affect referral initiation to cardiology or prompt medication change (O) within 10 weeks (T)? Evidence: In the realm of modern healthcare, it is crucial to recognize the impact of AI on Electronic Health Records (EHRs). This fusion of data analysis and health information technology provides an opportunity for healthcare treatments to become much more effective, resulting in better patient outcomes. Fifteen studies that matched the inclusion criteria were collected and used as substantiating evidence for this project. Intervention: AI software integrated into the EHR system computed comprehensive data analytics, consequently discovering a substantial cohort of patients with an elevated risk profile for ASCVD, accompanied by an LDL-C level that exceeded established clinical guidelines. Subsequently, an automated communication was sent to the APP, furnishing them with pertinent notifications and offering referral recommendations. Outcome: By integrating AI processes into the EHR, data management is streamlined and real-time disease prevention analysis is achieved. The primary goal was to identify high-risk ASCVD patient groups using AI within the EHR and assess the effectiveness of AI-generated electronic alerts with clinical guidance in encouraging behavior change. The clinical significance of this data collection and implementation was substantial. While the statistical analysis produced relevant metrics, it also exhibited applicability in the clinical context. The data exposed a patient population lacking aggressive medical management or referrals, a concern noted by APPs. Conclusion: Introducing AI-based tools can direct the pathway of care and bridge crucial gaps in care in high-risk populations. The result of this technology utilization and integration offers timely screening strategies, education, clinical decision support, and opportunities to address vital pathways for providers and health systems to address ASCVD treatment gaps

The use of extended reality and machine learning to improve healthcare and promote greenhealth

Com a Quarta Revolução Industrial, a propagação da Internet das Coisas, o avanço nas áreas de Inteligência Artificial e de Machine Learning até à migração para a Computação em Nuvem, o termo "Ambientes Inteligentes" cada vez mais deixa de ser uma idealização para se tornar realidade. Da mesma forma as tecnologias de Realidade Extendida também elas têm aumentado a sua presença no mundo tecnológico após um "período de hibernação", desde a popularização do conceito de Metaverse assim como a entrada das grandes empresas informáticas como a Apple e a Google num mercado onde a Realidade Virtual, Realidade Aumentada e Realidade Mista eram dominadas por empresas com menos experiência no desenvolvimento de sistemas (e.g. Meta), reconhecimento a nível mundial (e.g. HTC Vive), ou suporte financeiro e confiança do mercado. Esta tese tem como foco o estudo do potencial uso das tecnologias de Realidade Estendida de forma a promover Saúde Verde assim como seu uso em Hospitais Inteligentes, uma das variantes de Ambientes Inteligentes, incorporando Machine Learning e Computer Vision, como ferramenta de suporte e de melhoria de cuidados de saúde, tanto do ponto de vista do profissional de saúde como do paciente, através duma revisão literarária e análise da atualidade. Resultando na elaboração de um modelo conceptual com a sugestão de tecnologias a poderem ser usadas para alcançar esse cenário selecionadas pelo seu potencial, sendo posteriormente descrito o desenvolvimento de protótipos de partes do modelo conceptual para Óculos de Realidade Extendida como validação de conceito.With the Fourth Industrial Revolution, the spread of the Internet of Things, the advance in the areas of Artificial Intelligence and Machine Learning until the migration to Cloud Computing, the term "Intelligent Environments" increasingly ceases to be an idealization to become reality. Likewise, Extended Reality technologies have also increased their presence in the technological world after a "hibernation period", since the popularization of the Metaverse concept, as well as the entry of large computer companies such as Apple and Google into a market where Virtual Reality, Augmented Reality and Mixed Reality were dominated by companies with less experience in system development (e.g. Meta), worldwide recognition (e.g. HTC Vive) or financial support and trust in the market. This thesis focuses on the study of the potential use of Extended Reality technologies in order to promote GreenHealth as well as their use in Smart Hospitals, one of the variants of Smart Environments, incorporating Machine Learning and Computer Vision, as a tool to support and improve healthcare, both from the point of view of the health professional and the patient, through a literature review and analysis of the current situation. Resulting in the elaboration of a conceptual model with the suggestion of technologies that can be used to achieve this scenario selected for their potential, and then the development of prototypes of parts of the conceptual model for Extended Reality Headsets as concept validation

Mobile clinical decision support systems and applications: a literature and commercial review

The final publication is available at Springer via http://dx.doi.org/10.1007/s10916-013-0004-y[EN] Background: The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. Objective: The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Methods: Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Results: 92 relevant papers and 192 commercial apps were found. 44 papers were focused only on mobile clinical decision support systems. 171 apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. Conclusions: The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.This research has been partially supported by Ministerio de Economía y Competitividad, Spain. This research has been partially supported by the ICT-248765 EU-FP7 Project. This research has been partially supported by the IPT-2011-1126-900000 project under the INNPACTO 2011 program, Ministerio de Ciencia e Innovación.Martínez Pérez, B.; De La Torre Diez, I.; López Coronado, M.; Sainz De Abajo, B.; Robles Viejo, M.; García Gómez, JM. (2014). Mobile clinical decision support systems and applications: a literature and commercial review. Journal of Medical Systems. 38(1):1-10. https://doi.org/10.1007/s10916-013-0004-yS110381Van De Belt, T. H., Engelen, L. J., Berben, S. A., and Schoonhoven, L., Definition of Health 2.0 and Medicine 2.0: A systematic review. J Med Internet Res 2010:12(2), 2012.Oh, H., Rizo, C., Enkin, M., and Jadad, A., What is eHealth (3): A systematic review of published definitions. J Med Internet Res 7(1):1, 2005. PMID: 15829471.World Health Organization (2011) mHealth: New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth (Global Observatory for eHealth Series, Volume 3). World Health Organization. 2011. ISBN: 9789241564250Lin, C., Mobile telemedicine: A survey study. J Med Syst April 36(2):511–520, 2012.El Khaddar, M.A., Harroud, H., Boulmalf, M., Elkoutbi, M., Habbani, A., Emerging wireless technologies in e-health Trends, challenges, and framework design issues. 2012 International Conference on Multimedia Computing and Systems (ICMCS). 440–445, 2012.Luanrattana, R., Win, K. T., Fulcher, J., and Iverson, D., Mobile technology use in medical education. J Med Syst 36(1):113–122, 2012.Yang, S. C., Mobile applications and 4 G wireless networks: A framework for analysis. Campus-Wide Information Systems 29(5):344–357, 2012.Kumar, B., Singh, S.P., Mohan, A., Emerging mobile communication technologies for health. 2010 International Conference on Computer and Communication Technology, ICCCT-2010; Allahabad; pp. 828–832, 2010.Yan, H., Huo, H., Xu, Y., and Gidlund, M., Wireless sensor network based E-health system—implementation and experimental results. IEEE Transactions on Consumer Electronics 56(4):2288–2295, 2010.IDC (2013) Press release: Strong demand for smartphones and heated vendor competition characterize the worldwide mobile phone market at the end of 2012. http://www.idc.com/getdoc.jsp?containerId=prUS23916413#.UVBKiRdhWCn . Accessed 11 September 2013.IDC (2012) IDC Raises its worldwide tablet forecast on continued strong demand and forthcoming new product launches. http://www.idc.com/getdoc.jsp?containerId=prUS23696912#.US9x86JhWCl . Accessed 11 September 2013.International Data Corporation (2013) Android and iOS combine for 91.1 % of the worldwide smartphone OS market in 4Q12 and 87.6 % for the year. http://www.idc.com/getdoc.jsp?containerId=prUS23946013 . Accessed 11 September 2013.Jones, C., (2013) Apple and Google continue to gain US Smartphone market share. Forbes. http://www.forbes.com/sites/chuckjones/2013/01/04/apple-and-google-continue-to-gain-us-smartphone-market-share/ . Accessed 11 September 2013.Apple (2013) iTunes. http://www.apple.com/itunes/ . Accessed 11 September 2013.Google (2013) Google play. https://play.google.com/store . Accessed 11 September 2013.Rowinski, D., (2013) The data doesn’t lie: iOS apps are better than android. Readwrite mobile. http://readwrite.com/2013/01/30/the-data-doesnt-lie-ios-apps-are-better-quality-than-android . Accessed 11 September 2013.Rajan, S. P., and Rajamony, S., Viable investigations and real-time recitation of enhanced ECG-based cardiac telemonitoring system for homecare applications: A systematic evaluation. Telemed J E Health 19(4):278–286, 2013.Logan, A. G., Transforming hypertension management using mobile health technology for telemonitoring and self-care support. Can J Cardiol 29(5):579–585, 2013.Tamrat, T., and Kachnowski, S., Special delivery: An analysis of mHealth in maternal and newborn health programs and their outcomes around the world. Matern Child Health J 16(5):1092–1101, 2012.Martínez-Pérez, B., de la Torre-Díez, I., López-Coronado, M., and Herreros-González, J., Mobile Apps in Cardiology: Review. JMIR Mhealth Uhealth 1(2):e15, 2013.de Wit HA, Mestres Gonzalvo C, Hurkens KP, Mulder WJ, Janknegt R, et al., Development of a computer system to support medication reviews in nursing homes. Int J Clin Pharm. 26, 2013.Dahlström, O., Thyberg, I., Hass, U., Skogh, T., and Timpka, T., Designing a decision support system for existing clinical organizational structures: Considerations from a rheumatology clinic. J Med Syst 30(5):325–31, 2006.Lambin P, Roelofs E, Reymen B, Velazquez ER, Buijsen J, et al., ‘Rapid learning health care in oncology’ - An approach towards decision support systems enabling customised radiotherapy’. Radiother Oncol. 27, 2013.Graham, T. A., Bullard, M. J., Kushniruk, A. W., Holroyd, B. R., and Rowe, B. H., Assessing the sensibility of two clinical decision support systems. J Med Syst 32(5):361–8, 2008.Martínez-Pérez, B., de la Torre-Díez, I., and López-Coronado, M., Mobile health applications for the most prevalent conditions by the World Health Organization: Review and analysis. J Med Internet Res 15(6):e120, 2013.Savel, T. G., Lee, B. A., Ledbetter, G., Brown, S., LaValley, D., et al., PTT advisor: A CDC-supported initiative to develop a mobile clinical laboratory decision support application for the iOS platform. Online J Public Health Inform 5(2):215, 2013.Doctor Doctor Inc. (2009) iDoc. iTunes. https://itunes.apple.com/es/app/idoc/id328354734?mt=8 . Accessed 13 September 2013.Hardyman, W., Bullock, A., Brown, A., Carter-Ingram, S., and Stacey, M., Mobile technology supporting trainee doctors’ workplace learning and patient care: An evaluation. BMC Med Educ 13:6, 2013.Lee, N. J., Chen, E. S., Currie, L. M., Donovan, M., Hall, E. K., et al., The effect of a mobile clinical decision support system on the diagnosis of obesity and overweight in acute and primary care encounters. ANS Adv Nurs Sci 32(3):211–21, 2009.Divall, P., Camosso-Stefinovic, J., and Baker, R., The use of personal digital assistants in clinical decision making by health care professionals: A systematic review. Health Informatics J 19(1):16–28, 2013.Chignell, M, and Yesha, Y, Lo, J., New methods for clinical decision support in hospitals. In Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research (CASCON’10). Toronto, ON; Canada, 2010Charani, E., Kyratsis, Y., Lawson, W., Wickens, H., Brannigan, E. T., et al., An analysis of the development and implementation of a smartphone application for the delivery of antimicrobial prescribing policy: Lessons learnt. J Antimicrob Chemother 68(4):960–7, 2013.Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., et al., Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PLoS One 8(2):e56956, 2013.Hervás, R., Fontecha, J., Ausín, D., Castanedo, F., Bravo, J., et al., Mobile monitoring and reasoning methods to prevent cardiovascular diseases. Sensors (Basel) 13(5):6524–41, 2013.Di Noia, T., Ostuni, V. C., Pesce, F., Binetti, G., Naso, N., et al., An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst Appl 40(11):4438–4445, 2013.Velikova, M., van Scheltinga, J. T., Lucas, P. J. F., and Spaanderman, M., Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare. Int J Approx Reason, 2013. doi: 10.1016/j.ijar.2013.03.016 .Medical Data Solutions (2012) Pediatric clinical pathways. Google play. https://play.google.com/store/apps/details?id=com.ipathways . Accessed 17 September 2013.QxMD Medical Software Inc. (2013) Calculate by QxMD. Google play. https://play.google.com/store/apps/details?id=com.qxmd.calculate . Accessed 17 September 2013.Skyscape (2012) ACC pocket guides. Google play. https://play.google.com/store/apps/details?id=com.skyscape.packagefiveepkthreeundata.android.voucher.ui . Accessed 17 September 2013.Skyscape (2013) Skyscape medical resources. Google play. https://play.google.com/store/apps/details?id=com.skyscape.android.ui&hl=en . Accessed 17 September 2013.Pieter Kubben, M.D., (2012) NeuroMind. Google play. https://play.google.com/store/apps/details?id=eu.dign.NeuroMind . Accessed 17 September 2013.Mobile Systems, Inc. (2013) 2013 Medical diagnosis TR. Google play. https://play.google.com/store/apps/details?id=com.mobisystems.msdict.embedded.wireless.mcgrawhill.cmdt2013 . Accessed 17 September 2013.World Health Organization (2013) The global burden of disease: 2004 update. http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf . Accessed 18 September 2013.Martínez-Pérez, B., de la Torre-Díez, I., Candelas-Plasencia, S., and López-Coronado, M., Development and evaluation of tools for measuring the Quality of Experience (QoE) in mHealth applications. J Med Syst 37(5):9976, 2013

DESIGN AND EXPLORATION OF NEW MODELS FOR SECURITY AND PRIVACY-SENSITIVE COLLABORATION SYSTEMS

Collaboration has been an area of interest in many domains including education, research, healthcare supply chain, Internet of things, and music etc. It enhances problem solving through expertise sharing, ideas sharing, learning and resource sharing, and improved decision making. To address the limitations in the existing literature, this dissertation presents a design science artifact and a conceptual model for collaborative environment. The first artifact is a blockchain based collaborative information exchange system that utilizes blockchain technology and semi-automated ontology mappings to enable secure and interoperable health information exchange among different health care institutions. The conceptual model proposed in this dissertation explores the factors that influences professionals continued use of video- conferencing applications. The conceptual model investigates the role the perceived risks and benefits play in influencing professionals’ attitude towards VC apps and consequently its active and automatic use

Interactive Visual Displays for Results Management in Complex Medical Workflows

Clinicians manage medical orders to ensure that the results are returned promptly to the correct physician and followed up on time. Delays in results management occur frequently, physically harm patients, and often cause malpractice litigation. Better tracking of medical orders that showed progress and indicated delays, could result in improved care, better safety, and reduced clinician effort. This dissertation presents novel displays of rich tables with an interaction technique called ARCs (Actions for Rapid Completion). Rich tables are generated by MStart (Multi-Step Task Analyzing, Reporting, and Tracking) from a workflow model that defines order processes. Rich tables help clinicians perceive each order's status, prioritize the critical ones, and act on results in a timely fashion. A second contribution is the design of an interactive visualization called MSProVis (Multi-Step Process Visualization), which is composed of several PCDs (Process Completion Diagrams) that show the number and duration of in-time, late, and not-completed orders. With MSProVis, managers perform retrospective analyses to make decisions by studying an overview of the order process, durations of order steps, and performances of individuals. I visited seven hospitals and clinics to define sample results management workflows. Iterative design reviews with clinicians, designers, and researchers led to refinements of the rich tables, ARCs, and design guidelines. A controlled experiment with 18 participants under time pressure and distractions tested two features (showing pending orders and prioritizing by lateness) of rich tables. These changes statistically significantly reduce the time from nine to one minute to correctly identify late orders compared to the traditional chronologically-ordered lists. Another study demonstrated that ARCs speed performance up by 25% compared to state-of-the-art systems. A usability study with two clinicians and five novices showed that participants were able to understand MSProVis and efficiently perform representative tasks. Two subjective preference surveys suggested new design choices for the PCDs. This dissertation provides designers of results management systems with clear guidance about showing pending results and prioritizing by lateness, and tested strategies for performing retrospective analyses. It also offers detailed design guidelines for results management, tables, and integrated actions on tables that speed performance for common tasks

How to Add Value to your Business with CEA: A Practical Approach

Companies are always trying to differentiate themselves from the rest of the pack by applying different strategies such as improving customer service, increasing the efficiency of their operations, or reducing their costs. Most of the time, however, these goals are competing against each other for scarce resources, and managers often need to decide to concentrate on one. A small company can effectively and simultaneously accomplish these goals for a fraction of the cost by implementing communications-enabled business processes or solutions, which are a set of technology components that add real-time networking functionality to applications. One particular implementation of this framework is the one provided by Coral CEA. Coral CEA is a business ecosystem anchored around CEA functionalities that are offered as building blocks, out-of-the-box components that link the capabilities and intelligence of networks platforms with the power of current applications to provide a new set of features and functionalities. In this article, we show how a small company called Rezact, located in the ski resort town of Mont-Tremblant, Quebec, successfully implemented CEA capabilities within its own operations using Coral CEA services

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information

N/