569 research outputs found

    Addendum to Informatics for Health 2017: Advancing both science and practice

    Get PDF
    This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

    CDSSs for CVD Risk Management: An Integrative Review

    Get PDF
    Cardiovascular disease (CVD) is a preventable disease affecting almost half of adults in the United States (U.S.) and can have significant negative outcomes such as stroke and myocardial infarction, which can be fatal. Utilizing clinical decision support systems (CDSSs) in the primary care and community health setting can improve primary prevention of CVD by supporting evidence-based decision making at the point of care. This integrative review synthesizes the most up-to-date literature on the use of clinical decision support (CDS) tools to support guideline-based management of CVD risk. Using Whittemore and Knafl’s framework for integrative reviews, a systematic search of CINAHL, Cochrane, and Medline and ancestry search yielded 492 results; 17 articles were included in the final review after applying inclusion and exclusion criteria. Evidence-based CDSSs for CVD prevention improved guideline-based initiation and intensification of pharmacological treatment, increased frequency and accuracy of CVD risk screening, and facilitated shared decision-making discussions with patients about CVD risk; however, they were not effective in promoting smoking cessation and only sometimes effective in improving blood pressure (BP) control. This integrative review supports future evidence-based practice projects implementing CDSSs designed to improve guideline-based primary prevention of CVD as an, albeit partial, solution to improving prevention of CVD in the U.S. and globally

    Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

    Get PDF
    Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

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

    Full text link
    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

    The role of clinical decision support systems in preventing stroke in primary care: a systematic review.

    Get PDF
    Computerized clinical decision support systems (CDSS) are increasingly being used to facilitate the role of clinicians in complex decision-making processes. This systematic review evaluates evidence of the available CDSS developed and tested to support the decision-making process in primary healthcare for stroke prevention and barriers to practical implementations in primary care settings. A systematic search of Web of Science, Medline Ovid, Embase Ovid, and Cinahl was done. A total of five studies, experimental and observational, were synthesised in this review. This review found that CDSS facilitate decision-making processes in primary health care settings in stroke prevention options. However, barriers were identified in designing, implementing, and using the CDSS

    PICT-DPA: A Quality-Compliance Data Processing Architecture to Improve the Performance of Integrated Emergency Care Clinical Decision Support System

    Get PDF
    Emergency Care System (ECS) is a critical component of health care systems by providing acute resuscitation and life-saving care. As a time-sensitive care operation system, any delay and mistake in the decision-making of these EC functions can create additional risks of adverse events and clinical incidents. The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the aforementioned EC functions. However, the literature is scarce on how to implement and evaluate the EC-CDSS with regard to the improvement of PHOs, which is the ultimate goal of ECS. The reasons are twofold: 1) lack of clear connections between the implementation of EC-CDSS and PHOs because of unknown quality attributes; and 2) lack of clear identification of stakeholders and their decision processes. Both lead to the lack of a data processing architecture for an integrated EC-CDSS that can fulfill all quality attributes while satisfying all stakeholders’ information needs with the goal of improving PHOs. This dissertation identified quality attributes (PICT: Performance of the decision support, Interoperability, Cost, and Timeliness) and stakeholders through a systematic literature review and designed a new data processing architecture of EC-CDSS, called PICT-DPA, through design science research. The PICT-DPA was evaluated by a prototype of integrated PICT-DPA EC-CDSS, called PICTEDS, and a semi-structured user interview. The evaluation results demonstrated that the PICT-DPA is able to improve the quality attributes of EC-CDSS while satisfying stakeholders’ information needs. This dissertation made theoretical contributions to the identification of quality attributes (with related metrics) and stakeholders of EC-CDSS and the PICT Quality Attribute model that explains how EC-CDSSs may improve PHOs through the relationships between each quality attribute and PHOs. This dissertation also made practical contributions on how quality attributes with metrics and variable stakeholders could be able to guide the design, implementation, and evaluation of any EC-CDSS and how the data processing architecture is general enough to guide the design of other decision support systems with requirements of the similar quality attributes

    Mobile Clinical Decision Support Systems – A Systematic Review

    Get PDF
    In this review article, we provide a descriptive analysis of the current state of mobile decision support systems in the healthcare domain based on studies published in the following databases: Business Source Complete, CINAHL, Cochrane library, MEDLINE, PsycINFO, PubMed, ScienceDirect and Web of Science databases. A total of 29 studies were identified and analyzed to understand the current state of development, evaluation efforts, usability and challenges to adoption by patients and care providers. Our aim is to evaluate these systems and identify the key challenges which hinders their widespread adoption. Although, mobile based decision support systems in healthcare context have the potential to improve clinical decision making, the current state with low adoption rate and early stage of development need to be addressed for successful health outcomes

    Evaluation of a Clinical Decision Support System for Dyslipidemia Treatment (HTE-DLPR) by QoE questionnaire

    Get PDF
    Introduction: Clinical decision support systems (CDSS) are computer systems designed to assist clinicians with patient-related decision making, such as diagnosis and treatment. CDSS have shown to improve both patient outcomes and cost of care.Methods: A multi-center observational prospective study was conducted. Ten physicians agreed to participate. Seventy-seven patients with high or very high cardiovascular risk were included. After using CDSS for dyslipidemia (HTE-DLPR) for a 3 months period, participants were asked to evaluate their experience with HTE-DLPR using a quality of experience questionnaire (QoE) tool for mHealth applications.Results: Total score on the QoE was 3.89 out of 5. The highest scores were received for precision, ease of use and content quality. The lowest scores were given to security, appearance and performance. Physicians were in strong agreement with the 1st HTEDLPR recommendation in 86.1% and the system’s use was described as comfortablein 85% of cases. Users positively evaluated the development of a new version of HTEDLPR in the future receiving a total score of 4.25 out of 5.Conclusions: A CDSS for dyslipidemia (HTE-DLP) has been positively evaluated by physicians using QoE questionnaire

    A Clinical Decision Support System for Remote Monitoring of Cardiovascular Disease Patients: A Clinical Study Protocol

    Get PDF
    Funding: This work was partially supported by the Center for Innovative Care and Health Technology (ciTechcare), Polytechnic of Leiria, Portugal. The work of FV was funded by the Portuguese Foundation for Science and Technology (FCT), CEECINST/00103/2018. The funder had no role in the clinical study protocol.Introduction: Cardiovascular diseases (CVD) are the leading cause of death globally, taking an estimated 17. 9 million lives each year. Cardiac rehabilitation is shown to reduce mortality and hospital readmissions, while improving physical fitness and quality of life. Despite the recommendations and proven benefits, acceptance and adherence remain low. Mobile health (mHealth) solutions may contribute to more personalized and tailored patient recommendations according to their specific needs. This study protocol aims to assess the effectiveness of a user-friendly, comprehensive Clinical Decision Support System (CDSS) for remote patient monitoring of CVD patients, primarily on the reduction of recurrent cardiovascular events. Methods and Analysis: The study will follow a multicenter randomized controlled design involving two cardiology units in the Center Region of Portugal. Prospective CVD patients will be approached by the healthcare staff at each unit and checked for eligibility according to the predefined inclusion/exclusion criteria. The CDSS will suggest a monitoring plan for the patient, will advise the mHealth tools (apps and wearables) adapted to patient needs, and will collect data. The clinical study will start in January 2023. Discussion: The success of the mHeart.4U intervention will be a step toward the use of technological interfaces as an integrating part of CR programs. Ethics and Dissemination: The study will undergo ethical revision by the Ethics Board of the two hospital units where the study will unfold. The study was registered in ClinicalTrials.gov on 18th January 2022 with the number NCT05196802. The study findings will be published in international peer-reviewed scientific journals and encounters and in a user-friendly manner to the society.info:eu-repo/semantics/publishedVersio
    • …
    corecore