Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems

[EN] Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass.Authors would like to acknowledge the Laboratory for the Analysis for Human Behavior (www.sabien.upv.es/lach) and the Operative Program FEDER 2007/2013, for providing the necessary materials to undertake the presented research. The work done by A.L. was funded by the Ministry of Economy and Competitiveness: Promoting Youth Employment Program and Implementation of the (PEJ-2014-A-06813) Youth Guarantee 2014. The subsidized activity is part of the National System of Youth Guarantee and are co-financed under the Operational Program for Youth Employment, with financial resources from the Initiative Youth Employment (IYE) and the European Social Fund (ESF) for the period 2014-2020.Martínez Millana, A.; Bayo Montón, JL.; Lizondo García, A.; Fernández Llatas, C.; Traver Salcedo, V. (2016). Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems. Sensors. 16(2142):1-12. https://doi.org/10.3390/s16122142S112162142Abrahams, E., Ginsburg, G. S., & Silver, M. (2005). The Personalized Medicine Coalition. American Journal of PharmacoGenomics, 5(6), 345-355. doi:10.2165/00129785-200505060-00002Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20. doi:10.2196/jmir.3.2.e20The Truth about Google X: An Exclusive Look Behind the Secretive Lab’s Closed Doorshttps://www.fastcompany.com/3028156/united-states-of-innovation/the-google-x-factorGlauser, W. (2013). Doctors among early adopters of Google Glass. Canadian Medical Association Journal, 185(16), 1385-1385. doi:10.1503/cmaj.109-4607Google Glass at Workhttps://developers.google.com/glass/Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation, 9(1), 21. doi:10.1186/1743-0003-9-21Davis, C. R., & Rosenfield, L. K. (2015). Looking at Plastic Surgery through Google Glass. Plastic and Reconstructive Surgery, 135(3), 918-928. doi:10.1097/prs.0000000000001056Iversen, M. D., Kiami, S., Singh, K., Masiello, I., & von Heideken, J. (2016). Prospective, randomised controlled trial to evaluate the effect of smart glasses on vestibular examination skills. BMJ Innovations, 2(2), 99-105. doi:10.1136/bmjinnov-2015-000094Liebert, C. A., Zayed, M. A., Aalami, O., Tran, J., & Lau, J. N. (2016). Novel Use of Google Glass for Procedural Wireless Vital Sign Monitoring. Surgical Innovation, 23(4), 366-373. doi:10.1177/1553350616630142Longley, C., & Whitaker, D. (2015). Google Glass Glare: disability glare produced by a head-mounted visual display. Ophthalmic and Physiological Optics, 36(2), 167-173. doi:10.1111/opo.12264Trese, M. G. J., Khan, N. W., Branham, K., Conroy, E. B., & Moroi, S. E. (2016). Expansion of Severely Constricted Visual Field Using Google Glass. Ophthalmic Surgery, Lasers and Imaging Retina, 47(5), 486-489. doi:10.3928/23258160-20160419-15Jeroudi, O. M., Christakopoulos, G., Christopoulos, G., Kotsia, A., Kypreos, M. A., Rangan, B. V., … Brilakis, E. S. (2015). Accuracy of Remote Electrocardiogram Interpretation With the Use of Google Glass Technology. The American Journal of Cardiology, 115(3), 374-377. doi:10.1016/j.amjcard.2014.11.008Cicero, M. X., Walsh, B., Solad, Y., Whitfill, T., Paesano, G., Kim, K., … Cone, D. C. (2015). Do You See What I See? Insights from Using Google Glass for Disaster Telemedicine Triage. Prehospital and Disaster Medicine, 30(1), 4-8. doi:10.1017/s1049023x1400140xWu, T. S., Dameff, C. J., & Tully, J. L. (2014). Ultrasound-Guided Central Venous Access Using Google Glass. The Journal of Emergency Medicine, 47(6), 668-675. doi:10.1016/j.jemermed.2014.07.045Lewis, T. L., & Vohra, R. S. (2013). Smartphones make smarter surgeons. British Journal of Surgery, 101(4), 296-297. doi:10.1002/bjs.9328Albrecht, U.-V., von Jan, U., Kuebler, J., Zoeller, C., Lacher, M., Muensterer, O. J., … Hagemeier, L. (2014). Google Glass for Documentation of Medical Findings: Evaluation in Forensic Medicine. Journal of Medical Internet Research, 16(2), e53. doi:10.2196/jmir.3225Waxman, B. P. (2012). Medicine in small doses. ANZ Journal of Surgery, 82(11), 768-768. doi:10.1111/j.1445-2197.2012.06276.xKortuem, G., Bauer, M., & Segall, Z. (1999). Mobile Networks and Applications, 4(1), 49-58. doi:10.1023/a:1019122125996Zou, G., Gan, Y., Chen, Y., Zhang, B., Huang, R., Xu, Y., & Xiang, Y. (2014). Towards automated choreography of Web services using planning in large scale service repositories. Applied Intelligence, 41(2), 383-404. doi:10.1007/s10489-014-0522-4O’Brien, P. D., & Nicol, R. C. (1998). BT Technology Journal, 16(3), 51-59. doi:10.1023/a:1009621729979Muensterer, O. J., Lacher, M., Zoeller, C., Bronstein, M., & Kübler, J. (2014). Google Glass in pediatric surgery: An exploratory study. International Journal of Surgery, 12(4), 281-289. doi:10.1016/j.ijsu.2014.02.003Hwang, A. D., & Peli, E. (2014). An Augmented-Reality Edge Enhancement Application for Google Glass. Optometry and Vision Science, 91(8), 1021-1030. doi:10.1097/opx.0000000000000326Tully, J., Dameff, C., Kaib, S., & Moffitt, M. (2015). Recording Medical Students’ Encounters With Standardized Patients Using Google Glass. Academic Medicine, 90(3), 314-316. doi:10.1097/acm.0000000000000620Fox, B. I., & Felkey, B. G. (2013). Potential Uses of Google Glass in the Pharmacy. Hospital Pharmacy, 48(9), 783-784. doi:10.1310/hpj4809-783Nguyen, V., & Gruteser, M. (2015). First Experiences with GOOGLE GLASS in Mobile Research. ACM SIGMOBILE Mobile Computing and Communications Review, 18(4), 44-47. doi:10.1145/2721914.272193

When the Social Meets the Semantic: Social Semantic Web or Web 2.5

The social trend is progressively becoming the key feature of current Web understanding (Web 2.0). This trend appears irrepressible as millions of users, directly or indirectly connected through social networks, are able to share and exchange any kind of content, information, feeling or experience. Social interactions radically changed the user approach. Furthermore, the socialization of content around social objects provides new unexplored commercial marketplaces and business opportunities. On the other hand, the progressive evolution of the web towards the Semantic Web (or Web 3.0) provides a formal representation of knowledge based on the meaning of data. When the social meets semantics, the social intelligence can be formed in the context of a semantic environment in which user and community profiles as well as any kind of interaction is semantically represented (Semantic Social Web). This paper first provides a conceptual analysis of the second and third version of the Web model. That discussion is aimed at the definition of a middle concept (Web 2.5) resulting in the convergence and integration of key features from the current and next generation Web. The Semantic Social Web (Web 2.5) has a clear theoretical meaning, understood as the bridge between the overused Web 2.0 and the not yet mature Semantic Web (Web 3.0).Pileggi, SF.; Fernández Llatas, C.; Traver Salcedo, V. (2012). When the Social Meets the Semantic: Social Semantic Web or Web 2.5. Future Internet. 4(3):852-854. doi:10.3390/fi4030852S85285443Chi, E. H. (2008). The Social Web: Research and Opportunities. Computer, 41(9), 88-91. doi:10.1109/mc.2008.401Bulterman, D. C. A. (2001). SMIL 2.0 part 1: overview, concepts, and structure. IEEE Multimedia, 8(4), 82-88. doi:10.1109/93.959106Boll, S. (2007). MultiTube--Where Web 2.0 and Multimedia Could Meet. IEEE Multimedia, 14(1), 9-13. doi:10.1109/mmul.2007.17Fraternali, P., Rossi, G., & Sánchez-Figueroa, F. (2010). Rich Internet Applications. IEEE Internet Computing, 14(3), 9-12. doi:10.1109/mic.2010.76Lassila, O., & Hendler, J. (2007). Embracing «Web 3.0». IEEE Internet Computing, 11(3), 90-93. doi:10.1109/mic.2007.52Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud Computing: Distributed Internet Computing for IT and Scientific Research. IEEE Internet Computing, 13(5), 10-13. doi:10.1109/mic.2009.103Mangione-Smith, W. H. (1998). Mobile computing and smart spaces. IEEE Concurrency, 6(4), 5-7. doi:10.1109/4434.736391Greaves, M. (2007). Semantic Web 2.0. IEEE Intelligent Systems, 22(2), 94-96. doi:10.1109/mis.2007.40Bojars, U., Breslin, J. G., Peristeras, V., Tummarello, G., & Decker, S. (2008). Interlinking the Social Web with Semantics. IEEE Intelligent Systems, 23(3), 29-40. doi:10.1109/mis.2008.50Definition of Web 2.0http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.htmlZhang, D., Guo, B., & Yu, Z. (2011). The Emergence of Social and Community Intelligence. Computer, 44(7), 21-28. doi:10.1109/mc.2011.65Pentlan, A. (2005). Socially aware, computation and communication. Computer, 38(3), 33-40. doi:10.1109/mc.2005.104Staab, S., Domingos, P., Mika, P., Golbeck, J., Li Ding, Finin, T., … Vallacher, R. R. (2005). Social Networks Applied. IEEE Intelligent Systems, 20(1), 80-93. doi:10.1109/mis.2005.16The Semantic Webhttp://www.scientificamerican.com/article.cfm?id=the-semantic-webDecker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., … Horrocks, I. (2000). The Semantic Web: the roles of XML and RDF. IEEE Internet Computing, 4(5), 63-73. doi:10.1109/4236.877487OWL Web Ontology Language Overviewhttp://www.w3.org/TR/owl-features/Vetere, G., & Lenzerini, M. (2005). Models for semantic interoperability in service-oriented architectures. IBM Systems Journal, 44(4), 887-903. doi:10.1147/sj.444.0887Fensel, D., & Musen, M. A. (2001). The semantic web: a brain for humankind. IEEE Intelligent Systems, 16(2), 24-25. doi:10.1109/mis.2001.920595Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The Semantic Web Revisited. IEEE Intelligent Systems, 21(3), 96-101. doi:10.1109/mis.2006.62Dodds, P. S., & Danforth, C. M. (2009). Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents. Journal of Happiness Studies, 11(4), 441-456. doi:10.1007/s10902-009-9150-9Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173. doi:10.1002/asi.21662Blogmeterhttp://www.blogmeter.it/Christakis, N. A., & Fowler, J. H. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE, 5(9), e12948. doi:10.1371/journal.pone.0012948Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149Bernal, P. A. (2010). Web 2.5: The Symbiotic Web. International Review of Law, Computers & Technology, 24(1), 25-37. doi:10.1080/13600860903570145Mikroyannidis, A. (2007). Toward a Social Semantic Web. Computer, 40(11), 113-115. doi:10.1109/mc.2007.405Jung, J. J. (2012). Computational reputation model based on selecting consensus choices: An empirical study on semantic wiki platform. Expert Systems with Applications, 39(10), 9002-9007. doi:10.1016/j.eswa.2012.02.03

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

[EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscriptMartinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079S12618 (1)79Thomas, C. C., & Philipson, L. H. (2015). Update on Diabetes Classification. Medical Clinics of North America, 99(1), 1-16. doi:10.1016/j.mcna.2014.08.015Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), 840-846. doi:10.1038/nature05482Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2), 137-149. doi:10.1016/j.diabres.2013.11.002Beagley, J., Guariguata, L., Weil, C., & Motala, A. A. (2014). Global estimates of undiagnosed diabetes in adults. Diabetes Research and Clinical Practice, 103(2), 150-160. doi:10.1016/j.diabres.2013.11.001Hippisley-Cox, J., Coupland, C., Robson, J., Sheikh, A., & Brindle, P. (2009). Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ, 338(mar17 2), b880-b880. doi:10.1136/bmj.b880Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., … Cupples, L. A. (2008). Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes. New England Journal of Medicine, 359(21), 2208-2219. doi:10.1056/nejmoa0804742Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., & Khunti, K. (2007). Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ, 334(7588), 299. doi:10.1136/bmj.39063.689375.55Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. BMJ, 343(nov28 1), d7163-d7163. doi:10.1136/bmj.d7163Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1), 55. doi:10.7326/m14-0697Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., … Schroter, S. (2013). Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research. PLoS Medicine, 10(2), e1001381. doi:10.1371/journal.pmed.1001381Collins, G. S., & Moons, K. G. M. (2012). Comparing risk prediction models. BMJ, 344(may24 2), e3186-e3186. doi:10.1136/bmj.e3186Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016). External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, i3140. doi:10.1136/bmj.i3140Reilly, B. M., & Evans, A. T. (2006). Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions. Annals of Internal Medicine, 144(3), 201. doi:10.7326/0003-4819-144-3-200602070-00009Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009). Prognosis and prognostic research: validating a prognostic model. BMJ, 338(may28 1), b605-b605. doi:10.1136/bmj.b605Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009). Prognosis and prognostic research: what, why, and how? BMJ, 338(feb23 1), b375-b375. doi:10.1136/bmj.b375Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., … Kattan, M. W. (2010). Assessing the Performance of Prediction Models. Epidemiology, 21(1), 128-138. doi:10.1097/ede.0b013e3181c30fb2Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784-1789. doi:10.1016/j.eswa.2009.07.064Schmidt, M. I., Duncan, B. B., Bang, H., Pankow, J. S., Ballantyne, C. M., … Golden, S. H. (2005). Identifying Individuals at High Risk for Diabetes: The Atherosclerosis Risk in Communities study. Diabetes Care, 28(8), 2013-2018. doi:10.2337/diacare.28.8.2013Talmud, P. J., Hingorani, A. D., Cooper, J. A., Marmot, M. G., Brunner, E. J., Kumari, M., … Humphries, S. E. (2010). Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ, 340(jan14 1), b4838-b4838. doi:10.1136/bmj.b4838Sackett, D. L. (1997). Evidence-based medicine. Seminars in Perinatology, 21(1), 3-5. doi:10.1016/s0146-0005(97)80013-4Segagni, D., Ferrazzi, F., Larizza, C., Tibollo, V., Napolitano, C., Priori, S. G., & Bellazzi, R. (2011). R Engine Cell: integrating R into the i2b2 software infrastructure. Journal of the American Medical Informatics Association, 18(3), 314-317. doi:10.1136/jamia.2010.007914Semantic Webhttp://www.w3.org/2001/sw/Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Murphy, S., Churchill, S., Bry, L., Chueh, H., Weiss, S., Lazarus, R., … Kohane, I. (2009). Instrumenting the health care enterprise for discovery research in the genomic era. Genome Research, 19(9), 1675-1681. doi:10.1101/gr.094615.109Lindstrom, J., & Tuomilehto, J. (2003). The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725-731. doi:10.2337/diacare.26.3.725Alssema, M., Vistisen, D., Heymans, M. W., Nijpels, G., Glümer, C., … Dekker, J. M. (2010). The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia, 54(5), 1004-1012. doi:10.1007/s00125-010-1990-7Mann, D. M., Bertoni, A. G., Shimbo, D., Carnethon, M. R., Chen, H., Jenny, N. S., & Muntner, P. (2010). Comparative Validity of 3 Diabetes Mellitus Risk Prediction Scoring Models in a Multiethnic US Cohort: The Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology, 171(9), 980-988. doi:10.1093/aje/kwq030Stern, M. P., Williams, K., & Haffner, S. M. (2002). Identification of Persons at High Risk for Type 2 Diabetes Mellitus: Do We Need the Oral Glucose Tolerance Test? Annals of Internal Medicine, 136(8), 575. doi:10.7326/0003-4819-136-8-200204160-00006Abdul-Ghani, M. A., Abdul-Ghani, T., Stern, M. P., Karavic, J., Tuomi, T., Bo, I., … Groop, L. (2011). Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk. Diabetes Care, 34(9), 2108-2112. doi:10.2337/dc10-2201Rahman, M., Simmons, R. K., Harding, A.-H., Wareham, N. J., & Griffin, S. J. (2008). A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Family Practice, 25(3), 191-196. doi:10.1093/fampra/cmn024Guasch-Ferré, M., Bulló, M., Costa, B., Martínez-Gonzalez, M. Á., Ibarrola-Jurado, N., … Estruch, R. (2012). A Risk Score to Predict Type 2 Diabetes Mellitus in an Elderly Spanish Mediterranean Population at High Cardiovascular Risk. PLoS ONE, 7(3), e33437. doi:10.1371/journal.pone.0033437Wilson, P. W. F. (2007). Prediction of Incident Diabetes Mellitus in Middle-aged Adults. Archives of Internal Medicine, 167(10), 1068. doi:10.1001/archinte.167.10.1068Franzin, A., Sambo, F., & Di Camillo, B. (2016). bnstruct: an R package for Bayesian Network structure learning in the presence of missing data. Bioinformatics, btw807. doi:10.1093/bioinformatics/btw807Rood, B., & Lewis, M. J. (2009). Grid Resource Availability Prediction-Based Scheduling and Task Replication. Journal of Grid Computing, 7(4), 479-500. doi:10.1007/s10723-009-9135-2Ramakrishnan, L., & Reed, D. A. (2009). Predictable quality of service atop degradable distributed systems. Cluster Computing, 16(2), 321-334. doi:10.1007/s10586-009-0078-yKianpisheh, S., Kargahi, M., & Charkari, N. M. (2017). Resource Availability Prediction in Distributed Systems: An Approach for Modeling Non-Stationary Transition Probabilities. IEEE Transactions on Parallel and Distributed Systems, 28(8), 2357-2372. doi:10.1109/tpds.2017.2659746Weber, G. M., Murphy, S. N., McMurry, A. J., MacFadden, D., Nigrin, D. J., Churchill, S., & Kohane, I. S. (2009). The Shared Health Research Information Network (SHRINE): A Prototype Federated Query Tool for Clinical Data Repositories. Journal of the American Medical Informatics Association, 16(5), 624-630. doi:10.1197/jamia.m3191Martinez-Millana, A., Fico, G., Fernández-Llatas, C., & Traver, V. (2015). Performance assessment of a closed-loop system for diabetes management. 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pMineR: An Innovative R Library for Performing Process Mining in Medicine

Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given realworld data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare. In this paper we introduce pMineR, an R library specifically designed for performing Process Mining in the medical domain, and supporting human experts by presenting processes in a human-readable way

Process mining methodology for health process tracking using real-time indoor location systems

[EN] The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.The authors want to acknowledge the work MySphera Company and Hospital General for their invaluable support. This work was supported in part by several projects; FASyS-Absolutely Safe and Healthy Factory (Spanish Ministry of Industry. CEN-20091034), MOSAIC-Models and simulation techniques for discovering diabetes influence factors (ICT-FP7-600914) and HEARTWAYS-Advanced Solutions for Supporting Cardiac Patients in Rehabilitation (ICT-SME-315659) EU Projects; and organizations like Tecnologias para la Salud y el Bienestar (TSB S.A.) and the Universitat Politecnica de Valencia.Fernández Llatas, C.; Lizondo, A.; Montón Sánchez, E.; Benedí Ruiz, JM.; Traver Salcedo, V. (2015). Process mining methodology for health process tracking using real-time indoor location systems. Sensors. 12:29821-29840. https://doi.org/10.3390/s151229769S298212984012Weske, M., van der Aalst, W. M. P., & Verbeek, H. M. W. (2004). Advances in business process management. Data & Knowledge Engineering, 50(1), 1-8. doi:10.1016/j.datak.2004.01.001Davidoff, F., Haynes, B., Sackett, D., & Smith, R. (1995). Evidence based medicine. BMJ, 310(6987), 1085-1086. doi:10.1136/bmj.310.6987.1085Reilly, B. M. (2004). The essence of EBM. BMJ, 329(7473), 991-992. doi:10.1136/bmj.329.7473.991Weiland, D. E. (1997). Why use clinical pathways rather than practice guidelines? The American Journal of Surgery, 174(6), 592-595. doi:10.1016/s0002-9610(97)00196-7Hunter, B., & Segrott, J. (2008). Re-mapping client journeys and professional identities: A review of the literature on clinical pathways. International Journal of Nursing Studies, 45(4), 608-625. doi:10.1016/j.ijnurstu.2007.04.001Lenz, R., Blaser, R., Beyer, M., Heger, O., Biber, C., Bäumlein, M., & Schnabel, M. (2007). IT support for clinical pathways—Lessons learned. International Journal of Medical Informatics, 76, S397-S402. doi:10.1016/j.ijmedinf.2007.04.012Blaser, R., Schnabel, M., Biber, C., Bäumlein, M., Heger, O., Beyer, M., … Kuhn, K. A. (2007). Improving pathway compliance and clinician performance by using information technology. International Journal of Medical Informatics, 76(2-3), 151-156. doi:10.1016/j.ijmedinf.2006.07.006Schuld, J., Schäfer, T., Nickel, S., Jacob, P., Schilling, M. K., & Richter, S. (2011). Impact of IT-supported clinical pathways on medical staff satisfaction. A prospective longitudinal cohort study. International Journal of Medical Informatics, 80(3), 151-156. doi:10.1016/j.ijmedinf.2010.10.012Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Schilling, M., Richter, S., Jacob, P., & Lindemann, W. (2006). Klinische Behandlungspfade. DMW - Deutsche Medizinische Wochenschrift, 131(17), 962-967. doi:10.1055/s-2006-939876Zannini, L., Cattaneo, C., Peduzzi, P., Lopiccoli, S., & Auxilia, F. (2012). Experimenting clinical pathways in general practice: a focus group investigation with Italian general practitioners. Journal of Public Health Research, 1(2), 30. doi:10.4081/jphr.2012.e30Rubin, H. R. (2001). The advantages and disadvantages of process-based measures of health care quality. International Journal for Quality in Health Care, 13(6), 469-474. doi:10.1093/intqhc/13.6.469Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080. doi:10.1109/tsmcc.2007.905750Li, N., & Becerik-Gerber, B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Advanced Engineering Informatics, 25(3), 535-546. doi:10.1016/j.aei.2011.02.004Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D., & McCaughey, A. (2011). An evaluation of indoor location determination technologies. Journal of Location Based Services, 5(2), 61-78. doi:10.1080/17489725.2011.562927Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Stübig, T., Zeckey, C., Min, W., Janzen, L., Citak, M., Krettek, C., … Gaulke, R. (2014). Effects of a WLAN-based real time location system on outpatient contentment in a Level I trauma center. International Journal of Medical Informatics, 83(1), 19-26. doi:10.1016/j.ijmedinf.2013.10.001Najera, P., Lopez, J., & Roman, R. (2011). Real-time location and inpatient care systems based on passive RFID. Journal of Network and Computer Applications, 34(3), 980-989. doi:10.1016/j.jnca.2010.04.011Huang, Z., Dong, W., Ji, L., Gan, C., Lu, X., & Duan, H. (2014). Discovery of clinical pathway patterns from event logs using probabilistic topic models. Journal of Biomedical Informatics, 47, 39-57. doi:10.1016/j.jbi.2013.09.003Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., & Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44, 88-96. doi:10.1016/j.compbiomed.2013.10.015Bouarfa, L., & Dankelman, J. (2012). Workflow mining and outlier detection from clinical activity logs. Journal of Biomedical Informatics, 45(6), 1185-1190. doi:10.1016/j.jbi.2012.08.003Disco https://fluxicon.com/disco/Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Wantland, D. J., Portillo, C. J., Holzemer, W. L., Slaughter, R., & McGhee, E. M. (2004). The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes. Journal of Medical Internet Research, 6(4), e40. doi:10.2196/jmir.6.4.e40Bellazzi, R., Montani, S., Riva, A., & Stefanelli, M. (2001). Web-based telemedicine systems for home-care: technical issues and experiences. Computer Methods and Programs in Biomedicine, 64(3), 175-187. doi:10.1016/s0169-2607(00)00137-1Van der Aalst, W. (2012). Process Mining. ACM Transactions on Management Information Systems, 3(2), 1-17. doi:10.1145/2229156.2229157MySphera Company http://mysphera.com/Van der Aalst, W. M. P., & de Medeiros, A. K. A. (2005). Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance. Electronic Notes in Theoretical Computer Science, 121, 3-21. doi:10.1016/j.entcs.2004.10.01

Process mining for individualised behaviour modeling using wireless tracking in nursing homes

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detectionThe authors want to acknowledge the Spanish Government, the eMotiva Project (TSI-020110-2009-219) partners, Health Institute Carlos III through the RETICSCombiomed (RD07/0067/2001) and Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ05-02-03386), for their support and the professionals and residents of Centro Residencial San Sebastian en la Pobla De Vallbona and MySphera Enterprise for their active participation in the project.Fernández Llatas, C.; Benedí Ruiz, JM.; García Gómez, JM.; Traver Salcedo, V. (2013). Process mining for individualised behaviour modeling using wireless tracking in nursing homes. Sensors. 13(11):15434-15451. https://doi.org/10.3390/s131115434S15434154511311Hobson, P. (1999). The detection of dementia and cognitive impairment in a community population of elderly people with Parkinson’s disease by use of the CAMCOG neuropsychological test. Age and Ageing, 28(1), 39-43. doi:10.1093/ageing/28.1.39Tahan, H. A., & Sminkey, P. V. (2012). Motivational Interviewing. Professional Case Management, 17(4), 164-172. doi:10.1097/ncm.0b013e318253f029Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Khadivi, S., … Vilar, J.-M. (2009). Statistical Approaches to Computer-Assisted Translation. Computational Linguistics, 35(1), 3-28. doi:10.1162/coli.2008.07-055-r2-06-29Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003). Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237-267. doi:10.1016/s0169-023x(03)00066-1De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Remagnino, P., & Foresti, G. L. (2005). Ambient Intelligence: A New Multidisciplinary Paradigm. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 1-6. doi:10.1109/tsmca.2004.838456Tao Zhao, & Nevatia, R. (2004). Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1208-1221. doi:10.1109/tpami.2004.73Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2007). Learning and inferring transportation routines. Artificial Intelligence, 171(5-6), 311-331. doi:10.1016/j.artint.2007.01.006Zhou, Y., Law, C. L., Guan, Y. L., & Chin, F. (2011). Indoor Elliptical Localization Based on Asynchronous UWB Range Measurement. IEEE Transactions on Instrumentation and Measurement, 60(1), 248-257. doi:10.1109/tim.2010.2049185Chang, N., Rashidzadeh, R., & Ahmadi, M. (2010). Robust indoor positioning using differential wi-fi access points. IEEE Transactions on Consumer Electronics, 56(3), 1860-1867. doi:10.1109/tce.2010.5606338MySphera Enterprise, RTLS Sphera Indoor Positioning Systemhttp://mysphera.com/Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.2414

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?Gatta, R.; Vallati, M.; Fernández Llatas, C.; Martinez-Millana, A.; Orini, S.; Sacchi, L.; Lenkowicz, J.... (2020). What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper. International Journal of Environmental research and Public Health (Online). 17(18):1-19. https://doi.org/10.3390/ijerph17186616S1191718Guyatt, G. (1992). Evidence-Based Medicine. JAMA, 268(17), 2420. doi:10.1001/jama.1992.03490170092032Hripcsak, G., Ludemann, P., Pryor, T. A., Wigertz, O. B., & Clayton, P. D. (1994). Rationale for the Arden Syntax. Computers and Biomedical Research, 27(4), 291-324. doi:10.1006/cbmr.1994.1023Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. 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N., & Greenes, R. A. (1994). Improving Clinical Guidelines with Logic and Decision-table Techniques. Medical Decision Making, 14(3), 245-254. doi:10.1177/0272989x9401400306Peleg, M., Tu, S., Bury, J., Ciccarese, P., Fox, J., Greenes, R. A., … Stefanelli, M. (2003). Comparing Computer-interpretable Guideline Models: A Case-study Approach. Journal of the American Medical Informatics Association, 10(1), 52-68. doi:10.1197/jamia.m1135Karadimas, H., Ebrahiminia, V., & Lepage, E. (2018). User-defined functions in the Arden Syntax: An extension proposal. Artificial Intelligence in Medicine, 92, 103-110. doi:10.1016/j.artmed.2015.11.003Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004Marco-Ruiz, L., Moner, D., Maldonado, J. A., Kolstrup, N., & Bellika, J. G. (2015). Archetype-based data warehouse environment to enable the reuse of electronic health record data. International Journal of Medical Informatics, 84(9), 702-714. doi:10.1016/j.ijmedinf.2015.05.016Gooch, P., & Roudsari, A. (2011). Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. Journal of the American Medical Informatics Association, 18(6), 738-748. doi:10.1136/amiajnl-2010-000033Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., & Mossa, C. (2000). Guideline-based careflow systems. Artificial Intelligence in Medicine, 20(1), 5-22. doi:10.1016/s0933-3657(00)00050-6Schadow, G., Russler, D. C., & McDonald, C. J. (2001). Conceptual alignment of electronic health record data with guideline and workflow knowledge. International Journal of Medical Informatics, 64(2-3), 259-274. doi:10.1016/s1386-5056(01)00196-4González-Ferrer, A., ten Teije, A., Fdez-Olivares, J., & Milian, K. (2013). Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artificial Intelligence in Medicine, 57(2), 91-109. doi:10.1016/j.artmed.2012.08.008Shabo, A., Parimbelli, E., Quaglini, S., Napolitano, C., & Peleg, M. (2016). Interplay between Clinical Guidelines and Organizational Workflow Systems. Methods of Information in Medicine, 55(06), 488-494. doi:10.3414/me16-01-0006Mulyar, N., van der Aalst, W. M. P., & Peleg, M. (2007). A Pattern-based Analysis of Clinical Computer-interpretable Guideline Modeling Languages. Journal of the American Medical Informatics Association, 14(6), 781-787. doi:10.1197/jamia.m2389Grando, M. A., Glasspool, D., & Fox, J. (2012). A formal approach to the analysis of clinical computer-interpretable guideline modeling languages. Artificial Intelligence in Medicine, 54(1), 1-13. doi:10.1016/j.artmed.2011.07.001Kaiser, K., & Marcos, M. (2015). Leveraging workflow control patterns in the domain of clinical practice guidelines. BMC Medical Informatics and Decision Making, 16(1). doi:10.1186/s12911-016-0253-zMartínez-Salvador, B., & Marcos, M. (2016). Supporting the Refinement of Clinical Process Models to Computer-Interpretable Guideline Models. Business & Information Systems Engineering, 58(5), 355-366. doi:10.1007/s12599-016-0443-3Decision Model and Notation Version 1.0https://www.omg.org/spec/DMN/1.0Ghasemi, M., & Amyot, D. (2016). Process mining in healthcare: a systematised literature review. International Journal of Electronic Healthcare, 9(1), 60. doi:10.1504/ijeh.2016.078745Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. 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Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

[EN] Background: Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. Objective: Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. Methods: This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. Results: The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. Conclusions: By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.This paper was partially funded by the National Commission for Scientific and Technological Research, the Formation of Advanced Human Capital Program and the National Fund for Scientific and Technological Development (CONICYT-PCHA/Doctorado Nacional/2016-21161705 and CONICYT-FONDECYT/1150365; Chile). The authors would like to thank Ancora UC primary health care centers for their help with this research. The founding sponsors had no role in the design of the study in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.Conca, T.; Saint Pierre, C.; Herskovic, V.; Sepulveda, M.; Capurro, D.; Prieto, F.; Fernández Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. JOURNAL OF MEDICAL INTERNET RESEARCH. 20(4). https://doi.org/10.2196/jmir.8884S204Chen, C.-C., Tseng, C.-H., & Cheng, S.-H. (2013). Continuity of Care, Medication Adherence, and Health Care Outcomes Among Patients With Newly Diagnosed Type 2 Diabetes. 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(2012). The influence of power dynamics and trust on multidisciplinary collaboration: a qualitative case study of type 2 diabetes mellitus. BMC Health Services Research, 12(1). doi:10.1186/1472-6963-12-63Gucciardi, E., Espin, S., Morganti, A., & Dorado, L. (2016). Exploring interprofessional collaboration during the integration of diabetes teams into primary care. BMC Family Practice, 17(1). doi:10.1186/s12875-016-0407-1Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., & Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44, 88-96. doi:10.1016/j.compbiomed.2013.10.015Rothman, A. A., & Wagner, E. H. (2003). Chronic Illness Management: What Is the Role of Primary Care? 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Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Juxtaposing BTE and ATE – on the role of the European insurance industry in funding civil litigation

One of the ways in which legal services are financed, and indeed shaped, is through private insurance arrangement. Two contrasting types of legal expenses insurance contracts (LEI) seem to dominate in Europe: before the event (BTE) and after the event (ATE) legal expenses insurance. Notwithstanding institutional differences between different legal systems, BTE and ATE insurance arrangements may be instrumental if government policy is geared towards strengthening a market-oriented system of financing access to justice for individuals and business. At the same time, emphasizing the role of a private industry as a keeper of the gates to justice raises issues of accountability and transparency, not readily reconcilable with demands of competition. Moreover, multiple actors (clients, lawyers, courts, insurers) are involved, causing behavioural dynamics which are not easily predicted or influenced. Against this background, this paper looks into BTE and ATE arrangements by analysing the particularities of BTE and ATE arrangements currently available in some European jurisdictions and by painting a picture of their respective markets and legal contexts. This allows for some reflection on the performance of BTE and ATE providers as both financiers and keepers. Two issues emerge from the analysis that are worthy of some further reflection. Firstly, there is the problematic long-term sustainability of some ATE products. Secondly, the challenges faced by policymakers that would like to nudge consumers into voluntarily taking out BTE LEI