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

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

Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of the American Medical Informatics Association following peer review. The version of record is available online at: http://dx.doi.org/10.1093/jamia/ocv008[EN] [Objective] This systematic review aims to identify and compare the existing processes and methodologies that have been published in the literature for defining clinical information models (CIMs) that support the semantic interoperability of electronic health record (EHR) systems. [Material and Methods] Following the preferred reporting items for systematic reviews and meta-analyses systematic review methodology, the authors reviewed published papers between 2000 and 2013 that covered that semantic interoperability of EHRs, found by searching the PubMed, IEEE Xplore, and ScienceDirect databases. Additionally, after selection of a final group of articles, an inductive content analysis was done to summarize the steps and methodologies followed in order to build CIMs described in those articles. [Results] Three hundred and seventy-eight articles were screened and thirty six were selected for full review. The articles selected for full review were analyzed to extract relevant information for the analysis and characterized according to the steps the authors had followed for clinical information modeling. [Discussion] Most of the reviewed papers lack a detailed description of the modeling methodologies used to create CIMs. A representative example is the lack of description related to the definition of terminology bindings and the publication of the generated models. However, this systematic review confirms that most clinical information modeling activities follow very similar steps for the definition of CIMs. Having a robust and shared methodology could improve their correctness, reliability, and quality. [Conclusion] Independently of implementation technologies and standards, it is possible to find common patterns in methods for developing CIMs, suggesting the viability of defining a unified good practice methodology to be used by any clinical information modeler.This research has been partially funded by the Instituto de Salud Carlos III (Platform for Innovation in Medical Technologies and Health), grant PT13/0006/0036 and the Spanish Ministry of Economy and Competitiveness, grants TIN2010-21388-C02-01 and PTQ-12-05620.Moreno-Conde, A.; Moner Cano, D.; Da Cruz, WD.; Santos, MR.; Maldonado Segura, JA.; Robles Viejo, M.; Kalra, D. (2015). Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis. Journal of the American Medical Informatics Association. 22(4):925-934. https://doi.org/10.1093/jamia/ocv008S925934224Goossen W Goossen-Baremans A van der Zel M . Detailed clinical models: a review. Healthc Inform Res. 2010;16:201.Beeler, G. W. (1998). HL7 Version 3—An object-oriented methodology for collaborative standards development1Presented at the International Medical Informatics Association Working Group 16 Conference on Standardisation in Medical Informatics—Towards International Consensus and Cooperation, Bermuda, 12 September, 1997.1. International Journal of Medical Informatics, 48(1-3), 151-161. doi:10.1016/s1386-5056(97)00121-4Dolin, R. H., Alschuler, L., Boyer, S., Beebe, C., Behlen, F. M., Biron, P. V., & Shabo (Shvo), A. (2006). HL7 Clinical Document Architecture, Release 2. Journal of the American Medical Informatics Association, 13(1), 30-39. doi:10.1197/jamia.m1888Fast Health Interoperability Resources (FHIR). http://www.hl7.org/fhir/. Accessed July 18, 2014.Beale T . Archetypes: constraint-based domain models for futureproof information systems. OOPSLA 2002 Workshop Behav Semant. 2002.ISO 13606:2008 - Health informatics - Electronic health record communication. 2008. www.iso.org/iso/catalogue_detail.htm?csnumber=40784.OpenEHR. http://www.openehr.org/. Accessed July 18, 2014.Clinical Information Modeling Initiative (CIMI). http://www.opencimi.org/. Accessed July 18, 2014.Goossen WT . Using detailed clinical models to bridge the gap between clinicians and HIT. Stud Health Technol Inf. 2008;141:3–10.Oniki TA Coyle JF Parker CG . Lessons learned in detailed clinical modeling at Intermountain Healthcare. J Am Med Inform Assoc. 2014. Advance access published; doi:10.1136/amiajnl-2014-002875.Jacobson I Booch G Rumbaugh J . The Unified Software Development Process. Massachusetts, USA: Addison-Wesley Reading; 1999.Ahn, S., Huff, S. M., Kim, Y., & Kalra, D. (2013). Quality metrics for detailed clinical models. International Journal of Medical Informatics, 82(5), 408-417. doi:10.1016/j.ijmedinf.2012.09.006Kalra D . Editorial principles for the development of standards for the structure and content of health records. 2012. https://www.rcplondon.ac.uk/sites/default/files/documents/editorial-principles-for-the-development-of-record-standards.pdf. Accessed July 16, 2014.Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 339(jul21 1), b2535-b2535. doi:10.1136/bmj.b2535Transparent reporting of systematic reviews and meta-analyses (PRISMA). http://www.prisma-statement.org/. Accessed July 18, 2014.US National Library of Medicine. http://www.pubmed.gov. Accessed July 18, 2014.IEEE Xplore Digital Library. http://ieeexplore.ieee.org/. Accessed July 18, 2014.ScienceDirect. http://www.sciencedirect.com/. Accessed July 18, 2014.Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115. doi:10.1111/j.1365-2648.2007.04569.xRinner C Kohler M Hübner-Bloder G . Creating ISO/EN 13606 archetypes based on clinical information needs. In: Proceedings of EFMI Special Topic Conference, 14–15 April 2011, Lǎsko, Slovenia e-Health Across Borders Without Boundaries. 2011:14–15.Muñoz Carrero A Romero Gutiérrez A Marco Cuenca G . Manual práctico de interoperabilidad semántica para entornos sanitarios basada en arquetipos. Unidad de investigación en Telemedicina y e-Salud. Instituto de Salud Carlos III - Ministerio de Economía y Competitividad. 2013.Kalra D . Editorial principles for the development of standards for the structure and content of health records. 2012. https://www.rcplondon.ac.uk/sites/default/files/documents/editorial-principles-for-the-development-of-record-standards.pdf . Accessed July 18, 2015.Yuksel, M., & Dogac, A. (2011). Interoperability of Medical Device Information and the Clinical Applications: An HL7 RMIM based on the ISO/IEEE 11073 DIM. IEEE Transactions on Information Technology in Biomedicine, 15(4), 557-566. doi:10.1109/titb.2011.2151868Nagy M Hanzlicek P Precková P . Semantic interoperability in Czech healthcare environment supported by HL7 version 3. Methods Inf Med. 2010;49:186.LOPEZ, D., & BLOBEL, B. (2009). A development framework for semantically interoperable health information systems. International Journal of Medical Informatics, 78(2), 83-103. doi:10.1016/j.ijmedinf.2008.05.009Lopez DM Blobel B . Enhanced semantic interoperability by profiling health informatics standards. Methods Inf Med. 2009;48:170–177.Lopez DM Blobel B . Enhanced semantic interpretability by healthcare standards profiling. Stud Health Technol Inform. 2008;136:735.Knaup, P., Garde, S., & Haux, R. (2007). Systematic planning of patient records for cooperative care and multicenter research. International Journal of Medical Informatics, 76(2-3), 109-117. doi:10.1016/j.ijmedinf.2006.08.002Goossen, W. T. F., Ozbolt, J. G., Coenen, A., Park, H.-A., Mead, C., Ehnfors, M., & Marin, H. F. (2004). Development of a Provisional Domain Model for the Nursing Process for Use within the Health Level 7 Reference Information Model. Journal of the American Medical Informatics Association, 11(3), 186-194. doi:10.1197/jamia.m1085Anderson, H. V., Weintraub, W. S., Radford, M. J., Kremers, M. S., Roe, M. T., Shaw, R. E., … Tcheng, J. E. (2013). Standardized Cardiovascular Data for Clinical Research, Registries, and Patient Care. Journal of the American College of Cardiology, 61(18), 1835-1846. doi:10.1016/j.jacc.2012.12.047Jian, W.-S., Hsu, C.-Y., Hao, T.-H., Wen, H.-C., Hsu, M.-H., Lee, Y.-L., … Chang, P. (2007). Building a portable data and information interoperability infrastructure—framework for a standard Taiwan Electronic Medical Record Template. Computer Methods and Programs in Biomedicine, 88(2), 102-111. doi:10.1016/j.cmpb.2007.07.014Spigolon, D. N., & Moro, C. M. C. (2012). Arquétipos do conjunto de dados essenciais de enfermagem para atendimento de portadoras de endometriose. Revista Gaúcha de Enfermagem, 33(4), 22-32. doi:10.1590/s1983-14472012000400003Späth, M. B., & Grimson, J. (2011). Applying the archetype approach to the database of a biobank information management system. International Journal of Medical Informatics, 80(3), 205-226. doi:10.1016/j.ijmedinf.2010.11.002Smith, K., & Kalra, D. (2008). Electronic health records in complementary and alternative medicine. International Journal of Medical Informatics, 77(9), 576-588. doi:10.1016/j.ijmedinf.2007.11.005Bax, M. P., Kalra, D., & Santos, M. R. (2012). Dealing with the Archetypes Development Process for a Regional EHR System. Applied Clinical Informatics, 03(03), 258-275. doi:10.4338/aci-2011-12-ra-0074Moner D Moreno A Maldonado JA . Using archetypes for defining CDA templates. Stud Health Technol Inform. 2012;180:53–57.Moner D Maldonado JA Boscá D . CEN EN13606 normalisation framework implementation experiences. In: Seamless Care, Safe Care: The Challenges of Interoperability and Patient Safety in Health Care: Proceedings of the EFMI Special Topic Conference, June 2–4, 2010; Reykjavik, Iceland. IOS Press; 2010: 136.Marcos, 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.2013.05.004Leslie H . International developments in openEHR archetypes and templates. Health Inf Manag J. 2008;37:38.Kohl CD Garde S Knaup P . Facilitating secondary use of medical data by using openEHR archetypes. Stud Health Technol Inform. 2009;160:1117–1121.Garde, S., Hovenga, E., Buck, J., & Knaup, P. (2007). Expressing clinical data sets with openEHR archetypes: A solid basis for ubiquitous computing. International Journal of Medical Informatics, 76, S334-S341. doi:10.1016/j.ijmedinf.2007.02.004Garcia D Moro CM Cicogna PE . Method to integrate clinical guidelines into the electronic health record (EHR) by applying the archetypes approach. Stud Health Technol Inform. 2012;192:871–875.Duftschmid, G., Rinner, C., Kohler, M., Huebner-Bloder, G., Saboor, S., & Ammenwerth, E. (2013). The EHR-ARCHE project: Satisfying clinical information needs in a Shared Electronic Health Record System based on IHE XDS and Archetypes. International Journal of Medical Informatics, 82(12), 1195-1207. doi:10.1016/j.ijmedinf.2013.08.002Dias, R. D., Cook, T. W., & Freire, S. M. (2011). Modeling healthcare authorization and claim submissions using the openEHR dual-model approach. BMC Medical Informatics and Decision Making, 11(1). doi:10.1186/1472-6947-11-60Buck, J., Garde, S., Kohl, C. D., & Knaup-Gregori, P. (2009). Towards a comprehensive electronic patient record to support an innovative individual care concept for premature infants using the openEHR approach. International Journal of Medical Informatics, 78(8), 521-531. doi:10.1016/j.ijmedinf.2009.03.001Puentes, J., Roux, M., Montagner, J., & Lecornu, L. (2012). Development framework for a patient-centered record. Computer Methods and Programs in Biomedicine, 108(3), 1036-1051. doi:10.1016/j.cmpb.2012.06.007Liu, D., Wang, X., Pan, F., Yang, P., Xu, Y., Tang, X., … Rao, K. (2010). Harmonization of health data at national level: A pilot study in China. International Journal of Medical Informatics, 79(6), 450-458. doi:10.1016/j.ijmedinf.2010.03.002Liu, D., Wang, X., Pan, F., Xu, Y., Yang, P., & Rao, K. (2008). Web-based infectious disease reporting using XML forms. International Journal of Medical Informatics, 77(9), 630-640. doi:10.1016/j.ijmedinf.2007.10.011Kim, Y., & Park, H.-A. (2011). Development and Validation of Detailed Clinical Models for Nursing Problems in Perinatal care. Applied Clinical Informatics, 02(02), 225-239. doi:10.4338/aci-2011-01-ra-0007Khan, W. A., Hussain, M., Afzal, M., Amin, M. B., Saleem, M. A., & Lee, S. (2013). Personalized-Detailed Clinical Model for Data Interoperability Among Clinical Standards. Telemedicine and e-Health, 19(8), 632-642. doi:10.1089/tmj.2012.0189Jing, X., Kay, S., Marley, T., Hardiker, N. R., & Cimino, J. J. (2012). Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the Continuity of Care Record standard. Journal of Biomedical Informatics, 45(1), 82-92. doi:10.1016/j.jbi.2011.09.001Hsu, W., Taira, R. K., El-Saden, S., Kangarloo, H., & Bui, A. A. T. (2012). Context-Based Electronic Health Record: Toward Patient Specific Healthcare. IEEE Transactions on Information Technology in Biomedicine, 16(2), 228-234. doi:10.1109/titb.2012.2186149Hoy D Hardiker NR McNicoll IT . Collaborative development of clinical templates as a national resource. Int J Med Inf. 2009;78:S3–S8.Buyl, R., & Nyssen, M. (2009). Structured electronic physiotherapy records. International Journal of Medical Informatics, 78(7), 473-481. doi:10.1016/j.ijmedinf.2009.02.007D’Amore JD Mandel JC Kreda DA . Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc. 2014. Advance access published; doi:10.1136/amiajnl-2014-002883.Kalra D Tapuria A Austin T . Quality requirements for EHR archetypes. In: MIE; 2012: 48–52.Garde S Hovenga EJ Gränz J . Managing archetypes for sustainable and semantically interoperable electronic health records. Electron J Health Inform. 2007;2:e9.Madsen M Leslie H Hovenga EJS . Sustainable clinical knowledge management: an archetype development life cycle. Stud Health Technol Inform. 2010;151:115–132.Kohl CD Garde S Knaup P . Facilitating the openEHR approach-organizational structures for defining high-quality archetypes. Stud Health Technol Inform. 2008;136:437.Stroetmann VN Kalra D Lewalle P . Semantic interoperability for better health and safer healthcare. European Commission, Directorate-General Information Society and Media; 2009. http://dx.doi.org/10.2759/38514

Data Modeling Challenges of Advanced Interoperability

Progressive health paradigms, involving many different disciplines and combining multiple policy domains, requires advanced interoperability solutions. This results in special challenges for modeling health systems. The paper discusses classification systems for data models and enterprise business architectures and compares them with the ISO Reference Architecture. On that basis, existing definitions, specifications and standards of data models for interoperability are evaluated and their limitations are discussed. Amendments to correctly use those models and to better meet the aforementioned challenges are offered

Solving the Modeling Dilemma as a Foundation for Interoperability

Introduction: Progressive health paradigms, involving many different disciplines and combining multiple policy domains, requires advanced interoperability solutions. This results in special challenges for modeling health systems. Methods: The paper discusses classification systems for data models and enterprise business architectures and compares them with the ISO Reference Architecture. Results and Conclusions: Existing definitions, specifications and standards for data models enabling interoperability are analyzed, and their limitations are evaluated. Amendments to correctly use those models and to better meet the aforementioned challenges are offered

UML profile for MIF static models. Version 1.0

HL7 provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity and enhance knowledge transfer among all of our stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs and patients. In all of our processes we exhibit timeliness, scientific rigor and technical expertise without compromising transparency, accountability, practicality, or our willingness to put the needs of our stakeholders first. HL7 is holding a contest to encourage the development of HL7 tools. This document describes the specification of a UML Profile for MIF Static Models as a particular submission to the HL7 2012-2013 Tooling ChallengePreprin

Interoperability is more than just technology

This Special Issue of the European Journal for Biomedical Informatics is dedicated to the International HL7 Interoper- ability Conference (IHIC 2016) "Interoperability is more than just technology", 13-15 June 2016 in Genoa, Italy [1]. It con- tains papers selected by an independent peer review process, strictly performed by experts from countries dierent from the authors' country of residence. IHIC 2016 is the 16th event of the International HL7 In- teroperability Conference series, which has been inaugurated in 2000 by the Board of HL7 Germany and its unforget- table Chair and interoperability pioneer Joachim W. Dudeck. The rst event in Dresden, Germany, was entitled "Advanced Healthcare Information Standards". While the rst confer- ences have been characterized by focusing on CDA (Clinical Document Architecture), over the time, the scope of the conferences has been extended towards all aspects of health information interoperability. The concept of interoperability has dramatically changed from standardized electronic data interchange (EDI) based on data representation at applica- tion level, the 7th level of the ISO Open Systems Intercon- nection stack, having been the name giver for the Health Level 7 standards framework. Meanwhile, the semantics of shared data as well as service level interoperability are consid- ered, bringing terminology and ontology issues, but also im- plementation challenges such as Web services and RESTful technologies on board. As visible outcome of such develop- ment, requirements for National Interoperability Frameworks stated in the USA [2], but also hypes such as FHIR came up and are highlighted in the IHIC 2016 papers as well. So it is just consequent to address in 2016 also non-technological issues of interoperability

Transformation of Health and Social Care Systems—An Interdisciplinary Approach Toward a Foundational Architecture

Objective: For realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome. Methods: Using the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used. Results: The system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications

Principles and Standards for Designing and Managing Integrable and Interoperable Transformed Health Ecosystems

The advancement of sciences and technologies, economic challenges, increasing expectations, and consumerism result in a radical transformation of health and social care around the globe, characterized by foundational organizational, methodological, and technological paradigm changes. The transformation of the health and social care ecosystems aims at ubiquitously providing personalized, preventive, predictive, participative precision (5P) medicine, considering and understanding the individual’s health status in a comprehensive context from the elementary particle up to society. For designing and implementing such advanced ecosystems, an understanding and correct representation of the structure, function, and relations of their components is inevitable, thereby including the perspectives, principles, and methodologies of all included disciplines. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be based on international standards. A core standard for representing transformed health ecosystems and managing the integration and interoperability of systems, components, specifications, and artifacts is ISO 23903:2021, therefore playing a central role in this publication. Consequently, ISO/TC 215 and CEN/TC 251, both representing the international standardization on health informatics, declared the deployment of ISO 23903:2021 mandatory for all their projects and standards addressing more than one domain. The paper summarizes and concludes the first author’s leading engagement in the evolution of pHealth in Europe and beyond over the last 15 years, discussing the concepts, principles, and standards for designing, implementing, and managing 5P medicine ecosystems. It not only introduces the theoretical foundations of the approach but also exemplifies its deployment in practical projects and solutions regarding interoperability and integration in multi-domain ecosystems. The presented approach enables comprehensive and consistent integration of and interoperability between domains, systems, related actors, specifications, standards, and solutions. That way, it should help overcome the problems and limitations of data-centric approaches, which still dominate projects and products nowadays, and replace them with knowledge-centric, comprehensive, and consistent ones

Transformation of Health and Social Care Systems—An Interdisciplinary Approach Toward a Foundational Architecture

Objective: For realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome. Methods: Using the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used. Results: The system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications.publishedVersionPeer reviewe