Mobile-Based Patient Monitoring Systems: A Prioritisation Framework Using Multi-Criteria Decision-Making Techniques

This study presents a prioritisation framework for mobile patient monitoring systems (MPMSs) based on multicriteria analysis in architectural components. This framework selects the most appropriate system amongst available MPMSs for the telemedicine environment. Prioritisation of MPMSs is a challenging task due to (a) multiple evaluation criteria, (b) importance of criteria, (c) data variation and (d) unmeasurable values. The secondary data presented as the decision evaluation matrix include six systems (namely, Yale–National Aeronautics and Space Administration (NASA), advanced health and disaster aid network, personalised health monitoring, CMS, MobiHealth and NTU) as alternatives and 13 criteria (namely, supported number of sensors, sensor front-end (SFE) communication, SFE to mobile base unit (MBU) communications, display of biosignals on the MBU, storage of biosignals on the MBU, intra-body area network (BAN) communication problems, extra-BAN communication problems, extra-BAN communication technology, extra-BAN communication protocols, back-end system communication technology, intended geographic area of use, end-to-end security and reported trial problems) based on the architectural components of MPMSs. These criteria are adopted from the most relevant studies and are found to be applicable to this study. The prioritisation framework is developed in three stages. (1) The unmeasurable values of the MPMS evaluation criteria in the adopted decision evaluation matrix based on expert opinion are represented by using the best–worst method (BWM). (2) The importance of the evaluation criteria based on the architectural components of the MPMS is determined by using the BWM. (3) The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilised to rank the MPMSs according to the determined importance of the evaluation criteria and the adopted decision matrix. For validation, mean ± standard deviation is used to verify the similarity of systematic prioritisations objectively. The following results are obtained. (1) The BWM represents the unmeasurable values of the MPMS evaluation criteria. (2) The BWM is suitable for weighing the evaluation criteria based on the architectural components of the MPMS. (3) VIKOR is suitable for solving the MPMS prioritisation problem. Moreover, the internal and external VIKOR group decision making are approximately the same, with the best MPMS being ‘Yale–NASA’ and the worst MPMS being ‘NTU’. (4) For the objective validation, remarkable differences are observed between the group scores, which indicate the similarity of internal and external prioritisation results.

[1]  B. B. Zaidan,et al.  Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors , 2019, IEEE Access.

[2]  Aristides Lopes da Silva,et al.  Health and emergency-care platform for the elderly and disabled people in the Smart City , 2015, J. Syst. Softw..

[3]  Jin Qi,et al.  An integrated AHP and VIKOR for design concept evaluation based on rough number , 2015, Adv. Eng. Informatics.

[4]  Gaurav Paliwal,et al.  A Comparison of Mobile Patient Monitoring Systems , 2013, HIS.

[5]  Dalila B.M.M. Fontes,et al.  Multicriteria Decision Making: A Case Study in the Automobile Industry , 2013 .

[6]  B. B. Zaidan,et al.  Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review , 2018, Journal of Medical Systems.

[7]  Chung-Ming Chen,et al.  Computer-Aided Detection and Diagnosis in Medical Imaging , 2013, Comput. Math. Methods Medicine.

[8]  Ajith Abraham,et al.  Hybrid fuzzy-linear programming approach for multi-criteria decision making problems , 2003, Neural Parallel Sci. Comput..

[9]  A. A. Zaidan,et al.  An evaluation and selection problems of OSS-LMS packages , 2016, SpringerPlus.

[10]  Himanshu Gupta,et al.  Evaluating service quality of airline industry using hybrid best worst method and VIKOR , 2017 .

[11]  B. B. Zaidan,et al.  Open source EMR software: Profiling, insights and hands-on analysis , 2014, Comput. Methods Programs Biomed..

[12]  Seyed Amin Seyed Haeri,et al.  Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique , 2017 .

[13]  Lóránt Tavasszy,et al.  Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method , 2017 .

[14]  Shuo-Yan Chou,et al.  A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes , 2008, Eur. J. Oper. Res..

[15]  Mirna Muñoz Mata,et al.  Design and Customization of Telemedicine Systems , 2013, Comput. Math. Methods Medicine.

[16]  R. Goeree,et al.  Multi-criteria decision analysis (MCDA) in health care: A bibliometric analysis , 2013 .

[17]  Georges Adunlin,et al.  Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis , 2015, Health expectations : an international journal of public participation in health care and health policy.

[18]  B. B. Zaidan,et al.  Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017 , 2019, Comput. Oper. Res..

[19]  J. Rezaei Best-worst multi-criteria decision-making method: Some properties and a linear model , 2016 .

[20]  Arkalgud Ramaprasad,et al.  An ontology of and roadmap for mHealth research , 2017, Int. J. Medical Informatics.

[21]  B. B. Zaidan,et al.  MIRASS: Medical Informatics Research Activity Support System Using Information Mashup Network , 2014, Journal of Medical Systems.

[22]  B. B. Zaidan,et al.  Based blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication , 2019, Comput. Stand. Interfaces.

[23]  B. B. Zaidan,et al.  Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers , 2018, Telecommun. Syst..

[24]  B. B. Zaidan,et al.  Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology , 2018, Journal of Medical Systems.

[25]  Sanjay Kumar Malik,et al.  Major MCDM Techniques and their application-A Review , 2014 .

[26]  B. B. Zaidan,et al.  Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology , 2019, Neural Computing and Applications.

[27]  F. M. Jumaah,et al.  Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment , 2018 .

[28]  Miss Laiha Mat Kiah Impact of data privacy and confidentiality on developing telemedicine applications: A review participates opinion and expert concerns , 2011 .

[29]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  B. B. Zaidan,et al.  An Enhanced Security Solution for Electronic Medical Records Based on AES Hybrid Technique with SOAP/XML and SHA-1 , 2013, Journal of Medical Systems.

[31]  B. B. Zaidan,et al.  Real-time-based E-health systems: design and implementation of a lightweight key management protocol for securing sensitive information of patients , 2018, Health and Technology.

[32]  A. Mühlbacher,et al.  Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA , 2016, Applied Health Economics and Health Policy.

[33]  B. B. Zaidan,et al.  Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis , 2018, Health and Technology.

[34]  B. B. Zaidan,et al.  Meeting the Security Requirements of Electronic Medical Records in the ERA of High-Speed Computing , 2014, Journal of Medical Systems.

[35]  Ing Widya,et al.  Mobihealth: mobile health services based on body area networks , 2006 .

[36]  Mostefa Mesbah,et al.  EEG rhythm/channel selection for fuzzy rule-based alertness state characterization , 2016, Neural Computing and Applications.

[37]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[38]  Jingzheng Ren,et al.  Selection of sustainable prime mover for combined cooling, heat, and power technologies under uncertainties: An interval multicriteria decision‐making approach , 2018 .

[39]  Aviad Shapira,et al.  AHP-based analysis of the risk potential of safety incidents: Case study of cranes in the construction industry , 2017 .

[40]  B. B. Zaidan,et al.  Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions , 2019, Comput. Stand. Interfaces.

[41]  Valérie Gay,et al.  Body Sensor Networks for Mobile Health Monitoring: Experience in Europe and Australia , 2009, 2010 Fourth International Conference on Digital Society.

[42]  Ivan Petrovic,et al.  Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers , 2018, Expert Syst. Appl..

[43]  B. B. Zaidan,et al.  Design and Develop a Video Conferencing Framework for Real-Time Telemedicine Applications Using Secure Group-Based Communication Architecture , 2014, Journal of Medical Systems.

[44]  B. B. Zaidan,et al.  Challenges, Alternatives, and Paths to Sustainability: Better Public Health Promotion Using Social Networking Pages as Key Tools , 2015, Journal of Medical Systems.

[45]  P. H. Huang,et al.  A non-linear non-weight method for multi-criteria decision making , 2017, Ann. Oper. Res..

[46]  B. B. Zaidan,et al.  Novel Methodology for Triage and Prioritizing Using "Big Data" Patients with Chronic Heart Diseases Through Telemedicine Environmental , 2017, Int. J. Inf. Technol. Decis. Mak..

[47]  Walid Serrai,et al.  Towards an efficient and a more accurate web service selection using MCDM methods , 2017, J. Comput. Sci..

[48]  B. B. Zaidan,et al.  Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations , 2018, Journal of Medical Systems.

[49]  B. B. Zaidan,et al.  Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions , 2019, Int. J. Inf. Technol. Decis. Mak..

[50]  Telemaco Melia,et al.  IP flow mobility: smart traffic offload for future wireless networks , 2011, IEEE Communications Magazine.

[51]  Azzedine Boukerche,et al.  Monitoring patients via a secure and mobile healthcare system , 2010, IEEE Wireless Communications.

[52]  A. A. Zaidan,et al.  Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions , 2018 .

[53]  Walid Serrai,et al.  An efficient approach for Web service selection , 2016, 2016 IEEE Symposium on Computers and Communication (ISCC).

[54]  B. B. Zaidan,et al.  Suitability of adopting S/MIME and OpenPGP email messages protocol to secure electronic medical records , 2013, Second International Conference on Future Generation Communication Technologies (FGCT 2013).

[55]  B. B. Zaidan,et al.  A Security Framework for Nationwide Health Information Exchange based on Telehealth Strategy , 2015, Journal of Medical Systems.

[56]  Haoran Zhao,et al.  Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability , 2018, Environment, Development and Sustainability.

[57]  B. B. Zaidan,et al.  Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects , 2018, Journal of Medical Systems.

[58]  Miss Laiha Mat Kiah,et al.  Comprehensive review and analysis of anti-malware apps for smartphones , 2019, Telecommunication Systems.

[59]  B. B. Zaidan,et al.  Conceptual framework for the security of mobile health applications on Android platform , 2018, Telematics Informatics.

[60]  B. B. Zaidan,et al.  Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review , 2019, Journal of Medical Systems.

[61]  A. A. Zaidan,et al.  A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution , 2018, Health and Technology.

[62]  B. B. Zaidan,et al.  Towards on Develop a Framework for the Evaluation and Benchmarking of Skin Detectors Based on Artificial Intelligent Models Using Multi-Criteria Decision-Making Techniques , 2017, Int. J. Pattern Recognit. Artif. Intell..

[63]  B. B. Zaidan,et al.  Real-Time Medical Systems Based on Human Biometric Steganography: a Systematic Review , 2018, Journal of Medical Systems.

[64]  Valéria Farinazzo Martins Salvador,et al.  A Checklist to Evaluate Augmented Reality Applications , 2014, SVR.

[65]  Hermie Hermens,et al.  A framework for the comparison of mobile patient monitoring systems , 2012, J. Biomed. Informatics.

[66]  A. A. Zaidan,et al.  A New Approach based on Multi-Dimensional Evaluation and Benchmarking for Data Hiding Techniques , 2017 .

[67]  B. B. Zaidan,et al.  Sensor-Based mHealth Authentication for Real-Time Remote Healthcare Monitoring System: A Multilayer Systematic Review , 2019, Journal of Medical Systems.

[68]  J. Rezaei Best-worst multi-criteria decision-making method , 2015 .

[69]  B. B. Zaidan,et al.  Software and Hardware FPGA-Based Digital Watermarking and Steganography Approaches: Toward New Methodology for Evaluation and Benchmarking Using Multi-Criteria Decision-Making Techniques , 2017, J. Circuits Syst. Comput..

[70]  Mohd Bakri Ishak,et al.  Optimal selection of Iron and Steel wastewater treatment technology using integrated multi-criteria decision-making techniques and fuzzy logic , 2017 .

[71]  Pravin Amrut Pawar,et al.  Review of quality of service in the mobile patient monitoring systems , 2017, 2017 IEEE Region 10 Symposium (TENSYMP).

[72]  A. A. Zaidan,et al.  A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’ , 2017, Softw. Pract. Exp..

[73]  Rajendra M. Sonar,et al.  Analytic Hierarchy Process (AHP), Weighted Scoring Method (WSM), and Hybrid Knowledge Based System (HKBS) for Software Selection: A Comparative Study , 2009, 2009 Second International Conference on Emerging Trends in Engineering & Technology.

[74]  B. B. Zaidan,et al.  MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems , 2018, Neural Computing and Applications.

[75]  Miss Laiha Mat Kiah,et al.  Suitability of using SOAP protocol to secure electronic medical record databases transmission , 2010 .

[76]  Jacek Malczewski,et al.  GIS and Multicriteria Decision Analysis , 1999 .

[77]  B. B. Zaidan,et al.  Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects , 2018, Journal of Medical Systems.

[78]  Mahmoud Migdadi,et al.  Knowledge management enablers and outcomes in the small-and-medium sized enterprises , 2009, Ind. Manag. Data Syst..

[79]  Nor Badrul Anuar,et al.  The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations , 2015, Comput. Methods Programs Biomed..

[80]  B. B. Zaidan,et al.  Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS , 2015, J. Biomed. Informatics.

[81]  Josef Jablonsky,et al.  MS Excel based Software Support Tools for Decision Problems with Multiple Criteria , 2014 .

[82]  A. A. Zaidan,et al.  A methodology for football players selection problem based on multi-measurements criteria analysis , 2017 .

[83]  Gwo-Hshiung Tzeng,et al.  Extended VIKOR method in comparison with outranking methods , 2007, Eur. J. Oper. Res..

[84]  Zhang-peng Tian,et al.  An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods , 2018, Appl. Soft Comput..

[85]  K. I. Mohammed,et al.  Based Multiple Heterogeneous Wearable Sensors: A Smart Real-Time Health Monitoring Structured for Hospitals Distributor , 2019, IEEE Access.

[86]  Himanshu Gupta,et al.  Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS , 2017 .

[87]  B. B. Zaidan,et al.  A distributed framework for health information exchange using smartphone technologies , 2017, J. Biomed. Informatics.

[88]  Gabriel Urzaiz,et al.  Monitoring Architecture to collect measurement data and medical patient control through mobile devices , 2011 .

[89]  B. B. Zaidan,et al.  Real-Time Remote Health-Monitoring Systems in a Medical Centre: A Review of the Provision of Healthcare Services-Based Body Sensor Information, Open Challenges and Methodological Aspects , 2018, Journal of Medical Systems.

[90]  B. B. Zaidan,et al.  Multi-criteria analysis for OS-EMR software selection problem: A comparative study , 2015, Decis. Support Syst..

[91]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[92]  A. A. Zaidan,et al.  Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques , 2018 .

[93]  András Takács,et al.  Context-aware IPv6 Flow Mobility for Multi-sensor Based Mobile Patient Monitoring and Tele-consultation , 2014, MoWNet.

[94]  Aduwati Sali,et al.  Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection , 2017, 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT).

[95]  B. B. Zaidan,et al.  A security framework for mHealth apps on Android platform , 2018, Comput. Secur..

[96]  B. B. Zaidan,et al.  Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization “Large Scales Data” Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology , 2018, Journal of Medical Systems.

[97]  Stanley Zionts,et al.  MCDM---If Not a Roman Numeral, Then What? , 1979 .

[98]  B. B. Zaidan,et al.  Based Medical Systems for Patient’s Authentication: Towards a New Verification Secure Framework Using CIA Standard , 2019, Journal of Medical Systems.