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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

  • Systems-level Quality Improvement
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Abstract

This paper presents a new approach to prioritize “Large-scale Data” of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.

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References

  1. Nguyen, T. et al., Classification of healthcare data using genetic fuzzy logic system and wavelets. Expert Syst. Appl. 42(4):2184–2197, 2015.

    Article  Google Scholar 

  2. Raghupathi, W., and Raghupathi, V., Big data analytics in healthcare: promise and potential. Health Information. Sci. Syst. 2(1):1, 2014.

    Google Scholar 

  3. Fong, S., Wong, R., and Vasilakos, A. V., Accelerated PSO swarm search feature selection for data stream mining big data. IEEE Trans. Serv. Comput. 9(1):33–45, 2016.

    Google Scholar 

  4. Zhang, Y., et al., Parallel processing systems for big data: a survey. 2016.

    Google Scholar 

  5. Tsai, C.-W. et al., Big data analytics: a survey. J Big Data, 2015.

  6. Acampora, G. et al., A survey on ambient intelligence in healthcare. Proc. IEEE 101(12):2470–2494, 2013.

    Article  Google Scholar 

  7. Chen, M. et al., Body area networks: a survey. Mob. Netw. Appl. 16(2):171–193, 2011.

    Article  Google Scholar 

  8. Ravikumaran, P., and Devi, K. V., A review: big data and analytics in health care. Indian J. Eng. 13(31):1–10, 2016.

    Google Scholar 

  9. Mavandadi, S. et al., Crowd-sourced BioGames: managing the big data problem for next-generation lab-on-a-chip platforms. Lab Chip 12(20):4102–4106, 2012.

    Article  CAS  PubMed  Google Scholar 

  10. Kim, G.-H., Trimi, S., and Chung, J.-H., Big-data applications in the government sector. Commun. ACM 57(3):78–85, 2014.

    Article  Google Scholar 

  11. Páez, D. G., et al., Big data and IoT for chronic patients monitoring. In: Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services, Springer, p. 416–423, 2014.

  12. Alanazi, H. O., Zaidan, A. A., Zaidan, B. B., Mat Kiah, M. L., and Al-Bakri, S. H., Meeting the security requirements of electronic medical records in the ERA of high-speed computing. J. Med. Syst. 39(1):1–14, 2015.

    Article  Google Scholar 

  13. Alanazi, H. O., Alam, G. M., Zaidan, B. B., and Zaidan, A. A., Securing electronic medical records transmissions over unsecured communications: an overview for better medical governance. J. Med. Plant Res. 4(19):2059–2074, 2010.

    Article  Google Scholar 

  14. Mat Kiah, M. L., Zaidan, B. B., Zaidan, A. A., Nabi, M., and Ibraheem, R., MIRASS: medical informatics research activity support system using information mashup network. J. Med. Syst. 38(4):1–37, 2014.

    Google Scholar 

  15. Mat Kiah, M. L., Al-Bakri, S. H., Zaidan, A. A., Zaidan, B. B., and Hussain, M., Design and develop a video conferencing framework for real-time telemedicine applications using secure group-based communication architecture. J. Med. Syst. 38(10):1–13, 2014d.

    Article  Google Scholar 

  16. Mat Kiah, M. L., Nabi, M. S., Zaidan, B. B., and Zaidan, A. A., An enhanced security solution for electronic medical records based on AES hybrid technique with SOAP/XML and SHA-1. J. Med. Syst. 37(5):1–16, 2013.

    Google Scholar 

  17. Nabi, M. S. A., Mat Kiah, M. L., Zaidan, B. B., Zaidan, A. A., and Alam, G. M., Suitability of SOAP protocol in securing transmissions of EMR database. Int. J. Pharmacol. 6(6):959–964, 2011.

    Google Scholar 

  18. Zaidan, A. A. et al., Challenges, alternatives, and paths to sustainability: better public health promotion using social networking pages as key tools. J. Med. Syst. 39(2):1–14, 2015c.

    Article  Google Scholar 

  19. Zaidan, B. B., Haiqi, A., Zaidan, A. A., Abdulnabi, M., Mat Kiah, M. L., and Muzamel, H., A security framework for nationwide health information exchange based on telehealth strategy. J. Med. Syst. 39(5):1–19, 2015.

    Article  Google Scholar 

  20. Zaidan, B. B., Zaidan, A. A., and Mat Kiah, M. L., Impact of data privacy and confidentiality on developing telemedicine applications: a review participates opinion and expert concerns. Int. J. Pharmacol. 7(3):382–387, 2011.

    Article  Google Scholar 

  21. Jeong, S. et al., An integrated healthcare system for personalized chronic disease care in home–hospital environments. IEEE Trans. Inf. Technol. Biomed. 16(4):572–585, 2012.

    Article  PubMed  Google Scholar 

  22. Westergren, H., Ferm, M., and Häggström, P., First evaluation of the paediatric version of the Swedish rapid emergency triage and treatment system shows good reliability. Acta Paediatr. 103(3):305–308, 2014.

    Article  PubMed  Google Scholar 

  23. Salman, O. H. et al., Multi-sources data fusion framework for remote triage prioritization in telehealth. J. Med. Syst. 38(9):1–23, 2014.

    Article  Google Scholar 

  24. Sakanushi, K., et al. Electronic triage system: casualties monitoring system in the disaster scene. In: 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), IEEE, 2011.

  25. Godfrey, B., et al., Emergency medical guidelines, Third Edit. Sunshine Act of Florida, p. 245, 2000.

  26. Seising, R. and M.E. Tabacchi, Fuzziness and medicine: philosophical reflections and application systems in health care: a companion volume to Sadegh-Zadeh’s Handbook of analytical philosophy of medicine, vol. 302, Springer, 2013.

  27. Christensen, D. et al., Nurse-administered early warning score system can be used for emergency department triage. Dan. Med. Bull. 58:A4221, 2011.

    PubMed  Google Scholar 

  28. Zarabzadeh, A. et al., Variation in health care providers’ perceptions: decision making based on patient vital signs. J. Decis. Syst. 22(3):168–189, 2013.

    Article  Google Scholar 

  29. Sakanushi, K. et al., Electronic triage system for continuously monitoring casualties at disaster scenes. J. Ambient. Intell. Humaniz. Comput. 4(5):547–558, 2013.

    Article  Google Scholar 

  30. Pinto Júnior, D., Salgado, P. D. O., and Chianca, T. C. M., Predictive validity of the Manchester triage system: evaluation of outcomes of patients admitted to an emergency department. Rev. Lat. Am. Enfermagem 20(6):1041–1047, 2012.

    Article  Google Scholar 

  31. Mills, A. F., A simple yet effective decision support policy for mass-casualty triage. Eur. J. Oper. Res. 253(3):734–745, 2016.

    Article  Google Scholar 

  32. Ashour, O. M., and Okudan, G. E., Patient sorting through emergency severity index and descriptive variables' utility. In: IIE Annual Conference, Proceedings, Institute of Industrial Engineers-Publisher, 2010a.

  33. Ashour, O. M., and Kremer, G. E. O., Dynamic patient grouping and prioritization: a new approach to emergency department flow improvement. Health Care Manag. Sci. 19(2):192–205, 2016.

    Article  PubMed  Google Scholar 

  34. Childers, A. K., Mayorga, M. E., and Taaffe, K. M., Prioritization strategies for patient evacuations. Health Care Manag. Sci. 17(1):77–87, 2014.

    Article  PubMed  Google Scholar 

  35. Sung, I., and Lee, T., Optimal allocation of emergency medical resources in a mass casualty incident: Patient prioritization by column generation. Eur. J. Oper. Res. 252(2):623–634, 2016.

    Article  Google Scholar 

  36. Elalouf, A., and Wachtel, G., An alternative scheduling approach for improving patient-flow in emergency departments. Oper. Res. Health Care 7:94–102, 2015.

    Article  Google Scholar 

  37. Claudio, D., and Okudan, G. E., Utility function-based patient prioritisation in the emergency department. Eur. J. Ind. Eng. 4(1):59–77, 2010.

    Article  Google Scholar 

  38. Claudio, D. et al., A dynamic multi-attribute utility theory–based decision support system for patient prioritization in the emergency department. IIE Trans. Healthcare Syst. Eng. 4(1):1–15, 2014.

    Article  Google Scholar 

  39. Mizumoto, T., et al. Emergency medical support system for visualizing locations and vital signs of patients in Mass Casualty Incident. In: 2012 I.E. International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE, 2012.

  40. Kashiyama, A., Uchiyama, A., and Higashino, T., Depth limited treatment planning and scheduling for electronic triage system in MCI. In: Wireless Mobile Communication and Healthcare, Springer, p. 224–233, 2012.

  41. Ashour, O. M., and Okudan, G. E., Fuzzy AHP and utility theory based patient sorting in emergency departments. Int. J. Collab. Enterp. 1(3–4):332–358, 2010b.

    Article  Google Scholar 

  42. Göransson, K. E. et al., Thinking strategies used by Registered Nurses during emergency department triage. J. Adv. Nurs. 61(2):163–172, 2008.

    Article  PubMed  Google Scholar 

  43. Faulin, J. et al., Decision making in service industries: a practical approach. CRC Press, 2012.

  44. Zaidan, A. et al., Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. J. Biomed. Inform. 53:390–404, 2015.

    Article  CAS  PubMed  Google Scholar 

  45. Zaidan, A. et al., Multi-criteria analysis for OS-EMR software selection problem: a comparative study. Decis. Support. Syst. 78:15–27, 2015.

    Article  Google Scholar 

  46. Belal, N.-A., Nur, F.-E., Hazura, M., Zaidan, A. A., and Zaidan, B. B., An evaluation and selection problems of OSS-LMS packages. SpringerPlus 5:1–35, 2016.

    Article  Google Scholar 

  47. Jumaah, F. M., Zaidan, A. A., Zaidan, B. B., Bahbibi, R., Qahtan, M. Y., and Sali, A., 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. Telecommun. Syst. 67(176):1–19, 2017.

    Google Scholar 

  48. Mat Kiah, M. L., Haiqi, A., Zaidan, B. B., and Zaidan, A. A., Open source EMR software: profiling, insights and hands-on analysis. Comput. Methods Prog. Biomed. 117(2):360–382, 2014.

    Article  Google Scholar 

  49. Qader, M. A., Zaidan, B. B., Zaidan, A. A., Ali, S. K., Kamaluddin, M. A., and Radzi, W. B., A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement 111:38–50, 2017.

    Article  Google Scholar 

  50. Qahtan, M.-Y., Zadain, A. A., Zaidan, B. B., Lakulu, M. B., and Rahmatullah, B., Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artifitcial intelligent models using multi-criteria decision-making techniques. Int. J. Pattern Recognit. Artif. Intell. 31(3):1–24, 2017.

    Google Scholar 

  51. Salman, O. H., Zaidan, A. A., Zaidan, B. B., Kalid, N., and Hashim, M., Novel methodology for triage and prioritizing using “big data” patients with chronic heart diseases through telemedicine environmental. Int. J. Inf. Technol. Decis. Mak. 5(16):1211–1245, 2017.

    Article  Google Scholar 

  52. Yas Qahtan, M., Zaidan, A. A., Zaidan, B. B., and Abdul Karim, H., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: review. Open Issues & Challenges, and Recommended Solutions. Measurement 114:243–260, 2018.

    Article  Google Scholar 

  53. Zaidan, B. B., Zaidan, A. A., Abdul Karim, H., and Ahmad, N. N., A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. Int. J. Inf. Technol. Decis. Mak. 16:1–41, 2017.

    Article  Google Scholar 

  54. Zaidan, B. B., Zaidan, A. A., Karim, H. A., and Ahmad, N. N., A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’. Software Pract. Experience 47(7):1–14, 2017.

    Google Scholar 

  55. Zaidan, B. B., and Zaidan, A. A., Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques. J. Circuit Syst. Comput. 26(6):1–27, 2017.

    Google Scholar 

  56. Zaidan, B. B., and Zaidan, A. A., Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques. Measurement 117:277–294, 2018.

    Article  Google Scholar 

  57. Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., and Muzammil, H., Based real time remote health monitoring systems: a review on patients prioritization and related “Big Data” using body sensors information and communication technology. J. Med. Syst. 42:2–30, 2018.

    Article  Google Scholar 

  58. Jumaah, F. M., Zadain, A. A., Zaidana, B. B., Hamzaha, A. K., and Bahbibia, R., Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement 118:83–95, 2018.

    Article  Google Scholar 

  59. Triantaphyllou, E. et al., Multi-criteria decision making: an operations research approach. Encycl. Electr. Electron. Eng. 15:175–186, 1998.

    Google Scholar 

  60. Triantaphyllou, E., Multi-criteria decision making methods. In: Multi-Criteria Decision Making Methods: A Comparative Study, Springer, p. 5–21, 2000.

  61. Aruldoss, M., Lakshmi, T. M., and Venkatesan, V. P., A survey on multi criteria decision making methods and its applications. Am. J. Inf. Syst. 1(1):31–43, 2013.

    Google Scholar 

  62. Yoon, K. P., and Hwang, C.-L., Multiple attribute decision making: an introduction, Vol. 104, Sage Publications, 1995.

  63. P.-E. J., and Mollaghasemi, M., Technical briefing: making multiple objective decisions. Los Alamitos, California: IEEE Computer Society Press, 1997.

  64. Lesmes, D., Castillo, M., and Zarama, R., Application of the Analytic Network Process (ANP) to establish weights in order to re-accredit a program of a University. In: Proceedings of the International Symposium on the Analytic Hierarchy Process, 2009.

  65. Mazurek, J., and Kiszová, Z., Modeling dependence and feedback in ANP with fuzzy cognitive maps. In: Proceedings of the 30th International Conference Mathematical Methods in Economics, 2012.

  66. Nilsson, H., Nordström, E.-M., and Öhman, K., Decision support for participatory forest planning using AHP and TOPSIS. Forests 7(5):100, 2016.

    Article  Google Scholar 

  67. Saaty, T. L., and Ozdemir, M. S., Why the magic number seven plus or minus two. Math. Comput. Model. 38(3):233–244, 2003.

    Article  Google Scholar 

  68. Kandakoglu, A., Celik, M., and Akgun, I., A multi-methodological approach for shipping registry selection in maritime transportation industry. Math. Comput. Model. 49(3):586–597, 2009.

    Article  Google Scholar 

  69. Çalışkan, H., Selection of boron based tribological hard coatings using multi-criteria decision making methods. Mater. Des. 50:742–749, 2013.

    Article  Google Scholar 

  70. Ortíz, M. A. et al., An integrated approach of AHP-DEMATEL methods applied for the selection of allied hospitals in outpatient service. Int. J. Med. Eng. Inf. 8(2):87–107, 2016.

    Google Scholar 

  71. Barrios, M. A. O. et al., An AHP-topsis integrated model for selecting the most appropriate tomography equipment. Int. J. Inf. Technol. Decis. Mak. 15(04):861–885, 2016.

    Article  Google Scholar 

  72. Houghton, A., and Gray, D., Making sense of the ECG: a hands-on guide. CRC Press, 2014.

  73. Sung, W.-T., and Chang, K.-Y., Evidence-based multi-sensor information fusion for remote health care systems. Sensors Actuators A Phys. 204:1–19, 2013.

    Article  CAS  Google Scholar 

  74. ter Haar, C. C. et al., Difference vectors to describe dynamics of the ST segment and the ventricular gradient in acute ischemia. J. Electrocardiol. 46(4):302–311, 2013.

    Article  PubMed  Google Scholar 

  75. Weatherly, H. et al., Methods for assessing the cost-effectiveness of public health interventions: Key challenges and recommendations. Health Policy 93(2):85–92, 2009.

    Article  PubMed  Google Scholar 

  76. Shih, H.-S., Shyur, H.-J., and Lee, E. S., An extension of TOPSIS for group decision making. Math. Comput. Model. 45(7):801–813, 2007.

    Article  Google Scholar 

  77. Dvorski, D. D., Installing, configuring, and developing with Xampp. Skills Canada, 2007.

  78. Qader, M. et al., A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement, 2017.

  79. Wyte-Lake, T., Claver, M., and Dobalian, A., Assessing patients' disaster preparedness in home-based primary care. Gerontology 62(3):263–274, 2016.

    Article  PubMed  Google Scholar 

  80. Sarkar, P., and Sinha, D., An approach to continuous pervasive care of remote patients based on priority based assignment of nurse. In: IFIP International Conference on Computer Information Systems and Industrial Management, Springer, 2014.

  81. Tan, K. W., Dynamic queue management for hospital emergency room services. 2013.

  82. Chowdhury, M. A., Mciver, W., and Light, J., Data association in remote health monitoring systems. IEEE Commun. Mag. 50(6):144–149, 2012.

    Article  Google Scholar 

  83. Ashour, O., Patient family identification through group technology and its impact on static complexity and system performance in the emergency department. 2012.

  84. Abbasgholizadeh Rahimi, S. et al., Using fuzzy cost-based FMEA, GRA and profitability theory for minimizing failures at a healthcare diagnosis service. Qual. Reliab. Eng. Int. 31(4):601–615, 2015.

    Article  Google Scholar 

  85. Maslov, I. V., and Gertner, I., Multi-sensor fusion: an evolutionary algorithm approach. Inf. Fusion 7(3):304–330, 2006.

    Article  Google Scholar 

  86. Khaleghi, B. et al., Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14(1):28–44, 2013.

    Article  Google Scholar 

  87. Liggins, II, M., Hall, D., and Llinas, J., Handbook of multisensor data fusion: theory and practice. CRC press, 2008.

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Funding

This study was funded by FRGS No: MMUE/140083.

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Authors and Affiliations

  1. Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

    Naser Kalid, A. A. Zaidan, B. B. Zaidan, M. Hashim, O. S. Albahri & A. S. Albahri

  2. Al- Iraqia University, Al Adhmia, Haiba Khaton, Baghdad, Iraq

    Omar H. Salman

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  1. Naser Kalid
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Correspondence to A. A. Zaidan.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Appendix

Appendix

Table 11 TOPSIS sample results of the first and last forty patients for the first expert
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Table 12 TOPSIS sample results of the first and last forty patients for the second expert
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Table 13 TOPSIS sample results of the first and last forty patients for the third expert
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Table 14 TOPSIS sample results of the first and last forty patients for the fourth expert
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Table 15 TOPSIS sample results of the first and last forty patients for the fifth expert
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Table 16 TOPSIS sample results of the first and last forty patients for the sixth expert
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Kalid, N., Zaidan, A.A., Zaidan, B.B. 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. J Med Syst 42, 69 (2018). https://doi.org/10.1007/s10916-018-0916-7

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