Evaluation Model for the Application of Artificial Intelligence Medical Assistant System to the Development of Medical Ecology in China

Chich-Jen Shieh, Guang-Sheng Wan, Wei Wang, Yuzhou Luo

Ekoloji, 2019, Issue 107, Pages: 311-316, Article No: e107036

OPEN ACCESS

Download Full Text (PDF)

Abstract

People are increasing the requirements for medical ecology in past years so that the establishment of an electronic medical care mechanism becomes urgent. Along with the development of network technology and the advance of information technology, electronic data could be done the statistical analysis. By extracting electronic medical records through the Internet, physicians could rapidly grasp complete medical record information for the diagnosis to further enhance medical quality and assist hospital managers in making proper decisions. Aiming at upper first-class hospitals in Shanghai, total 12 representative hospitals with artificial intelligence medical assistant systems are selected as the research objects for this study. Based on the annual statistical reports of the hospitals, “Delphi Method” and “Data Envelopment Analysis” are utilized for the data analyses. The research results are summarized as followings. 1. One DMU presents strong efficiency on the artificial intelligence medical assistant system performance, 4 DMUs show the efficiency in 0.9-1, and 5 DMUs appear the efficiency lower than 0.9. 2. Sensitivity analysis is utilized for analyzing and finding out the critical factors in the application of artificial intelligence medical assistant systems to medical ecology, and the inputs and outputs are gradually removed for DEA to understand the sensitivity to efficiency. According to the results, suggestions are proposed, expecting to provide medical ecology with the application of artificial intelligence medical assistant systems extracting data through diagnosis systems to perfect the medical record information of patients for physicians’ diagnoses and to further promote medical quality.

Keywords

medical ecology, artificial intelligence, medical assistant system, performance evaluation

References

  • Bruun M, et al. (2018) Evaluating combinations of diagnostic tests to discriminate different dementia types. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring.
  • Cho I, Kim E, Choi WH, et al. (2016) Comparing Usability Testing Outcomes and Functions of Six Electronic Nursing Record Systems. International Journal of Medical Informatics 88: 78-85.
  • DeForge R, et al. (2017) Evaluating dementia home care practices: The reification of care norms. Journal of Aging Studies 43: 23-31.
  • Delbari SA, Ng SI, Aziz YA, Ho JA (2016) An Investigation of Key Competitiveness Indicators and Drivers of Full-Service Airlines Using Delphi and AHP Techniques. Journal of Air Transport Management, 23-34.
  • Duport Dejonckheere F, Smerieri A, Akrout AM, Haelterman M, Massar S (2016) Fully Analogue Photonic Reservoir Computer. Scientific Reports 6: 22381.
  • Gu L, Zeng D, Guo S, Barnawi A, Xiang Y (2017) Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Topics Comput., 5(1): 108-119.
  • Hussian CA, Vuha K, Rajani M, Vineeth JM (2017) Smart Health Care Monitoring Using Internet of Things and Android. International Journal of Advanced Research in Electronics and Communication Engineering, 6(3): 101-104.
  • Kalarthi ZM (2016) A Review Paper on Smart Health Care System Using Intetnet of Thing. International Journal of Research in Engineering and Technology, 5(3): 80-84.
  • Khosrow-Khavar F, Tavakolian K, Blaber A, Menon C (2017) Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Noninvasive Estimation of Cardiac Time Intervals. IEEE Transactions on Biomedical Engineering, 64(8): 1701-1710.
  • Liu ZQ, Liu B, Chen CW (2017) Transmission-rate-adaption assisted energy-efficient resource allocation with QoS support in WBANs. IEEE Sensors J., 17(17): 5767-5780.
  • Mishra M, Chatterjee S (2018) Application of Analytical Hierarchy Process (AHP) Algorithm to Income Insecurity Susceptibility Mapping: A study in the District of Purulia, India. Socio-Economic Planning Sciences, 56-74.
  • Mohan TY, Raja V (2016) Internet of Things: Benefits and Risk of Smart Healthcare Application. Journal of Information Systems Research and Innovation, 10(3): 37-42.
  • Roy S, et al. (2016) Executive Function and Personality Predict Instrumental Activities of Daily Living in Alzheimer Disease. The American Journal of Geriatric Psychiatry 24(11): 1074-1083.
  • Sadoughi F, Sarsarshahi A, Eerfannia I, Firouzabad SMAK (2016) Ranking Evaluation Factors in Hospital Information Systems. International Journal of the Bioflux Society, 8(2): 92-97.
  • Sahoo PK, Thakkar HK, Lin WY, Chang PC, Lee MY (2018) On the design of an efficient cardiac health monitoring system through combined analysis of ecg and scg signals. Sensors 18(2): 379.
  • Santhi H, Gayathri P (2016) Case Study on Health Care Using Internet of Things. International Journal of Current Trends in Engineering & Research, 2 (5): 368-374.
  • Shafiq G, Tatinati S, Ang WT, Veluvolu KC (2016) Automatic identification of systolic time intervals in seismocardiogram.Scientific reports, 6: 37524.
  • Yang L, Zheng Q, Fan X (2017) RSPP: A Reliable, Searchable and Privacy-Preserving e-Healthcare System for Cloud-Assisted Body Area Networks. arXiv preprint arXiv: 1702.03467.