Advertisement

Application of Cloud Computing for Emergency Medical Services: A Study of Spatial Analysis and Data Mining Technology

  • Jui-Hung KaoEmail author
  • Feipei Lai
  • Bo-Cheng Lin
  • Wei-Zen Sun
  • Kuan-Wu Chang
  • Ta-Chien Chan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 375)

Abstract

Out of Hospital Cardiac Arrest (OHCA) is an important medical and public health issue. Emergency first aid service prior to hospital admission is an important indicator for the quality evaluation of the emergency medical service. OHCA frequently occurs without warning, and while there are clear steps in emergency first aid concerning the treatment of OHCA patients, their survivability diminishes if they cannot receive emergency first aid services in time. Using statistical methods such as chi-square test, logistic regression, and decision tree, the influence factors were analyzed and extracted. In addition, combining the strengths of three independent spatial clustering analysis methods, namely, the Global Moran’s Index for finding the spatial clustering, as well as the Local Moran’s Index and spatial autocorrelation analysis Getis-Ord Gi* algorithm, a novel summary approach to identify high-risk OHCA areas. The Global Moran’s Index of OHCA event locations were 0.025861, with a Z-score of 8.178045, indicating significance spatial clustering phenomenon of OHCA locations, Getis-Ord Gi* covers more towns (urban areas), but the High-High area reaching statistical standards obtained through the Local Moran’s Index also has also appeared in the high clusters Area found through search using the Getis-Ord Gi*. In addition, the important factors found through the decision tree analysis method have more space distribution coverage. When OHCA occurs, based on findings in this study, the 119-dispatch duty officer may make further inquiries regarding medical history of heart disease or diabetes, which shall serve as a reference for future dispatch of senior technicians. Based on the OHCA-prone hot zone generated by the Getis-Ord Gi* and targeting OHCA patients’ past medical history of heart disease or diabetes, public health units may adopt information technology or wearable devices as intervention in order to increase the probability of eyewitnesses and prioritize the dispatch of emergency aid resources into the hot zone, thereby enhancing OHCA patient survival rates.

Keywords

Out-of-hospital cardiac arrest Cardiopulmonary resuscitation Geographic information systems Spatial statistics Public health interventions 

Notes

Acknowledgement

This research was supported by grant entitled “Multidisciplinary Health Cloud Research Program: Technology Development and Application of Big Health Data” from the Academia Sinica. We would also like to express our sincere gratitude to Mr. Kent M. Suárez for his English editing.

Competing Interests

The authors declare that we do not have any competing interests related to this study.

References

  1. 1.
    Go AS, Mozaffarian D, Roger VL et al (2014) Heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation 129(3):e28CrossRefGoogle Scholar
  2. 2.
    Nichol G, Thomas E, Callaway CW et al (2008) Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA 300(12):1423–1431CrossRefGoogle Scholar
  3. 3.
    Sasson C, Rogers MA, Dahl J et al (2010) Predictors of survival from out-of-hospital cardiac arrest a systematic review and meta-analysis. Circ Cardiovasc Qual Outcomes 3(1):63–81Google Scholar
  4. 4.
    McNally B, Valderrama AL (2011) Out-of-hospital cardiac arrest surveillance: Cardiac Arrest Registry to Enhance Survival (CARES), United States, Oct 1, 2005–Dec 31, 2010Google Scholar
  5. 5.
    Manuel B (2013) Will models of naturally occurring disease in animals reduce the bench-to-bedside gap in biomedical research? Zhonghua wei zhong bing ji jiu yi xue 25(1):5–7Google Scholar
  6. 6.
    Lee C-K (2010) The Analysis of Ilan’s Out-of-Hospital Cardiac Arrest (OHCA) PatientsGoogle Scholar
  7. 7.
    China MoHaWRo. The cause of death statistics. Secondary The cause of death statistics. http://www.mohw.gov.tw/cht/DOS/Statistic.aspx?f_list_no=312. Access date, 2015/05/18
  8. 8.
    Valenzuela TD, Roe DJ, Cretin S et al (1997) Estimating effectiveness of cardiac arrest interventions a logistic regression survival model. Circulation 96(10):3308–3313CrossRefGoogle Scholar
  9. 9.
    Sasson C, Keirns CC, Smith D et al (2010) Small area variations in out-of-hospital cardiac arrest: does the neighborhood matter? Ann Internal Med 153(1):19–22Google Scholar
  10. 10.
    Root ED, Gonzales L, Persse DE et al (2013) A tale of two cities: the role of neighborhood socioeconomic status in spatial clustering of bystander CPR in Austin and Houston. Resuscitation 84(6):752–759CrossRefGoogle Scholar
  11. 11.
    Sasson C, Meischke H, Abella BS et al (2013) Increasing cardiopulmonary resuscitation provision in communities with low bystander cardiopulmonary resuscitation rates a science advisory from the american heart association for healthcare providers, policymakers, public health departments, and community leaders. Circulation 127(12):1342–1350CrossRefGoogle Scholar
  12. 12.
    Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Methods 26(6):1481–1496MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, New YorkGoogle Scholar
  14. 14.
    Anselin L (1995) Local indicators of spatial association-LISA. Geograph Anal 27(2):93–115CrossRefGoogle Scholar
  15. 15.
    Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geograph Anal 24(3):189–206CrossRefGoogle Scholar
  16. 16.
    Geatz MW, Roiger R (2011) Data mining: a tutorial based primer. Pearson Education, LondonGoogle Scholar
  17. 17.
    Automated icd9-cm coding employing bayesian machine learning: a preliminary exploration. Simposio de Informtica y Salud; 2004Google Scholar
  18. 18.
    Hosmer DW Jr, Lemeshow S, Sturdivant RX (2000) Model-building strategies and methods for logistic regression. Third Edition, Applied Logistic Regression, pp 89–151Google Scholar
  19. 19.
    Chan T-C, Fu Y-c, Wang D-W, et al (2014) Determinants of receiving the pandemic (H1N1) 2009 vaccine and intention to receive the seasonal influenza vaccine in TaiwanGoogle Scholar
  20. 20.
    Gardner LS, Nguyen-Pham S, Greenslade JH et al (2014) Admission glycaemia and its association with acute coronary syndrome in Emergency Department patients with chest pain. Emerg Med J emermed-2014-204046Google Scholar
  21. 21.
    Tandon N, McCarthy M, Forehand B et al (2013) Comparison of intubation modalities in a simulated cardiac arrest with uninterrupted chest compressions. Emerg Med J emermed-2013-202783Google Scholar
  22. 22.
    Chang AM, Edwards M, Matsuura AC et al (2013) Relationship between renal dysfunction and outcomes in emergency department patients with potential acute coronary syndromes. Emerg Med J 30(2):101–105CrossRefGoogle Scholar
  23. 23.
    Henry K, Murphy A, Willis D et al (2012) Out-of-hospital cardiac arrest in Cork, Ireland. Emerg Med J emermed-2011-200888Google Scholar
  24. 24.
    Ong MEH, Wah W, Hsu LY et al (2014) Geographic factors are associated with increased risk for out-of hospital cardiac arrests and provision of bystander cardio-pulmonary resuscitation in Singapore. Resuscitation 85(9):1153–1160CrossRefGoogle Scholar
  25. 25.
    Lam SSW, Zhang J, Zhang ZC et al (2015) Dynamic ambulance reallocation for the reduction of ambulance response times using system status management. Am J Emerg Med 33(2):159–166MathSciNetCrossRefGoogle Scholar
  26. 26.
    Nhavoto JA, Grönlund Å (2014) Mobile technologies and geographic information systems to improve health care systems: a literature review. JMIR mHealth and uHealth 2(2)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Jui-Hung Kao
    • 1
    • 2
    Email author
  • Feipei Lai
    • 1
  • Bo-Cheng Lin
    • 2
  • Wei-Zen Sun
    • 1
  • Kuan-Wu Chang
    • 3
  • Ta-Chien Chan
    • 2
  1. 1.Department of Computer Science and Information Engineering, Department of Electrical Engineering, Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan
  2. 2.Center for Geographic Information Science, Research Center for Humanity and Social SciencesAcademia SinicaTaipeiTaiwan
  3. 3.Division of Emergency Medical Service, Fire DepartmentNew Taipei City GovernmentNew TaipeiTaiwan

Personalised recommendations