Skip to main content
SpringerLink
Log in

Statistical Analysis of Patients’ Characteristics in Neonatal Intensive Care Units

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The staff in the neonatal intensive care units is required to have highly specialized training and the using equipment in this unit is so expensive. The random number of arrivals, the rejections or transfers due to lack of capacity and the random length of stays, make the advance knowledge of the optimal staff; equipments and materials requirement for levels of the unit behaves as a stochastic process. In this paper, the number of arrivals, the rejections or transfers due to lack of capacity and the random length of stays in a neonatal intensive care unit of a university hospital has been statistically analyzed. The arrival patients are classified according to the levels based on the required nurse: patient ratio and gestation age. Important knowledge such as arrivals, transfers, gender and length of stays are analyzed. Finally, distribution functions for patients’ arrivals, rejections and length of stays are obtained for each level in the unit.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Turkey State Planning Organization: (http://ekutup.dpt.gov.tr/ekonomi/gosterge).

  2. Adomat, R., and Hicks, C., Measuring nursing workload in intensive care: an observational study using closed circuit video cameras. J. Adv. Nurs. 42(4):402–412, 2003. doi:10.1046/j.1365-2648.2003.02632.x.

    Article  Google Scholar 

  3. DoH., Circular HSG(95)8, NHS responsibilities for meeting continuing health care needs. London: DoH, 1996.

    Google Scholar 

  4. Miranda, D. R., Moreno, R., and Iapichino, G., Nine equivalents of nursing manpower use score (NEMS). Intensive Care Med. 23:760–765, 1997. doi:10.1007/s001340050406.

    Article  Google Scholar 

  5. Narang, A., Kiran, P. S., and Kumar, P., Cost of neonatal intensive care in a tertiary care center. Indian Pediatrics. 42-October 17; 42:989–997, 2005.

    Google Scholar 

  6. Jung, A. L., and Streeter, N. S., Total population estimate of newborn special-care bed needs. Pediatrics. 75:993–996, 1985.

    Google Scholar 

  7. Parmanum, J., Field, D., Rennie, J., and Ster, P., National census of availability of neonatal intensive care. BMJ. 321:727–729, 2000. doi:10.1136/bmj.321.7263.727.

    Article  Google Scholar 

  8. Gorunescu, F., McClean, S. I., and Millard, P. H., A queuing model for bed-occupancy management and planning of hospitals. J. Oper. Res. Soc. 53:19–24, 2002. doi:10.1057/palgrave/jors/2601244.

    Article  MATH  Google Scholar 

  9. Davies, R., Simulation for planning services for patients with coronary artery disease. Eur. J. Oper. Res. 71:323–332, 1994. doi:10.1016/0377-2217(94)90313-1.

    Article  Google Scholar 

  10. Hudson, P. E., Peel, V., and Rayner, A., Using computer simulation to plan accident & emergency services. Br. J. Health Care Manage. 3:160–162, 1997.

    Google Scholar 

  11. Jacobson, S., Hall, S., and Swisher, J., Discrete event simulation of health care systems. In: Delay Management in Healthcare Systems. Hall R. Springer in Health Care Management, 2006.

  12. Milne, E., and Whitty, P., Calculation of the need for pediatric intensive care beds. Arch. Dis. Child. 73:505–507, 1995. doi:10.1136/adc.73.6.505.

    Article  Google Scholar 

  13. Romanin-Jacur, G., and Facchin, P., Optimal planning of a pediatric semi-intensive care unit via simulation. Eur. J. Oper. Res. 29:192–198, 1987. doi:10.1016/0377-2217(87)90109-3.

    Article  Google Scholar 

  14. Hershey, J. C., Weiss, E. N., and Cohen, M. A., A stochastic service network model with application to hospital facilities. Oper. Res. 29:1–22, 1981. doi:10.1287/opre.29.1.1.

    Article  MATH  MathSciNet  Google Scholar 

  15. Cochran, J. K., and Bharti, A., A multi-stage stochastic methodology for whole hospital bed planning under peak loading. Int. J. Ind. Syst. Eng. 1:8–36, 2006. doi:10.1504/IJISE.2006.009048.

    Article  Google Scholar 

  16. Cochran, J. K., and Bharti, A., Stochastic bed balancing of an obstetrics hospital. Health Care Manage. Syst. 9:31–45, 2006. doi:10.1007/s10729-006-6278-6.

    Article  Google Scholar 

  17. Kokangul, A., A combination of deterministic and stochastic approaches to optimize bed capacity in a hospital unit. Comput. Methods Programs Biomed. 90:56–65, 2008. doi:10.1016/j.cmpb.2008.01.001.

    Article  Google Scholar 

  18. Costa, A. X., Ridley, S. A., Shahani, A. K., Harper, P. R., and De Senna, V., Mathematical modeling and simulation for planning critical care capacity. Anaesthesia. 58:320–327, 2003. doi:10.1046/j.1365-2044.2003.03042.x.

    Article  Google Scholar 

  19. http://www.statsoft.com

  20. http://www.mathworks.com

  21. Gorunescu, F., Mclean, S. I., and Millard, P. H., Using a queuing model to help plan bed allocation in a department of geriatric medicine. Health Care Manage. Sci. 5:307–312, 2002. doi:10.1023/A:1020342509099.

    Article  Google Scholar 

  22. Groothuis, S., Hasman, A., Pol, P. E. J., Lencer, N. H. M. K., Janssen, J. J. J., Jans, J., et al., Predicting capacities required in cardiology units for heart failure patients via simulation. Comput. Methods Programs Biomed. 74:129–141, 2004. doi:10.1016/S0169-2607(03)00080-4.

    Article  Google Scholar 

  23. Millard, P. H., Mackay, M., Vasilakis, C., and Christodoulou, G., Measuring and modeling surgical bed usage. Ann. R. Coll. Surg. Engl. 82:75–82, 2000.

    Google Scholar 

  24. Montgomery, D. C., Runger, G. C., and Hubele, N. F., Engineering statistics, 3rd edition, p. 461, Wiley, New York, 2004.

    Google Scholar 

  25. Ridge, J. C., Jones, S. K., Nielsen, M. S., and Shahani, A. K., Capacity planning for intensive care units. Eur. J. Oper. Res. 105:346–355, 1998. doi:10.1016/S0377-2217(97)00240-3.

    Article  MATH  Google Scholar 

  26. Utley, M., Gallivan, S., Treasure, T., and Valencia, O., Analytical methods for calculating the capacity required to operate an effective booked admissions policy for elective inpatient service. Health Care Manage. Sci. 6:97–104, 2003. doi:10.1023/A:1023333002675.

    Article  Google Scholar 

  27. Darzi, E. E., Vasilakis, C., Chaussalet, T., and Millard, P. H., A simulation modeling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department. Health Care Manage. Sci. 1:143–149, 1998. doi:10.1023/A:1019054921219.

    Article  Google Scholar 

  28. Navarro, A., and Thompson, W. A., Analysis of bed usage and occupancy following the introduction of geriatric rehabilitative care in a hospital in Huesca, Spain. Health Care Manage. Sci. 4:63–66, 2001. doi:10.1023/A:1009657817365.

    Article  Google Scholar 

  29. Utley, M., Gallivan, S., Davis, K., Daniel, P., Reeves, P., and Worrall, J., Estimating bed requirement for an intermediate care facility. Eur. J. Oper. Res. 150:92–100, 2003. doi:10.1016/S0377-2217(02)00788-9.

    Article  MATH  Google Scholar 

  30. Turkish Neonatology Association., Türkiye’de Yenidoğan Bakım Ünitelerinde Mortalite. Turk Neonatoloji Dernegi Bulteni. 7:7–12, 2003.

    Google Scholar 

  31. Shahani, A. K., Korve, N., Jones, K. P., and Paynton, D. J., Towards an operational model for preventing and treatment of asthma attacks. J. Oper. Res. Soc. 45:916–926, 1994.

    MATH  Google Scholar 

  32. Tu, J. V., Mazer, C. D., Levinton, C., Armstrong, P. V., and Naylor, C. D., A predictive index for length of stay in the intensive care unit following cardiac surgery. CMAJ. 151 (2)177–185, 1994.

    Google Scholar 

  33. Rawlings, J. S., Smith, F. R., and Garcia, J., Expected duration of hospital stay of low birth weight infants: Graphic depiction in relation to birth eight and gestational age. J. Pediatr. 123(2):307–309, 1993. doi:10.1016/S0022-3476(05)81708-1.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering and Architecture, Cukurova University, 01330, Adana, Turkey

    Ali Kokangul, Ayfer Ozkan, Serap Akcan & Mufide Narli

  2. Neonatal Intensive Care Unit, Cukurova University, 01330, Adana, Turkey

    Kenan Ozcan

Authors
  1. Ali Kokangul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayfer Ozkan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Serap Akcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenan Ozcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mufide Narli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serap Akcan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kokangul, A., Ozkan, A., Akcan, S. et al. Statistical Analysis of Patients’ Characteristics in Neonatal Intensive Care Units. J Med Syst 34, 471–478 (2010). https://doi.org/10.1007/s10916-009-9259-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9259-8

Keywords

Search

Navigation