A Unique Mathematical Framework for Optimizing Patient Satisfaction in Emergency Departments

Document Type : Research Paper

Authors

1 Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

In healthcare systems, emergency departments (EDs) are the most vital
elements, in that they provide critical and immediate healthcare services to the
patients 24 hours a day. Patient satisfaction is a crucial concept and a practical
tool for evaluating the performance of the EDs. This study presents a unique
framework for the performance optimization of an emergency department in a
big general hospital in Iran based on the standard patient satisfaction indicators.
Standard questionnaire is designed and used in a large and busy emergency
department. The reliability and validity of the questionnaires are obtained by
Cronbach’s alpha and parametric and non-parametric analysis of variance
(ANOVA), respectively. Afterwards, the most efficient data envelopment
analysis (DEA) model is selected and employed to assess the performance of the
emergency department based on the selected indicators. Results show that
certain indicators such as quality of equipment, performance of physicians and
treatment time have the greatest impact (weight) on overall patient satisfaction.
The framework of this study is a practical approach for all types of emergency
departments in the process of the improvement and optimization of patient
satisfaction

Keywords

Main Subjects

References

Abo-Hamad, W., & Arisha, A. (2013). Simulation-based framework to improve patient experience in an emergency department. European Journal of Operational Research, 224(1), 154-166.
Al-Refaie, A., Fouad, R. H., Li, M.-H., & Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 41, 59-72.
Athanassopoulos, A., & Gounaris, C. (2001). Assessing the technical and allocative efficiency of hospital operations in Greece and its resource allocation implications. European Journal of Operational Research, 133(2), 416-431.
Azadeh, A., Ghaderi, S., Anvari, M., Izadbakhsh, H., Rezaee, M. J., & Raoofi, Z. (2013). An integrated decision support system for performance assessment and optimization of decision-making units. The International Journal of Advanced Manufacturing Technology, 66(5-8), 1031-1045.
Azadeh, A., Saberi, M., Moghaddam, R. T., & Javanmardi, L. (2011). An integrated data envelopment analysis–artificial neural network–rough set algorithm for assessment of personnel efficiency. Expert Systems with Applications, 38(3), 1364-1373.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Boudreaux, E. D., d'Autremont, S., Wood, K., & Jones, G. N. (2004). Predictors of emergency department patient satisfaction: stability over 17 months. Academic Emergency Medicine, 11(1), 51-58.
Boudreaux, E. D., & O'Hea, E. L. (2004). Patient satisfaction in the emergency department: A review of the literature and implications for practice. The Journal of emergency medicine, 26(1), 13-26.
Charnes, A., Cooper, W. W., & Rhodes, E. (1979). Measuring the efficiency of decision-making units. European Journal of Operational Research, 3(4), 339-338.
Draper, M., Cohen, P., & Buchan, H. (2001). Seeking consumer views: What use are results of hospital patient satisfaction surveys? International journal for quality in health care, 13(6), 463-468.
Fiallos, J., Patrick, J., Michalowski, W., & Farion, K. (2017). Using data envelopment analysis for assessing the performance of pediatric emergency department physicians. Health care management science, 20(1), 129-140.
Gibbons, C., Singh, S., Gibbons, B., Clark, C., Torres, J., Cheng, M. Y., . . . Armstrong, A. W. (2018). Using qualitative methods to understand factors contributing to patient satisfaction among dermatology patients: A systematic review. Journal of Dermatological Treatment, 29(3), 290-294.
Grosskopf, S., & Valdmanis, V. (1993). Evaluating hospital performance with case-mix-adjusted outputs. Medical Care, 31(6), 525-532.
Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2016). Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European Journal of Industrial Engineering, 10(2), 196-223.
Gupta, D., Rodeghier, M., & Lis, C. G. (2013). Patient satisfaction with service quality in an oncology setting: Implications for prognosis in non-small cell lung cancer. International journal for quality in health care, 25(6), 696-703.
Halkos, G. E., & Tzeremes, N. G. (2011). A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures. Health policy, 103(1), 73-82.
Hall, M. F., & Press, I. (1996). Keys to patient satisfaction in the emergency department: Results of a multiple facility study. Journal of Healthcare Management, 41(4), 515.
Harrison, J. P., Coppola, M. N., & Wakefield, M. (2004). Efficiency of federal hospitals in the United States. Journal of Medical Systems, 28(5), 411-422.
Heiberger, R. M., & Neuwirth, E. (2009). R through Excel: A spreadsheet interface for statistics, data analysis, and graphics. New York: Springer, 323-330.
Hollingsworth, B. (2003). Non-parametric and parametric applications measuring efficiency in health care. Health care management science, 6(4), 203-218.
Hussey, P. S., De Vries, H., Romley, J., Wang, M. C., Chen, S. S., Shekelle, P. G., & McGlynn, E. A. (2009). A systematic review of health care efficiency measures. Health services research, 44(3), 784-805.
Kol, E., Arıkan, F., İlaslan, E., Akıncı, M. A., & Koçak, M. C. (2018). A quality indicator for the evaluation of nursing care: Determination of patient satisfaction and related factors at a university hospital in the Mediterranean Region in Turkey. Collegian, 25(1), 51-56.
Kounetas, K., & Papathanassopoulos, F. (2013). How efficient are Greek hospitals? A case study using a double bootstrap DEA approach. The European Journal of Health Economics, 14(6), 979-994.
Lertworasirikul, S., Fang, S.-C., Joines, J. A., & Nuttle, H. L. (2003). Fuzzy data envelopment analysis (DEA): A possibility approach. Fuzzy Sets and Systems, 139(2), 379-394.
Li, J., Wang, P., Kong, X., Liang, H., Zhang, X., & Shi, L. (2016). Patient satisfaction between primary care providers and hospitals: A cross-sectional survey in Jilin province, China. International journal for quality in health care, 28(3), 346-354.
Luscombe, R., & Kozan, E. (2016). Dynamic resource allocation to improve emergency department efficiency in real time. European Journal of Operational Research, 255(2), 593-603.
Mitropoulos, P., Mitropoulos, I., & Sissouras, A. (2013). Managing for efficiency in health care: The case of Greek public hospitals. The European Journal of Health Economics, 14(6), 929-938.
Nazari-Shirkouhi, S., & Keramati, A. (2017). Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm. Applied Mathematical Modelling, 50, 755-771.
Newcomb, P., Wilson, M., Baine, R., McCarthy, T., Penny, N., Nixon, C., & Orren, J. (2017). Influences on Patient Satisfaction Among Patients Who Use Emergency Departments Frequently for Pain-Related Complaints. Journal of Emergency Nursing, 43(6), 553-559.
O’Neill, L., Rauner, M., Heidenberger, K., & Kraus, M. (2008). A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Socio-Economic Planning Sciences, 42(3), 158-189.
Osman, I. H., Berbary, L. N., Sidani, Y., Al-Ayoubi, B., & Emrouznejad, A. (2011). Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit. Journal of Medical Systems, 35(5), 1039-1062.
Özpeynirci, Ö., & Köksalan, M. (2007). Performance evaluation using data envelopment analysis in the presence of time lags. Journal of Productivity Analysis, 27(3), 221-229.
Pink, G. H., Murray, M., & McKillop, I. (2003). Hospital efficiency and patient satisfaction. Health Services Management Research, 16(1), 24-38.
Popescu, C., Asandului, L., & Fatulescu, P. (2014). A Data Envelopment Analysis for Evaluating Romania’s Health System. Procedia-Social and Behavioral Sciences, 109, 1185-1189.
Schoenfelder, T., Klewer, J., & Kugler, J. (2011). Determinants of patient satisfaction: A study among 39 hospitals in an in-patient setting in Germany. International journal for quality in health care, 23(5), 503-509.
Sharma, S., & Sharma, S. (1996). Applied multivariate techniques.
Soares, A. M., & Farhangmehr, M. (2015). Understanding patient satisfaction in a hospital emergency department. International Review on Public and Nonprofit Marketing, 12(1), 1-15.
Taylor, D., Kennedy, M. P., Virtue, E., & McDonald, G. (2006). A multifaceted intervention improves patient satisfaction and perceptions of emergency department care. International journal for quality in health care, 18(3), 238-245.
Rezaie, K., Dalfard, V. M., Hatami-Shirkouhi, L., & Nazari-Shirkouhi, S. (2013). Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange. Neural Computing and Applications, 23(1), 1-17.
Valdmanis, V. (1992). Sensitivity analysis for DEA models: An empirical example using public vs. NFP hospitals. Journal of Public Economics, 48(2), 185-205.
Watson, W. T., Marshall, E. S., & Fosbinder, D. (1999). Elderly patients' perceptions of care in the emergency department. Journal of Emergency Nursing, 25(2), 88-92.
Welch, S. J. (2010). Twenty years of patient satisfaction research applied to the emergency department: A qualitative review. American Journal of Medical Quality, 25(1), 64-72.
Zavras, A. I., Tsakos, G., Economou, C., & Kyriopoulos, J. (2002). Using DEA to evaluate efficiency and formulate policy within a Greek national primary health care network. Journal of Medical Systems, 26(4), 285-292.
Interdisciplinary Journal of Management Studies
Volume 12, Issue 2
April 2019
Pages 255-279

Files

Share

How to cite

Statistics