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The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems

Year 2018, Volume: 21 Issue: 4, 699 - 718, 29.12.2018

Abstract

The aim of the study is to determine factors in health communication, when minorities are serviced in a language different from their mother tongue. Health care service satisfaction when doctor and patient speaking the common language or the mother tongue is an important area of investigation. On the other hand, when either of them speaking different languages, namely, generally patients in the minority position speaking a language different from the one doctor speaks, health communication becomes cumbersome for both sides resulting in low level of health care service satisfaction leading to ultimately wide range of complaints from minority patients. How attributes playing roles on health care service satisfaction in minority patients by modeling the relationship is conducted. Therefore, single attributes and interrelated ones are determined. The data is collected using a questionnaire form based on stratified sampling method, 387 participants are included in the analysis. Questionnaires were distributed among minorities living in Vienna area. The factors that are impact on health care service satisfaction are extracted by factor analysis. Questionnaire data collected as verbal statements representing the subjective evaluations of participants transformed into mathematical functions using fuzzy set theory enables to model attributes affecting health care service satisfaction using fuzzy logic called fuzzy rule based systems. Modeling tool called Fuzzy Rule Based Systems is a mathematical model in order to explain which single factors and/or interrelated ones having impact on health care service satisfaction are determined by employing fuzzy set theory and fuzzy logic, which are the components of the mentioned mathematical model. The findings suggest that the first expectation by minority patients from doctors is to respect to their cultural differences. If it is met at the first glance, then health care service satisfaction tends to increase with the positive effects of other attributes. If not, health care service satisfaction stays at lower with no other attributes playing major roles. According to the findings of the study, other attributes or interrelated ones play significant roles on the health care service satisfaction when they are singly evaluated, which lead to comprehend not only single attributes but also interrelated ones by minorities.

References

  • 1. Abdi F. (2018) Hospital Leanness Assessment Model: A Fuzzy MULTIMOORA Decision Making Approach. Journal of Industrial and Systems Engineering 11(3): 37-59. 2. Arthur V.A. (1995) Written Patient Information: A Review of the Literature. Journal of Advanced Nursing 21(6): 1081-1086. 3. Bas E. (2018) An Integrated OSH Risk Management Approach to Surgical Flow Disruptions in Operating Room. Safety Science 109: 281-293. 4. Basaran M. A., Kalayci N. and Atay M. T. (2011) A Novel Hybrid Method for Better Evaluation: Evaluating University Instructors Teaching Performance by Combining Conventional Content Analysis with Fuzzy Rule Based Systems. Expert Systems with Applications 38(10): 12565-12568. 5. Blendon R. J., Scheck A. C., Donelan K., Hill C. A., Smith M., Beatrice D. and Altman D. (1995) How White and African Americans View Their Health and Social Problems: Different Experiences, Different Expectations. JAMA 273(4): 341-346. 6. Booker R. (2005) Effective Communication with The Patient. Eur Respir Re 14(96): 93–96. 7. Canavese D., Regina N. and Ortega S. (2013) A Proposal of a Fuzzy Rule-Based System for The Analysis of Health and Health Environments in Brazil. Ecological Indicators 34:7-14. 8. Coşgun Ö., Ekinci Y. and Yanık S. (2014) Fuzzy Rule-Based Demand Forecasting for Dynamic Pricing of a Maritime Company. Knowledge-Based Systems 70: 88-96. 9. Demir M. O., Basaran M. A. and Simonetti B. (2016) Determining Factors Affecting Healthcare Service Satisfaction Utilizing Fuzzy Rule-Based Systems. Journal of Applied Statistics 43(13): 2474-2489. 10. Dubois D. and Prade H. (1980) Fuzzy Sets and Systems: Theory and Applications. Academic Press, USA. 11. Ferguson W. J. and Candib L. M. (2002) Culture, Language, and the Doctor-Patient Relationship. FMCH Publications, Australia. 12. Goh M., Shuya Z. and de Souza R. (2018) Operational Framework for Healthcare Supplier Selection Under a Fuzzy Multi-Criteria Environment. 23rd International Symposium on Logistics (ISL 2018) Big Data Enabled Supply Chain Innovations, 8–11th July 2018, Indonesia. 13. Greenfield S., Kaplan S. and Ware J. E. (1985) Expanding Patient Involvement in Care Effects On Patient Outcomes. Annals of Internal Medicine 102(4): 520-528. 14. Greenfield S., Kaplan S. H., Ware J. E., Yano E. M. and Frank H. J. (1988) Patients’ Participation in Medical Care. Journal of General Internal Medicine 3(5): 448-457. 15. Gregory R., Peters E. and Slovic P. (2011) Making Decisions About Prescription Drugs: A Study of Doctor–Patient Communication. Health, Risk and Society 13(4): 347-371. 16. Herndon J. H. and Pollick K. J. (2002) Continuing Concerns, New Challenges, and Next Steps in Physician-Patient Communication. The Journal of Bone and Joint Surgery 84(2): 309-315. 17. Ishibuchi H., Murata T. and Gen M. (1998) Performance Evaluation of Fuzzy Rule- Based Classification Systems Obtained by Multi-Objective Genetic Algorithms. Computers and Industrial Enginering 35(34): 575-578. 18. Hofstede G. H. (2001) Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. Sage Press, USA. 19. Huang X., Shi F., Gu W. and Chen S. (2009) SVM-Based Fuzzy Rules Acquisition System for Pulsed GTAW Process. Engineering Applications of Artifical Intelligence 22(8): 1245-1255. 20. Ishikawa H. and Kiuchi T. (2010) Health Literacy and Health Communication. Biopsychosoc Med 4(18): 1-5. 21. Johnson R. L., Roter D., Powe N. R. and Cooper L. A. (2004) Patient Race/Ethnicity and Quality of Patient-Physician Communication During Medical Visits. American Journal of Public Health 94(12): 2084-2090. 22. Kirschbaum K. (2012) Physician Communication in The Operating Room: Expanding Application of Face-Negotiation Theory to The Health Communication Context. Health Communication 27(3): 292-301. 23. Li C., Hao F., Zhao L., Song L. and Dong X. (2017) Analysis of Medical and Healthcare Data Based on Positive and Negative Association Rules. 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), China. 24. Maguire P. and Pitceathly C. (2002) Key Communication Skills and How to Acquire Them. BMJ: British Medical Journal 325(7366): 697. 25. Nguyen H. T. and Walker E. A. (2000) A First Course in Fuzzy Logic. Second Edition, Chapman and Hall, UK. 26. Nørgaard B., Ammentorp J., Kyvık K. O. and Kofoed P. E. (2012) Communication Skills Training Increases Self-Efficacy of Health Care Professionals. Journal of Continuing Education in the Health Professions 32(2): 90-97. 27. Park E. K. and Song M. (2005) Communication Barriers Perceived by Older Patients and Nurses. International Journal of Nursing Studies 42(2): 159-166. 28. Plunkett A. and Quine S. (1996) Difficulties Experienced by Carers from Non‐English‐Speaking Backgrounds in Using Health and Other Support Services. Australian and New Zealand Journal of Public Health 20(1): 27-32. 29. Rosenberg E. E., Lussier M. T. and Beaudoin C. (1997) Lessons for Clinicians from Physician–Patient Communication Literature. Archives of Family Medicine 6: 279– 283. 30. Roter D. and Hall J. A. (2006) Doctors Talking with Patients/Patients Talking with Doctors: Improving Communication in Medical Visits. Greenwood Publishing Group Publishing, United States. 31. Roter D. L., Hall J. A. and Aoki Y. (2002) Physician Gender Effects in Medical Communication: A Meta-Analytic Review. JAMA 288(6): 756-764. 32. Sanz J. A., Galar M., Jurio A., Brugos A., Pagola M. and Bustince H. (2014) Medical Diagnosis of Cardiovascular Diseases Using an Interval-Valued Fuzzy Rule-Based Classification System. Applied Soft Computing 20: 103-111. 33. Schouten B. C. and Meeuwesen L. (2006) Cultural Differences in Medical Communication: A Review of the Literature. Patient Education and Counseling 64: 21–34. 34. Shakhawat C., Tahir H. and Neil B. (2006) Fuzzy Rule-Based Modelling for Human Health Risk from Naturally Occurring Radioactive Materials in Produced Water. Journal of Environmental Radioactivity 89(1): 1-17. 35. Shpilko I. (2006) Russian–American Health Care: Bridging The Communication Gap Between Physicians and Patients. Patient Education and Counseling 64: 331–341. 36. Smoczek J. and Szpytko J. (2014) Evolutionary Algorithm-Based Design of a Fuzzy TBF Predictive Model and TSK Fuzzy Anti-Sway Crane Control System. Engineering Applications of Artifical Intelligence 28: 190-204. 37. Statistik Austria (2011) https://www.statistik.at/web_de/statistiken/index.html (Erişim Tarihi: 19.05.2011). 38. Stewart M. A. (1995) Effective Physician-Patient Communication and Health Outcomes: A Review. CMAJ: Canadian Medical Association Journal 152(9): 1423. 39. Sudha V. and Girijamma H. A. (2017) Novel Clustering of Bigger and Complex Medical Data by Enhanced Fuzzy Logic Structure. International Conference on Circuits, Controls, and Communications (CCUBE), India. 40. Ustundag A., Kılınç M. S. and Cevikcan E. (2010) Fuzzy Rule-Based System for The Economic Analysis of RFID Investments. Expert Systems with Applications 37(7): 5300-5306. 41. Wallace L. S., de Voe J. E., Heintzman J. D. and Fryer G. E. (2009) Language Preference and Perceptions of Healthcare Providers’ Communication and Autonomy Making Behaviors Among Hispanics. Journal of Immigrant and Minority Health 11(6): 453-459. 42. Wan K. and Alagar V. (2017) Analyzing Healthcare Big Data for Patient Satisfaction. 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), China. 43. Wong S. and Lee A. (2006) Communication Skills and Doctor Patient Relationship. Medical Bulletin 11(3): 7-9. 44. Zadeh L. A. (1965) Fuzzy Sets. Information and Control 8: 338-353. 45. Zadeh L. A. (1975a) The Concept of a Linguistics Variable Ant Its Application to Approximate Reasoning –I. Information Science 8:199-250. 46. Zadeh L. A. (1975b) The Concept of a Linguistics Variable Ant Its Application to Approximate Reasoning –II. Information Science 8: 301-357. 47. Zadeh L. A. (1975c) The Concept of a Linguistics Variable Ant Its Application to Approximate Reasoning –I. Information Science 8: 43-50.
Year 2018, Volume: 21 Issue: 4, 699 - 718, 29.12.2018

Abstract

References

  • 1. Abdi F. (2018) Hospital Leanness Assessment Model: A Fuzzy MULTIMOORA Decision Making Approach. Journal of Industrial and Systems Engineering 11(3): 37-59. 2. Arthur V.A. (1995) Written Patient Information: A Review of the Literature. Journal of Advanced Nursing 21(6): 1081-1086. 3. Bas E. (2018) An Integrated OSH Risk Management Approach to Surgical Flow Disruptions in Operating Room. Safety Science 109: 281-293. 4. Basaran M. A., Kalayci N. and Atay M. T. (2011) A Novel Hybrid Method for Better Evaluation: Evaluating University Instructors Teaching Performance by Combining Conventional Content Analysis with Fuzzy Rule Based Systems. Expert Systems with Applications 38(10): 12565-12568. 5. Blendon R. J., Scheck A. C., Donelan K., Hill C. A., Smith M., Beatrice D. and Altman D. (1995) How White and African Americans View Their Health and Social Problems: Different Experiences, Different Expectations. JAMA 273(4): 341-346. 6. Booker R. (2005) Effective Communication with The Patient. Eur Respir Re 14(96): 93–96. 7. Canavese D., Regina N. and Ortega S. (2013) A Proposal of a Fuzzy Rule-Based System for The Analysis of Health and Health Environments in Brazil. Ecological Indicators 34:7-14. 8. Coşgun Ö., Ekinci Y. and Yanık S. (2014) Fuzzy Rule-Based Demand Forecasting for Dynamic Pricing of a Maritime Company. Knowledge-Based Systems 70: 88-96. 9. Demir M. O., Basaran M. A. and Simonetti B. (2016) Determining Factors Affecting Healthcare Service Satisfaction Utilizing Fuzzy Rule-Based Systems. Journal of Applied Statistics 43(13): 2474-2489. 10. Dubois D. and Prade H. (1980) Fuzzy Sets and Systems: Theory and Applications. Academic Press, USA. 11. Ferguson W. J. and Candib L. M. (2002) Culture, Language, and the Doctor-Patient Relationship. FMCH Publications, Australia. 12. Goh M., Shuya Z. and de Souza R. (2018) Operational Framework for Healthcare Supplier Selection Under a Fuzzy Multi-Criteria Environment. 23rd International Symposium on Logistics (ISL 2018) Big Data Enabled Supply Chain Innovations, 8–11th July 2018, Indonesia. 13. Greenfield S., Kaplan S. and Ware J. E. (1985) Expanding Patient Involvement in Care Effects On Patient Outcomes. Annals of Internal Medicine 102(4): 520-528. 14. Greenfield S., Kaplan S. H., Ware J. E., Yano E. M. and Frank H. J. (1988) Patients’ Participation in Medical Care. Journal of General Internal Medicine 3(5): 448-457. 15. Gregory R., Peters E. and Slovic P. (2011) Making Decisions About Prescription Drugs: A Study of Doctor–Patient Communication. Health, Risk and Society 13(4): 347-371. 16. Herndon J. H. and Pollick K. J. (2002) Continuing Concerns, New Challenges, and Next Steps in Physician-Patient Communication. The Journal of Bone and Joint Surgery 84(2): 309-315. 17. Ishibuchi H., Murata T. and Gen M. (1998) Performance Evaluation of Fuzzy Rule- Based Classification Systems Obtained by Multi-Objective Genetic Algorithms. Computers and Industrial Enginering 35(34): 575-578. 18. Hofstede G. H. (2001) Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. Sage Press, USA. 19. Huang X., Shi F., Gu W. and Chen S. (2009) SVM-Based Fuzzy Rules Acquisition System for Pulsed GTAW Process. Engineering Applications of Artifical Intelligence 22(8): 1245-1255. 20. Ishikawa H. and Kiuchi T. (2010) Health Literacy and Health Communication. Biopsychosoc Med 4(18): 1-5. 21. Johnson R. L., Roter D., Powe N. R. and Cooper L. A. (2004) Patient Race/Ethnicity and Quality of Patient-Physician Communication During Medical Visits. American Journal of Public Health 94(12): 2084-2090. 22. Kirschbaum K. (2012) Physician Communication in The Operating Room: Expanding Application of Face-Negotiation Theory to The Health Communication Context. Health Communication 27(3): 292-301. 23. Li C., Hao F., Zhao L., Song L. and Dong X. (2017) Analysis of Medical and Healthcare Data Based on Positive and Negative Association Rules. 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), China. 24. Maguire P. and Pitceathly C. (2002) Key Communication Skills and How to Acquire Them. BMJ: British Medical Journal 325(7366): 697. 25. Nguyen H. T. and Walker E. A. (2000) A First Course in Fuzzy Logic. Second Edition, Chapman and Hall, UK. 26. Nørgaard B., Ammentorp J., Kyvık K. O. and Kofoed P. E. (2012) Communication Skills Training Increases Self-Efficacy of Health Care Professionals. Journal of Continuing Education in the Health Professions 32(2): 90-97. 27. Park E. K. and Song M. (2005) Communication Barriers Perceived by Older Patients and Nurses. International Journal of Nursing Studies 42(2): 159-166. 28. Plunkett A. and Quine S. (1996) Difficulties Experienced by Carers from Non‐English‐Speaking Backgrounds in Using Health and Other Support Services. Australian and New Zealand Journal of Public Health 20(1): 27-32. 29. Rosenberg E. E., Lussier M. T. and Beaudoin C. (1997) Lessons for Clinicians from Physician–Patient Communication Literature. Archives of Family Medicine 6: 279– 283. 30. Roter D. and Hall J. A. (2006) Doctors Talking with Patients/Patients Talking with Doctors: Improving Communication in Medical Visits. Greenwood Publishing Group Publishing, United States. 31. Roter D. L., Hall J. A. and Aoki Y. (2002) Physician Gender Effects in Medical Communication: A Meta-Analytic Review. JAMA 288(6): 756-764. 32. Sanz J. A., Galar M., Jurio A., Brugos A., Pagola M. and Bustince H. (2014) Medical Diagnosis of Cardiovascular Diseases Using an Interval-Valued Fuzzy Rule-Based Classification System. Applied Soft Computing 20: 103-111. 33. Schouten B. C. and Meeuwesen L. (2006) Cultural Differences in Medical Communication: A Review of the Literature. Patient Education and Counseling 64: 21–34. 34. Shakhawat C., Tahir H. and Neil B. (2006) Fuzzy Rule-Based Modelling for Human Health Risk from Naturally Occurring Radioactive Materials in Produced Water. Journal of Environmental Radioactivity 89(1): 1-17. 35. Shpilko I. (2006) Russian–American Health Care: Bridging The Communication Gap Between Physicians and Patients. Patient Education and Counseling 64: 331–341. 36. Smoczek J. and Szpytko J. (2014) Evolutionary Algorithm-Based Design of a Fuzzy TBF Predictive Model and TSK Fuzzy Anti-Sway Crane Control System. Engineering Applications of Artifical Intelligence 28: 190-204. 37. Statistik Austria (2011) https://www.statistik.at/web_de/statistiken/index.html (Erişim Tarihi: 19.05.2011). 38. Stewart M. A. (1995) Effective Physician-Patient Communication and Health Outcomes: A Review. CMAJ: Canadian Medical Association Journal 152(9): 1423. 39. Sudha V. and Girijamma H. A. (2017) Novel Clustering of Bigger and Complex Medical Data by Enhanced Fuzzy Logic Structure. International Conference on Circuits, Controls, and Communications (CCUBE), India. 40. Ustundag A., Kılınç M. S. and Cevikcan E. (2010) Fuzzy Rule-Based System for The Economic Analysis of RFID Investments. Expert Systems with Applications 37(7): 5300-5306. 41. Wallace L. S., de Voe J. E., Heintzman J. D. and Fryer G. E. (2009) Language Preference and Perceptions of Healthcare Providers’ Communication and Autonomy Making Behaviors Among Hispanics. Journal of Immigrant and Minority Health 11(6): 453-459. 42. Wan K. and Alagar V. (2017) Analyzing Healthcare Big Data for Patient Satisfaction. 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), China. 43. Wong S. and Lee A. (2006) Communication Skills and Doctor Patient Relationship. Medical Bulletin 11(3): 7-9. 44. Zadeh L. A. (1965) Fuzzy Sets. Information and Control 8: 338-353. 45. Zadeh L. A. (1975a) The Concept of a Linguistics Variable Ant Its Application to Approximate Reasoning –I. Information Science 8:199-250. 46. Zadeh L. A. (1975b) The Concept of a Linguistics Variable Ant Its Application to Approximate Reasoning –II. Information Science 8: 301-357. 47. Zadeh L. A. (1975c) The Concept of a Linguistics Variable Ant Its Application to Approximate Reasoning –I. Information Science 8: 43-50.
There are 1 citations in total.

Details

Primary Language English
Journal Section Makaleler
Authors

Mehmet Özer Demir

Murat Alper Başaran

Publication Date December 29, 2018
Published in Issue Year 2018 Volume: 21 Issue: 4

Cite

APA Demir, M. Ö., & Başaran, M. A. (2018). The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems. Hacettepe Sağlık İdaresi Dergisi, 21(4), 699-718.
AMA Demir MÖ, Başaran MA. The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems. HSİD. December 2018;21(4):699-718.
Chicago Demir, Mehmet Özer, and Murat Alper Başaran. “The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems”. Hacettepe Sağlık İdaresi Dergisi 21, no. 4 (December 2018): 699-718.
EndNote Demir MÖ, Başaran MA (December 1, 2018) The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems. Hacettepe Sağlık İdaresi Dergisi 21 4 699–718.
IEEE M. Ö. Demir and M. A. Başaran, “The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems”, HSİD, vol. 21, no. 4, pp. 699–718, 2018.
ISNAD Demir, Mehmet Özer - Başaran, Murat Alper. “The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems”. Hacettepe Sağlık İdaresi Dergisi 21/4 (December 2018), 699-718.
JAMA Demir MÖ, Başaran MA. The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems. HSİD. 2018;21:699–718.
MLA Demir, Mehmet Özer and Murat Alper Başaran. “The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems”. Hacettepe Sağlık İdaresi Dergisi, vol. 21, no. 4, 2018, pp. 699-18.
Vancouver Demir MÖ, Başaran MA. The Factors Affecting Minorities’ Satisfaction of Health Care Service Utilizing Fuzzy Rule Based Systems. HSİD. 2018;21(4):699-718.