Abstract
Patient experience is a key quality indicator driven by various patient- and provider-related factors in healthcare systems. While several studies provided different insights on patient experience factors, limited research investigates the interdependencies between provider-related factors and patient experience. This study aims to develop a data-driven Bayesian belief network (BBN) model that explores the role and relative importance of provider-related factors influencing patient experience. A BBN model was developed using structural learning algorithms such as tree augmented Naïve Bayes. We used hospital-level aggregated survey data from the British National Health Service to explore the impact of eight provider-related factors on overall patient experience. Moreover, sensitivity and scenario-based analyses were performed on the model. Our results showed that the most influential factors that lead to a high patient experience score are: (1) confidence and trust, (2) respect for patient-centered values, preferences, and expressed needs, and (3) emotional support. Further sensitivity and scenario analyses provided significant insights into the effect of different hypothetical interventions and how the patient experience is affected. The study findings can help healthcare managers utilize and allocate their resources more effectively to improve the overall patient experience in healthcare systems.
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References
“NHS Patient Surveys,” 2022. https://nhssurveys.org/surveys/ (accessed January 14, 2022).
Adamson, K., Bains, J., Pantea, L., Tyrhwitt, J., Tolomiczenko, G., & Mitchell, T. (2012). Understanding the patients’ perspective of emotional support to significantly improve overall patient satisfaction. Healthcare Quarterly, 15(4), 63–69. https://doi.org/10.12927/hcq.2012.23193
Al-Abri, R., & Al-Balushi, A. (2014). Oman medical specialty board patient satisfaction survey as a tool towards quality improvement. Oman Medical Journal, 29(1), 3–73.
Anderson, R. T., Camacho, F. T., & Balkrishnan, R. (2007). Willing to wait? The influence of patient wait time on satisfaction with primary care. BMC Health Services Research, 7, 31. https://doi.org/10.1186/1472-6963-7-31
Ankan, A., et al. (2016). pgmpy: Probabilistic graphical models using Python. Journal of Public Transportation, 3, 6–11. https://doi.org/10.5038/2375-0901.19.3.3
Baker, A. (2001). Crossing the quality chasm: A new health system for the 21st century, vol. 323, no. 7322. British Medical Journal Publishing Group.
Bari, V., et al. (2020). An approach to predicting patient experience through machine learning and social network analysis. Journal of the American Medical Informatics Association, 27(12), 1834–1843. https://doi.org/10.1093/JAMIA/OCAA194
Barnicot, K., Allen, K., Hood, C., & Crawford, M. (2020). Older adult experience of care and staffing on hospital and community wards: A cross-sectional study. BMC Health Services Research, 20(1), 583. https://doi.org/10.1186/s12913-020-05433-w
Batbaatar, E., Dorjdagva, J., Luvsannyam, A., Savino, M. M., & Amenta, P. (2017). Determinants of patient satisfaction: A systematic review. Perspectives in Public Health, 137(2), 89–101. https://doi.org/10.1177/1757913916634136
Bertakis, K. D., & Azari, R. (2011). Patient-centered care is associated with decreased health care utilization. Journal of the American Board of Family Medicine, 24(3), 229–239. https://doi.org/10.3122/jabfm.2011.03.100170
Brownlee, J. (2020). How to use discretization transforms for machine learning. Machine Learning Mastery. https://machinelearningmastery.com/discretization-transforms-for-machine-learning/. (accessed January 16, 2022).
Calnan, M. W., & Sanford, E. (2004). Public trust in health care: The system or the doctor? Quality & Safety in Health Care, 13(2), 92–97. https://doi.org/10.1136/qshc.2003.009001
Chakraborty, S., Mengersen, K., Fidge, C., Ma, L., & Lassen, D. (2016). A Bayesian Network-based customer satisfaction model: A tool for management decisions in railway transport. Decision Analytics. https://doi.org/10.1186/s40165-016-0021-2
Chao, Y. S., et al. (2017). A network perspective on patient experiences and health status: The Medical Expenditure Panel Survey 2004 to 2011. BMC Health Services Research, 17(1), 1–12. https://doi.org/10.1186/s12913-017-2496-5
Chen, V., Li, J., Kim, J. S., Plumb, G., & Talwalkar, A. (2021). Gaining insights into patient satisfaction through interpretable machine learning. IEEE Journal of Biomedical and Health Informatics, 19(6), 28–56. https://doi.org/10.1145/3511299
Churchill, N. (2013). Domain 4 Ensuring that people have a positive experience of care. Nhs.Uk, May 2013. https://www.england.nhs.uk/wp-content/uploads/2013/11/pat-expe.pdf (accessed October 03, 2021).
CMC. (2008). HCAHPS: Patients’ perspectives of care survey | CMS. Center for Medicare & Medicaid Services, 2008. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/HospitalHCAHPS (accessed October 09, 2021).
Constantinou, A. C., Fenton, N., Marsh, W., & Radlinski, L. (2016). From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artificial Intelligence in Medicine, 67, 75–93. https://doi.org/10.1016/j.artmed.2016.01.002
CQC, Consultation CQC’s NHS Patient Survey Programme. (2016, May). Care Quality Commission . Retrieved June 2023, from https://www.cqc.org.uk/sites/default/files/20160525_nhs_survey_consultation.pdf. (accessed May, 2016).
CQC. (2018). NHS Patient Survey Programme 2017 Adult Inpatient Survey Statistical release. https://www.cqc.org.uk/sites/default/files/20180613_ip17_statisticalrelease.pdf. (accessed January 15, 2022).
CQC. (2019). NHS Patient Survey Programme 2018 Adult Inpatient Survey Statistical release. https://www.cqc.org.uk/sites/default/files/20190620_ip18_statisticalrelease.pdf. (accessed January 15, 2022).
CQC. (2020). NHS Patient Survey Programme 2019 Adult Inpatient Survey Statistical release. https://nhssurveys.org/wp-content/surveys/02-adults-inpatients/04-analysis-reporting/2019/StatisticalRelease.pdf. (accessed January 15, 2022).
Croker, J. E., Swancutt, D. R., Roberts, M. J., Abel, G. A., Roland, M., & Campbell, J. L. (2013). Factors affecting patients’ trust and confidence in GPs: Evidence from the English national GP patient survey. British Medical Journal Open, 3(5), 1–8. https://doi.org/10.1136/bmjopen-2013-002762
Cugnata, F., Kenett, R. S., & Salini, S. (2016). Bayesian networks in survey data: Robustness and sensitivity issues. Journal of Quality Technology, 48(3), 253–264. https://doi.org/10.1080/00224065.2016.11918165
Danielsen, K., Bjertnæs, Ø. A., Garratt, A. M., & Pettersen, K. I. (2007). Patient experiences in relation to respondent and health service delivery characteristics: A survey of 26,938 patients attending 62 hospitals throughout Norway. Scandinavian Journal of Public Health, 35(1), 70–77. https://doi.org/10.1080/14034940600858615
Davidson, L., Scott, J., & Forster, N. (2021). Patient experiences of integrated care within the United Kingdom: A systematic review. International Journal of Care Coordination, 24(2), 39–56. https://doi.org/10.1177/20534345211004503
DeCourcy, A., West, E., & Barron, D. (2012). The National Adult Inpatient Survey conducted in the English National Health Service from 2002 to 2009: How have the data been used and what do we know as a result? BMC Health Services Research, 12(1), 71. https://doi.org/10.1186/1472-6963-12-71
Delen, D., Topuz, K., & Eryarsoy, E. (2020). Development of a Bayesian belief network-based DSS for predicting and understanding freshmen student attrition. European Journal of Operational Research, 281(3), 575–587. https://doi.org/10.1016/j.ejor.2019.03.037
de Silva, D. (2014). Helping measure person-centred care: A review of evidence about commonly used approaches and tools used to help measure person-centred care. Health Foundation. https://www.health.org.uk/publications/helping-measure-personcentred-care
DoH reveals the Abu Dhabi Healthcare Quality Index. Emirates News Agency - WAM, 2020. https://wam.ae/en/details/1395302881733 (accessed April 20, 2022).
DOH. (2022). Muashir-Resources-Department of Health. https://www.doh.gov.ae/en/programs-initiatives/muashir (accessed March 20, 2022).
Doyle, C., Lennox, L., & Bell, D. (2013). A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. British Medical Journal Open. https://doi.org/10.1136/bmjopen-2012-001570
Dubé, L., Trudeau, E., & Bélanger, M. C. (1994). Determining the complexity of patient satisfaction with foodservices. Journal of the American Dietetic Association, 94(4), 394–401. https://doi.org/10.1016/0002-8223(94)90093-0
Ekici, A., & Önsel Ekici, Ş. (2019). Understanding and managing complexity through Bayesian network approach: The case of bribery in business transactions. Journal of Business Research. https://doi.org/10.1016/J.JBUSRES.2019.10.024
Epstein, K. R., Laine, C., Farber, N. J., Nelson, E. C., & Davidoff, F. (1996). Patients’ perceptions of office medical practice: Judging quality through the patients’ eyes. American Journal of Medical Quality, 11(2), 73–80. https://doi.org/10.1177/0885713X9601100204
Ewart, L., Moore, J., Gibbs, C., & Crozier, K. (2014). Patient- and family-centred care on an acute adult cardiac ward. The British Journal of Nursing, 23(4), 213–218. https://doi.org/10.12968/bjon.2014.23.4.213
Fenton, N., & Neil, M. (2012). Risk assessment and decision analysis with Bayesian networks–Norman Fenton, Martin Neil-Google Books. CRC Press.
Flott, K., Darzi, A., & Mayer, E. (2018). Care pathway and organisational features driving patient experience: Statistical analysis of large NHS datasets. British Medical Journal Open. https://doi.org/10.1136/bmjopen-2017-020411
Foster, D. R. (2010). Intelligent-Board-2010. The Intelligient Board. https://healthcaregovernance.org.au/wp-content/uploads/2022/03/the-intelligent-board-patient-experience-2010.pdf (accessed March 17, 2021).
Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifier. Machine Learning, 29(2–3), 131–163. https://doi.org/10.1007/978-3-662-44845-8_14
Gavurova, B., Dvorsky, J., & Popesko, B. (2021). Patient satisfaction determinants of inpatient healthcare. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph182111337
Genie. (2020). GeNIe Modeler Manual. https://support.bayesfusion.com/docs/GeNIe.pdf.
Gesell, S. B., & Wolosin, R. J. (2016). Inpatients’ ratings of care in 5 common clinical conditions. Quality Management in Health Care, 13(4), 222–227. https://doi.org/10.1097/00019514-200410000-00005
Gleeson, H., Calderon, A., Swami, V., Deighton, J., Wolpert, M., & Edbrooke-Childs, J. (2016). Systematic review of approaches to using patient experience data for quality improvement in healthcare settings. British Medical Journal Open, 6(8), e011907. https://doi.org/10.1136/bmjopen-2016-011907
Goldsmith, L. J., et al. (2017). The importance of informational, clinical and personal support in patient experience with total knee replacement: A qualitative investigation. BMC Musculoskeletal Disorders, 18(1), 1–13. https://doi.org/10.1186/s12891-017-1474-8
Graham, C. (2016). Incidence and impact of proxy response in measuring patient experience: Secondary analysis of a large postal survey using propensity score matching. The International Journal for Quality in Health Care, 28(2), 246–252. https://doi.org/10.1093/intqhc/mzw009
Gu, X., & Itoh, K. (2015). Factors behind dialysis patient satisfaction: Exploring their effects on overall satisfaction. Therapeutic Apheresis and Dialysis, 19(2), 162–170. https://doi.org/10.1111/1744-9987.12246
Guler, P. H. (2017). Patient experience: A critical indicator of healthcare performance. Frontiers of Health Services Management, 33(3), 17–29. https://doi.org/10.1097/HAP.0000000000000003
Hekkert, K. D., Cihangir, S., Kleefstra, S. M., van den Berg, B., & Kool, R. B. (2009). Patient satisfaction revisited: A multilevel approach. Social Science and Medicine, 69(1), 68–75. https://doi.org/10.1016/J.SOCSCIMED.2009.04.016
Honeyford, K., Greaves, F., Aylin, P., & Bottle, A. (2017). Secondary analysis of hospital patient experience scores across England’s National Health Service: How much has improved since 2005? PLoS ONE. https://doi.org/10.1371/journal.pone.0187012
Ijegwa, A. D., Olufunke, V. R., Folorunso, O., & Richard, J. B. (2018). A bayesian based system for evaluating customer satisfaction in an online store. Advances in Intelligent Systems and Computing, 869, 1047–1061. https://doi.org/10.1007/978-3-030-01057-7_78
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. In Information science and statistics, 2nd ed. Springer.
Jones, C. H., O’Neill, S., McLean, K. A., Wigmore, S. J., & Harrison, E. M. (2017). Patient experience and overall satisfaction after emergency abdominal surgery. BMC Surgery, 17(1), 1–8. https://doi.org/10.1186/s12893-017-0271-5
Jordan, J. (2016). Grouping data points with k-means clustering. https://www.jeremyjordan.me/grouping-data-points-with-k-means-clustering/. (accessed January 15, 2022).
Kshirsagar, A. V., Tabriz, A. A., Bang, H., & Lee, S. Y. D. (2019). Patient satisfaction is associated with dialysis facility quality and star ratings. American Journal of Medical Quality, 34(3), 243–250. https://doi.org/10.1177/1062860618796310
LaVela, S. L., & Gallan, A. S. (2014). Evaluation and measurement of patient experience. Patient Experience Journal, 1(1), 28.
Lawrence JM, Ibne Hossain NU, Jaradat R, Hamilton M. (2020). Leveraging a Bayesian network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria. International Journal of Disaster Risk Reduction, 49, 101607. https://doi.org/10.1016/j.ijdrr.2020.101607
Luo, C. (2006). Neglected outcomes of customer satisfaction. Journal of Marketing, 71(2), 133–149.
Manary, M. P., Boulding, W., Staelin, R., & Glickman, S. W. (2013). The patient experience and health outcomes. New England Journal of Medicine, 368(3), 201–203. https://doi.org/10.1056/NEJMP1211775
Marcot, B. G. (2012). Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling, 230, 50–62. https://doi.org/10.1016/J.ECOLMODEL.2012.01.013
Marcot, B. G., & Hanea, A. M. (2021). What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics, 36(3), 2009–2031. https://doi.org/10.1007/S00180-020-00999-9/TABLES/5
Medina, L. A., Jankovic, M., OkudanKremer, G. E., & Yannou, B. (2013). An investigation of critical factors in medical device development through Bayesian networks. Expert Systems with Applications, 40(17), 7034–7045. https://doi.org/10.1016/j.eswa.2013.06.014
Milosevic, D., & Bayyigit, M. (1999). Quality improvement: What is in it for the patient? IEEE Transactions on Engineering Management. https://doi.org/10.1017/S1365100507060166
Murphy, T. (2017). The role of food in hospitals: The current state of food in hospitals.
Naidu, A. (2009). Factors affecting patient satisfaction and healthcare quality. International Journal of Health Care Quality Assurance, 22(4), 366–381. https://doi.org/10.1108/09526860910964834
Newell, S., & Jordan, Z. (2015). The patient experience of patient-centered communication with nurses in the hospital setting: A qualitative systematic review protocol. JBI Database of Systematic Reviews and Implementation Reports, 13(1), 76–87. https://doi.org/10.11124/jbisrir-2015-1072
Nguyen, J., Hunter, J., Smith, L., & Harnett, J. E. (2021). Can we all speak the same ‘language’ for our patients’ sake? Feedback on interprofessional communication and related resources. Global Advances in Health and Medicine, 10, 1–11. https://doi.org/10.1177/2164956121992338
NHS. (2011). NHS Patient Experience Framework.
NHS Institute for Innovation and Improvement, The Patient Experience Book. 2013.
OneView Blog. (2015). The eight principles of patient-centered care. OneView Revolutionising Patient Experience. https://www.oneviewhealthcare.com/blog/the-eight-principles-of-patient-centered-care/ (accessed October 09, 2021).
Pascale, A., & Nicoli, M. (2011). Adaptive Bayesian network for traffic flow prediction. In IEEE workshop on statistical signal processing proceedings (pp. 177–180). https://doi.org/10.1109/SSP.2011.5967651.
Pentescu, A., Orzan, M., Dragos, C., & Davila, C. (2020). Modelling patient satisfaction in healthcare. International Journal of Services and Operations Management, 35(3), 339–358. https://doi.org/10.1504/IJSOM.2020.105375
Prakash, B. (2010). Patient satisfaction. Journal of Cutaneous and Aesthetic Surgery, 3(3), 151–155. https://doi.org/10.4103/0974-2077.74491
Price, R. A., et al. (2014). Examining the role of patient experience surveys in measuring health care quality. Medical Care Research and Review, 71(5), 522–554. https://doi.org/10.1177/1077558714541480
Qazi, A., & Dikmen, I. (2021). From risk matrices to risk networks in construction projects. IEEE Transactions on Engineering Management, 68(5), 1449–1460. https://doi.org/10.1109/TEM.2019.2907787
Qazi, A., Dikmen, I., & Birgonul, M. T. (2020). Mapping uncertainty for risk and opportunity assessment in projects. Engineering Management Journal, 32(2), 86–97. https://doi.org/10.1080/10429247.2019.1664249
Quill, T. E., Arnold, R., & Back, A. L. (2009). Discussing treatment preferences with patients who want ‘everything.’ Annals of Internal Medicine, 151(5), 345–349. https://doi.org/10.7326/0003-4819-151-5-200909010-00010
Raleigh, V., Sizmur, S., Tian, Y., & Thompson, J. (2015). Impact of case-mix on comparisons of patient-reported experience in NHS acute hospital trusts in England. Journal of Health Services Research & Policy, 20(2), 92–99. https://doi.org/10.1177/1355819614552682
Raleigh, V. S., Frosini, F., Sizmur, S., & Graham, C. (2012). Do some trusts deliver a consistently better experience for patients? An analysis of patient experience across acute care surveys in English NHS trusts. BMJ Quality and Safety, 21(5), 381–390. https://doi.org/10.1136/bmjqs-2011-000588
Raleigh, V. S., Hussey, D., Seccombe, I., & Qi, R. (2009). Do associations between staff and inpatient feedback have the potential for improving patient experience? An analysis of surveys in NHS acute trusts in England. Quality & Safety in Health Care, 18(5), 347–354. https://doi.org/10.1136/qshc.2008.028910
Reeves, R., & West, E. (2015). Changes in inpatients’ experiences of hospital care in England over a 12-year period: A secondary analysis of national survey data. Journal of Health Services Research & Policy, 20(3), 131–137. https://doi.org/10.1177/1355819614564256
Robert, G., & Cornwell, J. (2013). Rethinking policy approaches to measuring and improving patient experience. The Journal of Health Services Research and Policy, 18(2), 67–69. https://doi.org/10.1177/1355819612473583
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. https://doi.org/10.1093/intqhc/mzr038
Scikit-Learn. (2017). https://scikit-learn.org/stable/. https://scikit-learn.org/0.18/_downloads/scikit-learn-docs.pdf. (accessed January 25, 2022).
Sharma, P. (2019). K means clustering | K means clustering algorithm in Python. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/. (accessed January 15, 2022).
Simsekler, M. C. E., Alhashmi, N. H., Azar, E., King, N., Luqman, R. A. M. A., & AlMulla, A. (2021). Exploring drivers of patient satisfaction using a random forest algorithm. BMC Medical Informatics and Decision Making, 21(1), 1–9. https://doi.org/10.1186/s12911-021-01519-5
Simsekler, M. C. E., & Qazi, A. (2020). Adoption of a data-driven Bayesian belief network investigating organizational factors that influence patient safety. Risk Analysis. https://doi.org/10.1111/risa.13610
The Berly Institute. (2016). Defining patient experience-The Beryl Institute-Improving the Patient Experience. https://www.theberylinstitute.org/page/DefiningPatientExp (accessed March 31, 2021).
Tian, Z., Si, B., Shi, X., & Fang, Z. (2018). An application of Bayesian Network approach for selecting energy efficient HVAC systems. The Journal of Building Engineering. https://doi.org/10.1016/j.jobe.2019.100796
van der Eijk, M., Faber, M. J., AlShamma, S., Munneke, M., & Bloem, B. R. (2011). Moving towards patient-centered healthcare for patients with Parkinson’s disease. Parkinsonism & Related Disorders, 17(5), 360–364. https://doi.org/10.1016/J.PARKRELDIS.2011.02.012
Verghese. (2011). Nursing home care quality: Insights from a Bayesian network approach. Bone, 23(1), 1–7.
Wolf, J. A. (2016). Patient experience: Driving outcomes at the heart of healthcare. Patient Experience Journal, 3(1), 1–4. https://doi.org/10.35680/2372-0247.1147
Wolf, J. A., Niederhauser, V., Marshburn, D., & Lavela, S. L. (2014). Defining patient experience. Patient Experience Journal, 1(1), 7–19. https://doi.org/10.35680/2372-0247.1004
Wu, J., Yang, M., Rasouli, S., & Xu, C. (2016). Exploring passenger assessments of bus service quality using bayesian networks. Journal of Public Transportation, 19(3), 36–54. https://doi.org/10.5038/2375-0901.19.3.3
Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2016). A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study. Expert Systems with Applications, 60, 141–155. https://doi.org/10.1016/j.eswa.2016.05.005
Yu, W., Liu, Q., Zhao, G., & Song, Y. (2021). Exploring the effects of data-driven hospital operations on operational performance from the resource orchestration theory perspective. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3098541
Zhang, L., Wu, X., Skibniewski, M. J., Zhong, J., & Lu, Y. (2014). Bayesian-network-based safety risk analysis in construction projects. Reliability Engineering and System Safety, 131, 29–39. https://doi.org/10.1016/j.ress.2014.06.006
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This work was supported, in part, by the Khalifa University of Science and Technology under Award RCII-2019-002-Research Center for Digital Supply Chain and Operations Management. The funding body had no direct involvement in the design, data collection, analysis, and interpretation or in writing the manuscript. The work in this paper was supported, in part, by the Faculty Research Grant (FRG23-E-B91) from the American University of Sharjah. This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah.
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Appendix: Survey questions
Appendix: Survey questions
Factors | Questions |
---|---|
T1 | “Did you know which nurse was in charge of looking after you (this would have been a different person after each shift change)?” |
“How much information about your condition or treatment was given to you?” | |
“Before you left hospital, were you given any written or printed information about what you should or should not do after leaving hospital?” | |
“Did hospital staff tell you who to contact if you were worried about your condition or treatment after you left hospital?” | |
T2 | “When you had important questions to ask a doctor, did you get answers that you could understand?” |
“When you had important questions to ask a nurse, did you get answers that you could understand?” | |
“Were you involved as much as you wanted to be in decisions about your care and treatment?” | |
“Were you given enough privacy when discussing your condition or treatment?” | |
“Did you feel you were involved in decisions about your discharge from hospital?” | |
T3 | “Did you find someone in the hospital staff to talk to about your worries and fears?” |
“Do you feel you got enough emotional support from hospital staff during your stay?” | |
T4 | “Did you have confidence and trust in the doctors treating you?” |
“Did you have confidence and trust in the nurses treating you?” | |
“Did you have confidence in the decisions made about your condition or treatment?” | |
T5 | “In your opinion, did the members of staff caring for you work well together?” |
“After leaving hospital, did you get enough support from health or social care professionals to help you recover and manage your condition?” | |
“Did hospital staff take your family or home situation into account when planning your discharge?” | |
“Did hospital staff discuss with you whether you may need any further health or social care services after leaving the hospital (e.g. services from a GP, physiotherapist or community nurse, or assistance from social services or the voluntary sector)?” | |
T6 | “Were you offered a choice of food?” |
T7 | “During your time in the hospital, did you get enough to drink?” |
T8 | “Overall, did you feel you were treated with respect and dignity while you were in the hospital?” |
T9 | “Overall patient experience” |
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Al Nuairi, A., Simsekler, M.C.E., Qazi, A. et al. A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector. Ann Oper Res 342, 1797–1817 (2024). https://doi.org/10.1007/s10479-023-05437-9
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DOI: https://doi.org/10.1007/s10479-023-05437-9