Abstract
Despite an ever-growing personalized demand for patients’ hospital choice, little systematic work has examined the decision process that considers the diversity of medical service demand. In this paper, we develop an intelligence decision framework to explore multi-source uncertain information in hospital choice. The framework employs a novel SMAA-DEX method to generate a ranking list of hospital alternatives based on Decision EXpert (DEX) hierarchical model and stochastic multicriteria acceptability analysis (SMAA) in personalized hospital choice. To conduct the multi-source information fusion under uncertainty, the SMAA-DEX method produces the central weight vector considering the ordinal weight and random weight and estimates the holistic acceptability indices for each alternative. By collecting hospital statistics, third-party evaluations and personal patient information in the real world, we verify our method for personalized hospital choice in terms of different preferences such as distance, ranking and income. The results of the experiments demonstrate the effectiveness of the proposed approach, which not only effectively processes various types of hospital choice, but also accomplishes uncertain reasoning of multi-source online information.
Similar content being viewed by others
References
Lu SF, Rui H (2018) Can we trust online physician ratings? Evidence from cardiac surgeons in Florida. Manag Sci 64:2557–2573. https://doi.org/10.1287/mnsc.2017.2741
Deng S, Huang L, Xu G et al (2017) On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw Learn Syst 28:1164–1177. https://doi.org/10.1109/TNNLS.2016.2514368
Lin Y, Hu X, Wu X (2014) Quality of information-based source assessment and selection. Neurocomputing 133:95–102. https://doi.org/10.1016/j.neucom.2013.11.027
Liu N, Finkelstein SR, Kruk ME, Rosenthal D (2018) When waiting to see a doctor is less irritating: understanding patient preferences and choice behavior in appointment scheduling. Manag Sci 64:1975–1996. https://doi.org/10.1287/mnsc.2016.2704
Jacobsen KH, Ansumana R, Abdirahman HA et al (2012) Considerations in the selection of healthcare providers for mothers and children in Bo, Sierra Leone: reputation, cost and location. Int Health 4:307–313. https://doi.org/10.1016/j.inhe.2012.09.004
Sivey P (2012) The effect of waiting time and distance on hospital choice for English cataract patients. Health Econ 21:444–456. https://doi.org/10.1002/hec.1720
Wahlstedt E, Ekman B (2016) Patient choice, Internet based information sources, and perceptions of health care: evidence from Sweden using survey data from 2010 and 2013. BMC Health Serv Res 16:1–11. https://doi.org/10.1186/s12913-016-1581-5
Varkevisser M, van der Geest SA, Schut FT (2012) Do patients choose hospitals with high quality ratings? Empirical evidence from the market for angioplasty in the Netherlands. J Health Econ 31:371–378. https://doi.org/10.1016/j.jhealeco.2012.02.001
Angst C, Agarwal R, Gao G et al (2014) Information technology and voluntary quality disclosure by hospitals. Decis Support Syst 57:367–375. https://doi.org/10.1016/j.dss.2012.10.042
Osadchiy N, Diwas KC (2017) Are patients patient? the role of time to appointment in patient flow. Prod Oper Manag 26:469–490. https://doi.org/10.1111/poms.12659
Mills AF, Argon NT, Ziya S (2018) Dynamic distribution of patients to medical facilities in the aftermath of a disaster. Oper Res 66:716–732. https://doi.org/10.1287/opre.2017.1695
Baker LC, Bundorf MK, Kessler DP (2016) The effect of hospital/physician integration on hospital choice. J Health Econ 50:1–8. https://doi.org/10.1016/j.jhealeco.2016.08.006
Helm JE, Van Oyen MP (2014) Design and optimization methods for elective hospital admissions. Oper Res 62:1265–1282. https://doi.org/10.1287/opre.2014.1317
Song J, Wen J (2015) A non-cooperative game with incomplete information to improve patient hospital choice. Int J Prod Res 53:7360–7375. https://doi.org/10.1080/00207543.2015.1077284
Brown PH, Theoharides C (2009) Health-seeking behavior and hospital choice in China’s New Cooperative Medical System. Health Econ 18:S47–S64. https://doi.org/10.1002/hec.1508
Al-Doghaither AH, Abdelrhman BM, Saeed AAW, Magzoub MEMA (2003) Factors influencing patient choice of hospitals in Riyadh, Saudi Arabia. J R Soc Promot Health 123:105–109. https://doi.org/10.1177/146642400312300215
Paul JA, MacDonald L, Hariharan G (2014) Modeling risk factors and disease conditions to study associated lifetime medical costs. Serv Sci 6:47–62. https://doi.org/10.1287/serv.2014.0063
Lee EK, Atallah HY, Wright MD, Post ET, Thomas IVC, Wu DT, Haley LL Jr (2015) Transforming hospital emergency department workflow and patient care. Interfaces 45:58–82. https://doi.org/10.1287/inte.2014.0788
Ang E, Kwasnick S, Bayati M et al (2016) Accurate emergency department wait time prediction. Manuf Serv Oper Manag 18:141–156. https://doi.org/10.1287/msom.2015.0560
Cheraghi-Sohi S, Hole AR, Mead N et al (2008) What patients want from primary care consultations: a discrete choice experiment to identify patients’ priorities. Ann Fam Med 6:107–115. https://doi.org/10.1370/afm.816
Senot C, Chandrasekaran A, Ward PT et al (2016) The impact of combining conformance and experiential quality on hospitals’ readmissions and cost performance. Manag Sci 62:829–848. https://doi.org/10.1287/mnsc.2014.2141
Lucidi S, Maurici M, Paulon L et al (2016) A simulation-based multiobjective optimization approach for health care service management. IEEE Trans Autom Sci Eng 13:1480–1491. https://doi.org/10.1109/TASE.2016.2574950
Thibaud M, Chi H, Zhou W, Piramuthu S (2018) Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: a comprehensive review. Decis Support Syst 108:79–95. https://doi.org/10.1016/j.dss.2018.02.005
Cosmus TC, Parizh M (2011) Advances in whole-body MRI magnets. IEEE Trans Appl Supercond 21:2104–2109. https://doi.org/10.1109/TASC.2010.2084981
Emmert M, Meier F (2013) An Analysis of Online Evaluations on a Physician Rating Website: evidence From a German Public Reporting Instrument. J Med Internet Res 15:e157. https://doi.org/10.2196/jmir.2655
Kallinikos J, Tempini N (2014) Patient data as medical facts: social media practices as a foundation for medical knowledge creation. Inf Syst Res. https://doi.org/10.1287/isre.2014.0544
Xia C, Wang Z, Zheng C, Guo Q, Shi Y, Dehmer M, Chen Z (2019) A new coupled disease-awareness spreading model with mass media on multiplex networks. Inform Sciences 471:185–200. https://doi.org/10.1016/j.ins.2018.08.050
Xia C, Li X, Wang Z, Perc M (2018) Doubly effects of information sharing on interdependent network reciprocity. New J Phys 20:075005. https://doi.org/10.1088/1367-2630/aad140
Shariat S, Orten B, Dasdan A (2017) Online evaluation of bid prediction models in a large-scale computational advertising platform: decision making and insights. Knowl Inf Syst 51:37–60. https://doi.org/10.1007/s10115-016-0972-6
Victoor A, Delnoij DMJ, Friele RD, Rademakers JJ (2012) Determinants of patient choice of healthcare providers: a scoping review. BMC Health Serv Res 12:272. https://doi.org/10.1186/1472-6963-12-272
Santos R, Gravelle H, Propper C (2017) Does quality affect patients’ choice of doctor? Evidence from England. Econ J 127:445–494. https://doi.org/10.1111/ecoj.12282
Qu X, Shi J (2011) Modeling the effect of patient choice on the performance of open access scheduling. Int J Prod Econ 129:314–327. https://doi.org/10.1016/j.ijpe.2010.11.006
Bohanec M, Rajkovič V (1990) DEX: an expert system shell for decision support. Sistemica 1:145–157
Ding S, Xia C, Wang C, Wu D, Zhang Y (2017) Multi-objective optimization based ranking prediction for cloud service recommendation. Decis Support Syst 101:16–114. https://doi.org/10.1016/j.dss.2017.06.005
Damij N, Boškoski P, Bohanec M, Mileva Boshkoska B (2016) Ranking of business process simulation software tools with DEX/QQ hierarchical decision model. PLoS ONE 11:e0148391. https://doi.org/10.1371/journal.pone.0148391
Pavlovic M, Cerenak A, Pavlovic V, Rozman C, Pazek K, Bohanec M (2011) Development of DEX-HOP multi-attribute decision model for preliminary hop hybrids assessment. Comput Electron Agr 75:181–189. https://doi.org/10.1016/j.compag.2010.11.002
Delibašić B, Radovanović S, Jovanović M et al (2018) Integrating knowledge from DEX hierarchies into a logistic regression stacking model for predicting ski injuries. J Decis Syst 27:201–208. https://doi.org/10.1080/12460125.2018.1460164
Lahdelma R, Hokkanen J, Salminen P (1998) SMAA—stochastic multiobjective acceptability analysis. Eur J Oper Res 106:137–143. https://doi.org/10.1016/S0377-2217(97)00163-X
Lahdelma R, Salminen P (2001) SMAA-2: stochastic multicriteria acceptability analysis for group decision making. Oper Res 49:444–454. https://doi.org/10.1287/opre.49.3.444.11220
Lahdelma R, Salminen P, Hokkanen J (2002) Locating a waste treatment facility by using stochastic multicriteria acceptability analysis with ordinal criteria. Eur J Oper Res 142:345–356. https://doi.org/10.1016/S0377-2217(01)00303-4
Lahdelma R, Miettinen K, Salminen P (2005) Reference point approach for multiple decision makers. Eur J Oper Res 164:785–791. https://doi.org/10.1016/j.ejor.2004.01.030
Lahdelma R, Salminen P (2006) Stochastic multicriteria acceptability analysis using the data envelopment model. Eur J Oper Res 170:241–252. https://doi.org/10.1016/j.ejor.2004.07.040
Lahdelma R, Salminen P (2009) Prospect theory and stochastic multicriteria acceptability analysis (SMAA). Omega 37:961–971. https://doi.org/10.1016/J.OMEGA.2008.09.001
Zhou H, Wang J, Zhang H (2017) Stochastic multicriteria decision-making approach based on SMAA-ELECTRE with extended gray numbers. Int Trans Oper Res. https://doi.org/10.1111/itor.12380
Yaraghi N, Tabesh P, Guan P, Zhuang J (2015) Comparison of AHP and Monte Carlo AHP under different levels of uncertainty. IEEE Trans Eng Manag 62:122–132. https://doi.org/10.1109/TEM.2014.2360082
Ahn BS (2017) The analytic hierarchy process with interval preference statements. Omega 67:177–185. https://doi.org/10.1016/J.OMEGA.2016.05.004
Eyvindson K, Öhman K, Nordström E-M (2018) Using uncertain preferential information from stakeholders to assess the acceptability of alternative forest management plans. J Multi-Criteria Decis Anal 25:43–52. https://doi.org/10.1002/mcda.1630
Rahman MM, Paatero JV, Lahdelma R (2013) Evaluation of choices for sustainable rural electrification in developing countries: a multicriteria approach. Energy Policy 59:589–599. https://doi.org/10.1016/j.enpol.2013.04.017
Dias LC, Passeira C, Malça J, Freire F (2016) Integrating life-cycle assessment and multi-criteria decision analysis to compare alternative biodiesel chains. Ann Oper Res. https://doi.org/10.1007/s10479-016-2329-7
van Valkenhoef G, Tervonen T (2016) Entropy-optimal weight constraint elicitation with additive multi-attribute utility models. Omega 64:1–12. https://doi.org/10.1016/J.OMEGA.2015.10.014
Angilella S, Catalfo P, Corrente S et al (2018) Robust sustainable development assessment with composite indices aggregating interacting dimensions: the hierarchical-SMAA-Choquet integral approach. Knowledge-Based Syst. https://doi.org/10.1016/J.KNOSYS.2018.05.041
Chen T-Y (2013) A signed-distance-based approach to importance assessment and multi-criteria group decision analysis based on interval type-2 fuzzy set. Knowl Inf Syst 35:193–231. https://doi.org/10.1007/s10115-012-0497-6
Razavi M, Shams Aliee F, Badie K (2011) An AHP-based approach toward enterprise architecture analysis based on enterprise architecture quality attributes. Knowl Inf Syst 28:449–472. https://doi.org/10.1007/s10115-010-0312-1
Wei G, Wang H-J, Lin R (2011) Application of correlation coefficient to interval-valued intuitionistic fuzzy multiple attribute decision-making with incomplete weight information. Knowl Inf Syst 26:337–349. https://doi.org/10.1007/s10115-009-0276-1
Leibovich M, Zuckerman I, Pfeffer A, Gal Y (2017) Decision-making and opinion formation in simple networks. Knowl Inf Syst 51:691–718. https://doi.org/10.1007/s10115-016-0994-0
Pan Y, Wu D, Olson DL (2017) Online to offline (O2O) service recommendation method based on multi-dimensional similarity measurement. Decis Support Syst 103:1–8. https://doi.org/10.1016/J.DSS.2017.08.003
Ding S, Ma XJ, Yang SL (2012) A software trustworthiness evaluation model using objective weight based evidential reasoning approach. Knowl Inf Syst 33(1):171–189. https://doi.org/10.1007/s10115-011-0442-0
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 91846107, 71571058, 71690235) and the Anhui Provincial Science and Technology Major Project (Grant Nos. 16030801121 and 17030801001).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chen, Y., Ding, S., Zheng, H. et al. Decision support for personalized hospital choice using the DEX hierarchical model with SMAA. Knowl Inf Syst 62, 3059–3082 (2020). https://doi.org/10.1007/s10115-020-01448-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-020-01448-1