Quality evaluation and preferences of healthcare services: the case of telemedicine in Sardinia

Abstract

This paper explores preferences towards cardiological e-health services, assessed on a representative quota sample of potential users in Sardinia, Italy. By a mixture model approach, it is possible to observe individual response behaviour expressed in terms of agreement and heterogeneity. While a substantial heterogeneity emerges for modes of consultation, the ranking of respondents’ preferences is clearly depicted for the location. The findings highlight a strong preference for a cardiological consultation at the family doctor and for the province of residence as a location. Notably, education and age are the covariates that most importantly affect choice processes.

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References

  1. Agresti, A.: Analysis of Ordinal Categorical Data, 2nd edn. Wiley, Hoboken (2010)

    Google Scholar 

  2. Aneja, S., Ross, J.S., Wang, Y., Matsumoto, M., Rodgers, G.P., Bernheim, S.M., Rathore, S.S., Krumholz, H.M.: US cardiologist workforce from 1995 to 2007: modest growth, lasting geographic maldistribution especially in rural areas. Health Aff. 30(12), 2301–09 (2011)

    Article  Google Scholar 

  3. Basoglu, N., Daim, T.U., Topacan, U.: Determining patient preferences for remote monitoring. J. Med. Syst. 36(3), 1389–401 (2012)

    Article  Google Scholar 

  4. Capecchi, S., Iannario, M.: Gini heterogeneity index for detecting uncertainty in ordinal data surveys. METRON 74(2), 223–232 (2016)

    Article  Google Scholar 

  5. Capecchi, S., Piccolo, D.: Dealing with heterogeneity in ordinal responses. Qual. Quant. 51(5), 2375–2393 (2017)

    Article  Google Scholar 

  6. Cowie, M.R., Bax, J., Bruining, N., Cleland, J.G., Koehler, F., Malik, M., Pinto, F., van der Velde, E., Vardas, P.: E-Health: a position statement of the European Society of Cardiology. Eur. Heart J. 37(1), 63–66 (2016)

    Article  Google Scholar 

  7. CRENoS: Economia della Sardegna. \(24^{\circ }\) Rapporto, CUEC, Cagliari (2017)

  8. D’Elia, A., Piccolo, D.: A mixture model for preference data analysis. Comput. Stat. Data Anal. 49(3), 917–934 (2005)

    Article  Google Scholar 

  9. Dávalos, M.E., French, M.T., Burdick, A.E., Simmons, S.C.: Economic evaluation of telemedicine: review of the literature and research guidelines for benefit–cost analysis. Telemed. e Health 15(10), 933–948 (2009)

    Article  Google Scholar 

  10. de Bekker-Grob, E.W., Ryan, M., Gerard, K.: Discrete choice experiments in health economics: a review of the literature. Health Econ. 21(2), 145–172 (2012)

    Article  Google Scholar 

  11. European Commission: Europa 2020, a European strategy for smart, sustainable and inclusive growth. http://ec.europa.eu/eu2020/pdf (2010). Accessed 15 Feb 2018

  12. GeoNue: Le regioni storiche della Sardegna. https://geonue.com/le-regioni-storiche-della-sardegna (2017). Accessed 15 Feb 2018

  13. Gini, C.: Variabilità e mutabilità. Studi economico-giuridici, Facoltà di Giurisprudenza, Università di Cagliari, A, III, parte II (1912)

  14. Iannario, M.: Modelling shelter choices in a class of mixture models for ordinal responses. Stat. Methods Appl. 21(1), 1–22 (2012)

    Article  Google Scholar 

  15. Iannario, M., Piccolo, D.: A generalized framework for modelling ordinal data. Stat. Methods Appl. 25(2), 163–189 (2016a)

    Article  Google Scholar 

  16. Iannario, M., Piccolo, D.: A comprehensive framework of regression models for ordinal data. METRON 74(2), 233–252 (2016b)

    Article  Google Scholar 

  17. Iannario, M., Piccolo, D., Simone, R.: CUB: a class of mixture models for ordinal data. R package version 1.2.0. http://CRAN.R-project.org/package=CUB (2018). Accessed 15 Feb 2018

  18. ISTAT: GeoDemo. http://demo.istat.it/pop2014/index.html (2017). Accessed 15 Feb 2018

  19. Laakso, M., Taagepera, R.: Effective number of parties: a measure with application to West Europe. Comp. Political Stud. 12(1), 3–27 (1989)

    Article  Google Scholar 

  20. Mahmud, N., Rodriguez, J., Nesbit, J.: A text message-based intervention to bridge the healthcare communication gap in the rural developing world. Technol. Health Care 18(2), 137–144 (2010)

    Google Scholar 

  21. Peterson, L.T., Ford, E.W., Eberhardt, J., Huerta, T.R., Menachemi, M.: Assessing differences between physicians’ realized and anticipated gains from electronic health record adoption. J. Med. Syst. 35(2), 151–161 (2011)

    Article  Google Scholar 

  22. Piccolo, D.: On the moments of a mixture of uniform and shifted binomial random variables. Quad. Stat. 5, 85–104 (2003)

    Google Scholar 

  23. Tutz, G.: Regression for Categorical Data. Cambridge University Press, Cambridge (2012)

    Google Scholar 

  24. Yin, S., Huang, K., Shieh, J., Liu, Y., Wu, H.: Telehealth services evaluation: a combination of SERVQUAL model and importance–performance analysis. Qual. Quant. 50(2), 751–766 (2016)

    Article  Google Scholar 

Download references

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Correspondence to Stefania Capecchi.

Additional information

M. Pulina and M. Meleddu acknowledge the Fondazione Banco di Sardegna (FBS) for financing the project Prot.U823.2013-A1.747.MGB- Prat.2013.1441 - “La telemedicina: quantificazione della disponibilitá a pagare dei potenziali fruitori ed intermediar”. This work has been partially supported by CUBREMOT project at University of Naples Federico II.

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Capecchi, S., Meleddu, M. & Pulina, M. Quality evaluation and preferences of healthcare services: the case of telemedicine in Sardinia. Qual Quant 53, 2339–2351 (2019). https://doi.org/10.1007/s11135-018-0743-4

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Keywords

  • E-medicine
  • Evaluation
  • Heterogeneity
  • Mixture models