Skip to main content
SpringerLink
Log in

Machine Learning for Digital Shadow Design in Health Insurance Sector

  • Research
  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available on request from the corresponding author, [R.R.].

References

  1. Timo E, Lauren P (n.d.). The Essentials for Successful Digital Transformation (DX). https://www.sap.com/latinamerica/insights/viewpoints/what-are-theessentials-for-successful-digital-transformation-dx.html

  2. SAP (n.d.). What is digital transformation? https://www.sap.com/latinamerica/insights/what-is-digital-transformation.html

  3. Ashkenas R (2013), April 16 Change Management Need to Change. https://hbr.org/2013/04/change-management-needs-to-cha

  4. World Health Organization. (n.d.). Basic information about COVID-19. WHO. Recover 2021 December 1, https://www.who.int/es/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19

  5. COVID Conference November 18. AMIS. Asociación Mexicana de Instituciones de Seguros, Recover (2021), November 18 2021 December 1, from https://www.amisprensa.org/wp-content/uploads/2021/11/Conferencia-COVID-19-61-V2.pdf

  6. Furr N, Shipilov A, Rouillard D, Hemon-Laurens A (2022), January 28 The 4 Pillars of Successful Digital Transformations. https://hbr.org/2022/01/the-4-pillars-of-successful-digital-transformations

  7. Chamorro-Premuzic. (2021), November 23 The Essential Components of Digital Transformation. https://hbr.org/2021/11/the-essential-components-of-digitaltransformation

  8. OECD (2021) Health at a glance 2021: OECDIndicators. OECD Publishing, Paris. https://doi.org/10.1787/ae3016b9-en

    Book  Google Scholar 

  9. Rodriguez-Aguilar R, Marmolejo-Saucedo JA, Landin AZ, Aguilar MR, Saucedo LM (2023) Out of pocket and catastrophic health spending in Mexico in the face of the COVID-19 pandemic. EAI Endorsed Transactions on Pervasive Health and Technology, p 9

  10. Khan IU, Aljabri M, Aljameel SS, Mariam Moataz AK, Alshamrani FM, Sara Mhd BC (2021) Computational intelligence-based model for mortality rate prediction in COVID-19 patients. Int J Environ Res Public Health 18(12):6429. https://doi.org/10.3390/ijerph18126429

    Article  Google Scholar 

  11. Kutzko JD (2018) 18 mayo). Computer-implemented systems and methods for predicting the health and therapeutic behavior of individuals using artificial intelligence, smart contracts, and blockchain. Google Patents. Recuperado 1 de diciembre de 2021, de https://patentimages.storage.googleapis.com/8b/5f/92/58fd6949c1ef12/US10991463.pdf

  12. Asociación Mexicana de Instituciones de Seguros (2021) 18 noviembre). Conferencia COVID 18 noviembre. AMIS. Recuperado 1 de diciembre de 2021, de https://www.amisprensa.org/wp-content/uploads/2021/11/Conferencia-COVID-19-61-V2.pdf

  13. Vera-Zertuche J, Mancilla-Galindo J, Tlalpa-Prisco M, Aguilar-Alonso P, Aguirre-García MM, Segura-Badilla O, de Vidal-Mayo J, J (2021) Obesity is a strong risk factor for short-term mortality and adverse outcomes in Mexican patients with COVID-19: a national observational study. Epidemiol Infect 149. https://doi.org/10.1017/S0950268821001023

  14. Economic Commission for Latin America and the Caribbean (ECLAC) (2013) Digital economy for structural change and equality. Santiago de Chile, CEPAL

    Google Scholar 

  15. Tapscott D (1995) The digital economy: promise and peril in the age of networked intelligence. McGraw-Hill: NY

  16. Jacquez-Hernández M, Torres V (2018) Ingeniería Industrial Actualidad y Nuevas Tendencias 6(20):61–78Modelos de evaluación de la madurez y preparación hacia la Industria 4.0: una revisión de literatura

  17. Uhlemann THJ, Lehmann C, Steinhilper R (2017) The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia Cirp 61:335–340

    Article  Google Scholar 

  18. Witkowski K (2017) Internet of things, big data, industry 4.0–innovative solutions in logistics and supply chains management. Procedia Eng 182:763–769

    Article  Google Scholar 

  19. Wagner R, Schleich B, Haefner B, Kuhnle A, Wartzack S, Lanza G (2019) Challenges and potentials of digital twins and industry 4.0 in product design and production for high-performance products. Procedia CIRP 84:88–93

    Article  Google Scholar 

  20. Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and industry 4.0: a 360-degree comparison. IEEE Access 6:3585–3593

    Article  Google Scholar 

  21. Mhlanga D (2020) Industry 4.0 in finance: the impact of artificial intelligence (Ai) on digital financial inclusion. Int J Financial Stud 8(3):45

    Article  MathSciNet  Google Scholar 

  22. Muñoz LDC (2020) Elementos clave de la innovación empresarial. Una revisión desde las tendencias contemporáneas. Revista Innova ITFIP 6(1):50–69

    Article  Google Scholar 

  23. Scavarda A, Daú G, Scavarda LF, Goyannes Gusmão Caiado R (2019) An analysis of the corporate social responsibility and the industry 4.0 with focus on the youth generation: a sustainable human resource management framework. Sustainability 11(18):5130

    Article  Google Scholar 

  24. Nagy J, Oláh J, Erdei E, Máté D, Popp J (2018) The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain— the case of Hungary. Sustainability 10(10):3491

    Article  Google Scholar 

  25. Harvard, Business Review. (s.f). Better Digital Transformation Depends on Aligning with How People Will Work. https://hbr.org/resources/pdfs/comm/BetterDigitalTransformationonAligningWithHowPeopleWillWork.pdf

  26. Sousa MJ, Rocha Á (2019) Digital learning: developing skills for digital transformation of organizations. Future Generation Computer Systems 91:327–334

    Article  Google Scholar 

  27. Jedynak M, Czakon W, Kuźniarska A, Mania K (2021) Digital transformation of organizations: what do we know and where to go next? J Organizational Change Manage 34(3):629–652. https://doi.org/10.1108/JOCM-10-2020-0336

    Article  Google Scholar 

  28. Morakanyane R, Grace AA, O’Reilly P (2017) Conceptualizing Digital Transformation in Business Organizations: A Systematic Review of Literature BLED 2017 Proceedings. 21. https://aisel.aisnet.org/bled2017/21

  29. Trushkina N, Abazov R, Rynkevych N, Bakhautdinova G (2020) Digital transformation of organizational culture under conditions of the information economy. Virtual Econ 3(1):7–38

    Article  Google Scholar 

  30. Leclère R (2018) octubre de El auge del gemelo digital gracias al IoT en la Industria 4.0. (A. d. España, Ed.) Ausape(57), 30–31. Recuperado el 30 de octubre de 2020, de https://ausape.com/documentos/Media/Publicaciones/Revistas/2018/R57_Ausape. pd

  31. Schlegel D, Kraus P (2021) Skills and competencies for digital transformation–a critical analysis in the context of robotic process automation. International Journal of Organizational Analysis

  32. Chiquito MV, Plua JCG, Chong MB, Chong CB (2020) Gemelos digitales y su evolución en la industria. Recimundo 4(4):300–308

    Article  Google Scholar 

  33. Berisha-Gawlowski A, Caruso C, Harteis C (2021) The concept of a digital twin and its potential for learning organizations. Digital transformation of learning organizations, pp 95–114

  34. Yan MR, Hong LY, Warren K (2021) Integrated knowledge visualization and the enterprise digital twin system for supporting strategic management decision. Management Decision

  35. Monteiro ACB, França RP, Estrela VV, Iano Y, Khelassi A, Razmjooy N (2018) Health 4.0: applications, management, technologies and review. Personalized Med 5(6). https://doi.org/10.26415/2572-004X-vol2iss1p262-276

  36. Bause M, Esfahani BK, Forbes H, Schaefer D (2019) Design for health 4.0: Exploration of a new area. Proceedings of the design society: international conference on engineering design. 1(1), 887–896. Cambridge University Press. https://doi.org/10.1017/dsi.2019.93

  37. García L, Vergara L, García Pereira A, Holdorf Lopez M (2014) Estado da arte em wearables para saúde. https://repositorio.uca.edu.ar/handle/123456789/7964

  38. Barajas-Ochoa A, Ponce-Horta AM (2018) Reconocer Los errores diagnósticos, un paso necesario para abordarlos. Salud pública De Andom 60:109–110. https://doi.org/10.21149/8418

    Article  Google Scholar 

  39. T-Systems (2016) Big data y salud: Predicción de enfermedades. Recuperado el 6 de octubre de 2022 de: https://www.t-systemsblog.es/big-data-y-salud-prediccion-de-enfermedades/

  40. Biundo E, Pease A, Segers K, de Groote M, d’Argent T, Schaetzen ED (2020) The socio-economic impact of AI in healthcare. Deloitte & MedTech Europe. https://www.medtecheurope.org/wp-content/uploads/2020/10/mte-ai_impact-in-healthcare_oct2020_report.pdf

  41. Lopez V, Akundi A (2022) A Conceptual Model-based Systems Engineering (MBSE) approach to develop Digital Twins, 2022 IEEE International Systems Conference (SysCon), Montreal, QC, Canada, 2022, pp. 1–5, https://doi.org/10.1109/SysCon53536.2022.9773869

  42. Aguilar-Ramirez JE, Marmolejo-Saucedo JA, Rodriguez-Aguilar R (2022) Digital twins and blockchain: Empowering the supply chain. In Intelligent Computing & Optimization: Proceedings of the 4th International Conference on Intelligent Computing and Optimization 2021 (ICO2021) 3 (pp. 450–456). Springer International Publishing

  43. Rodríguez-Aguilar R, Marmolejo-Saucedo JA (2020) Conceptual framework of Digital Health Public Emergency System: digital twins and multiparadigm simulation. EAI Endorsed Transactions on Pervasive Health and Technology 6(21):e3–e3

    Article  Google Scholar 

  44. Lozano-Diez J, Marmolejo-Saucedo J, Rodriguez-Aguilar R (2020) Designing a resilient supply chain: an approach to reduce drug shortages in epidemic outbreaks. EAI Endorsed Transactions on Pervasive Health and Technology, 6(21), e2

  45. Barbieri G, Bertuzzi A, Capriotti A et al (2021) A virtual commissioning based methodology to integrate digital twins into manufacturing systems. Prod. Eng. Res. Devel. 15, 397–412 (2021). https://doi.org/10.1007/s11740-021-01037-3

  46. Brecher C, Dalibor M, Rumpe B, Schilling K, Wortmann A (2021) An Ecosystem for Digital Shadows in Manufacturing. Procedia CIRP, 104, pp. 833–838, https://doi.org/10.1016/j.procir.2021.11.140

  47. Paredis R, Vangheluwe H. Exploring a Digital Shadow Design Workflow by Means of a Line Following Robot Use-Case, 2021 Annual Modeling and, Conference S (2021) (ANNSIM), Fairfax, VA, USA, 2021, pp. 1–12, https://doi.org/10.23919/ANNSIM52504.2021.9552143

  48. Bergs T, Gierlings S, Auerbach T, Klink A, Schraknepper D, Augspurger T (2021) The concept of digital twin and digital shadow in manufacturing. Procedia CIRP 101:81–84

    Article  Google Scholar 

  49. Nikula RP, Paavola M, Ruusunen M, Keski-Rahkonen J (2020) Towards online adaptation of digital twins. Open Eng 10(1):776–783

    Article  Google Scholar 

  50. Hoerl AE, Kennard RW (1970) Ridge Regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67

    Article  Google Scholar 

  51. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J Roy Stat Soc 58(1):267–288

    MathSciNet  Google Scholar 

  52. Zou H, Hastie T (2005) Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), vol. 67, no. 2, pp. 301–20. JSTOR, http://www.jstor.org/stable/3647580

  53. Breiman L (2001) Random Forests Machine Learning 45(1):5–32

    Article  Google Scholar 

  54. Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller KR (2021) Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE, 109(3), 247–278

  55. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

  56. Naimi AI, Balzer LB (2018) Stacked generalization: an introduction to super learning. Eur J Epidemiol 33:459–464

    Article  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.

Author information

Authors and Affiliations

  1. Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Augusto Rodin 498, Ciudad de México, 03920, México

    Román Rodríguez-Aguilar

  2. Facultad de Ingeniería, Universidad Nacional Autónoma de México, Ciudad de México, México

    José-Antonio Marmolejo-Suacedo

  3. Instituto Mexicano del Seguro Social (IMSS), México, México

    Miriam Rodríguez-Aguilar

  4. Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE), México, México

    Liliana Marmolejo-Saucedo

Authors
  1. Román Rodríguez-Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José-Antonio Marmolejo-Suacedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miriam Rodríguez-Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liliana Marmolejo-Saucedo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Data curation, R.R. and J.A.M., Writing (Original draft preparation), M.R. and L.M.; Conceptualization, R.R., J.A.M., M.R. and L.M.; Methodology, R.R. and J.A.M.; Reviewing and Editing, R.R., J.A.M., M.R. and L.M.

Corresponding author

Correspondence to Román Rodríguez-Aguilar.

Ethics declarations

Ethical Approval

Not applicable.

Competing Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodríguez-Aguilar, R., Marmolejo-Suacedo, JA., Rodríguez-Aguilar, M. et al. Machine Learning for Digital Shadow Design in Health Insurance Sector. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-023-02289-2

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-023-02289-2

Keywords

Search

Navigation