Highliting the Importance of Business Intelligence Maturity Models in the Healthcare Sector

Authors

  • João Silva Porto Accounting and Business School, Polytechnic Institute of Porto (PORTUGAL)
  • Célia Talma Gonçalves Porto Accounting and Business School, Polytechnic Institute of Porto, CEOS.PP, LIACC (PORTUGAL)
  • Catarina Félix Universidade Portucalense, Research on Economics, Management, and Information Technologies - REMIT; LIAAD - INESC TEC (PORTUGAL)

DOI:

https://doi.org/10.34630/bobcatsss.vi.4972

Keywords:

Business Intelligence, Maturity Models, Healthcare, Healthcare Business Intelligence Maturity Models

Abstract

The digital transformation associated with the huge volume of data that healthcare organizations deal with, nowadays, are on the basis of transforming this complex knowledge-driven industry to transform data into knowledge. The healthcare industry requires comprehensive models to help identifying the priorities to implement a Business Intelligence (BI) solution.

Business Intelligence can help organizations make better decisions by showing present and historical data within their business context.

In the recent digital transformation, the decision process is supported through data analysis. Established as a common denominator, small and large organizations transform their data into valuable knowledge and powerful capabilities to help them become more data-driven organizations. To become a data-driven organization means that organization leaders/managers make decisions supported on the data that is setting in the organization. The decision support systems together with technology helps the decision makers to gather insights to enhance organizations.

This paper presents and highlights a comprehensive review of existing healthcare maturity models and tries to identify the main features of the presented models as well as the common success factors in adopting a Business Intelligence Maturity Model in Healthcare organizations.

References

Analytics, H. (2018). 2018 Adoption Model for Analytics Maturity Overview. Retrieved from https://www.himssanalytics.org/presentation/2018-adoption-model-analytics-maturity-overview

Analytics, H. (2018). 2018 EMRAM Overview and Criteria Update. Retrieved from https://www.himssanalytics.org/2018-emram-overview-and-criteria-update

Ashrafi, N., Kelleher, L., & Kuilboer, J.-P. (2014). The Impact of Business Intelligence on Healthcare Delivery in the USA. Interdisciplinary Journal of Information, Knowledge, and Management, 9, 117– 130. https://doi.org/10.28945/1993

Barros & Almeida, 2017

Binoti, J. (2019). Utilização de Business Intelligence no Apoio à Tomada de Decisão e Estratégia das Organizações de Saúde [Instituto Universitário de Lisboa]. Retrieved from http://hdl.handle.net/10071/20053

Brooks, P., El-Gayar, O., & Sarnikar, S. (2013). Towards a Business Intelligence Maturity Model for Healthcare. In 46th Hawaii International Conference on System Sciences (pp. 3807–3816).

Carvalho, J. V., Rocha, Á., & Abreu, A. (2016). Maturity Models of Healthcare Information Systems and Technologies: A Literature Review. Journal of Medical Systems, 40(6), 131. https://doi.org/10.1007/s10916-016-0486-5

Correia & Silva, L. (2019). O Impacto da Aplicação de Modelos de Maturidade nas Áreas Clínicas do SNS. Universidade do Minho.

Côrte-Real, N. (2011). Avaliação da Maturidade da Business Intelligence nas Organizações [Universidade Nova de Lisboa]. Retrieved from http://hdl.handle.net/10362/7477

Foshay, N., & Kuziemsky, C. (2014). Towards an Implementation Framework for Business Intelligence in Healthcare. International Journal of Information Management, 34(1), 20–27. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2013.09.003

Garets, D., & Davis, M. (2016). Electronic Medical Records vs. Electronic Health Records: Yes, There Is a Difference.

Gomes, J., & Romão, M. (2018). Information System Maturity Models in Healthcare. Journal of Medical Systems, 42. https://doi.org/10.1007/s10916-018-1097-0

Hogan, D. (2013). Incorporating analytics into EMRs. Pushes for quality care are inspiring a deeper delving into analytics. Health Management Technology, 34(8), 15.

Ivan, M., & Velicanu, M. (2015). Healthcare Industry Improvement with Business Intelligence. Informatică Economică, 19, 81–89.

Kaye, R., Kokia, E., Shalev, V., Idar, D., & Chinitz, D. (2010). Barriers and success factors in health information technology: A practitioner’s perspective. Journal of Management & Marketing in Healthcare, 3(2), 163–175. https://doi.org/10.1179/175330310X12736577732764

Leite, N. (2018). Business intelligence no suporte à decisão: soluções open source. IPC - ISCAC - Instituto Superior de Contabilidade e Administração de Coimbra.

Marc Holland. (2009). The Future of Business and Clinical Intelligence in the U.S. Provider Market.

Mettler, T., & Vimarlund, V. (2009). Understanding business intelligence in the context of healthcare. Health Informatics Journal, 15(3), 254–264. https://doi.org/10.1177/1460458209337446

Muraina, I., & Ahmad, A. (2012). Healthcare Business Intelligence: The Case of University’s Health Center. Internacional Conference on E-CASE & E-TECH.

Pastori, E. (2012). Nível de Maturidade em Business Intelligence [Universidade do Vale do Rio dos Sinos]. Retrieved from http://www.repositorio.jesuita.org.br/handle/UNISINOS/6720

Rajterič, I. H. (2010). Overview of Business Intelligence Maturity Models. Management, Vol. 15, 20.

Rocha, Á. (2011). Evolution of Information Systems and Technologies Maturity in Healthcare. International Journal of Healthcare Information Systems and Informatics, 9.

Rocha, Álvaro & Vasconcelos, J. (2004). Os Modelos de Maturidade na Gestão dos SI. Revista Da Faculdade de Ciência e Tecnologia, 15. https://doi.org/http://dx.doi.org/10.34620/eduser.v2i2.24

Sanders, Burton & Protti (2018)

Sezões, Oliveira & Batista (2006)

Sharma, B. (2008). Electronic Healthcare Maturity Model (eHMM). Retrieved from http://www.quintegrasolutions.com/eHMM White Paper.pdf

Snowdon, A., & Sanders, D. (2019). The Healthcare Analytics Adoption Model: A Roadmap to Analytic Maturity. Retrieved from https://www.healthcatalyst.com/insights/healthcare-analytics-adoption-model-roadmap- analytic-maturity

Sousa, M. da C. R. (2018). Business Intelligence - Estudo Sobre a Utilização e Impacto na Tomada de Decisão Médica [Universidade do Porto]. Retrieved from https://sigarra.up.pt/fep/pt/pub_geral.show_file?pi_doc_id=175195

Watson, H., & Wixom, B. (2007). The Current State of Business Intelligence. Computer, 40, 96–99. https://doi.org/10.1109/MC.2007.331

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Published

2023-02-09

How to Cite

Silva, J., Gonçalves, C. T. ., & Félix, C. . (2023). Highliting the Importance of Business Intelligence Maturity Models in the Healthcare Sector. Bobcatsss, 148–160. https://doi.org/10.34630/bobcatsss.vi.4972