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The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.

A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.

From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.

The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.

To the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

The purpose of this paper is to evaluate a continuous improvement project (CIP) at a Mexican university designed to increase engineering graduate student loyalty.

A plan-do-check-act problem-solving methodology was implemented, and a SERVQUAL survey was conducted on 67 master’s engineering students.

Five factors were found to affect student loyalty: facility cleanliness; faculty teaching skills; evening student services; master’s degree student management roles at work; and master’s degree students’ ages. After the implementation of the improvement and control actions, there was a 7.7% increase in the engineering master’s degree students’ loyalty scores.

However, there were several research limitations: data availability (such as student loyalty, student satisfaction and a small master’s degree student population size) and factors outside the CIP’s scope (such as the country’s economic situation, university rankings, master’s programme accreditations and COVID-19).

The findings from this research study could be used by other higher education institutions (HEIs)to improve student loyalty and as a reference when conducting similar studies in other service organisations such as hospitals and hotels.

This research work took a different approach in assessing student satisfaction and student loyalty in a HEI by using the SERVQUAL survey as the data collection instrument for conducting CIP.