Tissue spheroids consist of a three-dimensional model of cells which is capable of imitating the complicated composition of healthy and unhealthy human tissue. Due to their unique properties, they can bring innovative solutions to tissue engineering and regenerative medicine, where they can be used as building blocks for the formation of organ and tissue models used in drug experimentation. Considering the rapid transformation of the health industry, it is crucial to assess the research dynamics of this field to support the development of innovative applications. In this research, a scientometric analysis was performed as part of a Competitive Technology Intelligence methodology, to determine the main applications of tissue spheroids. Papers from Scopus and Web of Science published between 2000 and 2019 were organized and analyzed. In total, 868 scientific publications were identified, and four main categories of application were determined. Main subject areas, countries, cities, authors, journals, and institutions were established. In addition, a cluster analysis was performed to determine networks of collaborations between institutions and authors. This article provides insights into the applications of cell aggregates and the research dynamics of this field, which can help in the decision-making process to incorporate emerging and innovative technologies in the health industry.

Tissue spheroids represent an innovative solution for tissue engineering and regenerative medicine. They constitute an in vitro three-dimensional cell culture model capable of mimicking the complex composition of a native tissue on a micro-scale; this model can function as a building block and be assembled into larger tissue constructs. Due to the potential tissue spheroids have for the evolution of the health industry, there is a need to assess the research dynamics of this field. Thus far, there have been no studies on their use as building blocks. To fill this gap, a study was performed to characterize the evolution of research where tissue spheroids were used as building blocks to generate tissue constructs. A scientometric analysis of the literature regarding tissue spheroid technologies was developed by quantification of bibliometric performance indicators. For this purpose, articles published during the period January 1, 2015 – December 31, 2021, from the Scopus database were organized and analyzed. The main subject areas, countries, cities, journals, institutions, and top-cited articles as well as the types of techniques, cells, culture time, and principal applications were identified. This research supports the definition and growth of research and development strategies for new technologies such as tissue spheroids.

In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.INDEX TERMS Data-driven, healthcare, scientometric, statistical process monitoring.