Advances in natural language processing and text mining provide a powerful approach to understanding trending themes in the health care management literature.
The aim of this study was to introduce machine learning, particularly text mining and natural language processing, as a viable approach to summarizing a subset of health care management research. The secondary aim of the study was to display the major foci of health care management research and to summarize the literature’s evolution trends over a 20-year period.
Article abstracts (N = 2,813), from six health care management journals published from 1998 through 2018 were evaluated through latent semantic analysis, topic analysis, and multiple correspondence analysis.
Using latent semantic analysis and topic analysis on 2,813 abstracts revealed eight distinct topics. Of the eight, three leadership and transformation, workforce well-being, and delivery of care issues were up-trending, whereas organizational performance, patient-centeredness, technology and innovation, and managerial issues and gender concerns exhibited downward trending. Finance exhibited peaks and troughs throughout the study period. Four journals, Frontiers of Health Services Management, Journal of Healthcare Management, Health Care Management Review, and Advances in Health Care Management, exhibited strong associations with finance, organizational performance, technology and innovation, managerial issues and gender concerns, and workforce well-being. The Journal of Health Management and the Journal of Health Organization and Management were more distant from the other journals and topics, except for delivery of care, and leadership and transformation.
There was a close association of journals and research topics, and research topics evolved with changes in the health care environment.
As scholars develop research agendas, focus should be on topics important to health care management practitioners for better informed decision-making.