Machine learning in healthcare strategic management: a systematic literature review

Abstract

PurposeThis study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.Design/methodology/approach The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.Findings Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.Research limitations/implications The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.Practical implications The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.Originality/value By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

Critical Review on the Contribution of Machine Learning to Health Science

“…Its algorithms can discover sophisticated patterns in these data sets that would be difficult to find using classical statistical methods. In addition, it has many applications, including diagnosis, treatment, and risk prediction [6][7][8] . Systematic literature reviews have identified over 10,000 ML algorithms used in healthcare, with the most common being neural networks, support vector machines, and random forests or decision trees 9 .…”

“…ML has significantly contributed to health science research by enabling the analysis of complex data sets 3,6,8 .…”

“…Its algorithms have been applied in various healthcare settings, including disease diagnosis, hospital management, and patient footfall management 3,6 . The increasing availability of data and computing power has made ML a viable approach to faster, more efficient healthcare delivery 9 .…”

“…The field of precision medicine has also seen significant applications of ML, using rich patient information to precisely target interventions 8 . ML has the potential to improve health outcomes and increase healthcare efficiency by analyzing massive datasets and extracting hidden information and patterns from them 6,9 .…”