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Healthcare has undergone multiple phases in gaining understanding, accepting and implementing quality and safety, with the last 3 decades being crucial and decisive in making progress. During that time, safety has always been quoted along with quality, but the cost of error in healthcare (both in human lives and monetary cost) has been continuing to rise.

This article discusses the authors’ expert perspective in comparison to the industry’s research and practice outputs.

Healthcare has not yet defined quality and safety. This is allowing the misconception that already established quality management systems (QMSs) are fit for safety purposes as well. Even though aviation has acted as a paradigm for healthcare, further alignment in embedding safety management systems (SMS) has yet to be realised.

In this paper, the distinct nature of safety and its detachment of quality is being discussed, along with the need for clear and safety specific processes. Setting common language is the first step in establishing appropriate safety processes within SMSs, operating in tandem with QMSs, to promote patient safety successfully.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.