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Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume and custom production, making conventional methods of machine qualification and health monitoring challenging to implement. Machine health has been difficult to separate from the effects of design and process decisions, and therefore inferring machine health through part quality has been similarly complicated.

This conceptual paper proposes a framework for monitoring machine health by monitoring two types of witness parts, in the form of witness builds and witness artifacts, to provide sources of data for potential indicators of machine health.

The proposed conceptual framework with witness builds and witness artifacts permits the implementation into AM techniques to monitor machine health according to part quality. Subsequently, probabilistic models can be used to optimize machine costs and repairs, as opposed to statistical approaches that are less ideal for AM. Bayesian networks, hidden Markov models and Markov decision processes may be well-suited to accomplishing this task.

Though variations of witness builds have been created for use in AM to measure build quality and machine capabilities, the literature contains no previously proposed framework that permits the evaluation of machine health and its influence on quality through a combination of witness builds and witness artifacts, both of which can be easily added into AM production.

Telemedicine has been advancing for decades and is more indispensable than ever in this unprecedented time of the COVID-19 pandemic. As shown, eHealth appears to be effective for routine management of chronic conditions that require extensive and repeated interactions with healthcare professionals, as well as the monitoring of symptoms and diagnostics. Yet much needs to be done to alleviate digital inequalities that stand in the way of making the benefits of eHealth accessible to all. The purpose of this paper is to explore the recent shift in healthcare delivery in response to the COVID-19 pandemic towards telemedicine in healthcare delivery and show how this rapid shift is leaving behind those without digital resources and exacerbating inequalities along many axes.

Because the digitally disadvantaged are less likely to use eHealth services, they bear greater risks during the pandemic to meet ongoing medical care needs. This holds true for both medical conditions necessitating lifelong care and conditions of particular urgency such as pregnancy. For this reason, the authors examine two case studies that exemplify the implications of differential access to eHealth: the case of chronic care diseases such as diabetes requiring ongoing care and the case of time-sensitive health conditions such as pregnancy that may be compromised by gaps in continuous care.

Not only are the digitally disadvantaged more likely to belong to populations experiencing greater risk – including age and economic class – but they are less likely to use eHealth services and thereby bear greater risks during the pandemic to meet ongoing medical care needs during the pandemic.

At the time of writing, almost 20% of Americans have been unable to obtain medical prescriptions or needed medical care unrelated to the virus. In light of the potential of telemedicine, this does not need to be the case. These social inequalities take on particular significance in light of the COVID-19 pandemic.

In light of the COVID-19 virus, ongoing medical care requires exposure to risks that can be successfully managed by digital communications and eHealth advances. However, the benefits of eHealth are far less likely to accrue to the digitally disadvantaged.