In parallel, AI that asserts recommendations through the process models continues to learn. Each iteration becomes more sensitive and better tuned to the affected cohort. When AI models assert insights directly to a process model, they can evolve in response to new clinical research without the need to change workflows to accommodate new iterations. Knowledge backlogs can be significantly reduced and replaced by an always-evolving, always-learning health system, which may define the future of healthcare. The long-sought goal of value-based care may also become a real possibility.

The U.S. is facing a doctor shortage that is getting worse as an aging population of Baby Boomers lives longer, increasing the demand for medical professionals. But doctors are not the only ones feeling the pinch. Pathologists, scientists who study disease, have also been hit hard, with an overall decline in professionals from 2007 to 2017. "With many senior pathologists expected to retire in the coming years, a'pathologist gap' is likely to increase through 2030," according to a 2018 study by the National Center for Biotechnology Information. David West, co-founder and CEO of digital startup Proscia, said his company is hoping to help pathologists use their time more efficiently.

There is a significant difference between automation and the use of artificial intelligence (AI) in the medtech space. Automation, within a healthcare setting, is defined as the use of hardware and software specifically programmed to save time. AI can be categorized as machine learning, meaning software and hardware working in conjunction to effectively mimic human decision-making -- just much, much faster. AI can learn outside of its programming, and the goal is for the software to make a decision of equivalent quality, compared to a human. The use of automation and AI is integral within the medtech space, as data is becoming increasingly important to manage and understand.

Nurse Florence Nightingale may be most well-known as the British Army's lady with the lamp, assiduously conducting night rounds and attending to the wounded by candle light. But by demonstrating the link between poor sanitary conditions and high mortality rates in hospitals, it was her pioneering use of data collection and visualisation that still resonates today. In 2018, medicine faces a different set of challenges, with longer life expectancies and population growth increasing the number of patients suffering with chronic conditions requiring ongoing care. This has led to the cost of delivering health care increasing faster than GDP and quickly becoming unsustainable. Over 160 years might have passed since Florence Nightingale's day, but addressing these challenges still depends on data.