Coding of unstructured clinical free-text to produce interoperable structured data is essential to improve direct care, support clinical communication and to enable clinical research.However, manual clinical coding is difficult and time consuming, which motivates the development and use of natural language processing for automated coding. This work evaluates the quality and consistency of both manual and automated clinical coding of diagnoses from hospital outpatient letters. Using 100 randomly selected letters, two human clinicians performed coding of diagnosis lists to SNOMED CT. Automated coding was also performed using IMO's Concept Tagger. A gold standard was constructed by a panel of clinicians from a subset of the annotated diagnoses. This was used to evaluate the quality and consistency of both manual and automated coding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2) a qualitative metric agreed upon by the panel of clinicians. Correlation between the two metrics was also evaluated. Comparing human and computer-generated codes to the gold standard, the results indicate that humans slightly out-performed automated coding, while both performed notably better when there was only a single diagnosis contained in the free-text description. Automated coding was considered acceptable by the panel of clinicians in approximately 90% of cases.

Trust is ubiquitous, but the understanding, building, and retaining of trust has become a key challenge of our time, with the trust narrative evolving across a dynamic duality. On one hand, concerns around data privacy, security, and the ethical development of artificial intelligence (AI) abound; on the other, the "art of the possible" has been demonstrated through the positive purposes to which data and technology have been applied. Another dynamic has also evolved recently: data literacy. Over the last year, our everyday lives have been dominated by data, heightening levels of awareness, and helping move beyond data ubiquity to make analytics more ubiquitous too. But as people understand more about how organizations are using their data, they are increasingly concerned, bringing trust center stage.

During the Covid-19 crisis, hospitals and healthcare companies have been rushed off their feet in trying to take care of affected patients. Alongside this has been the goal to find effective and safe treatments for the virus, which is still ongoing. However, digital technologies have continued to disrupt the healthcare sector, increasing efficiency and visibility, and AI is a key example. "Healthcare is a discipline perfectly suited to reap the rewards that even the most basic task-based AI can provide," said James Norman, chief information officer of healthcare at Dell Technologies. "Globally, the demand for healthcare is increasing at an unprecedented rate – far outstripping the supply of healthcare professionals trained globally. "While obviously true in the developing world, across Europe an ageing population and a rise in chronic disease is causing unprecedented strain on resources." Norman went on to explain how AI has aided pathologists in executing round-the-clock medical results, proving to be useful for treating cancer cases. "In Europe, the number of cancer cases continues to rise while the number of trained pathologists – those tasked with spotting cancerous cells – declines," he continued. "Traditional pathology requires that a GP take a tissue sample from a patient, send it to a lab for analysis in a lab, where it's manually placed on a glass slide to be examined, by a human pathologist, under a microscope.