Application of unsupervised machine learning to identify areas of blood product wastage in transfusion medicine.

Background: Wastage of blood products can be a significant cost to blood banks. However, the cause of wastage is often complex and makes it difficult to determine wastage-associated factors. Machine learning techniques may be useful tools to investigate these complex associations.

The successful implementation of an automated institution-wide assessment of hemoglobin and ABO typing to dynamically estimate red blood cell inventory requirements.

Background: Blood bank inventories must balance adequate supply with minimal outdate rates. The day-to-day practice of ordering red blood cell (RBC) inventory usually involves manually comparing current inventory levels with predetermined thresholds calculated from historical usage and ordering the difference. To date, there have been no published methods for ordering RBC inventory based on laboratory characteristics of admitted patients.

Reflex and reflective testing strategies for early detection of pituitary dysfunction.

Objective: The clinical presentation of pituitary dysfunction is typically variable and may often be insidious, resulting in delayed diagnosis by up to decades. The complexity of presentation and difficulty in pattern recognition of first line hormone tests result in challenges in early diagnosis of this condition. The aim of this study was to determine the impact of reflective testing and interpretive commenting on the early detection and management of such cases from primary care.