Evaluation of a New Digital Automated Glycemic Pattern Detection Tool

Digital microbiology

BACKGROUND

Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

OBJECTIVE

This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

SOURCES

We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

CONTENT

We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

IMPLICATIONS

We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

A Review of Emerging Technologies in Diabetes Management for Multiple-Dose Insulin-Injecting Patients With Type 2 Diabetes Who Self-monitor Blood Glucose

Objective: The management of diabetes mellitus requires a precise interpretation of blood glucose (BG) data by patients and providers and is increasingly associated with a need for medical technologies that aid in achieving patient-specific outcomes while making the process convenient. This review aims to summarize the current landscape in diabetes management technology, focusing specifically on devices that assist with pattern management in patients with type 2 diabetes (T2DM) who are on multiple-dose insulin regimens. Data Sources: The authors searched MEDLINE to identify articles from 2007 to 2018 that evaluated technologies for BG pattern management and diabetes monitoring. Additional references were generated through review of identified literature citations. Article selection was based on mutual agreement for inclusion. Data Selection and Data Extraction: Relevant articles were defined as English-language articles, describing technologies that assist with diabetes management in insulin-injecting patients with T2DM. Articles that focused exclusively on type 1 diabetes were excluded. Data Synthesis: The literature search yielded 334 articles, of which 21 were included for synthesis. The current BG monitoring practices emphasize the benefit of the structured self-monitoring of BG approach. Several randomized controlled trials conclude that the available technology aids in comprehensive data collection and facilitates communication between patients and providers. Digitally enabled “smart” devices are valuable tools that may help improve outcomes while providing a flexible, personalized approach. Conclusions: Integration of digital technology with diabetes management allows for accurate collection and analysis of data. Emergence of digital tools promotes a comprehensive, precise, and objective approach to glucose monitoring and encourages patient-provider collaborations.