Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques

Leveraging natural language processing to elucidate real-world clinical decision-making paradigms: A proof of concept study

BACKGROUND

Understanding how clinicians arrive at decisions in actual practice settings is vital for advancing personalized, evidence-based care. However, systematic analysis of qualitative decision data poses challenges.

METHODS

We analyzed transcribed interviews with Hebrew-speaking clinicians on decision processes using natural language processing (NLP). Word frequency and characterized terminology use, while large language models (ChatGPT from OpenAI and Gemini by Google) identified potential cognitive paradigms.

RESULTS

Word frequency analysis of clinician interviews identified experience and knowledge as most influential on decision-making. NLP tentatively recognized heuristics-based reasoning grounded in past cases and intuition as dominant cognitive paradigms. Elements of shared decision-making through individualizing care with patients and families were also observed. Limited Hebrew clinical language resources required developing preliminary lexicons and dynamically adjusting stopwords. Findings also provided preliminary support for heuristics guiding clinical judgment while highlighting needs for broader sampling and enhanced analytical frameworks.

CONCLUSIONS

This study represents the first use of integrated qualitative and computational methods to systematically elucidate clinical decision-making. Findings supported experience-based heuristics guiding cognition. With methodological enhancements, similar analyses could transform global understanding of tailored care delivery. Standardizing interdisciplinary collaborations on developing NLP tools and analytical frameworks may advance equitable, evidence-based healthcare by elucidating real-world clinical reasoning processes across diverse populations and settings.

Artificial Intelligence to Diagnose Complications of Diabetes

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes.Artificial intelligence is advantageous because it can handle large and complex datasets from a variety of sources. With each additional type of data incorporated into a clinical picture of a patient, the calculation becomes increasingly complex and specific. Artificial intelligence is the foundation of emerging medical technologies; it will power the future of diagnosing diabetes complications.

Improving Clinical Practice Through Patient Registries in Allergy and Immunology

Patient registries are a mechanism for collecting data on allergic and immunologic diseases that provide important information on epidemiology and outcomes that can ultimately improve patient care. Key criteria for establishing effective registries include the use of a clearly defined purpose, identifying the target population and ensuring consistent data collection. Registries in allergic diseases include those for diseases such as inborn errors of immunity (IEI), food allergy, asthma and anaphylaxis, pharmacological interventions in vulnerable populations, and adverse effects of pharmacologic interventions including hypersensitivity reactions to drugs and vaccines. Important insights gained from patient registries in our field include contributions in phenotype and outcomes in IEI, the risk for adverse reactions in food-allergic patients in multiple settings, the benefits and risk of biologic medications for asthma during pregnancy, vaccine safety, and the categorization and genetic determination of risk for severe cutaneous adverse reactions to medications. Impediments to the development of clinically meaningful patient registries include the lack of funding resources for registry establishment and the quality, quantity, and consistency of available data. Despite these drawbacks, high-quality and successful registries are invaluable in informing clinical practice and improving outcomes in patients with allergic and immunological diseases.