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The Liver Meeting 2020
Postgraduate Course - Part 1 Paradigm Shifts in He ...
Postgraduate Course - Part 1 Paradigm Shifts in Hepatology: Rethinking Conventional Wisdom
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Video Summary
Quality measurement in healthcare encompasses process and outcome measures to assess the effectiveness of care provided. Indicators like fibroscan for liver fibrosis or scheduling colonoscopies on time are examples of process measures, while reduced mortality or fewer complications represent outcome measures. However, measuring quality is complex due to various indicators structured differently. Data classification following the Donabedian model and choosing meaningful indicators are essential. Analyzing collected data through statistical interrogation helps understand clinic performance and areas for improvement. Quality metrics aim to reduce unwarranted variation, ensure appropriate care, and enhance efficiency.<br /><br />In addressing liver diseases, understanding the gut microbiome's role is crucial. Specific pathogenic microbes can contribute to conditions like non-alcoholic steatohepatitis (NASH). The "bad bug hypothesis" emphasizes targeting such microbes for improved treatment. In drug-induced liver injury (DILI), identifying associations like HLA genotypes can predict and prevent adverse reactions. While steroids may not treat DILI effectively, stopping the offending drug is crucial. N-acetylcysteine and antioxidants can aid in reducing hepatocyte stress. Utilizing genotyping and ongoing research can enhance causality assessment and improve outcomes in DILI cases, leading towards personalized medicine approaches.
Keywords
Quality measurement in healthcare
Process measures in healthcare
Outcome measures in healthcare
Donabedian model
Quality metrics
Data classification
Statistical interrogation
Clinic performance analysis
Liver diseases
Gut microbiome
Non-alcoholic steatohepatitis (NASH)
Drug-induced liver injury (DILI)
HLA genotypes
N-acetylcysteine
Personalized medicine
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