Healthcare organizations are looking to the promise of artificial intelligence (AI) and advanced analytics to position for next-generation healthcare. The growing sophistication of health IT infrastructures is opening new doors to strategies that can help consolidate costs, generate new revenue streams and solve complex healthcare challenges. Data quality sits at the heart of these initiatives as the insights derived from AI and analytics strategies are only as good as the information feeding them. Across the healthcare continuum, stakeholders collect data in a variety of ways—through EHRs, patient-generated data and unstructured notes to name a few—and in multiple formats, such as clinical, claims and reference data. The challenges of bringing all this information together in a complete and accurate way to inform analytics and AI initiatives is not lost on today’s healthcare executives. This presentation will explore the downstream impact of poor data quality as well as three key steps any healthcare organization should take to establish a foundation of accurate and reliable data. Presenters will also explore the potential of AI-powered solutions, such as clinical natural language processing and machine learning, and present common use cases for applying AI to ensure accurate documentation for risk adjustment, identify gaps in care for quality measure reporting and generate predictive analytics.