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Tuesday, July 16, 2013
Learn from the analytics group at Stanford Hospital & Clinics as they share the components that have proven critical to their success leading a data-driven approach to improving population health management. Core to their strategy is an analytics platform that pulls data from multiple sources to improve outcomes, reduce costs, and enhance the patient experience by implementing interventions based on a team based care approach. Fundamental to their strategy, Stanford gathered information to allow their
The use of clinical analytics is a staple in the healthcare industry. So where is the ROI for investing in real-time analytics? It is learning to use and deploy real-time clinical data for Clinical Decision Support (CDS) for improved diagnostic and therapeutic interventions. It is defining and trending operational measures on the fly to improve processes. It is monitoring and measuring Stage 1 and 2 Meaningful Use compliance, as it happens. It is even creating
In 2010, Dignity Health launched the initiative to develop the Ambulatory Information Management (AIM) program to address GPRO reporting and provider profiling analytics. This session discusses the successes and challenges in bringing together big data from 20+ different Ambulatory & Hospital clinical systems to support the vision. How the solution expanded to support clinical analytics for Meaningful Use, PQRS, Point-of-Care Clinical Decision Support, Surgical Site Infection Reporting, Patient Centered Medical Home (PCMH), and population health programs (Clinical Integration and Accountable Care Organization) analytical reporting. Gain first hand information on the value of an enterprise clinical analytical platform, challenges encountered and addressed: data standards, data quality, privacy, patient attribution, technical, and Provider workflows, governance and organization model, and Provider involvement. Learn how an enterprise-wide clinical analytics strategic vision is supported through development of an agile tactical solution.
Wednesday, July 17, 2013
An emerging function of clinical analytics is to match patients to the right services at the point of need. One method to perform this matching is to use predictive analytics. We will share how Allina Health has employed a strategy of quasi-real-time risk score calculations based on various data sources available in an enterprise data warehouse to pair patients with those services, including the development and implementation of a readmission predictive model. Then, we will
Advances in EHR adoption and analytics have created enormous opportunities in the field of healthcare epidemiology. In this session, we will explore predictive modeling, clinical decision support, and analytics that combine molecular with large-scale clinical data to manage epidemiological challenges in novel ways. Examples will include rationalizing test usage through risk-based testing, tracking the movement of pathogens through a hospital system, identifying patients at risk for recurrent contagious intestinal infections and community-wide surveillance for syndromes such as flu, pertussis and asthma. The potential for these new technologies to reduce costs and improve care will be highlighted.