The Predictive Analytics World for Healthcare program will feature sessions and case studies across Healthcare Business Operations and Clinical applications so you can witness how data science and machine learning are employed at leading enterprises and resulting in improved outcomes, lower costs, and higher patient satisfaction.

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raditional methods of streamlining patient flow through lean process events, dashboards, bed meetings based on retrospective data and excessive provider alerts - while filled with good intention - are often not sustainable, nor do they solve the actual challenges with managing inpatient bed capacity. Streamlining patient flow requires 24/7 front-line and system-wide visibility into real-time data. Leveraging this information empowers frontline staff and providers to make impactful decisions in real-time with greater confidence and delivers some significant results.

This presentation will discuss how the delivery of actionable information through the use of predictive and prescriptive analytics will enable your front-line and leadership teams to efficiently:

• Predict discharges and admissions by unit by time of day
• Uncover admission bottlenecks by service and level of care
• Highlight high-impact transfers

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Even after significant EHR investment, it is imperative to invest in predictive/prescriptive operational management and analytics tools particularly in the operating room.

Join this session as Matt Ruby, Business Operations Director Surgical Services at Northwestern Memorial Hospital, shares how Northwestern is leveraging predictive and prescriptive analytic tools and adopted a culture of data transparency to achieve positive results and increased ROI.

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In many healthcare organizations, the experience of managing through COVID-19 uncertainty has been a catalyst to reevaluate current planning tools and processes to improve planning efficiency and agility. In this session, we’ll highlight the planning trends that have emerged in our industry and examine what modern planning looks like in industries outside of healthcare to project what our future might look like.

A part of this forward-looking session will also explore how data science is evolving and becoming more integrated into planning tools and processes through machine learning and more predictive analytics.

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As the world begins to emerge from the first global pandemic in over 100 years which has exposed the vulnerabilities of the global healthcare system, it has never been more critical to develop AI enabled and connected devices. Developing essential systems that sense, process, store, mine and analyze will pave the way for the next generation of secure, smart healthcare. This conference track will provide the latest developments in tool sets and best practices for applying big data analytics with machine learning to achieve seamless integration for the IoT of healthcare.

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The analytics pathway is often described as moving upward in complexity from descriptive to diagnostic to predictive to prescriptive, with each step adding more complexity and business value. For health systems, it starts with building a team with the right mix of expertise, including data architects, operational champions, and subject matter experts. Panelists will discuss lessons learned creating the infrastructure to develop and deploy predictive analytics solutions in both clinical and business settings.

Delayed care leads to poorer outcomes and higher healthcare costs. Understanding when patients seek care and promoting early intervention can enable physician success. Providers must transition away from episodic care towards prevention, but doing so requires data analytic technologies that enable population health.

The Predictive Analytics Summit hosted by HealthITAnalytics will examine how technology can enable data-driven interventions that promote population health. Artificial intelligence, big data, and predictive analytics all play a role in helping providers close care gaps and improve clinical outcomes.