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Full Agenda – Healthcare 2017
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DAY 1, Monday, October 30, 2017

8:00-8:30am

Registration & Networking Breakfast


8:30-8:35am

Conference Founder Remarks

Eric Siegel
Founding Chair
Predictive Analytics World

8:35-8:50am

Conference Chair Welcome

Jeff Deal
Conference Chair
Predictive Analytics World Heathcare

8:50-9:45am

Keynote
TBA

Dr. George Savage
Co-Founder & Chief Medical Officer
Proteus Digital Health

[ Top of this page ] [ Agenda overview ]


9:45-10:00am

Gold Sponsor Presentation


10:00-10:30am

Exhibits & Morning Coffee Break


10:30-11:15am

Risk Modeling
Case Study: OSF Healthcare System
The Tale of Two Models: Identifying High Risk Patients in an Ambulatory Setting

To be relevant, models must be useful. To be useful they must provide actionable intelligence to end users. This session will highlight two models answering similar questions differently, given their intended audiences and uses. To provide a single risk stratification for primary care, the Advanced Analytics team at OSF Healthcare developed and deployed our Patient Utilization Model. Simultaneously the Advanced Analytics team built a specialized cost model for our CMS ACO patient population to assist our complex care management organization. This session will highlight the utility of each model and address how each provides value.

Chris Franciskovich
Senior Data Scientist
OSF Healthcare

Jason Weinberg
Statistician
OSF Healthcare

[ Top of this page ] [ Agenda overview ]


11:20am-12:05pm

Oncology Analytics
Case Study: Quintiles IMS
Using Deep Learning to Identify the Key Triggers of Initiating Patient First Line Treatment: An Oncology Case Study

Recent breakthrough in deep learning technology has demonstrated superior model performance and witnessed various applications. In oncology patient predictive studies, the medical data present many challenges such as high dimensionality, sparsity, high variability and look alike patient profiles etc., which cause poor model performance. Traditional models also need extensive clinical knowledge to manually design predictors. We construct a deep and wide neural networks model to carry out end-to-end learning. The novel model automatically identifies the key triggers of initiating first line treatment without the need of hand designing features. It also shows consistent accuracy improvement over benchmark methods.

Yilian Yuan
VP Advanced Analytics
Quintiles IMS

[ Top of this page ] [ Agenda overview ]


12:05-1:30pm

Lunch in the Exhibit Hall


1:30-2:15pm

Special Plenary Session
TBA

Dr. John Elder
CEO & Founder
Elder Research, Inc.

2:15-2:35pm

Sponsor Presentation


2:40-3:00pm

Quality Evaluation
Case Study: Public Health Data
Evaluating the Quality of State's Healthcare Using Big Data Analytics

In this talk, BDA are applied to healthcare data that is collected from multiple state-level sources to gain quality insights and apprehend best practices of the field (using new healthcare-specific data tools). The US states are unceasingly pursuing potential improvements to their healthcare's Quality of Service. Recent changes in data sharing provisions, such as the disposition of the recent Affordable Health Care Act (ACA), changed the rules of the game. This multidisciplinary talk examines historical health data from all over the country, assesses the medical QoS for multiple US states using a new healthcare-specific analytical infrastructure, and provides data-driven results.

Feras Batarseh
Research Assistant Professor
George Mason University - George Washington University

3:05-3:25pm

Healthcare Resource Planning
Case Study: Disease Outbreak Simulation
Are We Ready for Zika? Applicability of Healthcare Analytical Systems During Crisis

This project demonstrates and evaluates the use of a simulation method to address a hypothetical scenario where a Zika Virus outbreak occurs near a local healthcare clinic. In this particular proposed situation, the hospital uses a healthcare analytical system to predict the necessary resources to govern the emergency situation. More specifically, based on historical data, a discrete-event simulation of resource scheduling will be estimated, the effect on the wait times examined, and the need for additional nurses and doctors will be predicted. The results of this simulation test will be analyzed, fine-tuned, and used to expand the study to other.

Leyla Zhuhadar
Assistant Professor in Business Data Analytics
Western Kentucky University

[ Top of this page ] [ Agenda overview ]


3:25-3:55pm

Exhibits & Afternoon Break


3:55-4:40pm

Assessing Risk of Antibiotic Resistant Superbugs
Case Study: University of Virginia Medical Center
Predicting Patient Risk of Acquiring Antibiotic Resistant Superbugs from the Environment

The University of Virginia Health System has sustained a multi-year low frequency transmission of antibiotic resistant environmental organisms with a unique, common genetic signature. In order to identify patients at greater risk of infection and describe the clinical risk factors for acquisition we developed and deployed a patient risk model with an AUC 0.73. Additionally, we used this risk model to examine the risk of patient acquisition of antibiotic resistant organisms from the environment using a treatment effects model.

John Ainsworth
Senior Data Scientist
UVA Medical Center

[ Top of this page ] [ Agenda overview ]


4:45-5:30pm

Influencing Behavior
Creating Engaging Patient Journeys with Persuasion Modeling

Many applications of predictive analytics in healthcare identify a pool of patients at high risk of a preventable poor health outcome, and take steps to engage those individuals to lower their risk. However, less work has been done to identify the messages, frequency, and channels that are most effective at influencing those individuals' behavior. These factors can strongly shape the efficacy of health-related outreach and the costs associated with these programs.

We will present a framework, originally developed by political campaigns to engage voters, to test several messaging strategies, identify which individuals are moved by each health-related message and mode of outreach, and ultimately construct predictive models that enable providers, payers, and community groups effectively engage with each patient. Through this individual-centered persuasion approach, healthcare organizations can focus high touch messaging, such as in-person visits or multiple live phone calls, on those who require it to change their behavior, while reaching a broader, more easily "nudged" population with lower-touch, lower-cost tools such as push notifications and e-mail.

Erek Dyskant

Bluelabs

5:30-7:00pm

Networking Reception

[ Top of this page ] [ Agenda overview ]


DAY 2, Tuesday, October 31, 2017

8:00-8:45am

Registration & Networking Breakfast


8:45-8:50am

Conference Chair Welcome

Jeff Deal
Conference Chair
Predictive Analytics World Heathcare

8:50-9:45am

Keynote
TBA

Dr. Pamela Peele
Chief Analytics Officer
UPMC Health Plan & UMPC Enterprises

[ Top of this page ] [ Agenda overview ]


9:45-10:00am

Gold Sponsor Presentation


10:00-10:45am

Analytics for Emergency Response
Case Study: Disease Outbreak in New York City
Legionnaires' Disease in New York City: Analytics of the Built Environment for Emergency Services

Predictive analytics has proven to be a highly useful tool in the public sector, but what happens when an emergency strikes and we have to build an entire analytics infrastructure from scratch? In this case study the NYC Mayor's Office of Data Analytics (MODA) will walk you through how the City of New York built a system to collect, monitor, and predict the presence of potentially disease-carrying cooling towers among New York\'s one-million plus buildings in less than a week.

Simon Rimmele
Associate, Analytics
NYC Mayor's Office of Data Analytics


10:45-11:15am

Exhibits & Morning Coffee Break


11:15am-12:00pm

Industry Update
State of Precision Medicine: Where it is Headed and How to Discern the Signal from the Noise

Precision medicine is gaining momentum throughout the healthcare industry. This presentation will examine current adoption rates of precision medicine solutions across the United States, based on HIMSS Analytics research from the past two years. Additionally, we will look at key trends in this burgeoning field, current technology usage and predictions on the future of precision medicine.

Despite the potential it offers, precision medicine has not been widely adopted across the U.S. healthcare market. This research highlights barriers to entry (limited funds, technology, and expertise); high levels of reliance upon outside entities; and ways to stay ahead of the curve.

Brendan Fitzgerald
Director of Research
HIMSS Analytics

12:00-1:15pm

Lunch in the Exhibit Hall


1:15-2:00pm

Keynote
TBA

[ Top of this page ] [ Agenda overview ]


2:00-2:15pm

Vendor Elevator Pitches


2:15-3:00pm

Expert Panel
TBA

[ Top of this page ] [ Agenda overview ]


3:00-3:30pm

Exhibits & Afternoon Break


3:30-4:15pm

Drug Development Analytics
Case Study: Merck
Predicting Survival in Lung Cancer Based on Early Clinical Readouts using Modeling of Literature Data

Cancer care has been brimming with the promise of new therapies, especially those in immuno oncology. This is really exciting and hopeful time for patients but, at the same time, it is particularly important to understand who can benefit from these treatments. In this talk, we present the case study of Keytruda (pembrolizumab) for how one can leverage early readouts in a clinical trial to understand if a treatment is having a potential survival benefit as compared to standard of care. We believe that approaches like that can strengthen the decision making in a rapidly evolving field and ultimately benefit patients by giving them early access to best possibe cancer care.

Having accurate, unbiased prognosis information can help patients and providers make better decisions about what course of treatment to take. Using a comprehensive dataset of all colorectal cancer patients in California, we generate predictive models that estimate short-term and medium-term survival probabilities for patients based on their clinical and demographic information. This talk will discuss how the model was developed, how it improves on previous models, how it should be used, and the impact on the approach to treatment of colorectal cancer patients.

Anna Kondic
Executive Director, Predictive Economic Modeling
Merck

4:15-5:00pm

Building an Analytics Team
Case Study: OSF Healthcare System
The Benefits and Challenges of Building an In-House Data Science Team

Defining a healthcare data science strategy can be daunting. Do you build a team or buy services as needed? Where do you find and validate talent? Can an internal team really produce enough value to make it worth the effort and cost? What do you need in place to get started? At OSF Healthcare, we built our Advanced Analytics team, comprised of data scientists and statisticians, more than three years ago and have learned a lot along the way. In this candid session, we'll share what we've learned by highlighting some of our key projects' wins and lessons.

Juli Plack
Vice President of Information Delivery
OSF Healthcare System

Chris Franciskovich
Senior Data Scientist
OSF Healthcare System


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Program by: Elder Research, Inc.
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