Identifying Patients Before They’re at Risk: Case Study in Partnership and Machine Learning

Until recently, healthcare has not understood the root causes of diseases well enough for prevention; the main approach has historically been to treat patients after onset. While primary prediction scoring systems are routine for CVD patients, the goal is to reach patients before primary events occur. Amgen and a startup partner are co-developing a machine learning solution that uses existing EMR data to develop statistical and machine learning models predicting secondary CVD events. Having more accurate risk prediction models could significantly impact approaches to disease prevention. The session will also cover the role of partnership in sourcing, prototyping, piloting, and scaling novel technologies.

Jorn op den Buijs
Senior Scientist Philips Research

Sara Golas
Senior Data Specialist
Partners HealthCare
Pivot Labs

Early Identification of Elderly at Risk of Emergency Department Visits

Emergency departments have seen a dramatic increase in the number of visits from elderly patients. Many elderly use a personal emergency response system (PERS) to signal for help in case of an incident such as a fall or breathing problems. At Partners Healthcare, we are testing a predictive model that uses PERS data to predict elderly at high risk of emergency department visits. Clinical staff from our homecare program perform interventions with high-risk patients. This presentation will cover the development of the predictive model and its deployment in a randomized controlled trial.

The Ethical Use of Big Data in Healthcare

With the advent of big data and machine learning, there is an opportunity to combat rising healthcare costs by leveraging data in an ethical and privacy compliant way to establish more consistency and implantation of preventative care. We need to ensure there is a fundamental set of rules and responsibilities in place among healthcare organizations to protect their patient's privacy. In this presentation, we will address this challenge and speak to the importance of creating an ethical and privacy compliant approach to aggregating multiple data sources which then can be used to improve patient outcomes.

Health Data Science: The Future of Care

Healthcare has always used statistical analysis and analytic capabilities for accounting, reimbursement, actuarial and fiscal projection purposes. New developments in advanced statistical and predictive analytics techniques promise to revolutionize health and medical outcomes, and care delivery. These new techniques utilize modern machine learning and Artificial Intelligence methods to predict and prescribe at the individual level, instead of using traditional statistics. Learn how new machine learning techniques are being used for value-based purchasing, population health, healthcare consumerism and precision medicine. Peer into the future of Healthcare Data Science with predictions from industry leaders.