Rapid Learning Systems to Improve Patient Outcomes and Reduce Healthcare Costs

Bharat Rao (Siemens Health Services)
Healthcare Systems of the Future
Location: Plaza Room B Level: Intermediate
Average rating: *****
(5.00, 1 rating)

Trends over the past two decades indicate that the quantity and precision of diagnostic data available for a single patient has increased dramatically, the amount of published medical knowledge is doubling every few years, and a number of promising therapies have been developed. Despite all these advances, medicine remains largely mired in a ‘one size fits all’ paradigm that has led to an explosive increase in patient costs without a concomitant improvement in patient care.

We are on the verge of a paradigm shift in healthcare. Traditionally, medical knowledge has being derived from carefully conducted clinical studies, namely evidence-based-medicine; now, a new form of evidence is emerging – that created by rapid learning systems that will mine vast amounts of electronic patient data collected in routine care, to create “evidence generated medicine.” Thus, mining the millions of patient records collected routinely in the daily care of patients has tremendous potential to individualize care to the specific patient.

In this presentation, I will describe a first-of-its-kind US/Euro health IT network consisting of 10 cancer centers in 5 nations. In this network, cancer centers are able to securely learn personalized models from patient data collected across all centers. Learned models for predicting patient survival and side effects for 3 different cancers (lung, rectal, larynx) have been made available to the public and physicians at www.predictcancer.org.

Creating models from patient data collected across multiple centers provides statistical power, but leads to several challenges:

  1. The foremost among these is to protect patient privacy. We have developed privacy preserving data mining methods that allow us to securely mine all patients in the network, while ensuring that all patient data remains with the firewalls of each institution. We are able to derive models that have better performance than models based on smaller data sets from individual institutions.
  2. Patient data that has been collected for routine care have many problems from the mining perspective. The data are noisy, have errors and omissions, and in many cases, are biased due to the variance in care across nations and between and within institutions. We deal with these issues by leveraging the large numbers of patients in our multi-national population.
  3. Mining patient data is a multi-data-source problem. Rich clinical data is available not just from structured demographic, lab and drug databases, it needs to be extracted from unstructured sources, such as medical images, treatment plans and various omics. All this information needs to be leveraged to learn better, more personalized models.
  4. Each institution stores patient data in a variety of multi-vendor source system and in different formats. Each institution collects different kinds of patient data, at varying levels of detail, in different languages and uses varying terminology. Our approach allows us to normalize data across centers on an as-needed basis, thus reducing the normally-intractable problem of mapping all data to a manageable one.
  5. Finally patients are unique. Our system has to scale to learn from patients with very little data (e.g., on their first visit) as well as from patients who have records spanning decades.

In this presentation, we will describe how we overcome these issues to learn personalized models that have been statistically validated and published in leading conferences and journals. Additionally, we describe how pharma companies can mine these patient records to more efficiently find patients for clinical trials. The majority of the talk will present case studies and results that illustrate some of the challenges and opportunities unique to mining healthcare data. We conclude with a glimpse of a more-efficient healthcare future, where treatment decisions are driven by evolving knowledge that is continuously mined from patient records collected in health systems all over the world.

Photo of Bharat Rao

Bharat Rao

Siemens Health Services

Bharat Rao, PhD is Senior Director and Head of the newly-formed Center for Innovations in the Health Services (HS) business unit in Siemens Healthcare, headquartered in Malvern PA. The Siemens HS unit develops and markets enterprise information technology and business intelligence solutions for hospitals and other healthcare providers. The Center for Innovations was established in May 2012 with the vision to foster thought-leadership for Siemens in the dynamic field of healthcare IT. The Center’s goals are to create a continuous-innovation pipeline of new products, services and capabilities; to develop and rollout processes to translate innovation into commercial success; establish collaborations with luminary customers, academic & industry partners; and to drive an innovation agenda that impacts the entire HS portfolio and workforce.

Previously, Dr. Rao led the Knowledge Solutions group, Healthcare Analytics and Business Intelligence which develops and deploys data analytics solutions that analyze millions of patient records, impacting three major areas in healthcare. These include, automated quality measurement and decision-support from hospitals EMR’s, computer-aided diagnosis systems to identify suspicious lesions on medical images, and predictive models for personalized medicine. The group launched the first-to-market startup offering in healthcare quality, Soarian Quality Measures (and its cloud counterpart, the Quality Reporting Service) which is now an essential part of Siemens solution to satisfy the meaningful use requirements for US health reform.

Dr. Rao has received multiple international awards, including the ACM SIGKDD (Data Mining society) Service Award in 2011 for “service to society for pioneering data mining applications in healthcare products that reduce healthcare costs and improve patient care.” He was also named the Siemens Inventor of the Year in 2005, awarded yearly to one employee in Siemens Healthcare (45,000 employees worldwide) for the REMIND data mining platform. He is the only two-time winner of the International Data Mining Case Studies & Practice Prize, for the best deployed industrial and government data mining application, awarded by IEEE & ACM respectively.

Dr. Rao is recognized as a leading international expert in machine learning, healthcare analytics and mining ‘big data.’ He has been granted 45 patents (50 more pending), received multiple best paper awards and has published over 100 scholarly publications and one book. He is currently leading an international consortium to develop a Euro-US cancer research health IT network to develop personalized therapies for lung cancer.

Dr. Rao received a B.Tech in Electronics Engineering from the Indian Institute of Technology, Madras, and an M.S. and Ph.D. focusing on machine learning from the Dept. of Electrical & Computer Engineering, University of Illinois, Urbana-Champaign, in 1993. After his PhD, he joined Siemens Corporate Research, and formed the Data Mining group. In 2002, he moved to Siemens Healthcare to help found the “Computer-Aided Diagnosis & Therapy” group.


For information on exhibition and sponsorship opportunities at the conference, contact Sharon Pierce at (203) 304-9476 or spierce@oreilly.com

For information on trade opportunities with O'Reilly conferences contact mediapartners

For media-related inquiries, contact Maureen Jennings at maureen@oreilly.com

View a complete list of Strata Rx contacts