PAM : Big Data in Healthcare: Getting Past the Hype and Understanding Real World Applications Spring 2014

Transcription

1 CORNELL UNIVERSITY COLLEGE OF HUMAN ECOLOGY DEPARTMENT OF POLICY ANALYSIS AND MANAGEMENT PAM : Big Data in Healthcare: Getting Past the Hype and Understanding Real World Applications Spring 2014 Class Meeting Times and Locations: Instructor: Saturday, February 22, 2014, MVR 153 9:00 am 5:00 pm (With a lunch break from 12:45 pm 1:30 pm) Arnaub Chatterjee, MHA, MPA Contact Information: arnaubchatterjee@gmail.com Cell Phone: Course Description: The era of big data is upon us today, there are more than 50 petabytes of data in the healthcare system this number is expected to grow to 25,000 by the year Propelled by government regulation, advances in technology and growth in new markets, the healthcare industry has started to witness the information surge. While there is little doubt about the influx and massive digitization of information in healthcare, there are many unanswered questions about how to make this data actionable and exactly how it will change the industry. Increasingly, healthcare stakeholders are collecting, analyzing, and applying information in new ways to transform care. The purpose of this one credit course is to introduce students to big data in healthcare, understand how different players of the healthcare ecosystem are taking advantage of data liquidity and how to align the practice of medicine to evidence based care. Students will understand the evolution of big data in the healthcare ecosystem and different types of structured and unstructured data will be reviewed. The use of technology and analytics to structure and present information will be examined. The course will focus heavily on real world case studies to draw insights from payer, provider, government and pharmaceutical domains on how organizations are using real world data to create pathways to value.

2 Learning Objectives: [Sloan Competencies in brackets] 1) The student will understand the historical development of big data in the healthcare industry [Competency #3] 2) The student will understand the drivers of volume of big data as well as how government regulations such as the HITECH Act and the Affordable Care Act are impacting the landscape [Competency #3] 3) The student will become familiar with the primary pools of big data in healthcare, with a specific focus on both structured and unstructured data: [Competency #2, #3] a. Traditional or existing forms of data, including: i. Claims data used by payors and providers for utilization of care, cost estimate information ii. Clinical data used by providers and created by electronic medical records to generate information about diagnoses, lab values, etc. b. Newer healthcare data streams, including: i. Scientific data genomic or clinical trial data to accelerate translational research ii. Patient behavior and sentiment data generated from biometric sensors or retail purchases iii. Social media data sources from social networks such as Facebook and Twitter and the development of the field of digital epidemiology c. How each type of data is used by stakeholders (patients, providers, researchers, government, pharma/med device companies/etc., developers) in the healthcare system 4) The student will become familiar with the technologies and analytic tools enabling data to be cross linked and analyzed as well as the evolving ecosystem of organizations developing big data solutions [Competency #1, #3, #17] 5) The student will develop an understanding of the challenges in adopting big data, including: [Competency #3, #4, #5] a. Privacy and security concerns legal and ethical implications b. Data quality issues ( data veracity ) c. Data ownership who owns the data? d. The regulatory world 6) The student will develop a general understanding of how the big data revolution is driving economic growth in healthcare and spurring a new wave of innovation in the industry [Competency #13] 7) The student will gain experience in working with groups, conducting analyses and developing frameworks to evaluate multi faceted decisions [Competency #1, #11, #12, #17, #19] Sloan Competencies Competency #1: Statistical analysis Competency #2: Population health, social and behavioral determinants of health, and epidemiological analysis Competency #3: Understanding healthcare financing, regulatory, and delivery systems Competency #4: Legal perspectives on health policy and management issues

3 Competency #5: Research design Competency #11: Advocacy, persuasion, and negotiation skills Competency #12: Leadership and team based collaboration skills Competency #13: Economic analysis, including cost effectiveness analysis Competency #15: Oral communication and presentation skills Competency #17: Problem solving skills Competency #19: Working within diverse groups Course Structure and Academic Expectations 1. Class format will be lecture/discussion and group activities. 2. Students will be expected to attend all classes and participate in class discussions. 3. Invited speakers will present subject matter expertise. 4. Students will be expected to review and understand assigned readings prior to their discussion in the classroom. 5. Each student will also be expected to take an active and equal role in the group projects. Course Pre Requisites/Requirements 1. Must be a 1 st or 2 nd year Sloan Student or by individual exception approved by instructor. It is recommended that students complete PAM 5570 Health Care Organization prior to class enrollment. PAM 5280 Population Health for Health Managers is also recommended. a. To request admission to the course through exception, please send a current resume to Professor Julie Carmalt (jhc48@cornell.edu) along with a statement why you feel you should be granted an exception (e.g. previous experience analyzing data in healthcare). Assessment (S/U or Letter Grade) The final grade for the course will be based on how well each student performs in three areas: 1) Preparation (20%) Student attestations will be reviewed and preparation will be gauged based on questions asked during class. 2) Class participation (30%) Students are expected to access the assigned readings and be prepared to discuss during class. 3) Final paper (40%) Groups of 3 4 will work together to elaborate on a salient topic in big data and healthcare. A list of sample topics will be provided but students are encouraged to identify new problems and applications and may select their own topic subject to instructor approval. Teams must demonstrate participation from each member in the final paper and will be required to submit an assessment of each individual. Papers are expected to be 3 4 pages long. The course grade is computed based on the three criteria above. The letter grade is assigned on a straight scale: 90% and above leading to A, 80% 89% leading to B, etc.

4 Academic Integrity Absolute integrity is expected of every Cornell student in all academic undertakings. Integrity entails a firm adherence to a set of values, and the values most essential to an academic community are grounded on the concept of honesty with respect to the intellectual efforts of oneself and others. Academic integrity is expected not only in formal coursework situations but in all University relationships and interactions connected to the educational process, including the use of University resources. A Cornell student's submission of work for academic credit indicates that the work is the student's own. All outside assistance should be acknowledged, and the student's academic position truthfully reported at all times. In addition, Cornell students have a right to expect academic integrity from each of their peers. For information regarding the Cornell Code of Academic Integrity see Required Reading Assignments (3 hour estimate) Readings should be completed prior to the first course session. All readings are available on the course Blackboard page except the Beck article which is available online. Byrd RJ, Steinhubl, SR, et al; Automatic Identification of Heart Failure Diagnostic Criteria, Using Text Analysis of Clinical Notes From Electronic Health Records, International Journal of Medical Informatics; January Available from: Groves, Peter; Kayalli, Basel; Knott, David; Van Kuiken, Steve; The Big Data Revolution in Healthcare Accelerating Value and Innovation, McKinsey and Company Center for US Health Reform; January Available from: 8.ashx Institute of Medicine Roundtable on Value & Science Driven Health Care; Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good: Workshop Summary, Washington (DC): National Academies Press (US); Read only 'U.S. Healthcare Data Today: Current State of Play" (pp 69-90). Salathe, Marcel et al; Digital Epidemiology, PLoS Computational Biology; July 2012, Vol. 8 Issue 7. Available from: Tyree, Patrick, et al; Challenges of Using Medicare Insurance Claims Data for Utilization Analysis; American Journal of Medical Quality July/August : Available from:

5 Instructor Bio: Arnaub Chatterjee is the Associate Director for Health Information Partnerships within the Merck Medical Information and Innovation Team (M2I2). In this capacity, he is responsible for evaluating emerging business opportunities and driving the development of health information partnerships. He has led a number of academic industry collaborations in utilizing data and analytics to create sustainable value and manages a diverse portfolio of projects spanning multiple areas across therapeutics and healthcare IT. Previously, he served as Special Assistant to the first Chief Technology Officer at the U.S. Department of Health and Human Services, Todd Park, and current Chief Technology Officer Bryan Sivak. In his role, he worked on healthcare initiatives at the intersection of technology and innovation and helped to lead efforts for the health data initiative and healthdata.gov. He initiated and developed the HHSentrepreneurs (formerly HHS Innovation Fellows) program, which enables agencies to bring onboard innovation experts and entrepreneurs to work with HHS in taking on some of the Department s toughest challenges. He was also active in establishing mobile health initiatives at HHS, providing recommendations to the Secretary about health text messaging as well as sitting on the National Steering Committee for text4baby and the HHS mhealth Community of Practice. Prior to this role, Arnaub served as lead policy analyst in the Secretary s Budget Office for the Health Care Fraud and Abuse Control (HCFAC) program. He began his career as a strategy consultant in the Strategy and Operations practice at Deloitte Consulting, focusing on health data and information exchange. He earned his Master in Health Administration (MHA) and Master in Public Administration (MPA) degrees from Cornell University and his Bachelor of Science degree at the University of Michigan.

6 PAM Big Data in Healthcare: Getting Past the Hype and Understanding Real World Applications Spring 2014 College of Human Ecology Cornell University Class Schedule, Spring 2014 Saturday, February 22, 2014, MVR 153 Course Introduction Review of syllabus, course expectations, grading Evolution of big data in healthcare What s Driving Big Data? How HITECH Act and the Affordable Care Act are impacting the landscape Primary Pools of Big Data in Healthcare Part 1 Claims and clinical data 9:00 a.m. 9:30 a.m. 9:30 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. Break 11:00 a.m. 11:15 a.m. Primary Pools of Big Data in Healthcare Part 2 Imaging, genetic, consumer driven data Lunch 11:15 a.m. 12:00 p.m. 12:00 p.m. 1:00 p.m. Big Data Cases and Solutions Putting Data to Work Practical examples and case studies in big data 1:00 p.m. 2:00 p.m. Digital Epidemiology Guest Lecture by Dr. John Brownstein, Harvard Medical School Break Privacy and Security Legal Challenges in Big Data Adoption Data quality, data ownership and how to navigate the regulatory world Big Data, the Innovation Ecosystem and Entrepreneurship How data science and innovative uses of data can improve health outcomes Putting It All Together Group Papers 2:00 p.m. 3:00 p.m. 3:00 p.m. 3:15 p.m. 3:15 pm. 3:45 p.m. 3:45 p.m. 4:30 p.m. 4:30 p.m. 4:45 p.m. Questions and Answers 4:45 p.m. 5:00 p.m.