Agenda. What Data Science Can Learn from Training in Biomedical Informa8cs: The OHSU Experience

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1 What Data Science Can Learn from Training in Biomedical Informa8cs: The OHSU Experience William Hersh, MD, FACP, FACMI Professor and Chair Department of Medical Informa8cs & Clinical Epidemiology Oregon Health & Science University Portland, OR, USA Web: Blog: hop://informa8csprofessor.blogspot.com 1 Agenda Data science and informa8cs OHSU experience in informa8cs training Graduate programs Professional programs, including MD What data science can learn from informa8cs educa8on 2 1

2 What is done by data science has been part of informa8cs for a long 8me Use of data, informa8on, and knowledge to improve health and healthcare Capable data science requires competent informa8cs Data quality, standards, and interoperability Usability, workflow, and human factors Sociotechnical people and organiza8onal issues Research methodology beyond associa8ons 3 Need to think beyond Big Data: Use cases for biomedical data science Clinical Clinical decision support Quality measurement and improvement Business intelligence Pa8ent engagement Research Prospec8ve studies Retrospec8ve studies Data science and informa8cs research Educa8on Data users and managers clinicians, and others Data science and informa8cs professionals Data science and informa8cs researchers 4 2

3 OHSU biomedical informa8cs educa8onal program Graduate- level programs at Cer8ficate, Master s, and PhD levels One of largest and well- established (Hersh, 2007) hop:// educa8on Aims to produce researchers, professionals, and end users of informa8cs Recent extensions Clinical informa8cs subspecialty fellowship MD curriculum 5 OHSU categories of informa8cs educa8on Category Descrip/on Program Academics/ Researchers Conduct research Provide leadership PhD Postdoc Master s Professionals Implement systems Master s Graduate Cer8ficate Users/Clinicians Use informa8cs to improve their work Professional programs Con8nuing educa8on 6 3

4 OHSU informa8cs tracks, degrees and cer8ficates, and availability Track Degree/Cer/ficate PhD Master of Science Master of Biomedical Informa/cs Graduate Cer/ficate Clinical Informa8cs On- line On- line On- line Bioinforma8cs and Computa8onal Biology 7 Informa8cs competence is fundamental to clinician prac8ce 21 st century physicians and other clinicians must have competence in clinical informa8cs Many medical school curricula being revised to include clinical informa8cs Interdisciplinary nature makes incorpora8on into curriculum challenging Aided by AMA Accelera'ng Change in Medical Educa'on ini8a8ve 8 4

5 1/19/16 In reality, applicable to all healthcare professional students 9 A thread in new MD curriculum 10 5

6 1/19/16 Some specific educa8onal ac8vi8es Interac8ve lectures, e.g., Informa8on is Different Now That You re a Doctor Friday Closure Sessions Informa8cs, EBM, and Cri8cal Thinking Pearls weekly 7-10 minute asynchronous recording, 8ed to weekly case Clinical skills e.g., EHR, calcula8ng and applying quality measures Enrichment (op8onal) in- depth topics (EHR), clinical informa8cs careers 11 Do we really need to teach this to today s medical students? Computer- literate in many areas Communica8ons (e.g., , tex8ng) Social media Googling Less so with informa8cs and data science skills Finding and accessing medical resources Cri8cal appraisal Applica8on of data science/analy8cs 12 6

7 What can data science learn from informa8cs educa8on? Must s8ll teach cri8cal thinking, research methods, larger context of data, value to human health, etc. Some aspects to consider Volume is Only One of the Four "V"s of Big Data, Especially for the Right Data Beyond Predic8on: Data Analy8cs/Data Science/Big Data Must Demonstrate Value Is Medicine Precise Enough to Achieve Precision Medicine? Data Scien8sts Must Also Be Research Methodology Scien8sts From Implementa8on to Analy8cs: The Future Work of Informa8cs 13 7