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Curriculum Vitae

Date Prepared: 25 October 2017

Name: Ziad Obermeyer

Office Address: Department of Emergency Medicine Brigham and Women’s Hospital Neville House, 75 Francis St Boston, MA 02115

Home Address: 7 Dana Place, Cambridge, MA 02138

Work Phone: (617) 525-3133

Work Email: zobermeyer@bwh.harvard.edu

Work Fax: (617) 264-6848

Education

2001 A.B. (Magna cum laude with highest honors in field)

History and Science Harvard College, Cambridge, MA

2002 M.Phil. History and Philosophy of Science University of Cambridge, Cambridge, UK

2008 M.D.

(Magna cum laude) Medicine Harvard Medical School,

Boston, MA

Faculty Academic Appointments

05/13-9/14 Assistant Professor Medicine (Emergency Medicine) Harvard Medical School

10/14- Assistant Professor Emergency Medicine Harvard Medical School 07/14- Assistant Professor Health Care Policy Harvard Medical School Appointments at Hospitals/Affiliated Institutions

07/12- Associate Physician Emergency Medicine Brigham and Women’s Hospital, Boston, MA

07/12- Affiliated Faculty Institute for Quantitative Social Science

Harvard University, Cambridge, MA

12/13- Associate Faculty Ariadne Labs Brigham and Women’s Hospital/Harvard School of Public Health, Boston, MA

6/15- Faculty Member Dana Farber/Harvard Cancer Center Dana Farber Cancer Institute, Boston, MA

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Postdoctoral Training

07/08-06/12 Resident Emergency Medicine Harvard-Affiliated Emergency Medicine Residency (Brigham and Women’s Hospital / Massachusetts General Hospital), Boston, MA

Other Professional Positions

2002-2003 Business Analyst

McKinsey & Co., Florham Park, NJ

2003-2007 Research Associate

Harvard Initiative for Global Health, Cambridge, MA

2007-2008 Research Scientist Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA

2008 Rwanda Country Coordinator, Five-Year Evaluation

Global Fund to Fight AIDS, Tuberculosis, and Malaria, Geneva, Switzerland

2015- Scientific adviser National Cancer Institute, Surveillance Informatics Branch, Surveillance Research Program, Bethesda, MD

2016- Affiliate ideas42, New York, NY

2016 Visiting fellow Initiative on Global Markets, University of Chicago Booth School of Business, Chicago, IL

Committee Service Local

2013-15 Mortality Review Committee Brigham and Women’s Hospital Emergency Medicine representative 2014 Expert panel: best communication practices for

surgeons caring for seriously ill older patients Ariadne Labs/Brigham and Women’s Hospital Panel member

2015-17 Research Review Committee Brigham and Women’s Hospital Department of Emergency Medicine

Oversight and review of departmental research 2017- Physiology Research and Analytics Core Brigham and Women’s Hospital Founding co-chair (with Scirica B) National

2013- Global Emergency Care Committee

Society for Academic Emergency Medicine Data Collection and Management Workgroup

2013- Institutional Review Board

National Bureau of Economic Research, Cambridge, MA

2016 Reviewer

Machine Learning and Health Care Conference, Los Angeles, CA

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2016 Program Committee

Association for Computing Machinery Conference on Bioinformatics, Computational Biology, and Health Informatics, Workshop on Methods and Applications in Healthcare Analytics, Seattle, WA

2017 Reviewer

Machine Learning and Health Care Conference, Boston, MA

International

2013-16 Scientific Committee African Federation of Emergency Medicine Member

2013-16 Executive Committee Acute Care Development Consortium Consortium leader

2016-17 Advisory Committee on Emergency and Post-

Conflict Health Systems World Bank Group Committee chair

Editorial Activities Ad hoc reviewerAcademic Emergency Medicine Bulletin of the World Health Organization Cambridge University Press (Mathematical Sciences) Critical Care The BMJ BMJ Global Health BMJ Supportive and Palliative Care Demography EMJ Healthcare: The Journal of Delivery Science and Innovation JAMA JAMA Oncology Journal of Clinical Oncology Journal of Medical Economics Journal of Peace Research Lancet New England Journal of Medicine PNAS Pancreas PLoS Medicine Population and Development Review Prehospital and Disaster Medicine World Journal of Surgery

Other Editorial Roles

5/2015- Associate Editor, Emergency Medicine Journal

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Honors and Prizes

1998-2000 John Harvard and Harvard College Scholarships

Harvard College Academic excellence

2001 Phi Beta Kappa Harvard College

Academic excellence

2001 Rothschild Award Harvard University Department of the History of Science, Cambridge, MA

Best Senior Thesis

2001 Frank Knox Scholarship Harvard College Study at Cambridge

2004 Summer Research Award Harvard Medical School

2004 Certificate of Distinction in Teaching

Harvard College Teaching undergraduates

2008 Rose Seegal Prize Harvard Medical School Research

2010 Richard C. Wuerz Award Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA

Research

2012 Early Independence Award

Office of the Director, National Institutes of Health, Bethesda, MD

Research

2014 Certificate of Excellence in Tutoring

Harvard Medical School Teaching medical students

2015 Young Investigator Award

Society for Academic Emergency Medicine

Research

Report of Funded and Unfunded Projects Funding Information Past 2007-08 Measuring adult mortality in the developing world Bill & Melinda Gates Foundation / Institute for Health Metrics and Evaluation, Seattle, WA Investigator As part of the larger Global Burden of Disease effort, this study developed new methods for

measuring mortality in the developing world on the basis of household survey data.

2011-13 Measuring death after discharge from the Emergency Department (ED) Partners HealthCare Center of Excellence in Quality and Patient Safety, Boston, MA

Department of Emergency Medicine, Brigham and Women’s Hospital PI This study aims to identify patients who die within two weeks of being sent home from an

Emergency Department visit, and understand clinical factors that might predict these events.

2014-16 Population health analytics: Identifying patient phenotype cohorts for end-of-life care Dana Farber Cancer Institute Co-I ($75,000) The project seeks to optimize end of life care for a cohort of 30,000 patients undergoing treatment

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for genitourinary cancers. We use machine learning to predict six-month prognosis, and feed predictions back to treating oncologists. We track uptake of palliative and hospice care, chemotherapy and surgical interventions, and place of death in these patients, and compare these outcomes to a control group of patients about whom no prognostic information was given to physicians.

2015-16 Bad incentives or bad predictions? Rethinking low-value health care using machine learning Harvard University Interfaculty Collaboration Fund Co-PI ($50,000), with Mullainathan S This interdisciplinary project combines insights from behavioral economics and clinical medicine

with machine learning, to identify human misjudgments in medical practice. For a set of important medical decisions, we use machine learning to predict the utility of a procedure or test, and compare this prediction to the physician’s decision. This method can identify both mistakes, and predictable mistakes, and help design interventions to reduce them.

2015-16 Improving the Effectiveness and Efficiency of High-Risk Care Management through Machine Learning

National Institute for Health Care Management PI ($50,000) This project applies machine learning to identify individuals who will benefit from so-called ‘care

management programs’ for high-risk or high-cost patients. The aim is to create more effective and efficient programs, and amplify the ability of these initiatives to provide patients with better care at lower cost.

2012-17 Unexpected death after medical encounters: Measurement, reporting, and analysis Office of the Director, National Institutes of Health / DP5 OD012161 PI ($1,229,397) The project studies patients who die unexpectedly after being sent home from a medical encounter.

In order to reliably identify expected and unexpected deaths, the project also aims to improve existing methods for predicting short-term mortality in general outpatients. Finally, rate of early unexpected death after discharge is proposed as a novel outcome measure of the quality of health services.

Current 2014-17 Adaptive Quality Measurement Arnold Foundation Co-I ($10,000,000) This project aims to develop and pilot test an innovate approach to quality measurement, based on

adaptive approaches and grounded in measurement science. The new approach will not be overly burdensome or easily gameable. Because a single measurement system cannot serve all purposes, we will develop a measurement system intended to ensure system level quality of care in systems held accountable for economic and clinical outcomes (e.g. in ACOs or MA plans).

2015-18 Strengthening emergency care in conflict zones World Bank Co-PI ($485,000), with Mowafi H This project seeks to assess needs for emergency care in conflict zones in the Middle East. It will

collect preliminary data from emergency departments, and develop a focused training and quality improvement intervention as a function of needs. This intervention will be deployed in a block-randomized fashion to half of the hospitals in which data collection is taking place, to measure impact.

2015-17 Predicting acute complications in cancer with machine learning National Cancer Institute PI, Intergovernmental Personnel Agreement ($90,000) In collaboration with researchers at the Division of Cancer Control and Population Sciences, this

projects will use large observational datasets—Medicare claims, electronic health record data—to

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understand and predict acute complications of cancer care. This will range from medical complications like infections and venous thromboembolic disease, to complications of treatment, especially novel chemotherapeutic agents.

2015-18 Physician judgment and machine predictions: understanding and improving medical decisions

using machine learning Robert Wood Johnson Foundation Co-PI ($499,600), with Navathe A This project seeks to improve the value of care and reduce health disparities by developing a set of

powerful algorithms to consistently improve upon human clinical judgments. Our test case will be detecting sepsis in patients in the emergency department of the University of Pennsylvania Health System. We will measure the potential impact of improved decision-making on both low-value care and health disparities.

2016-18 Learning more about effectiveness for less: Applying novel statistical techniques to claims data National Institute on Aging, National Institutes of Health / P01 AG005842 (pilot project) Co-PI ($50,000) with Baicker K We aim to use Medicare claims data to quantify experimental effects for drugs as they are

prescribed, using "incidental experimentation" and the tools of machine learning. These data are uniquely well-suited to this analysis, both because of the scope of the information captured and the scale of the population represented. We will draw on the large literature on doctor prescription pattern variation as well as the natural experiment of drug introduction to generate difference-in-difference and IV strategies.

2016-17 Incorporating physiological sensor data into household surveys World Bank PI ($32,000) The advent of sensors and wearables for physiological monitoring could transform how we collect

data in household surveys. Just as dried blood allowed us to test for HIV or diabetes, physiological monitors like accelerometers, ECG, pulse oximeters, or digital retinal photography could allow us to screen reliably for a wide range of other conditions, including COPD, prior heart attack, and stroke. This project aims to review the literature and do preliminary field testing on high-performance physiological sensors, to lay the groundwork for incorporation into a large World Bank funded household survey.

2017-18 Low-Value Care: Moral Hazard or Mis-Prediction? Pershing Square Fund for Research on the Foundations of Human Behavior Initiative, Harvard Co-PI ($40,000) with Mullainathan S Economics provides a compelling explanation for why the return on health care dollars can be low:

moral hazard, i.e. physicians provide too much care because they are incentivized to do so. The proposed work will provide another explanation for low-value care, grounded in a behavioral perspective on medical decision-making: doctors make mistakes. Critically, this account is capable of accounting for potential under-use, not just over-use. Deciding who should receive care is hard and biases can lead to the wrong people being tested: this is neither over- nor under-use, but misuse. We will examine both sides of this coin in a concrete decision: deciding who should be tested for acute coronary syndromes in the emergency setting.

2017-18 Assessing the Overuse and Underuse of Diagnostic Testing National Institute on Aging, National Institutes of Health / P30 AG012810 (pilot project) Co-PI ($50,000) with Baicker K Low-value health care—care that provides little health benefit in light of its costs—is a central

concern for policymakers. Diagnostic testing is a particularly important example: the use of high-cost diagnostic tests has skyrocketed, but for many tests the “yield”—the frequency with which tests identify new diagnoses or trigger effective interventions—appears low. This project draws on advances in machine learning to (1) gauge the extent of over- and under-use of diagnostic tests nationally using Medicare claims, and (2) develop the basis for a clinical decision support tool using data from the Harvard hospital system.

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2017-18 Integrated Model of Palliative and Primary Care in Seriously Ill Older Adults National Institute on Aging, National Institutes of Health / R56 AG055728 Co-PI ($382,595) with Temel J Despite major advances in palliative care for patients with specific diseases, we know little about

how to deliver palliative interventions ‘upstream’—earlier in the disease trajectory for older adults with multiple chronic conditions. This project applies advanced predictive modeling techniques (‘machine learning’) to identify older patients in a primary care setting who would benefit most from palliative care: those whose complex interplay of chronic conditions puts them at high risk of near-term death. Building on our team’s strong infrastructure for clinical trials in palliative care, we will enroll the highest-risk patients in a randomized controlled trial, comparing usual primary care to primary care integrated with palliative care.

2017-22 Assessing the Overuse and Underuse of Diagnostic Testing National Institute on Aging, National Institutes of Health / P01 AG005842 (R01) Co-PI (R01 budget TBD) with Baicker K This project draws on advances in machine learning to gauge the extent of over- and under-use of

diagnostic tests nationally, and to develop the basis for a clinical decision support tool. Building on early evidence that machine learning algorithms combined with massive datasets can make highly accurate predictions, we will assess the extent to which doctors are testing patients with predictably low benefit or failing to test patients with predictably high benefit. After identifying the most promising opportunities to optimize testing using Medicare claims, we will use electronic health records (EHR) to predict yield using data available to doctors at the time of decision. This will pave the way for real-time clinical decision support tools.

Current Unfunded Projects: N/A

Report of Local Teaching and Training Teaching of Students in Courses 2004 Global Health Challenges (SA76) Harvard College Undergraduates Head Teaching Fellow: supervised 6 graduate

Teaching Fellows, taught weekly one-hour section, developed lectures and other course content, won teaching award.

2007 Patient-Doctor II Harvard Medical School / Massachusetts General Hospital

Medical students Peer tutor: met weekly with groups of 4-6 HMS2s to teach physical exam skills.

2010-12 Emergency Medicine (SU705.J) Harvard Medical School 4th year medical students Two-hour lecture; hands-on supervision at

simulation sessions during eight-week course

2010-14 Public health research in complex emergencies (GHP 537)

Harvard School of Public Health, Boston, MA

PhD, MPH, and MS students Two-hour lecture on measuring war deaths

2013 Independent study: Global Health and Health Policy (91r)

Harvard College

Undergraduate (Ndon) Supervisor for independent study; weekly one-on-one one-hour meeting

2013 Independent study: Health systems technology Harvard Medical School

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(SOC900.41) Medical student (Smith) Supervisor; weekly one-on-one one-hour

meeting

2014 Epidemiological methods in health services research (EPI 235)

Harvard School of Public health One-hour lecture

MSc and PhD students

2014 Independent study: Machine learning (SOC900.41)

Harvard Medical School

Medical student (Pierce) Supervisor; weekly one-on-one one-hour meeting

2014- Health care policy (HC750.0) Harvard Medical School Medical students Tutorial leader; 2 hours/week, month of January 2014 Independent study: End of life care research

(SOC900.41) Harvard Medical School

Medical student (Powers) Supervisor; weekly one-on-one one-hour meeting

2015-17 Independent study: High risk care management research (SOC900.41)

Harvard Medical School

Medical student (Powers) Supervisor; weekly one-on-one one-hour meeting, more as needed for specific projects

2016 Data science and medicine (ECON 1160) Harvard University Undergraduate and graduate Co-teaching, with Sendhil Mullainathan

3 hours/week, Spring semester 2016 Chemotherapy at the end of life Harvard Medical School Medical student (Pany) Lab Head, supervising MD-PhD program full-

time summer laboratory rotation, more as needed for specific projects

2017 Independent study, Applied Math: Machine learning and medicine (AM 91r)

Harvard College

Undergraduate (Zhang) Supervisor for independent study; weekly one-on-one one-hour meeting

2018 Econometric Methods for Applied Research II (Ec 2115 / HKS API-115)

Harvard Kennedy School

Graduate students Four two-hour lectures on machine learning

Formal Teaching of Residents, Clinical Fellows and Research Fellows (post-docs) 2007-8 Lectures on global public health organization,

financing, and leadership; data visualization and presentation; and career development

Institute for Health Metrics and Evaluation, University of Washington

Post-bachelor research fellows 30-40 minute lectures, 1-2 per semester

2009 Measuring war mortality Brigham and Women’s Hospital Emergency medicine residents Half-hour lecture

2009 Abdominal pain in the elderly Massachusetts General Hospital, Boston, MA Emergency medicine residents Half-hour lecture

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2010 Missed myocardial infarction Massachusetts General Hospital Emergency medicine residents Half-hour lecture

2012 Evidence-based cardiology in the ED Massachusetts General Hospital Emergency medicine residents One-hour lecture

2012 Defibrillation, cardioversion, external pacing Brigham and Women’s Hospital Incoming interns Three-hour simulation-based teaching session

2014 Anticipated difficult airway Brigham and Women’s Hospital Emergency medicine residents One-hour simulation-based teaching session

2014 Basic airway skills Brigham and Women’s Hospital Incoming interns Three-hour simulation-based teaching session

2015 Resident report Brigham and Women’s Hospital Residents Harvard Affiliated Emergency Medicine

Residency

2015 Health policy and machine learning Brigham and Women’s Hospital Residents Internal Medicine Residency, Management

Leadership Track One-hour lecture

2016 Clinical and operational research in the emergency department

Brigham and Women’s Hospital

Fellows International Emergency Development Leadership Administrative Fellowship Program One-hour workshop

Clinical Supervisory and Training Responsibilities 2012- Supervising and training PGY1-4 residents in

wide-ranging clinical care, airway management, and other essential procedural skills

Brigham and Women’s Hospital (300-400 hours/year in the Emergency Department)

Laboratory and Other Research Supervisory and Training Responsibilities 2005-07 Supervision of post-bachelor fellows: met to

direct research, assist with statistical coding, and review publication drafts / Harvard Initiative for Global Health, Cambridge, MA

Weekly mentorship for two years

2007-08 Supervision of post-bachelor fellows: met multiple times daily with individual fellows to review results, develop statistical coding algorithms, and create tables and graphs in preparation for publication / Institute for Health Metrics and Evaluation, University of Washington

Daily mentorship for one year

2013- Supervision of 4-6 research assistants, one physician assistant, one HMS student, two Ph.D. students/ Brigham and Women’s Hospital

Daily laboratory meetings and research (400 hours per year of mentorship, supervising laboratory research)

Formally Supervised Trainees

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Harvard medical students 2011-12 Laura Myers, M.D. / Resident, Internal Medicine, Massachusetts General Hospital, Boston, MA Supervised chart review and preliminary analysis in preparation for a case-control study of death

after emergency visits while she was an HMS student, counseling on career development and specialty selection, research mentorship.

2013-14 Monique Smith, A.B. / student, Harvard Medical School, Boston, MA Supervised independent study course, counseling on career development in international health

and emergency medicine

2014 Michael Pierce, B.A.. / student, Harvard Medical School, Boston, MA Supervised independent study course, counseling on career development in public health and

emergency medicine

2014- Brian Powers, B.A.. / student, Harvard Medical School, Boston, MA Coauthored multiple papers, counseling on career development in health care policy

2016- Max Pany, B.A. / M.D.-Ph.D. student, Harvard Medical School, Boston, MA Supervised for 2 years as a full time research analyst, with multiple co-authored papers in

submission. Continuing to mentor and collaborate through M.D.-Ph.D. program.

Other medical students 2017- Adrian Haimovitch, B.A. / M.D./Ph.D. candidate / Yale Medical School Counseling on career development and specialty selection, research mentorship in machine

learning and medicine.

Harvard PhD students 2015- Andy Miller / Ph.D. student, Computer Science, Harvard University, Cambridge, MA Supervising one Ph.D. paper, mentorship, career advice.

2016- Stephen Coussens / Ph.D. student, Public Policy, Harvard Kennedy School of Government,

Cambridge, MA Dissertation committee. Harvard undergraduates 2013 Sifon Ndon / Senior, Harvard College, Cambridge, MA Supervised one semester long independent study course, counseling on career development in

international health and medicine

2017- Amanda Zhang / Junior, Harvard College, Cambridge, MA Supervised one semester long independent study course, counseling on career development in

machine learning and medicine

Other students 2017- Kiyan Rajabi / M.Sc. student, Computer Science, Cornell Tech, New York, NY Advising on research in machine learning and medicine, mentorship, career advice.

Residents and fellows 2012-15 Samantha Stoll, M.D. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston, MA Supervised literature review with goal of publishing manuscript, now published with Dr Stoll as co-

author, counseling on career development in international health and emergency medicine.

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2014- Cindy Y. Chang, M.D. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston, MA Supervising multiple projects on vital sign monitoring, co-authored multiple papers on global

emergency care, counseling on career development in health services research and emergency medicine.

2015- David Silvestri, M.D., M.B.A. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston, MA

Supervising project on emergency care and data infrastructure development in Ethiopia, counseling on career development in global health and emergency medicine./

2015- Shaw Natsui, M.D., M.P.P. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston, MA

Supervising project on emergency care development and health policy, counseling on career development in health services research and emergency medicine.

2015- Kenneth Michaelson, M.D. / Fellow, Emergency Department, Children’s Hospital, Boston, MA Supervising project on emergency care metrics, counseling on career development in health

services research and emergency medicine

2015- Ravi Parikh, M.D. / Resident, Internal Medicine, Brigham and Women’s Hospital, Boston, MA Supervising project on oncology and palliative care, counseling on career development in data

science research and oncology.

2017- Rachel Rizal, M.D. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston, MA Mentorship, advising on career in business and medicine as well as research interests.

2017- Jonathan Van Ornam, M.D. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston,

MA Supervising project on clinical decision making and decision support, counseling on career

development in health services research and emergency medicine

2017- Shuhan He, M.D. / Resident, Harvard-Affiliated Emergency Medicine Residency, Boston, MA Mentorship, advising on career in sensor technology and medicine as well as research interests.

2017- Alexander Melamed, M.D., M.P.H. / Fellow, Gynecologic Oncology, BWH, Boston, MA Mentorship, advising on career in research in machine learning and health services.

2017- Mariam Fofana, M.D., Ph.D. / Resident, Harvard-Affiliated Emergency Medicine Residency,

Boston, MA Counseling on career development in health services research and emergency medicine.

Junior faculty 2013- Matthew Niedzwiecki, Ph.D. / Assistant Professor, Department of Emergency Medicine and Philip

R. Lee Institute for Health Policy Studies Supervised post-doctoral research leading to published paper, and mentorship in junior faculty

position.

2014- Aymen Elfiky, M.D., M.P.H. / Instructor, Harvard Medical School, Associate Physician, Dana-Farber Cancer Institute, Boston, MA

Senior mentor on a grant from Dana Farber investigating end of life care, advising on methods and development of statistical skills, career advice

2016- Michael Barnett, M.D., M.P.H. / Assistant Professor, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA

Advising on application of machine learning to claims data, career advice

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2016- Jonathan Slutzman, M.D. / Assistant Professor, UMass Medical School, Worcester, MA Advising on health services and operations research with hospital and health system administrative

datasets, career advice

2017- Daniel Dworkis, M.D., Ph.D. / Assistant Professor, University of Southern California, Los Angeles, CA

Advising on health services and delivery research, career advice

Physician assistants 2014- Mackenzie Bohlen, P.A.-C. / Physician assistant, Brigham and Women’s Emergency Department,

Boston, MA Supervising research assistance, career advising, mentorship, co-authored publication.

Research assistants 2005-07 Jesse Abbott-Klafter, M.D., M.P.H. / University of Washington Internal Medicine Residency

(Primary Care), Seattle, WA Published a manuscript, counseling on career development including medical school.

2007-08 Chang Park, B.A. / Medical student, Mount Sinai School of Medicine, New York, NY Published a manuscript, counseling on career development including medical school.

2013-15 Maggie Makar, B.A. / Ph.D. student in computer science, Massachusetts Institute of Technology,

Cambridge, MA Supervised for 2 years as a full time research assistant, with multiple co-authored papers published

including one with Maggie as lead author.

2014-16 Max Pany, B.A. / M.D.-Ph.D. student, Harvard Medical School, Boston, MA Supervised for 2 years as a full time research analyst, with multiple co-authored papers in

submission. Continuing to mentor and collaborate through M.D.-Ph.D. program.

2014-16 Margaret Robinson, B.A. / M.D. student, Stanford Medical School, Palo Alto, CA Supervised for 1 year as a full time research assistant, co-authored paper.

2014- Clara Marquardt, B.A., Analyst, Laboratory for Systems Medicine, Boston, MA Supervising as a research analyst, then research manager. Mentorship on leadership, start-up, and

career plans.

2016- Shreyas Lakhtakia, B.A., Analyst, Laboratory for Systems Medicine, Boston, MA Supervising as a full time research analyst, with career mentorship and advising on graduate

school.

2016- Jasmeet Samra, B.A., Analyst, Laboratory for Systems Medicine, Boston, MA Supervising as a full time research analyst, with career mentorship and advising on graduate

school.

2016- Ally Valliani, B.A., Analyst, Laboratory for Systems Medicine, Boston, MA Supervising as a full time research analyst, with career mentorship and advising on graduate

school.

2017- Adam Baybutt, B.A., Analyst, Laboratory for Systems Medicine and Abdul Latif Jameel Poverty Action Lab, Boston, MA

Supervising as a part time research analyst, with career mentorship and advising on graduate school.

2017- Christian Covington, B.A., Analyst, Laboratory for Systems Medicine, Boston, MA

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Supervising as a full time research analyst, with career mentorship and advising on graduate school.

2017- Katie Lin, B.A., M.A., Analyst, Laboratory for Systems Medicine, Boston, MA Supervising as a full time research analyst, with career mentorship and advising on graduate

school.

2017- Lia Petrose, B.A., Analyst, Laboratory for Systems Medicine, Boston, MA Supervising as a full time research analyst, with career mentorship and advising on graduate

school.

Formal Teaching of Peers (e.g., CME and other continuing education courses) No presentations below were sponsored by outside entities. 2011-13 1. Basic principles of statistical inference

2. Measuring war deaths 3. Impact evaluation methods

Three two-hour lectures

Global Health Emergencies CME Course, Cornell Weill Medical College

New York, NY

2015 Does the Medicare Hospice Benefit improve quality and

decrease costs? An evidence-based presentation One-hour plenary session

Practical Aspects of Palliative Care, Center for Palliative Care, Harvard Medical School

Cambridge, MA

Local Invited Presentations No presentations below were sponsored by outside entities. 2011 Morbidity and mortality conference Department of Emergency Medicine, Massachusetts General Hospital

2011 Morbidity and mortality conference Department of Emergency Medicine, Brigham and Women’s Hospital

2013 Unexpected death after medical encounters Faculty Research Group, Department of Emergency Medicine, Brigham and Women’s Hospital

2013 Insights from the end of life Health Services Research Group, Department of Emergency Medicine, Brigham and Women’s

Hospital

2013 Improving Prognosis Ariadne Labs, Brigham and Women’s Hospital and HSPH

2014 Accounting for care at the end of life Department of Health Care Policy, HMS

2014 Hot spots Ariadne Labs, Brigham and Women’s Hospital and HSPH

2014 Predictive analytics Ariadne Labs, Brigham and Women’s Hospital and HSPH

2014 Current debates on costs and hospice use at the end of life Grand Rounds, Division of Palliative Care, Massachusetts General Hospital

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2014 Unexpected death after emergency department visits Grand Rounds, Physician Assistants, Massachusetts General Hospital

2015 Unexpected death after emergency department visits: Insights from Medicare claims Faculty Research Group, Department of Emergency Medicine, Brigham and Women’s Hospital

2015 Predicting mortality in Medicare beneficiaries with cancer Outcomes Research Group, Dana Farber/Harvard Cancer Center

2015 Rethinking emergency care TEDx Harvard

2016 Are we over-testing? Understanding doctors’ decisions with machine learning Bioinformatics seminar, Harvard Medical School

2016 Are we over-testing? Understanding doctors’ decisions with machine learning Seminar, Ariadne Labs, Harvard School of Public Health/Brigham and Women’s Hospital

2017 Uses and misuses of machine learning in medicine Seminar, TIMI Study Group, Brigham and Women’s Hospital

Report of Regional, National and International Invited Teaching and Presentations No presentations below were sponsored by outside entities. Invited Presentations and Courses National 2007 Measuring war deaths: Data from the World Health Surveys Harvard Humanitarian Health Conference, Cambridge, MA

2007 A reality check on time-series cross sectional methods: In silico evidence Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA

2013 Unexpected death after medical encounters NIH Director’s Early Independence Award Symposium, Bethesda, MD

2013 Emergency Department Discharge (Moderator) Society for Academic Emergency Medicine Annual Meeting, Atlanta, GA

2013 Predictive modeling at the end of life Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA

2013 Improving prognosis National Institutes of Health High-Risk/High-Reward Symposium, Bethesda, MD

2014 Emergency care delivery in low and middle income countries Grand Rounds, Department of Emergency Medicine, Yale School of Medicine, New Haven, CT

2014 Clinically relevant predictive analytics Weill Cornell School of Medicine, Department of Healthcare Policy and Research, New York, NY

2015 End of life care and the new clinical data sciences: understanding and augmenting decision

making with machine learning Seminar speaker, Associate Secretary for Planning and Evaluation, Department of Health and

Human Services, Washington, DC

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2015 Understanding clinical decisions at the end of life: insights from national Medicare claims National Cancer Institute, Symposium on Oncologic Emergencies, Bethesda, MD

2015 Cancer-related dyspnea: understanding and improving clinical diagnosis National Cancer Institute, Symposium on Oncologic Emergencies, Bethesda, MD

2015 Understanding clinical decisions at the end of life: insights from national Medicare claims National Heart, Lung, and Blood Institute, Big Data Symposium, Bethesda, MD

2015 Machine learning in medicine Harvard Department of Economics, Cambridge, MA

2015 Does the Medicare Hospice Benefit Improve Quality and Decrease Costs? Plenary session, Practical Aspects of Palliative Care, Harvard Medical School, Cambridge, MA

2015 Predicting mortality for Medicare Beneficiaries with Cancer American Medical Informatics Association Annual Conference, San Francisco, CA

2015 Are we over-testing? Using machine learning to understand physician decision-making Department of Healthcare Management, University of Pennsylvania, Philadelphia, PA

2016 Are we over-testing? Using machine learning to understand physician decision-making Department of Emergency Medicine, New York University, New York, NY

2016 Are we over-testing? Using machine learning to understand physician decision-making Department of Health Policy, Cornell Medical School, New York, NY

2016 Bits, Bytes, and Better Care: Technology-Enabled Innovations for Advanced Care National Summit on Advanced Illness Care, National Academy of Sciences, Washington, DC

2016 Are we over-testing? Using machine learning to understand physician decision-making Applied Microeconomics Seminar, Chicago Booth School of Business, Chicago, IL

2016 Machine learning and clinical oncology Surveillance Research Program, National Cancer Institute, Rockville, MD

2017 Will machine learning simply automate physician biases and moral hazard?

American Economic Association, Chicago, IL

2017 Uses and misuses of machine learning in medicine Machine Learning & AI in Healthcare Conference, Boston, MA

2017 Uses and misuses of machine learning in medicine The Dartmouth Institute, Hanover, NH

2017 Uses and misuses of machine learning in medicine IBM Research, Yorktown, NY

2017 Uses and misuses of machine learning in medicine Briefing to Jason Study Group, La Jolla, CA

2017 Can a machine predict your death? Should it? TEDx Boston, Boston, MA

2017 (scheduled)

Are we over-testing? Using machine learning to understand physician decision-making Health Economics Seminar, University of Chicago, Chicago, IL

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2017 (scheduled)

Uses and misuses of machine learning in medicine Microsoft Research, Cambridge, MA

International 2007 Has the Directly Observed Treatment Short course (DOTS) strategy impacted tuberculosis case

detection or treatment success? / Research presentation (abstract) International Health Economics Association, Copenhagen, Denmark

2007 Mortality measurement using sibling survival from household surveys Population Health Metrics Consortium, Bill & Melinda Gates Foundation, Hyderabad, India

2008 Tuberculosis impact evaluation Global Fund to Fight AIDS, TB, and Malaria, Five-Year Impact Evaluation, Glion, Switzerland

2008 Estimating war mortality from surveys Centre for Research on the Epidemiology of Disasters, Documenting Mortality in Conflicts,

Brussels, Belgium

2013 Documenting the burden of acute disease African Federation for Emergency Medicine, Consensus Conference, Cape Town, South Africa

2013 Unexpected death after ED visits Department of Emergency Medicine, American University of Beirut Medical Center, Beirut,

Lebanon

2014 New approaches to predicting mortality with routine administrative data Department of Primary Care and Public Health, Imperial College, London, UK

2014 Emergency care delivery in low- and middle-income countries. African Conference on Emergency Medicine, Addis Ababa, Ethiopia

2015 Unexpected death and misdiagnosis in the ED: Insights from US Medicare data. National conference on emergency care, Helsingborg, Sweden

2015 Measuring coverage of emergency care and essential surgery. World Health Organization Conference on Universal Health Care Coverage, Bellagio, Italy 2016 Predicting risk of myocardial infarction using electronic health record data Halland Region research seminar, Halland, Sweden

2016 Machine learning for high-performance health systems Skåne University Hospital seminar, Malmö, Sweden

2016 Strengthening health sector resilience in fragile, conflict and violence settings Global Symposium on Health Systems Research, Vancouver, BC, Canada

2017 Machine learning and clinical science Seminar, Department of Internal Medicine, Hospital da Luz, Lisbon, Portugal

2017 Predictive health policy and machine learning Institute of Public Administration Australia and Department of the Prime Minister and Cabinet of the

Australian Government, Canberra, Australia

2017 Biological age and machine learning Malmö Diet and Cancer Study Screening Group, Department of Internal Medicine, Lund University,

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Malmö, Sweden

Report of Clinical Activities and Innovations Current Licensure and Certification

2011- Massachusetts Medical License 2013- Diplomate, American Board of Emergency Medicine Practice Activities 2012- Emergency Department

Brigham and Women’s Hospital 300 hours per year

Report of Education of Patients and Service to the Community Educational Material for Patients and the Lay Community No materials below were sponsored by outside entities. Articles in newspapers or magazines Numerous national and global popular and scientific media (on-line, in print, radio, broadcast news, etc.) have covered my research, including: Machine learning

STAT News: Patients aren’t told that death is near until too late. We can do better. Available on-line at https://www.statnews.com/2015/11/20/patients-arent-told-that-death-is-near-until-too-late-we-can-do-better. Fierce Healthcare: 3 ways machine learning will transform healthcare. Available on-line at http://www.fiercehealthcare.com/analytics/3-ways-machine-learning-will-transform-healthcare. Health Imaging: 3 ways machine learning will disrupt radiology—and the rest of medicine with it. Available on-line at http://www.healthimaging.com/topics/imaging-informatics/3-ways-machine-learning-will-disrupt-radiology%E2%80%94and-rest-medicine-it. STAT News: Making the modern radiologist obsolete? How machine learning may revolutionize medicine. Available on-line at https://www.statnews.com/2016/10/03/machine-learning-medicine-health/. Marketplace (NPR): Self-learning machines predict life and death. March 28, 2017. Available on-line at: https://www.marketplace.org/2017/03/28/tech/self-learning-machines-predict-life-and-death STAT News: Roger Ailes thought he knew how much longer he had to live. He was right. May 18, 2017. Available on-line at https://www.statnews.com/2017/05/18/roger-ailes-longevity-research/

Health policy

Voice of America: Only one in five women in developing world receive effective cervical cancer screening. Available on-line at http://www.voanews.com/english/news/a-13-2008-07-03-voa30-66670942.html.

Health Affairs blog: Physician-Level Practice Variation: Who You See Is What You Get. 23 September 2015. Available on-line at http://healthaffairs.org/blog/2015/09/23/physician-level-practice-variation-who-you-see-is-what-you-get/

HealthFoo: Computers Can Make Medicine More Human. 17 May 2015. Available on-line at https://www.youtube.com/watch?v=Rm9x9TyiAjA

Brookings Institution blog: Rethinking emergency care is a key part of ‘Health for All’. 25 August 2015. Available on-line at http://www.brookings.edu/blogs/future-development/posts/2015/08/25-emergency-health-care-obermeyer TEDx Harvard Talk: Rethinking emergency care. 9 November 2015. Available on-line at

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https://www.youtube.com/watch?v=3OJ5h4GLdGw

STAT News: Medicare patient deaths shortly after leaving the ER raise questions about rural hospitals. February 1, 2017. Available on-line at https://www.statnews.com/2017/02/01/medicare-emergency-room-deaths-hospitals/

TIME Magazine: The Scary Reason Healthy People Die After an ER Visit. February 01, 2017. Available on-line at time.com/4656619/emergency-room-doctors-hospital/

ED Legal Letter: ED Care Difficult to Defend if Patient Died Shortly After Discharge. September 2017.

War deaths Nature: Cressey L. War survey points to millions more dead: Study triples estimated number of violent war deaths since 1955. Published on-line 19 June 2008. Available on-line at http://www.nature.com/news/2008/080619/full/news.2008.901.html.

Science: Bohannon J. War deaths grossly underreported—study. Published 20 June 2008. Available on-line at http://sciencenow.sciencemag.org/cgi/content/full/2008/620/4.

ABC News: War’s human toll, 1955-2002: War deaths from 1955 to 2002 may be three times higher than previously thought. Available on-line at http://abcnews.go.com/Health/popup?id=5204447.

Reuters: Deaths in Vietnam, other wars undercounted: study. Published 19 June 2008. Available on-line at http://www.reuters.com/article/2008/06/19/us-war-deaths-idUSN1928547620080619.

Independent (UK): Death rates dropping in wartime in most countries: study. Published 23 January 2010. Available on-line at http://www.independent.co.uk/life-style/health-and-families/health-news/death-rates-dropping-in-wartime-in-most-countries-study-1877265.html

Wall Street Journal: Bialik C. Calculating the toll of war. Published 27 June 2008. Available on-line at http://blogs.wsj.com/numbersguy/calculating-the-toll-of-war-366/. US News & World Report: Worldwide war deaths underestimated: Three times as many killed as once thought in 50 years of conflicts, new analysis suggests. Published 20 June 2008. Available on-line at http://health.usnews.com/articles/health/healthday/2008/06/20/worldwide-war-deaths-underestimated.html.

Harvard Magazine: Counting the war dead. John Harvard’s Journal section. Published November/December 2007. Available on-line at http://harvardmagazine.com/2007/11/counting-the-war-dead.html. Voice of America: War fatalities three times higher than acknowledged. Last updated 1 November 2009. Available on-line at http://www.voanews.com/english/news/a-13-2008-06-20-voa2.html.

New Scientist: Giles J. War death estimates “should be tripled.” Published 23 June 2008. Available on-line at http://www.newscientist.com/article/dn14184-war-death-estimates-should-be-tripled.html?DCMP=ILC-hmts&nsref=news2_head_dn14184. Times of India: 269,000 people died in Bangladesh war, says new study. Published 20 June 2008. Available on-line at http://timesofindia.indiatimes.com/World/Rest_of_World/269000_people_diedin_Bangladesh_war_ says_new_study_/articleshow/3147513.cms.

Telegraph (Calcutta): Roy A. Bangla war toll raised fivefold .Published 21 June 2008. Available on-line at http://www.telegraphindia.com/1080621/jsp/frontpage/story_9442797.jsp.

Gawker.com: War: even more horrible than previously estimated. Available on-line at http://gawker.com/5018570/war-even-more-horrible-than-previously-estimated. MSNBC/Associated Press: War: Is it getting more hellish, or less? Updated 12 July 2009. Available on-line at http://www.msnbc.msn.com/id/31877209/ns/us_news-military/t/war-it-getting-more-hellish-or-less/. Hospice

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New York Times: Span P. The New Old Age: An Easier Death, and Less Costly, Too. 20 Nov 2014. Available on-line at http://newoldage.blogs.nytimes.com/2014/11/20/an-easier-death-and-less-costly-too. Boston Globe (front page): Lazar K. End-of-life counsel from doctors seen as lacking. 9 Jun 2015. Available on-line at https://www.bostonglobe.com/metro/2015/06/08/doctors-play-pivotal-role-determining-end-life-decisions-brigham-study-finds/vlkw2Cb5urAtgguvp7n7QL/story.html .

US News: Preidt R. Doctors Can Influence End of Life Care. 8 Jun 2015. Available on-line at http://health.usnews.com/health-news/articles/2015/06/08/doctors-can-influence-end-of-life-care.

Reuters: Seaman A. Hospice patients more likely to die at home, receive efficient care. 11 Nov 2014. Available on-line at http://www.reuters.com/article/2014/11/11/us-hospice-care-cost-idUSKCN0IV25C20141111.

Boston Magazine: Malamut M. New Brigham and Women’s Study Examines End of Life Care. 12 Nov 2014. Available on-line at http://www.bostonmagazine.com/health/blog/2014/11/12/new-brigham-womens-study-examines-end-life-care.

El Diario (Spain): Pinto T. Morir dignamente depende del médico que nos atiende (Dying with dignity depends on the doctor treating us). 15 Jun 2015. Available on-line at http://www.eldiario.es/sociedad/Muerte_digna-cuidados_paliativos-cancer_0_397911077.html.

JAMA Report: Dolf C. Multimedia: Association Between the Medicare Hospice Benefit and Health Care Utilization and Costs for Patients With Poor-Prognosis Cancer. 11 Nov 2014. Available on-line at http://jama.jamanetwork.com/multimediaPlayer.aspx?mediaid=7929450.

Medscape: Nelson R. Hospice Care Lowers Cost and ICU Use in Cancer Patients. 11 Nov 2014. Available on-line at http://www.medscape.com/viewarticle/834760.

Medscape: Nelson R. Physicans Are Biggest Influences of Hospice Enrollment. 17 Jun 2015. Available on-line at http://www.medscape.com/viewarticle/846630.

Life Matters Media: Nelson R. Hospice Leads To Better Care, Lower Costs At End Of Life. 11 Dec 7 2014. Available on-line at http://www.lifemattersmedia.org/2014/12/hospice-leads-better-care-lower-costs-end-life-jama/.

Neurology Today: Hiscott R. Should More Patients at the End of Life Be Referred to Hospice? 4 Dec 2014. Available on-line at http://journals.lww.com/neurotodayonline/Fulltext/2014/12040/Should_More_Patients_at_the_End_of_Life_Be.3.aspx.

CBS Boston: Stern D. Study: Hospice Patients Less Likely To Die In Hospitals. 12 Nov 2014. Available on-line at http://boston.cbslocal.com/2014/11/12/study-hospice-patients-less-likely-to-die-in-hospitals.

Report of Scholarship Peer reviewed publications in print or other media Research investigations 1. Ottmani S, Obermeyer Z, Bencheikh N, Mahjour J. Beliefs and behaviors surrounding tuberculosis in Morocco. Eastern Mediterranean Health Journal. 2008 Mar-Apr;14(2):298-304. 2. Obermeyer Z, Abbott-Klafter J, Murray CJL. Has the DOTS strategy impacted case detection or treatment success? An empirical assessment. PLoS One. 2008 Mar 5;3(3):e1721. 3. Gakidou E, Nordhagen S, Obermeyer Z. Coverage of cervical cancer screening in 57 countries: Low average levels and large inequalities. PLoS Medicine. 2008 Jun 17;5(6):e132. 4. Obermeyer Z, Murray CJL, Gakidou E. Fifty years of violent war deaths from Vietnam to Bosnia: Analysis of data from the world health survey programme. BMJ. 2008 Jun 28;336(7659):1482-6. 5. Obermeyer Z, Rajaratnam JK, Park CH, Gakidou E, Hogan MC, Lopez AD, Murray CJL. Measuring adult mortality using sibling survival: A new analytical method and new results for 44 countries, 1974-2006. PLoS Medicine. 2010 Apr 13;7(4):e1000260. 6. Obermeyer Z, Makar M, Abujaber S, Dominici F, Cutler DM. Association Between the Medicare Hospice Benefit and Health Care Utilization and Costs for Patients With Poor-Prognosis Cancer. JAMA. 2014 12 Nov;

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312(18):1888-1896.

7. Bobb J, Obermeyer Z, Wang Y, Dominici F, Cutler DM. Cause-Specific Risk of Hospital Admission Related to Extreme Heat in Older Adults. JAMA. 2014 24/31 Dec; 312(24):2659-2667.

* Paper of the year, National Institute of Environmental Health Sciences, National Institutes of Health (2015)

8. Makar M, Ghassemi M, Cutler DM, Obermeyer Z. Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data. International Journal of Machine Learning and Computing. 2015 Jun; 5(3): 192-7.

9. Ezzati M, Obermeyer Z, Tzoulaki I, Mayosi BM, Elliott P, Leon DA. The contributions of risk factor trends to cardiovascular mortality trends. Nature Reviews Cardiology. 2015 16 Jun; 82: 1-23. 10. Powers B, Makar M, Jain SH, Obermeyer Z. Cost Savings Associated with Expanded Hospice Use in Medicare. Journal of Palliative Medicine. 2015 May; 18(5): 400-1. 11. Obermeyer Z, Powers B, Makar M, Keating NL, Cutler DM. Physician Characteristics Strongly Predict Patient Enrollment In Hospice. Health Affairs. 2015 Jun; 34(6): 993-1000.

* Recognized as “One of the year’s major achievements in clinical cancer research and care,” American Society of Clinical Oncology's Clinical Cancer Advances (2016)

12. Liu S, Chang Y, Obermeyer Z, Narayan K. Frequency of ED revisits and death among older adults after a fall. American Journal of Emergency Medicine. 2015 Aug; 33(8): 1012-8. 13. Obermeyer Z, Abujaber S, Makar M, Stoll S, Kayden SR, Wallis LA, Reynolds TA. Emergency care delivery in 60 low- and middle-income countries: Systematic review and descriptive analysis. Bulletin of the World Health Organization. 2015 Aug 1; 93(8):577-586. 14. Obermeyer Z, Clarke AC, Makar M, Schuur JD, Cutler DM. Association of the Medicare Hospice Benefit with Emergency Utilization. Journal of the American Geriatrics Society. 2016 Feb; 64(2): 323-9. 15. Niedzwiecki M, Wilson M, Cutler DM, Obermeyer Z. Short-term Outcomes for Medicare Beneficiaries after Low-acuity Visits to Emergency Departments and Clinics. Medical Care. 2016 May; 54(5): 498-503. 16. Chang CJ, Abujaber S, Reynolds T, Camargo CA, Obermeyer Z. Burden of emergency conditions and emergency care utilization: New estimates from 40 countries. Emergency Medicine Journal. 2016 Nov;33(11):794-800. 17. Abujaber S, Chang CJ, Reynolds T, Mowafi H, Obermeyer Z. Developing metrics for emergency care research in low- and middle-income countries. African Journal of Emergency Medicine. 2016 Sep;6(3): 116–124. 18. Lakin JR, Robinson MG, Bernacki RE, Powers BW, Block SD, Cunningham R, Obermeyer Z. Predicting One-Year Mortality for High-Risk Primary Care Patients Using the “Surprise” Question. JAMA Internal Medicine. 2016 Dec 1;176(12):1863-1865. 19. Chang B, Pany MJ, Obermeyer Z. Early death after emergency department discharge in patients with psychiatric illness. American Journal of Emergency Medicine. 2016 Nov 17. pii: S0735-6757(16)30868-3. 20. Venkatesh A, Mei H, Kocher K, Obermeyer Z, Spatz E, Granovsky M, Rothenberg C, Krumholz H, Lin Z. Identification of Emergency Department Visits in Medicare Administrative Claims: Approaches and Implications. Academic Emergency Medicine. 2017 Apr;24(4):422-431. 21. Obermeyer Z, Cohn B, Wilson M, Jena AB, Cutler DM. Early death after discharge from emergency departments: analysis of national US insurance claims data. BMJ. 2017 Feb 1;356:j 239. 22. Asaria P, Elliott P, Douglass M, Obermeyer Z, Soljak M, Majeed A, Ezzati M. Half of all acute myocardial infarction deaths occur in the 28 days following hospitalisation and in one third of these acute myocardial infarction is not recorded during the admission. Lancet Public Health. 2017 Apr; 2(4): 191–201.

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23. Ouchi K, Jambaulikar G, George NR, Xu W, Obermeyer Z, Aaronson EL, Schuur JD, Schonberg MA, Tulsky JA, Block SD. The "Surprise Question" Asked of Emergency Physicians May Predict 12-Month Mortality among Older Emergency Department Patients. J Palliat Med. 2017 Aug 28. 24. Obermeyer Z, Samra JK, Mullainathan S. Individuals’ body temperatures vary meaningfully and predict mortality. Accepted to BMJ (22 October 2017). 25. Elfiky A, Pany MJ, Parikh RB, Obermeyer Z. Predicting mortality in patients with cancer at the start of chemotherapy. In submission; preprint at bioRxiv 204081; doi: https://doi.org/10.1101/204081.

* Awarded “Most promising idea” at Dana Farber Junior Investigators in Cancer Research Symposium (poster; 2016)

Other peer-reviewed publications 1. Makhlouf MV, Obermeyer Z. Bipedicle flap for wounds following Achilles tendon repair. Plastic and Reconstructive Surgery. 2008 Apr;121(4):235e-6e. 2. Martindale JL, Senecal EL, Obermeyer Z, Nadel ES, Brown DF. Altered mental status and hypothermia. Journal of Emergency Medicine. 2010 Oct;39(4):491-6. 3. Hasegawa K, Obermeyer Z, Milne LW. Eczema Herpeticum. Journal of Emergency Medicine. Epub 2011 Aug 16. 4. Mowafi H, Dworkis D, Bisanzo M, Hansoti B, Seidenberg P, Obermeyer Z, Hauswald M, Reynolds TA. Making Recording and Analysis of Chief Complaint a Priority for Global Emergency Care Research in Low-income Countries. Academic Emergency Medicine. 2013 Dec;20(12):1241-5. 5. Reynolds TA, Bisanzo M, Dworkis D, Hansoti B, Obermeyer Z, Seidenberg P, Hauswald M, Mowafi H. Research Priorities for Data Collection and Management Within Global Acute and Emergency Care Systems. Academic Emergency Medicine. 2013 Dec;20(12):1246-1250. 6. Bernacki R, Obermeyer Z. A need for more, better, and earlier conversations with cancer patients about goals of care. Evidence Based Oncology. 2015 Apr; 21(6): 166-7. 7. Brown JA, Grudzen C, Kyriacou D, Obermeyer Z, Quest T, Rivera D, Stone S, Wright J, Shelburne N. The Emergency Care of Patients with Cancer. Annals of Emergency Medicine. 2016 Dec;68(6):706-711. 8. Kleinberg J, Ludwig J, Mullainathan S, Obermeyer Z. Prediction Policy Problems. American Economic Review: Papers and Proceedings 2015, 105(5): 491–495. 9. Obermeyer Z, Emanuel E. Predicting the Future: Big Data, Machine Learning, and the Future of Clinical Medicine. NEJM. 2016 Sep 29;375(13):1216-9. 10. Mullainathan S, Obermeyer Z. Does Machine Learning Automate Moral Hazard and Error? Accepted (July 14 2016) to American Economic Review: Papers and Proceedings. 2017 107(5): 1–5. 11. Obermeyer Z. Is less more, or is it less? The growing evidence on high-intensity hospital care. Emerg Med J. 2017 Aug 18 [epub ahead of print]. 12. Obermeyer Z, Lee TH. Lost in Thought—The limits of the human mind and the future of medicine. NEJM. 2017 Sep 27;377(13):1209-11. Non-peer reviewed scientific or medical publications/materials in print or other media Reviews, chapters, monographs, and editorials

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1. Obermeyer Z. A dirty business. J Surg Educ. 2007 Jan-Feb;64(1): 61. 2. Obermeyer Z. Closing the evidence gap for public health interventions in developing countries. Young Voices in Research for Health. Geneva: Global Forum for Health Research; 2007. 3. Ravishankar N, Gakidou E, Lim S, Obermeyer Z, Murray CJL. Population Health Implementation & Training Partnership research framework. New York: Doris Duke Charitable Foundation; 2008. 4. Parikh RB, Obermeyer Z, Bates DW. Making Predictive Analytics a Routine Part of Patient Care. Harvard Business Review. April 21, 2016. Thesis

Obermeyer Z. Has the global dots strategy worked for tuberculosis control? an empirical assessment. Thesis (M.D., magna cum laude), Harvard Medical School, 2008.