My name is Erkin Ötleş & I’m a physician-engineer (in perpetual training). My mission is to advance health by harnessing the power of data. When I’m not working on AI for healthcare, I enjoy cooking, traveling, and jumping around!
My work is in the intersection of artificial intelligence (AI) and medicine, with specific research interests spanning clinical informatics, machine learning, and operations research. I am a seventh-year Medical Scientist Training Program Fellow (MD-PhD student) at the University of Michigan. My doctoral research focused on creating AI tools for patients, physicians, and health systems. I have led work across the AI lifecycle with projects advancing from model development to validation, technical integration, and workflow implementation. I was co-advised by Dr. Brian Denton (Industrial and Operations Engineering) and Dr. Jenna Wiens (Computer Science and Engineering).
I am also interested in incorporating education about AI tools into medical curricula. I have a professional background in health IT development, having worked at Epic and later leading a healthcare data science team. I have a Master’s of Engineering from the University of Wisconsin. After completion of my MD-PhD training, I plan on pursuing residency training.
University of Michigan Medical School
Medical Scientist Training Program
MD expected May 2024
Medicine has been a central aspect of my journey and has profoundly shaped my professional and personal interests.
I started my medical training as a Medical Scientist Training Program (MSTP) Fellow at the University of Michigan in 2016. The Michigan MSTP is a National Institute of Health-funded program designed to train physician-scientists by integrating medical education with scientific training. In addition to rigorous training, the Michigan MSTP has allowed me to work with and mentor other physician-scientists in training. I was a founding editor of “The Pipette,” the semi-official Michigan MSTP journal (newsletter). The program even let me co-chair our 2021 annual scientific retreat.
In my first two years in the program, I completed my medical school didactic and core clinical coursework. This exposure to real-life clinical decision-making and hands-on experience in patient care greatly informed my PhD research. I was able to draw on my clinical knowledge to develop data-driven solutions to pressing healthcare problems. Additionally, clinical needs remained an essential driver of my research focus. Working closely with physician-researchers was one of the best aspects of my PhD training.
After defending my dissertation, I returned to medical school. I am currently an “M3” and am trying to decide on my clinical specialty. Procedural specialties that allow for a balance between research and clinical duty, such as anesthesia, Emergency Medicine, and Ophthalmology, are on my shortlist.
I have found clinical medicine to be challenging and rewarding. There are many challenges to tackle, ranging from imperfect information to operational issues to poor software design. It can be daunting! However, building clinical decision-making and procedural skills and utilizing these skills in service of humankind has been immensely gratifying.
University of Michigan College of Engineering
Industrial and Operations Engineering & AI Lab
PhD defended August 2022
University of Wisconsin College of Engineering
Decisions Science & Operations Research (Concentration: Computer Science)
MS May 2016
University of Wisconsin College of Engineering
Industrial and Systems Engineering
BS May 2011
As an engineer, I am passionate about using data-driven approaches to develop innovative solutions to complex problems in medicine and health. With a strong background in industrial engineering and computer science, I have a unique perspective on the intersection of engineering and healthcare.
During my training, I gained extensive experience in developing and applying computational methods to healthcare data. I have expertise in machine learning, natural language processing, and data visualization, and I have applied these skills to a wide range of healthcare challenges, from predicting patient outcomes to identifying patterns in electronic medical record data.
I am also interested in the development of novel technologies to improve healthcare delivery. I believe that technologies such as wearables, mobile apps, and telemedicine have the potential to transform the way we approach healthcare, and I am excited to be part of this innovation.
Through my work as an engineer, I am committed to advancing health by harnessing the power of data and technology. Whether through developing new algorithms, building innovative tools, or collaborating with clinicians, I am dedicated to using my skills to make a positive impact on healthcare.
Deep experience integrating AI in healthcare; millions of predictions on patients. Ex-Epic engineer.
I’ve developed a deep understanding of healthcare IT systems and the role they play in improving patient care. During my time as a solutions engineer at Epic, I actively supported the EMR installation and maintenance processes and was certified on multiple Epic modules (Ambulatory, MiChart, In Patient, and various data models). I helped manage PQRS and meaningful use reporting, and developed data-driven approaches to debug software. I also contributed to the development of registry tools for accountable care organizations within the EpicCare Ambulatory module.
My current work has allowed me to further explore the intersection of healthcare and technology. In particular, my dissertation involved deploying AI tools in EMRs to help clinicians. I have implemented multiple AI tools into the EMR and using both Epic (Nebula/ECCP) and non-Epic tools. These tools have been run millions of times on hundreds of thousands of hospitalized patient. As a result of this ongoign wokr I have a keen understanding of the technical challenges associated with implementing AI in healthcare.
EpicCare Ambulatory Solution Engineer
2012 – 2013
Designed and developed analytics tools & strategies for Health Systems moving towards ACO models of care. Led technical support of two customer implementations. Managed and resolved install issue escalations often managing teams containing internal developers, customer technical and clinical teams.
Health Systems Engineering Intern
University of Wisconsin Hospital and Clinics
2010 – 2012
Led design of next generation of inpatient rooms. Developed data mining tools & processes to analyze quality of care as well as patient satisfaction.
HR Analytics Intern
Mining data from various recruiting systems, databases, and email archives. Built and analyzed databases to help identify key attributes of the best candidates and employees.
Industrial Engineer Co-op
Optimized the production lines of two next-gen cardiac pacing leads. Scheduled clean-room & production shutdowns for machine and utilities installations. Developed new machine acceptance program.
Electronic Theatre Controls
Some design for manufacturability & line design. Got my hands dirty by machining, wiring, assembling new testing fixtures.