Exploring the Intersection Between Medicine and Data Science
July 19, 2017
We are here as health professionals to deliver high-quality, safe, satisfying care at the lowest possible cost. How then can we achieve that mission without this new shift into the data era? How can this shift happen with clinicians on the outside looking in?
Terms like “big data” and precision medicine have been circulating the healthcare sphere for the past decade. Underlying the buzz is a paradigm shift towards data analytics that even while in its infancy is changing medicine, as it has changed marketing, finance, and politics.
The most impactful future strides in patient advocacy—and informed clinical care—will hinge on our ability to use personalized patient information. Through the experiences I’m about to share, I hope to inspire other clinicians-in-training to seriously consider the value of a data science education in their lifelong medical endeavors.
In medical school, I started noticing ample untapped opportunities to actualize clinical data science as the nexus of medicine—both to help physicians make more informed, safer clinical decisions and to allow patients to take control of their health. I wanted to be able to approach any data science problem and be fully confident—in my skills, in my creativity to approaching the problem, and in my ability to communicate the results.
After scouring the internet, I came across Metis, an accredited 12-week data science training bootcamp that requires completion of multiple real-world projects, and grounds those projects in the teaching of theory and understanding of data science concepts. It also provides consistent career advice and resources to bolster connections and future work potential. Metis appeared to provide the perfect opportunity to blend education, real projects, research, and networking.
After going through Metis’ competitive application process and getting accepted, my challenge was now getting Metis approved for medical school credit. At the University of Virginia School of Medicine, during the fourth year we are allotted up to 12 weeks of research which requires a UVA physician supervisor sign off on a detailed research plan. Two research mentors at UVA graciously agreed to function as my supervisors. After six proposals for three different projects, I was finally awarded 12 weeks of credit and could fully capitalize on the experience.
At Metis, I gained a strong foundation of the theory and quantitative groundwork from experienced instructors, one of whom had worked extensively in clinical data science. I also exponentially grew my network of healthcare data scientists and created a robust LinkedIn presence. Most importantly, I completed five data science projects, some individually and some in collaboration with colleagues from vastly different career backgrounds.
For one project, I applied advanced classification machine learning techniques to predict mortality in the ICU from a time series database of 40,000 patients and visualized the model performance through d3.js (a data visualization language).
For my final project, I built a partnership with another major hospital system at UCSF (facilitated through my own research advisor at UVA) that would likely not have been actualized otherwise. I developed the aforementioned custom-built random forest algorithm to personalize heart rate alarm thresholds and thereby, reduce alarm fatigue in the intensive care unit in the hospital and improve patient safety.
Large amounts of patient data enable improvement of diagnostic accuracy and efficiency and focus evaluation and treatment on individual patients rather than the incomplete “one size fits all” model. Wearables allow for more holistic, longitudinal monitoring of diseases, whether acute or chronic, and can facilitate prevention of disease. Algorithm development can reduce hospital readmissions, preempt decompensation in the hospital, and cut healthcare costs.
The premise of the clinician serving only as the end-user of these tools comes with its many pitfalls: confusion and inundation with electronic health records (EHRs), notification fatigue, and a lack of awareness of the breadth and application of technology tools, to name a few. Hence, clinicians need to have at least a basic understanding of statistics, probability, programming, and data analysis tools in order to be able to collaborate and communicate with others in data science.
The experience I gained at Metis has already been invaluable in my medical career. I have continued to work on my final bootcamp project with UCSF. I noted my experience on my Internal Medicine residency applications and the bootcamp came up as a talking point and a strength in the majority of my interviews. I see my future in medicine involving indelibly connected roles as both a practicing Internal Medicine physician and a clinical data scientist to advocate for my patients both on individual and systemic bases.
In a few months, I will have the privilege of working as an Internal Medicine resident at UT Southwestern in Dallas, and I believe that my experience in clinical data science at Metis was a strong catalyst. The path to a healthcare data science foundation might not yet be paved for physicians from different educational backgrounds, but I hope that my experience provides insight into one robust and effective path.
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