by Sanaz Cordes, MD
Sometimes friends, clients, students, or family ask: “Don’t you miss being a real doctor?” I always pause for a moment when I get this question. I’ve given up explaining that being a doctor is like being a Marine... you kind of always get to “be” one, even after you’re no longer in active service. I once even had a local newspaper reporter refer to me as “Sanaz Cordes, a ‘former’ physician,” in an article that I’m certain no one read.
But, I must admit that, as I work with health tech startups, I long for the ability to be teleported back to the early 2000’s (we’ll leave the backdating vague), when I was actively practicing inpatient and outpatient medicine. When I think about the “tools” we used back then, as compared to what’s available now, I long for a “do-over.” Some of my biggest frustrations with practicing medicine centered around the piecemeal, manual workflow of caring for patients.
At the time, the shiniest object in the room was the EMR! Almost no practices (and very few hospitals) had EMRs for charting, CPOE, or even results viewing. I remember carrying spiral Mead “memo” pads and a pencil while walking back and forth between the hospital’s nurses’ station, the patients’ rooms, and the floor secretary’s desk dozens of times each night! Saturday morning rounds used to take upwards of 4 hours. I always fretted missing a critical lab result, forgetting to place an important order, leaving out relevant chart notes for consultants, or even forgetting to see a patient altogether! And, sadly, one of these things would normally occur weekly.
It’s sometimes hard for me to comprehend the types of tools and technologies that are available for physicians today. I spent most of 2014-2015 focusing on the physician shortage in this country. Most of the early solutions were centered around enticing more providers into the profession. But, I think we’re starting to see the pendulum swing. Yes, we need more physicians and advanced practitioners, but the healthcare industry is finally allowing technology to help solve this challenge as well. In no other industry have we seen such a “horse-and-buggy” mindset as we do in healthcare.
But, there is light at the end of this long and windy tunnel! If you had told me 10 years ago, when I transitioned to a career in healthcare technology, about things like healthcare Artificial Intelligence and Machine Learning, it would have triggered a blank stare as my mind wandered to episodes of Star Trek: The Next Generation. It would have seemed like a beautiful but impossible dream.
Today, there are companies building tools that predict a bad outcome before it happens. These AI tools provide a risk score and alert the right resource, at the right point of care, before it’s too late. And, the tool isn’t just predicting a “one-size-fits-all” outcome based on the disease using EMR data like labs and vitals. It’s predicting and preventing an adverse outcome for one specific patient by interpreting structured and unstructured EMR data, social data, population analytics, and “truths” about that patient across the continuum of care.
If I just let that sink in, it almost drives this “former” doctor to tears. I remember manually carrying tubes of my patients’ blood, spinal fluid, and even less glamorous specimens down a creepy hallway in the bowels of Parkland Memorial Hospital to the lab. I feared that my patients’ tests and the results I desperately needed would vanish. Then, I would spend the next several hours religiously checking for results on the original Macintosh dinosaur that required a whack on the case to stop flickering.
Technologies like cognitive processing can immediately flag a chronic congestive heart failure (CHF) patient with diabetes for a pharmacist consult when he arrives in the E.D. with a broken wrist – because he hasn’t filled his meds in over 8 weeks. The enormity of marrying ambulatory data, social behavior, and inpatient EMR data to preemptively keep this CHF patient from returning with a hypertensive or diabetic crisis is mind-boggling. Some forward-thinking health systems that are using these tools are seeing results like a 31% reduction in readmission rates among CHF patients.
Technologies like machine learning can independently aggregate and interpret new data to reveal hidden insights that humans could never manually process. Not only are these technologies significantly improving the quality of patient care, but they driving enormous cost and efficiency savings by allocating resources in a prioritized manner and reducing costly adverse outcomes. These technologies are enabling personalized surveillance, prediction, action, and reporting for patients – whether they’re at home, in their primary care provider’s office, in the E.D., or even in the ICU.
I spent some of the happiest years of my career scurrying around the ward with my tattered copies of Pharmacopeia and Sanford Guide in hand. Then, the smart phone, loaded with the Epocrates app, arrived. It was life-changing. My colleagues and I were convinced that it couldn’t get any better than that. Technology had peaked. But, fast forward a decade or so (again, no need for exact math), and the disruption of our technology-averse industry continues! I often tell my colleagues who are still practicing to seize the new technologies available to them. After all, embracing innovation is why we are no longer whacking that old, flickering Macintosh.