We are in some exciting discussions with Epic, so while we wait for the possibilities of that news, we're going to skip the Hyperspace Integration Post for some Emergency Medicine talk.


In his New York Times Bestseller, Blink - The Power of Thinking Without Thinking, Malcolm Gladwell writes about the remarkable power that humans have to make very quick and accurate decisions in very challenging, information heavy or information lacking environments. He gives several illustrations of this from as wide a variety of fields as fire fighters being able to determine whether a house's foundation will be sturdy enough to enter, to art critics being able to instantly spot a forgery.  He even relates a story of psychologists developing a process that can accurately determine whether a couple will be divorced within five years simply by observing fractions of seconds of their facial expressions during a conversation. This 'gut-instinct' that we have, Gladwell describes as the psychological principle of 'thin-slicing'. 



Either I shouldn't take this new job or that burrito doesn't like the idea of moving to Kansas.


The most interesting chapter to me was Gladwell's narrative of Cook County Hospital's Emergency Department during the late 1990's.  One of the country's busiest trauma centers and the basis of the television drama series ER had a very big problem.  Their Emergency Room was being overloaded with patients.  With an astronomical quarter million patients being seen in their ED annually, the lines were endless with wait times necessitating patient families pack multiple meals.  Patient evaluations we're being done in hallways on gurneys and triage of the sickest patients was being delayed by the sheer volume of waiting patients. 


As with many ED's one of the most common reasons people would come in was for a suspected Myocardial Infarction.  As a facility with very limited resources, Cook County simply didn't have the capacity or staff to admit every patient that had a suspected heart attack.  Additionally, a heart attack is a problem that is incredibly tricky to nail down.  The tests that were typically run and a patients individual make-up all contributed to the possibility of whether that patient was having a heart attack or not.  Furthermore, the only test that could conclusively tell if a person was having one took several hours to be resulted.  Since it was too expensive to house every patient that fell into a 'gray area', the hospital created short-stay and observation units for patients who potentially were having a heart attack. Unfortunately, this just diverted the problem to a different area of the hospital and the incidence of false admissions remained high.





What Cook County needed was a way to more accurately and more quickly triage these patients.  For that they turned to the research of a cardiologist named Lee Goldman.  In the 1970's Dr. Goldman looked at the volumes of information and case studies of heart attack patients and worked with mathematicians to devise the most relevant and important risk factors that contribute to an actual MI.  What Goldman developed was coined the 'Goldman Algorithm' and it boiled down the possibility of a MI in a patient to four key factors. (1) Their ECG (2) unstable angina (3) fluid in lungs, and (4) systolic blood pressure '< 100'. At the time, Goldman's contemporaries scoffed at the thought that medicine and treatment of such a serious problem could be simplified so much.  When Cook County implemented Goldman's algorithm, the results spoke for themselves.  The Goldman alogorithm was a whopping 70 percent better at identifying true MI's and also significantly safer and more accurate in determining the severity of the problem as well as recommending a governed progression of treatment.


This flies in the face of the practical 'defensive medicine' that many physicians are employing today.  While only an estimated 10% of the patients admitted for a possible heart attack were actually having one, somewhere in the other 90% were the patients that would've been sent home based on the Goldman Algorithm with potentially disastrous results.  Furthermore, many detractors to this simplified method of diagnosis argue that the aims of medicine shouldn't be to keep patients out of the hospital but to not miss the correct diagnosis of someone who actually needs help. 


What if the insights from massive data volume, and the power that can be drawn from the 'gut instinct' (thin-slicing) are not mutually exclusive?  What if we could transform large amounts of data into easily digestible snippets for physicians to make use of more accurately in their judgments.  When IBM developed Watson, it wasn't just to be a gimmick to show that computers were better at storing and analyzing massive amounts of data.  (We all know our future metal overlords are good at that)  What it was meant to highlight was the possibility of a tool like this in assisting in the decision-making  process.  The lessons from Watson and the power of thin-slicing can be coupled today in QlikView!


Last fall, one of QlikView's customers, Allina Healthcare based in Minneapolis was gracious enough to present a webcast on analytics they'd done in QlikView.  One of the key subject areas they focused on was readmissions reductions.  The challenge they faced was a familiar one - they didn't have the resources to devote to every inpatient admission but they also knew that the data existed to provide powerful insight into how they could effectively target patients that had a high probability of readmission. Echoing the same efforts of Dr. Goldman, Allina recruited a particle physicist who used QlikView to develop a predictive model for readmission.  Each and every patient was assigned a risk-score and inter-disciplinary resources were focused on patients that fell into the high-risk category for readmission.  In just the first 2 months since implementing workflow changes, they saw a reduction in readmissions by more than 10% and it continues to trend down.


These are the types of opportunities that really lend themselves to QlikView.  Our tool can help make sense of the enormous volumes of data to allow care providers to make significantly more informed decisions for their patients and for the business. 


Let's send patients home and know it was the right decision to do so.