Data Series: Human-Computing Symbiosis in Healthcare
"They talk about AI as separate from us, but all interesting machines are hybrids of human and machine… and I think of the human-machine symbiosis as a trend that is probably bigger than the internet, and bigger than open source, and of which AI is one manifestation."
- Tim O'Reilly, interviewed by Tieman Ray, ZD Net, 9-25-2019
One of the most successful and famous artificial intelligence applications was demonstrated for three days of competition on Jeopardy, the long-running game show. Ken Jennings and Brad Rutter, the two most successful champions in the game's history, were pitted against IBM's Watson computer. Knowing how Watson was programmed to beat the best of the best Jeopardy players can help us understand how to apply data science tools in healthcare appropriately.
Watson's somewhat surprising victory didn't depend on just filling the computer with searchable facts (a la Google, Wikipedia, or all the encyclopedias in the world). One of the key ideas was for Watson's human programming team to recognize the need to provide context for the information programmed into the computer so that Watson could identify the appropriate patterns. Instead of asking the Jeopardy champions to recommend what knowledge should be put into the computer, the team realized that the real context would come from Jeopardy's question writers. They programmed into Watson all prior questions and answers ever written for Jeopardy, going back to the first episode on March 30, 1964.
In a multiyear, iterative process—many testing phases were performed—Watson's ability to predict the correct answer improved over time. There were failures along the way, and the outcome of the competition was not guaranteed, but the result was impressive, with Watson scoring $77,147 compared with Ken Jennings' $24,000 and Brad Rutter's $21,600.
Since the early days of computing, there has been a lower brain fear that computers could replace or even be a danger to humans. Many famous and intelligent people have popularized these fears, including Elon Musk and others. In part, these fears come from the Turing test concept, the likelihood that one day we would not be able to determine if we are speaking with a human or a computer during a normal conversation. But the Turing test is flawed, and these fears are misguided because we will never have computers that can think like humans. We humans have the potential to be creative and use critical thinking to discover new knowledge and innovations. Computers will always be dependent on humans to program what goes into the computer and interpret the output.
Before Watson, IBM had another supercomputer called Deep Blue, which was programmed to play chess. In a famous re-match against world champion Gary Kasparov in 1997, Deep Blue won, and computers have consistently beat the world's greatest chess players since then. But with the appropriate application of data science, there is something that now regularly defeats the fastest supercomputers at chess, centaurs. This human-computing symbiosis combines the intuition, creativity, and empathy of human team members with supercomputers' massive computing capabilities. These different strengths are complementary.
Our hernia team learned these data science principles over the past decade using a human-computing symbiosis applied to real patient care. It wasn't an easy process; we didn't have a roadmap or textbook. We were trying to take the principles of a scientific paradigm used in other industries: financial industry, baseball, etc. and apply it to healthcare.
One issue was that data science in other industries was used to improve the organization's revenue and profit or win against other competitors. It was not being used to enhance the value for the customers as we were attempting to do. So, we had a lot of trial and error.
At first, we measured way too many data points. Because I'm such a hernia nerd, I wanted to see everything we could think of. We spent too much time and resources capturing data and not nearly enough time and resources figuring out how to measure outcomes in terms of value. As we realized we measured too many process measures, we went from collecting over 600 process data points to only a few dozen, focusing on the ones that we thought mattered the most.
We also needed to use various analytics and data visualization tools to gain insight through feedback loops to improve value. After a few years, the hospital noticed that they were no longer losing money on our complex hernia patients. They even began to make a modest net positive margin on each patient.
Our most unexpected and vital discovery came a few years after we started. It took another few years to mature our understanding of our findings' impact and learn what improvements we could implement to address this discovery.
One day, we were having a hernia team clinical quality improvement (CQI) meeting, and we were looking at the data for patients who had complications and other less than ideal outcomes. We looked at our operative techniques and the typical patient factors like BMI and smoking, but nothing seemed to explain a pattern for these patients who had bad outcomes. Then Brandie, our patient care manager, spoke up and noted that the patients who had less than optimal results seemed to be the same patients that were more challenging to deal with before surgery.
Brandie described patterns in these patients – some were angry; some had unrealistic expectations or were looking for a "quick fix;" some had high anxiety, depression, or a controlling personality. We didn't yet know how to measure this, but Brandie convinced us there was a pattern. We needed some sort of measurement tool. Lacking much expertise in this area at the time, we settled on a subjective measure we called "Emotional Complexity," and we put patients in categories of either high, medium, or low.
As the next 6-9 months went by, we recorded emotional complexity and a few dozen other data points. The following factor analysis of the data showed that emotional complexity was the highest modifiable factor predicting our patients' outcomes. The only elements with a higher correlation to outcomes were the hernia size and the number of prior hernia recurrences, neither of which could be modified.
Since we found that this was such an important factor, we invited a small group of social scientists to our next CQI meeting to develop a more robust measurement tool. We came up with a 12-question tool to help identify the specific issues that might be impacting outcomes. Two of the questions asked about anger toward a prior surgeon and anger toward a mesh company.
We found that in some cases, if patients were angry, this could impact their outcomes if they didn't go through a process of healing and addressing their anger before surgery. This discovery didn't come from any preconceived belief in the importance of a patient's emotional state. Remember, I'm a surgeon, I believed that a patient's outcome was almost entirely due to my excellent surgical skills (at that time). These findings came from continuous improvement and data science principles using various analytical tools as a part of our human-computing symbiosis. I was shocked at first. How could a patient's emotional state be more important than my surgical skills?
As our team began to learn more about the neuro-cognitive/emotional state that can develop after traumatic experiences (violence, sexual abuse, financial stress, psychological abuse, etc.), we learned there could be a neurophysiologic change in the brain that leads to a chronic stress state. This chronic stress can negatively impact the hormonal and immune systems and increase inflammation in the body.
Fortunately, we also learned that various cognitive therapies could re-wire the brain to help resolve this chronic stress state. With the results from our analysis of data and our creativity to find potential solutions from other medical disciplines, we implemented the concept of Prehabilitation with pre-operative Cognitive Behavioral Therapy (CBT) for patient sub-populations with chronic pain and other signs of prior trauma. Since implementing Prehabilitation, we've seen a positive impact on a complex group of patients suffering from chronic pain after hernia repair and have published our findings.
Over the decade, as we learned to apply these principles, we made many mistakes and overcame many challenges. But as we became more skilled in using data science principles, we've seen the potential for our healthcare system to be transformed. Through human-computing symbiosis, any measured outcomes can be improved for patients suffering from any health problem. The challenge ahead is implementing a data and analytics infrastructure in each local clinical environment, so the potential of the human-computing symbiosis can be achieved. We've done it in chess and baseball, why not healthcare?