Someone commented to me that the concept of competing algorithms was very science-fictiony and hard to take at face value outside of the specific application of high frequency trading on Wall Street. I can understand how that could be argued, at first glance.
However, consider that systems are algorithms (you may want to re-read Part 6 of the What Medicine can learn from Wall Street series). We have entire systems (in some cases, departments) set up in medicine to handle the process of insurance billing and accounts receivable. Just when our billing departments seem to get very good at running claims, the insurers implement a new system or rule set which increases our denials. Our billers then adapt to that change to return to their earlier baseline of low denials.
Are you still sure that there are no competing algorithms in healthcare? They are hard-coded in people and processes not soft-coded in algorithms & software.
If you are still not sure, consider legacy retailers who are selling commodity goods. If everyone is selling the same item at the same price, you can only beat your competition by successful internal processes that give you increased profitability over your competitors, allowing you to out-compete them. You win because you have better algorithms.
Systems trading on Wall Street in the early days (pre 1980’s) was done by hand or by laborious computation. Systems traded off indicators – hundreds of indicators, exist but most are either trend or anti-trend. Trending indicators range from the ubiquitous and time-honored Moving Average, to the MACD, etc… Anti-trend indicators tend to be based on oscillators such as relative strength (RSI), etc. In a trending market, the moving average will do well, but it will get chopped around in a non-trending market with frequent wrong trades. The oscillator solves some of this problem, but in a strongly trending market, tends to underperform and miss the trend. Many combinations of trend and anti-trend systems were tried with little success to develop a consistent model that could handle changing market conditions from trend to anti-trend (consolidation) and back.
The shift towards statistical models in the 2000’s (see Evidence-Based Technical Analysis by Aronson) provided a different way to analyze the markets with some elements of both systems. While I would argue that mean reversion has components of an anti-trend system, I’m sure I could find someone to disagree with me. The salient point is that it is a third method of evaluation which is neither purely trend or anti-trend.
Finally, the machine learning algorithms that have recently become popular give a fourth method of evaluating the markets. This method is neither trend, anti-trend, or purely statistical (in the traditional sense), so it provides additional information and diversification.
Combining these models through ensembling might have some very interesting results. (It also might create a severely overfitted model if not done right).
Sidebar: I believe that the market trades in different ways at different times. It changes from a technical market, where predictive price indicators are accurate, to a fundamental market, driven by economic data and conditions, to a psychologic market, where ‘random’ current events and investor sentiment are the most important aspects. Trending systems tend to work well in fundamental markets, anti-trend systems work well in technical or psychologic markets, statistical (mean reversion) systems tend to work well in technical or fundamental markets, and I suspect machine learning might be the key to cracking the psychologic market. What is an example of a psychologic market? This – the S&P 500 in the fall of 2008 when the financial crisis hit its peak and we were all wondering if capitalism would survive.
By the way, this is why you pay a human to manage your money, instead of just turning it over to a computer. At least for now.
So why am I bringing this up? I’m delving more deeply into Queuing & operations theory these days, wondering if it would be helpful in developing an ensemble model – part supervised learning(statistics), part unsupervised (machine) learning, part Queue Theory algorithms. Because of this, I’m putting this project on hold. But it did make me think about the algorithms involved, and I had an aha! moment that is probably nothing new to Industrial Engineering types or Operations folks who are also coders.
Algorithms, like an ensemble model composed of three separate models: a linear model (Supervised Learning), a machine learning model (Unsupervised learning) and a rule based models (Queueing theory), are software coded rule sets. However, the systems we put in place in physical space are really just the same thing. The policies, procedures and operational rule sets that exist in our workplace (e.g. the hospital) are hard-coded algorithms made up of flesh and blood, equipment and architecture, operating in an analogue of computer memory – the wards and departments of the hospital.
If we only optimize for one value (profit, throughput, quality of care, whatever), we may miss the opportunity to create a more robust and stable model. What if we ensembled our workspaces to optimize for all parameters?
The physical systems we have in place, which stem from policies, procedures, management decisions, workspace & workflow design, are a real-life representation of a complex algorithm we have created, or more accurately has grown largely organically, to serve the function of delivering care in the hospital setting.
What if we looked at this system as such and then created an ensemble model to fulfill the triple (quad) aim?
I came across this wonderful piece by Bruce Davis MD on Physician’s Weekly.com about “The Etiquette of Help”. How do you help a colleague emergently in a surgical procedure where things go wrong? As proceduralists, we are always cognizant that this is a possibility.
“Any Surgeon to OR 6 STAT. Any Surgeon to OR 6 STAT.
No surgeon wants to hear or respond to a call like that. It means someone is in deep kimchee and needs help right away.”
I was called about an acute lower GI bleed with a strongly positive bleeding scan. I practice in a resort area, and an extended family had come here with their patriarch, a man in his late 50’s. (Identifying details changed/withheld – image above is NOT from this case). He had been feeling woozy in the hot sun, went to the men’s room, evacuated a substantial amount of blood, and collapsed.
As an interventional radiologist, I was asked to perform an angiogram and embolize the bleeder if possible. The patient was brought to the cath lab; I gained access to the right femoral artery, and then consecutively selected the celiac, superior mesenteric, and inferior mesenteric arteries to evaluate abdominal blood supply. The briskly bleeding vessel was identifiable in the right colonic distribution as an end branch off the ileocolic artery. I guided my catheter, and then threaded a smaller micro-catheter through it, towards the vessel that was bleeding.
When you embolize a vessel, you are cutting off blood flow. Close off too large a region, and the bowel will die. Also, collateral vessels in the colon will resupply the bleeding vessel, so you have to be precise.
Advancing a microcatheter under fluoroscopy to an end vessel is slow, painstaking work requiring multiple wire exchanges and contrast injections. After one injection, I asked my assisting scrub tech to hand me back the wire.
“Sir, I’m sorry. I dropped the wire on the floor.”
“That’s OK. Just open up another one.”
“Sir, I’m sorry. That was the last one in the hospital.”
“There’s an art to coming in to help a colleague in trouble. Most of us have been in that situation, both giving and receiving help. A scheduled case that goes bad is different from a trauma. In trauma, you expect the worst. Your thinking and expectations are already looking for trouble. In a routine case, trouble is an unwelcome surprise, and even an experienced surgeon may have difficulty shifting from routine to crisis mode.”
We inquired how quickly we could get another wire. It would take hours, if we were lucky. The patient was still actively bleeding and requiring increasing fluid and blood support to maintain pressure. After a few creative attempts at solving this problem, it was clear that it was not going to be solved by me, today, in that room. It was time to pull the trigger and make the call the interventionalist dreads – the call to the surgeon.
The general surgeon came down to the angio suite and I explained what was happening. I marked the bowel with a dye to assist him in surgery, and sent the patient with him to the OR. The patient was operated on within 30 minutes from leaving my cath lab, and OR time was perhaps 45 minutes. After the procedure was done the surgeon remarked to me that it was one of the easiest resections ever, as he knew exactly where to go from my work. The surgeon never said anything negative to me, and we had a very good working relationship thereafter.
“The first thing to remember when stepping into a bad situation is that you are the cavalry. You didn’t create the situation, and recriminations and blame have no place in the room. You need to be the calm center to a storm that started before you got involved. Sometimes that’s all that is needed. A fresh perspective, a few focused questions, and the operating surgeon can calm down and get back on track.”
I saw the patient the next day, sitting up with a large smile on his face. He explained to me how happy he was that he had come here for vacation, that it was the trip of a lifetime for him, and that he was looking forward to attending his youngest daughter’s wedding later that year. He told me he lived in a rural Midwest area, hours from a very small hospital without an interventionalist, and if this had happened at home, well, who knows?
If I had not objectively assessed my inability to finish the case because of equipment issues, well, who knows?
If I had been prideful and unwilling to accept my limitations at that time, well, who knows?
If I had been more concerned with my reputation or what my partners would think, well, who knows?
I sincerely hope that my patient has enjoyed many years of happiness with his family in his bucolic rural Midwestern home. I will never see him again, but I do think of him from time to time.