Value and Risk: the Radiologist’s perspective (Value as risk series #4)

Public DomainMuch can be written about Value-based care. I’ll focus on imaging risk management from a radiologist’s perspective. What it looks like from the Hospital’s perspective , the Insurer’s perspective, and in general have been discussed previously.

When technology was in shorter supply, radiologists were gatekeepers of limited Ultrasound, CT and MRI resources. Need-based radiologist approval was necessary for ‘advanced imaging’. The exams were expensive and needed to be protocoled correctly to maximize utility. This encouraged clinician-radiologist interaction – thus our reputation as “The Doctor’s doctor.”

In the 1990’s-2000’s , there was an explosion in imaging utilization and installed equipment. Imaging was used to maximize throughput, minimize patient wait times and decrease length of hospital stays. A more laissez-faire attitude prevailed where gatekeeping was frowned upon.

With a transition to value-based care, the gatekeeping role of radiology will return. Instead of assigning access to imaging resources on basis of limited availability, we need to consider ROI (return on investment) in the context of whether the imaging study will be likely to improve outcome vs. cost. (1) Clinical Decision Support (CDS) tools can help automating imaging appropriateness and value. (2)

The bundle’s economics are capitation of a single care episode for a designated ICD-10 encounter. This extends across the inpatient stay and related readmissions up to 30 days after discharge (CMS BPCI Model 4). A review of current Model 4 conditions show mostly joint replacements, spinal fusion, & our example case of CABG (Coronary Artery Bypass Graft).

Post CABG, a daily Chest X-ray (CXR) protocol may be ordered – very reasonable for an intubated & sedated patient. However, an improving non-intubated awake patient may not need a daily CXR. Six Sigma analysis would empirically classify this as waste – and a data analysis of outcomes may confirm it.

Imaging-wise, patients need a CXR preoperatively, & periodically thereafter. A certain percentage of patients will develop complications that require at least one CT scan of the chest. Readmissions will also require re-imaging, usually CT. There will also be additional imaging due to complications or even incidental findings if not contractually excluded (CT/CTA/MRI Brain, CT/CTA neck, CT/CTA/US/MRI abdomen, Thoracic/Lumbar Spine CT/MRI, fluoroscopy for diaphragmatic paralysis or feeding tube placement, etc…). All these need to be accounted for.


In the fee-for-service world, the ordered study is performed and billed.  In bundled care, payments for the episode of care are distributed to stakeholders according to a pre-defined allocation.

Practically, one needs to retrospectively evaluate over a multi-year period how many and what type of imaging studies were performed in patients with the bundled procedure code. (3) It is helpful to get sufficient statistical power for the analysis and note trends in both number of studies and reimbursement. Breaking down the total spend into professional and technical components is also useful to understand all stakeholder’s viewpoints. Evaluate both the number of studies performed and the charges, which translates into dollars by multiplying by your practice’s reimbursement percentage. Forward-thinking members of the Radiology community at Nieman HPI  are providing DRG-related tools such as ICE-T to help estimate these costs (used in above image). Ultimately one ends up with a formula similar to this:

CABG imaging spend = CXR’s+CT Chest+ CTA chest+ other imaging studies.

Where money will be lost is at the margins – patients who need multiple imaging studies, either due to complications or incidental findings. With between a 2% to 3% death rate for CABG and recognizing 30% of all Medicare expenditures are caused by the 5% of beneficiaries that die, with 1/3 of that cost in the last month of life (Barnato et al), this must be accounted for. An overly simplistic evaluation of the imaging needs of CABG will result in underallocation of funds for the radiologist, resulting in per-study payment dropping  – the old trap of running faster to stay in place.

Payment to the radiologist could either be one of two models:

First, fixed payment per RVU. Advantageous to the radiologist, it insulates from risk-sharing. Ordered studies are read for a negotiated rate. The hospital bears the cost of excess imaging. For a radiologist in an independent private practice providing services through an exclusive contract, allowing the hospital to assume the risk on the bundle may be best.

Second, a fixed (capitated) payment per bundled patient for imaging services may be made to the radiologist. This can either be in the form of a fixed dollar amount or a fixed percentage of the bundle.  (Frameworks for Radiology Practice Participation, Nieman HPI)  This puts the radiologist at-risk, in a potentially harmful way. The disconnect is that the supervising physicians (cardio-thoracic surgeon, intensivist, hospitalist) will be focusing on improving outcome, decreasing length of stay, or reducing readmission rates, not imaging volume. Ordering imaging studies (particularly advanced imaging) may help with diagnostic certitude and fulfill their goals. This has the unpleasant consequence of the radiologist’s per study income decreasing when they have no control over the ordering of the studies and, in fact, it may benefit other parties to overuse imaging to meet other quality metrics. The radiology practice manager should proceed with caution if his radiologists are in an employed model but the CT surgeon & intensivists are not. Building in periodic reviews of expected vs. actual imaging use with potential re-allocations of the bundle’s payment might help to curb over-ordering. Interestingly, in this model the radiologist profits by doing less!

Where the radiologist can add value is in analysis, deferring imaging unlikely to impact care. Reviewing data and creating predictive analytics designed to predict outcomes adds value while, if correctly designed, avoiding more than the standard baseline of risk. (see John’s Hopkins Sepsis prediction model). In patients unlikely to have poor outcomes, additional imaging requests can be gently denied and clinicians reassured. I.e. “This patient has a 98% chance of being discharged without readmission. Why a lumbar spine MRI?” (c.f. AK Moriarty et al) Or, “In this model patients with these parameters only need a CXR every third day. Let’s implement this protocol.” The radiologist returns to a gatekeeping role, creating value by managing risk, intelligently.

Let’s return to our risk/reward matrix:


For the radiologist in the bundled example receiving fixed payments:


Low Risk/Low Reward: Daily CXR’s for the bundled patients.


High Risk/Low Reward: Excess advanced imaging (more work for no change in pay)


High Risk/High Reward: Arbitrarily denying advanced imaging without a data-driven model (bad outcomes = loss of job, lawsuit risk)


Low Risk/High Reward: Analysis & Predictive modeling to protocol what studies can be omitted in which patients without compromising care.


I, and others, believe that bundled payments have been put in place not only to decrease healthcare costs, but to facilitate transitioning from the old FFS system to the value-based ‘at risk’ payment system, and ultimately capitated care. (Rand Corp, Technical Report TR-562/20) By developing analytics capabilities, radiology providers will be able to adapt to these new ‘at-risk’ payment models and drive adjustments to care delivery to improve or maintain the community standard of care at the same or lower cost.

  1. B Ingraham, K Miller et al. Am Coll Radiol 2016 in press
  2. AK Moriarty, C Klochko et al J Am Coll Radiol 2015;12:358-363
  3. D Seidenwurm FJ Lexa J Am Coll Radiol 2016 in press

How an health insurer uses risk to define value (Value as risk series)

RiskLets continue with value as risk. If you missed it, here’s the first post.

Providers assert that insurers hold most if not all the cards, collecting premiums and denying payment while holding large datasets of care patterns. I’ve heard, “if only we had access to that data, we could compete on a level playing field.”

I am neither an apologist for nor an insider in the insurance industry, but is this a “grass is always greener” problem? True, the insurer has detailed risk analysis on the patient & provider. Yes, the insurer does get to see what all providers are charging and coding in their coverage. And the insurer can deny or delay payment knowing that a certain percentage of these claims will not be re-submitted.

But the insurer also has deep institutional knowledge in risk-rating their clients. Consider the history of health insurance in the US.  Advancing medical knowledge advanced treatment cost. When medical cost inflation exceeded CPI  insurers modeled and predicted estimated spend with hard data. If individuals had medical conditions which would cost more ultimately than premiums received they failed medical underwriting. The insurers are private, for-profit businesses, and will not operate at a loss willingly.

To optimize profitability, insurers collected data from not only the insurance application, but also claims data, demographic data from consumer data brokers, financial data, information from other insurers (auto, home, life), and probably now Internet data (Facebook, etc…) to risk-rate the insured. Were they engaged in a risky lifestyle? Searching the net for serious genetic diseases?

Interestingly, the ACA changed this to only permit 1) Age 2) Smoking 3) Geographic location as pricing factors in the marketplace products. The marketplace products have been controversial, with buyers complaining of networks so narrow to be unusable , and insurers complaining of a lack of profitability, which has caused them to leave the market. Because the marketplace pools must take all comers, and many who entered the pools had not had insurance, there is some skew towards high-cost, sicker patients.

Consider a fictional medium-sized regional health insurer in three southern states specializing in group (employer) insurance – Southern Health. They are testing an ACA marketplace product. The geographic area they serve has a few academic medical centers, many community hospitals competing with each other, and only a few rural hospitals. In the past, they could play the providers off one another and negotiate aggressively, even sometimes paying lower rates than Medicare.

However, one provider – a fictional two-hospital system – Sun Memorial – hired a savvy CEO who developed profitable cardiac and oncology service lines leveraging reputation. Over the last 5 years, the two-hospital group has merged & acquired hospitals forming a 7-hospital system, with 4 more mergers in late-stage negotiations. The hospital system changed its physicians to an employed model and then at next contract renewal demanded above Medicare rates. As such, Southern Health did not renew their contract with Sun Memorial. In the past, such maneuvers ended conflict quickly as the hospital suffered cash flow losses. However, now with fewer local alternatives to Sun Memorial; patients were furiously complaining to both Southern Health and their employer’s HR department that their insurance would not cover their bills.   Pushback on the insurer by the local businesses purchasing benefits through Southern Health happened as they now threatened not to renew! The contract was eventually resolved at Medicare rates, with retroactive coverage.

The marketplace product is most purchased by the rural poor, operating on balance neutral to a slight loss. As the Southern Health’s CEO, you have received word that the your largest customer, a university, has approached Sun Memorial about creating a capitated product – cutting you out entirely. The CEO of Sun Memorial has also contacted you about starting an ACO together.

           Recall the risk matrix:


Low Risk/Low return: who cares?

High Risk/Low return: cancelling provider contracts as a negotiating ploy.

High Risk/High return: Entering into an ACO with Sun Memorial. Doing so shares your data with them & teaches them how to do analytics. This may negatively impact future negotiations and might even help them to structure the capitated contract correctly.

Low Risk/High Return: Pursue lobbying and legal action at the state/federal level to prevent further expansion of Sun Memorial. Maintain existing group business. Withdraw from unprofitable ACA marketplace business.

As CEO of Southern Health, you ultimately decide to hinder the chain’s acquisition strategy. You also withdraw from the marketplace but may reintroduce it later. Finally, you do decide to start an ACO – but with the primary competitor of Sun Memorial. You will give them analytic support as they are weak in analytics, thereby maintaining your competitive advantage.

From the insurer’s perspective the low risk and high return move is to continue the business as usual (late stage, mature company) and maintain margins in perpetuity. Adding new products is a high-risk high reward ‘blue ocean’ strategy that can lead to a new business line and either profit augmentation or revitalization of the business. However, in this instance the unprofitable marketplace product should be discontinued.


For the insurer, value is achieved by understanding, controlling, and minimizing risk.


Next, we’ll discuss things from the hospital system’s CEO perspective.


Defining value in healthcare through risk


For a new definition of value, it’s helpful to go back to the conceptual basis of payment for medical professional services under the RBRVS. Payment for physician services is divided into three components: Physician work, practice expense, and a risk component.

Replace physician with provider, and then extrapolate to larger entities.

Currently, payer (insurer, CMS, etc…) and best practice (specialty societies, associations like HFMA, ancillary staff associations) guidelines exist. This has reduced some variation among providers, and there is an active interest to continue this direction. For example, level 1 E&M clearly differs from a level 5 E&M – one might disagree whether a visit is a level 3 or 4, but you shouldn’t see the level 1 upcoded to 5. Physician work is generally quantifiable in either patients seen or procedures done, and for any corporate/employed practice, most physicians will be working towards the level of productivity they have contractually agreed to, or they will be let go/contracts renegotiated. Let’s hope they are fairly compensated for their efforts and not subjected solely to RVU production targets, which are falling out of favor vs. more sophisticated models (c.f. Craig Pedersen, Insight Health Partners).

Unless there is mismanagement in this category, provider work is usually controllable, measurable, and with some variation due to provider skill, age, and practice goals, consistent. For those physicians who have been vertically integrated, their current EHR burdens and compliance directives may place a cap on productivity.

Practice expenses represent those fixed expenses and variable expenses in healthcare – rent, taxes, facility maintenance, and consumables (medical supplies, pharmaceuticals, and medical devices). Most are fairly straightforward from an accounting standpoint. Medical supplies, pharmaceuticals, and devices are expenses that need management, with room for opportunity. ACO and super ACO/CIO organizations and purchasing consortiums such as Novation, Amerinet, and and Premier have been formed to help manage these costs.

Practice expense costs are identifiable, and once identified, controllable. Initially, six sigma management tools work well here. For all but the most peripheral, this has happened/is happening, and there are no magic bullets out there beyond continued monitoring of systems & processes as they evolve over time as drift and ripple effects may impact previously optimized areas.

This leaves the last variable – risk. Risk was thought of as a proxy for malpractice/legal costs. However, in the new world of variable payments, there is not only downside risk in this category, but the pleasant possibility of upside risk.

It reasons that if your provider costs are reasonably fixed, and practice expenses are as fixed as you can get them at the moment, that you should look to the risk category as an opportunity for profit.

As a Wall St. options trader, the only variable that really mattered to me for the price of the derivative product was the volatility of the option – the measure of its inherent risk. We profited by selling options (effectively, insurance) when that implied volatility was higher than the actual market volatility, or buying them when it was too low. Why can’t we do the same in healthcare?

What is value in this context? The profit or loss arising from the assumption and management of risk. Therefore, the management of risk in a value-based care setting allows for the possibility of a disproportionate financial return.

The sweet spot is Low Risk/High Return. This is where discovering a fundamental mispricing can return disproportionately vs. exposure to risk.

Apply this risk matrix to:

  • 1 – A medium sized insurer, struggling with hospital mergers and former large employers bypassing the insurer directly and contracting with the hospitals.
  • 2 – A larger integrated hospital system with at-risk payments/ACO model, employed physicians, and local competitors which is struggling to provide good care in the low margin environment.
  • 3 – group radiology practice which contracts with a hospital system and a few outpatient providers.

& things get interesting. On to the next post!

Some reflections on the ongoing shift from volume to value

As an intuitive and inductive thinker, I often use facts to prove or disprove my biases. This may make me a poor researcher, though I believe I would have been popular in circa 1200 academic circles. Serendipity plays a role; yes I’m a big Nassim Taleb fan – sometimes in the seeking, unexpected answers appear. Luckily, I’m correct more often than not. But honestly – in predicting widely you miss more widely.

One of my early mentors from Wall St. addressed this with me in the infancy of my career – take Babe Ruth’s batting average of .342 . This meant that two out of three times at bat, Babe Ruth struck out. However, he was trying to hit home runs. There is a big difference between being a base hit player and a home run hitter. What stakes are you playing for?

With that said, this Blog is for exploring topics I find of interest pertaining mostly to healthcare and technology. The blog has been less active lately, not only due to my own busy personal life (!) but also because I have sought more up-to-date information about advancing trends in both the healthcare payment sector and the IT/Tech sector as it applies to medicine. I’m also diving deeper into Radiology and Imaging. As I’ve gone through my data science growth phase, I’ll probably blog less on that topic except as it pertains to machine learning.

The evolution of the volume to value transition is ongoing as many providers are beginning to be subject to at least a degree of ‘at-risk’ payment. Stages of ‘at-risk’ payment have been well characterized – this slide by Jacque Sokolov MD at SSB solutions is representative:

Sokolove - SSB solutions slide 1

In 2015, approximately 20% of medicare spend was value-based, with CMS’s goal 50% by 2020. Currently providers are ‘testing the waters’ with <20% of providers accepting over 40% risk-based payments (c.f. Kimberly White MBA, Numerof & Associates). Obviously the more successful of these will be larger, more data-rich and data-utilizing providers.

However, all is not well in the value-based-payment world. In fact, this year United Health Care announced it is pulling its insurance products out of most of the ACA exchange marketplaces. While UHC products were a small share of the exchanges, it sends a powerful message when a major insurer declines to participate. Recall most ACO’s (~75%) did not produce cost savings in 2014, although more recent data was more encouraging (c.f. Sokolov).   Notably, out of the 32 Pioneer ACO’s that started, only 9 are left (30%) (ref. CMS). The road to value is not a certain path at all.

So, with these things in mind, how do we negotiate the waters? Specifically, as radiologists, how do we manage the shift from volume to value, and what does it mean for us? How is value defined for Radiology? What is it not? Value is NOT what most people think it is. I define value as: the cost savings arising from the assumption and management of risk. We’ll explore this in my next post.

Catching up with the “What medicine can learn from Wall St. ” Series

The “What medicine can learn from Wall Street” series is getting a bit voluminous, so here’s a quick recap of where we are up to so far:

Part 1 – History of analytics – a broad overview which reviews the lagged growth of analytics driven by increasing computational power.

Part 2 – Evolution of data analysis – correlates specific computing developments with analytic methods and discusses pitfalls.

Part 3 – The dynamics of time – compares and contrasts the opposite roles and effects of time in medicine and trading.

Part 4 – Portfolio management and complex systems – lessons learned from complex systems management that apply to healthcare.

Part 5 – RCM, predictive analytics, and competing algorithms – develops the concept of competing algorithms.

Part 6 – Systems are algorithms – discusses ensembling in analytics and relates operations to software.


What are the main themes of the series?

1.  That healthcare lags behind wall street in computation, efficiency, and productivity; and that we can learn where healthcare is going by studying Wall Street.

2.  That increasing computational power allows for more accurate analytics, with a lag.  This shows up first in descriptive analytics, then allows for predictive analytics.

3.  That overfitting data and faulty analysis can be dangerous and lead to unwanted effects.

4.  That time is a friend in medicine, and an enemy on Wall Street.

5.  That complex systems behave complexly, and modifying a sub-process without considering its effect upon other processes may have “unintended consequences.”

6.  That we compete through systems and processes – and ignore that at our peril as the better algorithm wins.

7.  That systems are algorithms – whether soft or hard coded – and we can ensemble our algorithms to make them better.


Where are we going from here?

– A look at employment trends on Wall Street over the last 40 years and what it means for healthcare.

– More emphasis on the evolution from descriptive analytics to predictive analytics to proscriptive analytics.

– A discussion for management on how analytics and operations can interface with finance and care delivery to increase competitiveness of a hospital system.

– Finally, tying it all together and looking towards the future.


All the best to you and yours and great wishes for 2016!



Some thoughts on Revenue Cycle Management, predictive analytics, and competing algorithms

After some reflection, this is clearly Part 5 of the “What medicine can learn from Wall Street” series.

It occurred to me while thinking about the staid subject of revenue cycle management (RCM) that this is a likely hotspot for analytics.   First, there is data – tons of data.  Second, it is relevant – folks tend to care about payment.

RCM is the method by which healthcare providers get paid, beginning from patient contact, leading to evaluation and treatment, and submitting charges by which we are ultimately paid via contractual obligation.  Modern RCM goes beyond billing, to include marketing, pre-authorization, completeness in the medical records to decrease denials, and ‘working’ the claims until payment is made.

Providers get paid by making claims.  Insurers ‘keep the providers honest’ by claim denials when the claims are not properly 1) pre-authorized, 2)documented, 3)medically indicated (etc).  There is a tug of war between both entities, which usually results in a relationship that ranges somewhere between grudging wariness to outright war (with contracts terminated and legal challenges fired off).  The providers profit by extracting the maximum profit they are contractually allowed to, the insurer by denying payment so that they can obtain investment returns on the pool of reserves they have.  Typically, the larger the reserve pool, the larger the profit.
Insurers silently fume at ‘creative coding’ where a change of coding rules causes a procedure/illness that has previously been paid at a lower level now paid at a much higher level.  Providers seethe at ‘capricious’ denials which require  staff work to provide whatever documentation requested (perhaps relevant, perhaps not) and ‘gotcha’ downcoding due to a single missing piece of information.  In any case, there is plenty of work for the billing & IT folks on either side.

Computerized revenue cycle management seems like a solution until you realize that the business model of either entity has not changed, and now the same techniques on either side can be automated.  Unfortunately, if the other guy does it, you probably need to too – here’s why.

We could get into this scenario:   A payor (insurer) when evaluating claims, decides that there is too much spend ($) on a particular ICD-9 diagnosis (or ICD-10 if you prefer) than expected and targets these for claim denials.  A provider would submit claims for this group, be denied on many of them, re-submit, be denied, and then either start ‘working’ the claims to gain value from them or if they had a sloppy or lazy or limited billing department, simply let them go (with resultant loss of the claim).  That would be a 3-12 month process.   However, a provider that was using descriptive analytics (see part 1) on say a weekly or daily basis would be able to see something was wrong more quickly – probably within three months – and gear up for quicker recovery.  A determined (and agressive) payor could shift their denial strategy to a different ICD-9 and something similar would occur.  After a few cycles of this, if the provider was really astute, they might data mine the denials to identify what codes were being denied and set up a predictive algorithm to compare new denials relative to their old book of business.  This would identify statistical anomalies in new claims, and could alert the provider about the algorithm the payor was using to target claims for denial.  By anticipating these denials, and either re-coding them or providing superior documentation to force the payor to pay (negating the beneficial effects of the payor’s claim denial algo), claims are paid in a timely and expected manner.  I haven’t checked out some of the larger vendors’ RCM offerings but I suspect that this is not far in the offing.

I could see a time where a very aggressive payor (perhaps under financial strain) strikes back with an algorithm designed to deny some, but not all claims on a semi-random basis to ‘fly under the radar’ and escape the provider’s more simple detection algorithms.  A more sophisticated algorithm based upon anomaly detection techniques could then be used to identify these denials….  This seems like a nightmare to me.  Once things get to this point, it’s probably only a matter of time until these games are addressed by the legislature.

Welcome to the battles of the competing algorithms.  This is what happens in high-frequency trading.  Best algorithm wins, loser gets poorer.

One thing is sure: in negotiations, the party who holds & evaluates the data holds the advantage.   The other party will forever be negotiating from behind..

P.S.  As an aside, with the ultra-low short term interest rates after the 2008 financial crisis, the time value of money is near all-time lows.  Delayed payments are an annoyance but apart from a cash-flow basis there is not any real advantage to delaying payments.   Senior management who lived through or studied the higher short term interest rates of the 1970’s-1980’s will recall the importance of managing the ‘float’ and good treasury/receivables operations.  Changing economic conditions could make this even more of a hot topic.

What Medicine can learn from Wall Street – Part 4 – Portfolio Management and complex systems

attrib: Asy ArchLet’s consider a single security trader.

All they trade is IBM.  All they need to know is that security and  its included indexes.  But start trading another security, such as Cisco (CSCO), while they have a position in IBM, and they have a portfolio.   Portfolios behave differently – profiting or losing on an aggregate basis from the combination of movements in multiple securities.  For example, if you hold 10,000 shares of IBM and CSCO, and IBM appreciates by a dollar while CSCO loses a dollar, you have no net gain or loss.  That’s called portfolio risk.

Everything in the markets is connected.  For example, if you’re an institutional trader, with a large (1,000,000 shares +) position in IBM, you know that you can’t sell quickly without tanking the market.  That’s called execution risk.  Also, once the US market closes (less of a concern these days than 20 years ago) there is less liquidity.  Imagine you are this large institutional trader, at home at 11pm.   A breaking news story develops about a train derailment of toxic chemicals near IBM’s research campus causing fires.   You suspect that it destroyed all of their most prized experimental hardware which will take years to replace.  Immediately, you know that you have to get out of as much IBM as possible to limit your losses.  However, when you get over to your trading terminal, the first bid in the market is $50 lower than the price that afternoon for a minuscule 10,000 shares.  If you sell at that price, the next price will be even lower for a smaller amount.   You’re stuck.  However, there is a relationship between IBM and the general market called a beta which is a correlation coefficient.  Since you cannot get out of your IBM directly, you sell a defined number of short S&P futures in the open market to simulate a short position in IBM.  You’re going to take a bath, but not as bad as the folks that went to bed early and didn’t react to the news.

A sufficiently large portfolio with >250 stocks will approximate broader market indexes (such as the S&P 500 or Russell index) depending upon composition.  It’s beta will be in the 0.9-1.1 range with 1.0 equaling a perfect correlation coefficient (r).  Traders attempt to improve upon this expected rate of return by strategic buys and sells of the portfolio components.  Any extra return above the expected rate of return of the underlying is alpha.   Alpha is what you pay managers for instead of just purchasing the Vanguard S&P 500 index and forgetting about it.  It’s said that most managers underperform the market indexes.  A discussion of Modern Portfolio Theory is beyond the scope of this blog, but you can go here for more.

So, excepting an astute manager delivering alpha (or an undiversified portfolio), the larger & more diversified the portfolio is the more it behaves like an index and the less dependent it is upon the behavior of any individual security.  Also, without knowing the exact composition of the portfolio and it’s proportions, it’s overall behavior can be pretty opaque.

MAIN POINT: The portfolio behaves as it’s own process; the sum of the interactions of its constituents.


Courtesy Arnold C.I postulate that the complex system of healthcare delivery behaves like a multiple security portfolio.  It is large, complex, and without a clear understanding of its constituent processes, potentially opaque.  The individual components of care delivery summate together to form an overall process of care delivery.  The over-arching hospital, outpatient, office care delivery process is a derivative process – integrating multiple underlying sub-processes.

We trace, review, and document these sub-processes to better understand them.  Once understood, metrics can be established and process improvement tools applied.  The PI team is called in, and a LEAN/Six Sigma analysis performed.  Six sigma process analytics typically focus on one sub-process at a time to improve its efficiency.  Improving a sub-process’ efficiency is a laudable & worthwhile goal which can result in cost savings, better care outcomes, and reduced healthcare prices.  However, there is also the potential for Merton’s ‘unintended consequences‘.

Most importantly, the results of the six sigma PI need to be understood in the context of the overall enterprise – the larger complex system.  Optimizing the sub-process when causing a bottleneck in the larger enterprise process is not progress!
This is because a choice of the wrong metric or overzealous overfitting may, while improving the individual process, create a perturbation in the system (a ‘bottleneck’) the negative effects of which are, confoundingly, more problematic than the fix.  Everyone thinks that they are doing a great job, but things get worse, and senior management demands an explanation.   Thereafter, a lot of finger pointing occurs.  These effects are due to dependent variables  or feedback loops that exist in the system’s process.  Close monitoring of the overall process will help in identifying unintended consequences of process changes.  I suspect most senior management folks will recall the time when an overzealous cost-cutting manager decreased in-house transport to the point where equipment idled and LOS increased.  I.E. The .005% saved by patient transport re-org cost the overall institution 2-3% until the problem was fixed.

There is a difference between true process improvement and goosing the numbers.  I’ve written a bit about this in real vs. fake productivity and my post about cost shifting.  I strongly believe it is incumbent upon senior management to monitor middle management & prevent these outcomes.  Well thought out metrics and clear missions and directives can help.  Specifically – senior management needs to be aware that optimization of sub-processes exists in the setting of the larger overall process and that optimization must also optimize the overall care process (the derivative process) as well.   An initiative that fails to meet both the local and global goals is a failed initiative!

It’s the old leaky pipe analogy – put a band-aid on the pipe to contain one leak, and the increased pressure in the pipe causes the pipe to burst somewhere else, necessitating another band-aid.  You can’t patch the pipe enough – too old.  The whole pipe needs replacement.  And the sum of repairs over time exceeds the cost of simply replacing it.

I’m not saying that process improvement is useless – far from it, it is necessary to optimize efficiency and reduce waste to survive in our less-than-forgiving healthcare business environment.  However, consideration of the ‘big picture’ is essential – which can be mathematically modeled.  The utility of modeling is to gain an understanding of how the overall complex process responds to changes – to avoid unintended consequences of system perturbation.

What medicine can learn from Wall Street – Part 3 – The dynamics of time

This a somewhat challenging post with cross-discipline correlations, some unfamiliar terminology, and concepts.  There is a payoff!

You can recap part 1 and part 2 here. 

The crux of this discussion is time.  Understanding the progression towards shorter and shorter time frames on Wall Street enables us to draw parallels and differences in medical care delivery particularly pertaining to processes and data analytics.  This is relevant because some vendors tout real-time capabilities in health care data analysis.  Possibly not as useful as one thinks.

In trading, the best profit one is a risk-less one.  A profit that occurs by simply being present, is reliable, and reproducible, and exposes the trader to no risk.  Meet arbitrage.  Years ago, it was possible for the same security to be trading at different prices on different exchanges as there was no central marketplace.  A network of traders could execute a buy of a stock for $10 in New York, and then sell those same shares on the Los Angeles exchange for $11.  If one imagines a 1000 share transaction, a $1 profit per share yields $1000.  It was made by the head trader holding up two phones to his head and saying ‘buy’ into one and ’sell’ into the other.*   These relationships could be exploited over longer periods of time and represented an information deficit.  However, as more traders learned of them, the opportunities became harder to find as greater numbers pursued them.  This price arbitrage kept prices reasonably similar before centralized, computerized exchanges and data feeds.

As information flow increased, organizations became larger and more effective, and time frames for executing profitable arbitrages decreased.  This led traders to develop simple predictive algorithms, like Ed Seykota did, detailed in part 1.  New instruments re-opened the profit possibility for a window of time, which eventually closed.  The development of futures, options, indexes, all the way to closed exchanges (ICE, etc…) created opportunities for profit which eventually became crowded.  Since the actual arbitrages were mathematically complex (futures have an implied interest rate, options require a solution of multiple partial differential equations, and indexes require summing instantaneously hundreds of separate securities) a computational model was necessary as no individual could compute the required elements quickly enough to profit reliably.  With this realization, it was only a matter of time before automated trading (AT) happened, and evolved into high-frequency trading with its competing algorithms operating without human oversight on millisecond timeframes.

The journey from daily prices to ever shorter prices over the trading day to millisecond prices was driven by availability of good data and reliable computing which could be counted to act on those flash prices.  Once a game of location (geographical arbitrage) turned into a game of speed (competitive pressures on geographical arbitrage) turned into a game of predictive analytics (proprietary trading and trend following) turned into a more complex game of predictive analytics (statistical arbitrage) was then ultimately turned back into a game of speed and location (High frequency trading).

The following chart shows a probability analysis of an ATM straddle position on IBM.  This is an options position.  It is not important to understand the instrument, only to understand what the image shows.  For IBM, the expected variance that exists in price at one standard deviation (+/- 1 s.d.) is plotted in below.  As time (days) increases along the X axis, the expected range widens, or becomes less accurate.

credit: TD Ameritrade
credit: TD Ameritrade

Is there a similar corollary for health care?

Yes, but.

First, recognize the distinction between the simpler price-time data which exists in the markets, vs the rich, complex multivariate data in healthcare.  

Second, assuming a random walk hypothesis , security price movement is unpredictable, and at best can only be calculated so that the next price will be in a range defined by a number of standard deviations according to one’s model as seen above in the picture. You cannot make this argument in healthcare.  This is because the patient’s disease is not a random walk.  Disease follows proscribed pathways and natural histories which allow us to make diagnoses and implement treatment options.

It is instructive to consider Clinical Decision Support tools.  Please note that these tools are not a substitute for expert medical advice (and my mention does not employ endorsement).  See Esagil and diagnosis pro.  If you enter “abdominal pain” into either of the algorithms, you’ll get back a list of 23 differentials (woefully incomplete) in Esagil and 739 differentials (more complete, but too many to be of help) in Diagnosis Pro.  But this is a typical presentation to a physician – a patient complains of “abdominal pain” and the differential must be narrowed.

At the onset, there is a wide differential diagnosis.  The possibility that the pain is a red herring and the patient really has some other, unsuspected, disease must be considered.  While there are a good number of diseases with a pathognomonic presentation, uncommon presentations of common diseases are more frequent than common presentations of rare diseases.

In comparison to the trading analogy above, where expected price movement is generally restricted to a quantifiable range based on the observable statistics of the security over a period of time, for a de novo presentation of a patient, this could be anything, and the range of possibilities is quite large.

Take, for example, a patient that presents to the ER complaining “I don’t feel well.”  When you question them, they tell you that they are having severe chest pain that started an hour and a half ago.  That puts you into the acute chest pain diagnostic tree.

Reverse Tree

With acute chest pain, there is a list of differentials that needs to be excluded (or ‘ruled out’), some quite serious.  A thorough history and physical is done, taking 10-30 minutes.  Initial labs are ordered (5-30 minutes if done in a rapid, in-ER test, longer if sent to the main laboratory) an EKG and CXR (chest X-ray) are done for their speed,(10 minutes for each)  and the patient is sent to CT for a CTA (CT Angiogram) to rule out a PE (Pulmonary embolism).  This is a useful test, because it will not only show the presence or absence of a clot, but will also allow a look at the lungs to exclude pneumonias, effusions, dissections, and malignancies. Estimate that the wait time for the CTA is at least 30 minutes.  

The ER doctor then reviews the results (5 minutes)- troponins are negative, excluding a heart attack (MI), the CT scan eliminated PE, Pneumonia, Dissection, Pneumothorax, Effusion, malignancy in the chest.  The Chest X-Ray excludes fracture.  The normal EKG excludes arrhythmia, gross valvular disease, and pericarditis.   The main diagnoses left are GERD, Pleurisy, referred pain, and anxiety.  ER doctor goes back to the patient (10 minutes) , patient doesn’t appear anxious & no stressors, so panic attack unlikely.  No history of reflux, so GERD unlikely.  No abdominal pain component, and labs were negative, so abdominal pathologies unlikely.  Point tenderness present on the physical exam at the costochondral junction – and the patient is diagnosed with costochondritis.  The patient is then discharged with a prescription for pain control.  (30 minutes).  

Ok, if you’ve stayed with me, here’s the payoff.

As we proceed down the decision tree, the number of possibilities narrows in medicine.

In comparison, price-time data – in which the range of potential prices increase as you proceed forward in time.

So, in healthcare the potential diagnosis narrows as you proceed down the x-axis of time.  Therefore, time is both one’s friend and enemy – friend as it provides for diagnostic and therapeutic interventions which establish the patient’s disease process; enemy as payment models in medicine favor making that diagnostic and treatment process as quick as possible (when a hospital inpatient).

We’ll continue this in part IV and compare it relevance to portfolio trading.

*As an aside, the phones in trading rooms had a switch on the handheld receiver – you would push them in to talk.  That way, the other party would not know that you were conducting an arbitrage!  They were often slammed down and broken by angry traders – one of the manager’s jobs was to keep a supply of extras in his desk, and they were not hard-wired but plugged in by a jack expressly for that purpose! trader's phone

**Yes, for the statisticians reading this, I know that there is an implication of a gaussian distribution that may not be proven.  I would suspect the successful houses have modified for this and have instituted non-parametric models as well.  Again, this is not a trading, medical or financial advice blog.


What Medicine can learn from Wall Street – Part 2 – evolution of data analysis

If you missed the first part, read it  here.  Note: For the HIT crowd reading this, I’ll offer (rough) comparison to the HIMSS stages (1-7).

1.     Descriptive Analytics based upon historical data.

Hand Drawn Chart of the Dow Jones Average
Hand Drawn Chart of the Dow Jones Average

     This was the most basic use of data analysis.   When newspapers printed price data (Open-High-Low-Close or OHLC), that data could be charted (on graph paper!) and interpreted using basic technical analysis, which was mostly lines drawn upon the chart. (1)  Simple formulas such as year-over-year (YOY) percentage returns could be calculated by hand. This information was merely descriptive and had no bearing upon future events.  To get information into a computer required data entry by hand, and operator errors could throw off the accuracy of your data.  Computers lived in the accounting department, with the data being used to record position and profit and loss (P&L).   At month’s end a large run of data would produce a computer-generated accounting matrix
     A good analogue to this system would be older laboratory reporting systems where laboratory test values were sent to a dedicated lab computer.  If the test equipment interfaced with the computer (via IEEE-488 & RS-232 interfaces) the values were sent automatically.  If not, data entry clerks had to enter these values.  Once in the system, data could be accessed by terminals throughout the hospital.  Normal ranges were typically included, with an asterisk indicating the value was abnormal.  The computer database would be updated once a day (end of day type data).  For more rapid results, you would have to go to the lab yourself and ask.  On the operations side, a Lotus 1-2-3 spreadsheet on the finance team’s computer of  quarterly charges, accounts receivable, and perhaps a few very basic metrics would be available to the finance department and CEO for periodic review.  
     For years, this delayed, descriptive data was the standard.  Any inference would be provided by humans alone, who connected the dots.  A rough equivalent would be HIMSS stage 0-1.

2.     Improvements in graphics, computing speed, storage, connectivity.

     Improvements in processing speed & power (after Moore’s Law), cheapening memory and storage prices, and improved device connectivity resulted in more readily available data.  Near real-time price data was available, but relatively expensive ($400 per month or more per exchange with dedicated hardware necessary for receipt – a full vendor package could readily run thousands of dollars a month from a low cost competitior, and much more if you were a full service institution).    An IBM PC XT of enough computing power & storage ($3000) could now chart this data.  The studies that Ed Seykota ran on weekends would run on the PC – but analysis was still manual. The trader would have to sort through hundreds of ‘runs’ of the data to find the combination of parameters which led to the most profitable (successful) strategies, and then apply them to the market going forward.  More complex statistics could be calculated – such as Sharpe Ratios, CAGR, and maximum drawdown – and these were developed and diffused over time into wider usage.  Complex financial products such as options could now be priced more accurately in near-real time with algorithmic advances (such as the binomial pricing model).
     The health care corollary would be in-house early electronic record systems tied in to the hospital’s billing system.  Some patient data was present, but in siloed databases with limited connectivity.  To actually use the data you would ask IT for a data dump which would then be uploaded into Excel for basic analysis.  Data would come from different systems and combining it was challenging.  Because of the difficulty in curating the data (think massive spreadsheets with pivot tables), this could be a full-time job for an analyst or team of analysts, and careful selection of what data was being followed and what was discarded would need to be considered, a priori.  The quality of the analysis improved, but was still human labor intensive, particularly because of large data sets & difficulty in collecting the information.  For analytic tools think Excel by Microsoft or Minitab.
     This corresponds to HIMSS stage 2-3.

3.     Further improvement in technology correlates with algorithmic improvement.Pretty
     With new levels of computing power, analysis of data became quick and relatively cheap allowing automated analysis.  Taking the same data set of computed results from price/time data that was analyzed by hand before; now apply an automated algorithm to run through ALL possible combinations of included parameters.  This is brute-force optimization.   The best solve for the data set is found, and a trader is more confident that the model will be profitable going forward.
ACTV5MA     For example, consider ACTV(2).  Running a brute force optimization on this security with a moving average over the last 2 years yields a profitable trading strategy that returns 117% with the ideal solve.  Well, on paper that looks great.  What could be done to make it even MORE profitable?  Perhaps you could add a stop loss.  Do another optimization and theoretical return increases.  Want more?  Sure.  Change the indicator and re-optimize.  Now your hypothetical return soars.  Why would you ever want to do anything else? (3,4)
     But it’s not as easy as it sounds.  The best of the optimized models would work for a while, and then stop.  The worst would immediately diverge and lose money from day 1 – never recovering.  Most importantly : what did we learn from this experienceWe learned that how the models were developed matteredAnd to understand this, we need to go into a bit of math.
    Looking at security prices, you can model (approximate) the price activity as a function, F(X)= the squiggles of a chart.  The model can be as complex or simple as desired.  Above, we start with a simple model (the moving average), and make it progressively more complex adding additional rules and conditions.  As we do so, the accuracy of the model increases, so the profitability increases as well.  However, as we increase the accuracy of the model, we use up degrees of freedom, making the model more rigid and less resilient.
     Hence the system trader’s curse – everything works great on paper, but when applied to the market, the more complex the rules, and the less robustly the data is tested, the more likely the system will fail due to a phenomenon known as over-fitting.  Take a look at the 3D graph below which shows a profitability model of the above analysis:3D optimization
     You will note that there is a spike in profitability using a 5 day moving average at the left of the graph, but profitability sharply falls off after that, rises a bit, and then craters.  There is a much broader plateau of profitability in the middle of the graph, where many values are consistently and similarly profitable.  Changes in market conditions could quickly invalidate the more profitable 5 day moving average model, but a model with a value chosen in the middle of the chart might be more consistently profitable over time.  While more evaluation would need to be done, the less profitable (but still profitable) model is said to be more ‘Robust’.

To combat this, better statistical sampling methods were utilized, namely cross-validation where an in-sample set is used to test an out-of-sample set for performance.  This gave a system which was less prone to immediate failure, i.e. more robust.  A balance between profitability and robustness can be struck, netting you the sweet spot in the Training vs. Test-set performance curve I’ve posted before.
So why didn’t everyone do this?  Quick answer: they did.  And by everyone analyzing the same data set of end-of-day historical price data in the same way, many people began to reach the same conclusions as each other.  This created an ‘observer effect’ where you had to be first to market to execute your strategy, or trade in a market that was liquid enough (think the S&P 500 index) that the impact of your trade (if you were a small enough trader – doesn’t work for a large institutional trader) would not affect the price.  Classic case of ‘the early bird gets the worm’. here
     The important point is that WE ARE HERE in healthcare.  We have moderately complex computer systems that have been implemented largely due to Meaningful Use concerns, bringing us to between HIMSS stages 4-7.  We are beginning to use the back ends of computer systems to interface with analytic engines for useful descriptive analytics that can be used to inform business and clinical care decisions.  While this data is still largely descriptive, some attempts at predictive analytics have been made.  These are largely proprietary (trade secrets) but I have seen some vendors beginning to offer proprietary models to the healthcare community (hospitals, insurers, related entities) which aim at predictive analytics.  I don’t have specific knowledge of the methods used to create these analytics, but after the experience of Wall Street, I’m pretty certain that a number of them are going to fall into the overfitting trap.  There are other, more complex reasons why these predictive analytics might not work (and conversely, good reasons why they may), which I’ll cover in future posts.  
     One final point – application of predictive analytics to healthcare will succeed in the area where it fails on Wall Street for a specific reason.  On Wall Street, the relationship once discovered and exploited causes the relationship to disappear.  That is the nature of arbitrage – market forces reduce arbitrage opportunities since they represent ‘free money’ and once enough people are doing it, it is no longer profitable.  However, biological organisms don’t response to gaming the system in that manner.  For a conclusive diagnosis, there may exist an efficacious treatment that is consistently reproducible.  In other words, for a particular condition in a particular patient with a particular set of characteristics (age, sex, demographics, disease processes, genetics) if accurately diagnosed and competently executed, we can expect a reproducible biologic response, optimally a total cure of the individual.  And that reproducible response applies to processes present in the complex dynamic systems that comprise our healthcare delivery system.  That is where the opportunity lies in applying predictive analytics to healthcare.

(1) Technical Analysis of Stock Trends, Edwards and Magee, 8th Edition, St. Lucie Press
(2) ACTIVE Technologies, acquired (taken private) by Vista Equity Partners and delisted on 11/15/2013.  You can’t trade this stock.   
(3) Head of Trading, First Chicago Bank, personal communication
(4) Reminder – see the disclaimer for this blog!  And if you think you are going to apply this particular technique to the markets to be the next George Soros, I’ve got a piece of the Brooklyn Bridge to sell you.

What medicine can learn from Wall Street – Part I – History of analytics

floorWe, in healthcare, lag in computing technology and sophistication vs. other fields.  The standard excuses given are: healthcare is just too complicated, doctors and staff won’t accept new ways of doing things, everything is fine as it is, etc…  But we are shifting to a new high-tech paradigm in healthcare, with ubiquitous computing supplanting or replacing traditional care delivery models.  Medicine has a ‘deep moat’ – both regulatory and through educational barriers to entry.  However, the same was said of the specialized skill sets of the financial industry.  Wall St. has pared its staffing down and has automated many jobs & continues to do so.  More product (money) is being handled by fewer people than before, an increase in real productivity.

Computing power in the 1960’s-1970’s on Wall street was large mainframe & mini-frame systems which were used for back-office operations.  Most traders operated by ‘seat of your pants’ hunches and guesses, longer term macro-economic plays, or using their privileged position as market-makers to make frequent small profits.  One of the first traders to use computing was Ed Seykota, who applied Richard Donchian’s trend following techniques to the commodity markets.  Ed would run computer programs on an IBM 360 on weekends, and over six months tested four systems with variations (100 combinations), ultimately developing an exponential moving average trading system that would turn a $5000 account into $15,000,000.(1)  Ed would run his program and wait for the output.  He would then manually select the best system for his needs (usually most profitable).  He had access to delayed, descriptive data which required his analysis for a decision.

In the 1980’s – 1990’s computing power increased with the PC, and text-only displays evolved to graphical displays.  Systems traders became some of the most profitable traders in large firms.  Future decisions were being made on historical data (early predictive analytics).   On balance well-designed systems traded by experienced traders were successful more often than not.  Testing was faster, but still not fast (a single security run on a x386 IBM PC would take about 8 hours).  As more traders began to use the same systems, the systems worked less well.  This was due to an ‘observer effect’., with traders trying to exploit a particular advantage quickly causing the advantage to disappear!  The system trader’s ‘edge’ or profitability was constantly declining, and new markets or circumstances were sought. ‘Program’ trades were accused of being the cause of the 1987 stock market crash.  

There were some notable failures in market analysis – Fast Fourier Transformations being one.  With enough computing power, you could fit a FFT to the market perfectly – but it would hardly ever work going forward.  The FFT fails because it presumes a cyclical formula, and the markets while cyclical, are not predictably so.  But an interesting phenomenon was that the better the fit in the FFT, the quicker and worse it would fall apart.  That was due to the phenomenon of curve-fitting.  ‘Fractals’ were all the rage later & failed just as miserably – same problem.  As an aside, it explains why simpler linear models in regression analysis are frequently ‘better’ than a high-n polynomial spline fit to the data, particularly when considered for predictive analytics.  The closer you fit the data, the less robust the model becomes and more prone to real-world failure.

Further advances in computing and computational statistics followed in the 1990’s-2000’s.  Accurate real-time market data became widely available and institutionally ubiquitous, and time frames became shorter and shorter.   Programs running on daily data were switched to multi-hour, hour, and then in intervals of minutes.The trend-following programs of the past became failures as the market became more choppy, and anti-trend (mean reversion) systems were popular.  Enter the quants –  the statisticians.(2)   With fast, cheap, near-ubiquitous computing, the scope of the systems expanded.   Now many securities could be analyzed at once, and imbalances exploited.  Hence the popularity of ‘pairs’ trading. Real-time calculation of indices created index arbitrage, which were able to execute without human intervention.

The index arbitrage (index-arb) programs relied on speed and proximity to the exchanges to have advantages in execution.  Statistical Arbitrage (Stat-arb) programs were the next development. These evolved into today’s High-Frequency-Trading programs (HFT’s) which dominate systems trading  These programs are tested extensively on existing data, and then are let loose on the markets to be run – with only high-level oversight.  They make thousands of trading decisions a second, incur real profits and losses, and compete against other HFT algorithms in a darwinian environment where the winners make money and are adapted further, and the losers dismissed with a digital death.  Master governing algorithms coordinate individual algorithms. (4)

The floor traders, specialists, market-makers, and scores of support staff that once participated in the daily business have been replaced by glowing boxes sitting in a server rack next to the exchange.  

Not to say that automated trading algorithms are perfect.  A rogue algorithm with insufficient oversight caused a forced sale of Knight Capital Group (KCG) in 2012.  (3)  The lesson here is significant – there ARE going to be errors once automated algorithms are in greater use – it is inevitable.

So reviewing the history, what happened on wall st.?
1.  First was descriptive analytics based upon historical data.
2.  Graphical Interfaces were improved.
3.  Improving technology led to more complicated algorithms which overfit the data. (WE ARE HERE)
4.  Improving data accuracy led to real-time analytics.
5.  Real time analytics led to shorter analysis timeframes
6.  Shorter analysis timeframes led to dedicated trading algorithms operating with only human supervision
7.  Master algorithms were created to coordinate the efforts of individual trading algorithms.

Next post, I’ll show the corollaries in health care and use it to predict where we are going.


(1) Jack Schwager, Market Wizards, Ed Seykota interview pp151-174.
(2) David Aronson, Evidence-based Technical Analysis, Wiley 2007
(3) Wall St. Journal, Trading Error cost firm $440 million, Marketbeat  

(4)Personal communication, HFT trader (name withheld)