Do we need more medical imaging?


Fanpic of the starship enterprise with deep dream

The original captain of the starship Enterprise, James T. Kirk addressed his ship with the invocation of, “Computer, …” .  For an audience in the late 1960’s it was a imagined miracle hundreds of years in the future.  In the early 1990’s, MIT’s SAIL Laboratory was dreaming of Project Oxygen – an ever-present, voice activated computer that could be spoken to and give appropriate responses.


“Hi, Siri” circa 2011
“Hello Alexa” circa 2016










Cloud computing, plentiful memory, on-demand massive storage and GPU-powered deep learning brought this future into our present.  Most of us already have the appliance (a smartphone) capable of connecting us to scalable cloud computing resources. Comparing current reality to the 1960’s expectations, this advancing world of ubiquitous computing is small, cheap, and readily available.

But imaging is not.  The current paradigm holds imaging as a rare, special, and expensive medical procedure.  In the days of silver-film radiology, with tomographic imaging and cut-film feeders for interventional procedures, it was a scarce resource.  In the first days of CT and MRI, requests for anything more complicated than an x-ray needed to pass through a radiologist.  These machines, and the skills necessary to operate them, were expensive and in short supply.

But is it still?  In a 2017 ER visit – the point of access to health care for > 50% of patients –  if your symptoms are severe enough, it is almost a certainty you will receive imaging early in your ER visit.  Belly pain? – CT that.  Worst headache of your life? – CT again.   Numbness on one side of your body?  Diffusion Weighted MRI.  And it is ordered on a protocol circumventing Radiology approval – why waste time in the era of 24/7 imaging with final interpretations available in under an hour.

I’ve written briefly about how a change to value-based care will upend traditional fee for service (FFS) delivery patterns.  But with that change from FFS, and volume to value, should we think about Radiology and other diagnostic services differently?  Perhaps medical imaging should be not rationed, but readily and immediately available – an equal to the history and physical.

I call this concept Ubiquitous Imaging ©, or Ubiquitous Radiology.   Ubiquitous Imaging is the idea that imaging is so necessary for the diagnosis and management of disease that it should be an integral part of every diagnostic workup, and guide every treatment plan where it is of benefit.  “A scan for every patient, when it would benefit to the patient.”

This is an aggressive statement.  We’re not ready for it just yet.  But let me explain why Ubiquitous Imaging is not so far off.

  1.  Imaging is no longer a limited good in the developed world
  2.  Artificial intelligence will increase imaging productivity, similar to PACS
  3.  Concerns about radiation dose will be salved by improvements in technology
  4.  Radiomics will greatly increase the value of imaging
  5.  Contrast use may be markedly decreased by an algorithm
  6.  Imaging will change from a cost center to an accepted part of preventative care in a value-based world.
  7. Physicians may shift from the current subspecialty paradigm to a Diagnosis-Acute Treatment-Chronic Care Management paradigm to better align with value based care.

Each of these points may sound like science fiction.  But the groundwork for each of these is being laid now:

In the US in 2017, there are 5,564 hospitals registered with the AHA.  Each of these will have some inpatient radiology services.  As of 2007, there were at least 10,335 CT Scanners operating in the US, and 7810 MRI scanners.  Using OECD library data from 2015, with 41 CT’s & 39 MRI’s per million inhabitants of the US, and a total US census of 320,000,000 we can calculate the number of US CT and MRI scanners in 2015 to be 13,120 and 12,480 respectively.

If proper procedures are followed with appropriate staffing and a lean/six sigma approach to scanning, it is conceivable that a modern multislice CT could scan one patient every ten minutes (possibly better), and be run almost 24/7 (downtime for maintenance & QA).  Thus, one CT scanner could image 144 patients daily. 144 scans/day x 365 days/year x 13120 CT scanners = 689,587,200 potential scans yearly – two scans a year for every US resident!

MRI imaging is more problematic because physics dictates the length of scans.  The T1 and T2 relaxation times are set by the length of the sequence in milliseconds, and making scans faster runs up against the laws of physics.  While there are some ‘shortcuts’, we pay for those with T2* effects and decreased resolution.  Stronger magnets & gradients help, but at higher cost and a risk of energy transfer to the patient.  So at optimal efficiency and staffing, the best you could probably get is 22 studies daily (a very aggressive number).  22 MRI studies/day x 365 days/year x 12480 MRI’s = 100,214,400 studies yearly.  Or enough to scan 1/3 of the US population yearly.  (Recent discussions at RSNA 2017 suggest MRI scans might be able to be shortened to the length of CT)

Think about this.  We can CT scan every US citizen twice in a one year period, and we continue to think about imaging as a scarce resource.  One in three US citizens can be scanned with MRI annually.  Imaging is not scarce in the developed world.

X-ray is the most commonly performed imaging procedure, including mammography & fluoroscopy, accounting for up to 50% of radiology studies.  The CT/MR/US and nuclear medicine studies occupy the other 50%.  A bit of backing out on the number above will suggest capacity on the order of 2.256 billion possible studies a year.

We’ve done the studies – how will we interpret them?  A physician (MD) examines every study and interprets them, delivering a report.  There are about 30,656 radiologists in the USA (2012 AMA physician masterfile).  Nieman HPI suggests that estimate may be low, and gives an upper range of 37,399 radiologists.

A busy radiologist on a PACS system could interpret 30,000 studies a year.  30,656 x 30,000 = 919,680,000 potentially interpretable studies from our workforce.  Use the high estimate and the capacity number rises to 1.12 billion.  That’s a large variance from the 2.256 billion studies performed.  However, it is suggested that about 50% of studies, usually X-ray and Ultrasound, are performed and interpreted by non-radiologists.  So, that gets us back to 1.12 billion studies.

Recall that Radiologists did not always interpret studies on computer monitors (PACS).  Prior to PACS, a busy radiologist would read 18,000 studies a year.  Radiologists experienced a jump in productivity when we went from interpreting studies based on film to interpreting studies on PACS systems.

Artificial Intelligence algorithms are beginning to appear in Radiology at a rapid pace.  While it is early in the development of these products, there is no question in the minds of most informed Radiologists that computer algorithms will be a part of radiology.  And because AI solutions in radiology will not be reimbursed additionally, cost justification needs to come from productivity.  An AI algorithm in Radiology needs to justify its price by making the radiologist more efficient, so that cost is borne by economies of scale.

Now imagine that the AI algorithms develop accuracy similar to a radiologist.  Able to ‘trust’ the algorithms and thereby streamline their daily work processes, Radiologists no longer are limited to interpreting 30,000 studies a year.  Perhaps that number rises to 45,000.  Or 60,000.  I can’t in good conscience consider a higher number.  The speed of AI introduction, if rapid and widespread, may cause some capacity issues, but the aging population, retiring radiologists, well-informed medical students responding to the “invisible hand” and perpetual trends toward increasing demand for imaging services will form a new equilibirum.  Ryan Avent of the Economist (who’s book Wealth of Humans is wonderful reading) has a more resigned opinion, however.

One of the additional functions of Radiologists is to manage the potentially harmful effects of the dose of ionizing radiation used in X-rays.  We know that high levels of ionizing radiation cause cancerWhether lower levels of radiation cause cancer is controversialHowever, it is likely that some (low) percentage of cancer is actually CAUSED by medical imaging.  To combat this, we have used the ALARA paradigm in medical imaging, and in recent years to combat concerns associated with higher doses received in advanced imaging, the image gently campaign.

Recently, James Brink MD of the American College of Radiology (ACR) testified to the US congress about the need for contemporary research on the effects of the radiation doses encountered in medical imaging.  Without getting too much into the physics of imaging, more dose usually yields crisper, “prettier” images at higher resolution.

But what if there was another way to do this?  Traditionally, Radiologists have relied upon equipment makers to improve hardware and extract better signal/noise ratios which would allow for a lower radiation dose.  But in a cost-concious era, it is difficult to argue for more expensive new technologies if there is no reimbursement advantage.

However, an interesting pilot study used an AI technique on CT scans to ‘de-noise’ the images, improving their appearance.   The noise was added after artificially after the scan, rather than present at the time of imaging.  A number of papers at NIPS 2017 dealt with super-resolution.  Could similar technologies exist for imaging?  Paras Lahkani seems to think so.

Put hardware & software improvement together and we might be able to substantially decrease dose in ionizing radiation.  If this dose is low enough, and research bears out that there is a dose threshold below which radiation doesn’t cause any real effects, we could “image gently” with impunity.

Are we using the information in diagnostic imaging effectively?  Probably not.  There is just too much information on a scan for a single radiologist to report entirely.  But with AI algorithms also looking at diagnostic images, there is much more information that we can extract from the scan than we currently are.  The obvious use case is volumetrics.

The burgeoning science of Radiomics includes not only volumetrics, but also relationships between the data present on the scan we may not be able to perceive directly as humans.  Dr. Luke Oakden-Rayner caused a brief internet stir with his preliminary precision radiology article in 2017, using an AI classifier (a CNN) to predict patient survival from CT images.  While small, it showed the possibility of advanced informational discovery on existing datasets and application of those findings in a practical manner.  Radiomics feature selection has similar problems to that of genomics feature selection, in that the large number of data variables may predispose to more chance correlations than in traditionally designed, more focused experiments.

At the RSNA 2017, a number of machine learning companies were making their debut.  One of the more interesting offerings was Subtle Medical, a machine learning application designed to reduce contrast dose in imaged patients.  Not only would this be disruptive to the contrast industry by reducing the amount of administered contrast by a factor of 5 or higher (!), but it would remove one of the traditional concerns about contrast – its potential toxicity.  CT uses iodinated contrast, and MRI uses Gadolinium-based contrast.  Using less implies less toxicity and less cost, so this is a win all-around.

The economics of imaging could fill a book, let alone a blog post.  In a fee-for service world, imaging was a profit center, and increasing capacity and maximizing the number of imaging services was sensible to encourage a profitable service line.  With declining reimbursement, it has become less so (but still profitable).  However, as we transition to value-based care, how will radiology be seen?  Will it be seen as a cost-center, with radiologists fighting over a piece of the bundled payment pie, or something else?  Will it drive reduced or increased imaging utilization?  Target metrics and ease of attainment in the ACO drive this decision, with easier targets correlated with greater imaging. Particularly if imaging is seen as providing greater value, utilization should continue to rise.

Specialty training as it exists currently may not be sufficient to prepare for the way medicine is practiced in the future.  A specialty (and sup-specialty) approach was reasonable when information was not freely available, and the amount of information to know was overwhelming without specialization.  But as we increase efficiencies in medical care, care access goes along a definable path: Patient complaint -> Investigation -> Diagnosis -> Acute Treatment ->Chronic Treatment.  Perhaps it would make more sense to organize medicine along those lines as well?  Particularly in the field of diagnosis, I am not the only physician recognizing the shift occurring.  A well-thought out opinion piece written by Saurabh Jha MD and Eric Topol MD, Radiologists and Pathologists as Information Specialists, broaches that there is more similarity between the two specialties than differences, particularly in an age of artificial intelligence.  Should we call for a new Flexner report, ending the era of physician-basic scientists and beginning the dominance of physician-informaticists and physician-empaths?

Perhaps it is time to consider imaging not as a limited commodity, but instead to recognize it as a widely available resource, to be used as much as is reasonable.  By embracing AI, radiomics, new payment models, the radiologist as an informatician, and basic research on radiation safety, we can get there.

©2017 – All rights reserved

Where does risk create value for a hospital? (Value as Risk series post #3)

towers1Let’s turn to the hospital side.

For where I develop the concept of value as risk management go here 1st, and where I discuss the value in risk management from an insurer’s perspective click here 2nd.

The hospital is an anxious place – old fat fee-for-service margins are shrinking, and major rule set changes keep coming. To manage revenue cycles requires committing staff resources (overhead) to compliance related functions, further shrinking margin. More importantly, resource commitment postpones other potential initiatives. Maintaining compliance with Meaningful Use (MU) 3 cum MACRA, PQRS, ICD-10 (11?) and other mandated initiatives while dealing with ongoing reviews Read more

Follow up to “The Etiquette of Help”

c.f. mark ong at
Superior Mesenteric Angiogram demonstrating a right colonic bleed.

I came across this wonderful piece by Bruce Davis MD on Physician’s 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.

A conversation with Farzad Mostashari MD

I participated in a webinar with Farzad Mostashari MD, scM, former director of the ONC (Office of the National Coordinator for Health IT)  sponsored by the data analytics firm Wellcentive   He is now a visiting fellow at the Brookings Institution.  Farzad spoke on points made in a recent article in the American Journal of Accountable Care, Four Key Competencies for Physician-led Accountable Care Organizations.  

The hour-and-a-half format lent itself well to a Q&A format, and basically turned into a small group consulting session with this very knowledgeable policy leader!  

1.  Risk Stratification.  Begin using the EHR data by ‘hot spotting.’  Hot spotting refers to a technique of identifying outliers in medical care and evaluating these outliers to find out why they are consuming resources significantly beyond that of the average.  The Oliver Wyman folks wrote a great white paper that references Dr. Jeffrey Brenner of the Camden Coalition who identified the 1% of Medicaid patients responsible for 30% of the city’s medical costs.  Farzad suggests that data mining should go further and “identify populations of ‘susceptibles’ with patterns of behavior that indicate impending clinical decomposition & lack of resilience.”   He further suggests that we go beyond a insurance-like “risk score” to understand how and why these patients fail, and then apply targeted interventions to prevent susceptibles from failing and over utilizing healthcare resources in the process.  My takeaway from this is in the transition from volume to value, bundled payments and ACO style payments will incentivize physicians to share and manage this risk, transferring a role onto them traditionally filled only by insurers.

2.  Network Management.  Data mining the EHR enables organizations to look at provider and resource utilization within a network.  (c.f. the recent Medicare physician payments data release).  By analyzing this data, referral management can be performed.   By sending patients specifically to those providers who have the best outcomes / lowest costs for that disease, the ACO or insurer can meet shared savings goals.  This would help to also prevent over-utilization – by changing existing referral patterns and excluding those providers who always choose the highest-cost option for care (c.f. the recent medicare payment data for ophthalmologists performing intraocular drug injections – wide variation in costs).  This IS happening – Aetna’s CEO Mark Bertolini, said so specifically during his HIMSS 2014 keynote.   To my understanding, network analysis is mathematically difficult (think eigenfunctions, eigenvalues, and linear algebra) – but that won’t stop a determined implementer from it (it didn’t stop Facebook, Google, or Twitter).  Also included in this topic was workflow management, which is sorely broken in current EHR implementations, clinical decision support tools (like ACRSelect), and traditional six sigma process analytics.

3.  ADT Management.  This was something new.  Using the admission/discharge/transfer data from the HL7 data feed, you could ‘push’ that data to regional health systems.  It achieves a useful degree of data exchange not currently present without a regional data exchange.   Patients who bounce from one ER to the next could be identified this way.  Its also useful to push to the primary care doctors (PCP) managing those patients.  Today, where PCP’s function almost exclusively on an outpatient basis and hospitalists manage the patient while in the hospital, the PCP often doesn’t know about a patient’s hospitalization until they present to the office.  Follow-up care in the first week after hospitalization may help to prevent readmissions. According to Farzad, there is a financial incentive to do so – a discharge alert can enable a primary care practice to ensure that every discharged patient has a telephone follow-up within 48 hours and an office visit within 7 days which would qualify for a $250 “transition in care” payment from Medicare.  (aside – I wasn’t aware of this. I’m not a PCP, and I would carefully check medicare billing criteria closely for eligibility conditions before implementing, as consequences could be severe.  Don’t just take my word for it, as I may be misquoting/misunderstanding and medicare billers are ultimately responsible for what they bill for.  This may be limited to ACO’s.  Due your own due diligence)

4.  Patient outreach and engagement.  One business point is that for the ACO to profit, patients must be retained.  Patient satisfaction may be as important to the business model as the interventions the ACO is performing, particularly as the ACO model suggests a shift to up-front costs and back-end recovery through shared savings.  If you as an ACO invest in a patient, to only lose that patient to a competing ACO, you will let your competitor have the benefit of those improvements in care and eat those sunk costs!  To maintain patient satisfaction and engagement, behavioral economics (think Cass Sunstein’s  paper), gamification (Jane McGonigal ), A/B Testing (Tim Ferriss) marketing techniques.  Basically, we’re applying customer-centric marketing to healthcare, with not only the total lifetime revenue of the patient considered, but also the total lifetime cost!

It was a very worthwhile discussion and thanks to Wellcentive for hosting it!  

On Mentoring, compassion, curing and healing.

Once, I had a not-so-brief flirtation with Neurosurgery.  In medical school, I was awed by the structural and functional specificity of the brain, and fascinated by the almost priestly status of the neurosurgery attendings.  Unlike other attendings, they did EVERYTHING themselves – from operating to NICU management (including respirators) to clinics to research.  The gung-ho spirit of the specialty is infectious to those it speaks to – which is good because Neurosurgery demands a commitment so overwhelming it is more of a lifestyle choice than a profession.
Unfortunately, my medical school did not have a top-ranked Neurosurgery program, so in my fourth year of medical school, it was up to the Mecca in Boston to steep myself in its culture.  I was a curiosity, but the department was professionally committed to my education, which I appreciated.  Daily rounds were done with different attendings as I tried to soak up as much as possible so that I could learn how to be a great, academic neurosurgeon.

One night, after a long day of operative procedures and clinics which started at 6am, I was rounding alone with the patriarch of the Neurosurgery department.  This man was one of the greats – a society chairman, an expert in an esoteric and challenging area of neurosurgery, a prolific paper and book writer – a man who had dedicated his life to the pursuit of knowledge and surgical skill as a penultimate goal in his 60+ years of life.  I was honored that he allowed me to round with him, in truth.

We were seeing the last patient of the day – late at night.  It was a woman who had come to him with a brain tumor deemed inoperable by all others.  The surgeon had taken her to the OR a few days ago, and tried to wrest the cancer from her brainstem.  He proceeded painstakingly, with extreme care.  The movements of his hands were precise and slight during the operation.  Every time he tried to extirpate the tumor, it caused physiologic instability.  It was nerve-wracking to observe.  After a number of hours, he closed.  

The patient was awake and awaiting the surgeon.  She asked him, “Did you get it?”  He answered, “No.”   She then asked, “Am I going to die?”  He answered softly, “Yes.”

The patient started to cry.  And then I saw this man, this giant, this scientist and clinician beyond reproach – sit down on the bed and put his arm around the patient.  He held her until she stopped crying.  It was more than a perfunctory few minutes.

This man, at the pinnacle of his field, could act any way he wanted.  He could spin on his heel and leave the room, snap at the patient and tell her to get herself together – and nobody would ever reproach him.   Such a gifted surgeon could act any way he wished.

But instead he reached out in a more compassionate way to a suffering patient than many physicians I have known.   It was not just programmed, scripted ‘compassion’ learned from a patient experience consultant – he waited there until she had exhausted her grief at that moment.   She knew that he was there for her in a human, healing way, now that he could no longer cure her.  I stood still, listening to her muffled sobs for at least 15 minutes.

The lesson learned – if the best of the best could show such compassion, so could I.  Perhaps other surgeon’s responses I had seen lacking empathy were really a marker of a lesser degree of competence, a cover-up for personal or professional inadequacies instead of a mark of importance.

I ultimately did not choose Neurosurgery as my specialty.  But the lesson stayed with me.  I hope that in my practice I was able to show this degree of caring to my patients, many of whom came to me in extreme sickness, many of whom I would never be able to cure.  I hope that I was able to heal them in some way.

As might be expected with many years passage, he is now gone.  But the picture of him is how I remember him, and I will share it with you. (from the MGH dept. of Neurosurgery web site)

OjemannPortrait2008x600wGood night, Dr. Ojemann. (1931-2010)