Khosla writes well in his techcrunch article Do We Need Doctors Or Algorithms; talking about how technology and algorithms that will replace 80 percent of physicians.
Khosla writes well in his techcrunch article Do We Need Doctors Or Algorithms; talking about how technology and algorithms that will replace 80 percent of physicians.
It’s hard to not to get excited with the vision and examples that Khosla brings in the article but it’s easy to peel off the thin veneer of a VC hyping his portfolio companies once you stop panting from the excitement.
The vital signs could all be determined with the help of mobile devices, the operation of which do not require years of training and a certification. You will be able to do this by yourself—Philips already is using the iPhone camera to try to measure vital indicators, others will be even more innovative and as an insurance company it would be cost-effective to give them to every insured person for free. Skin Scan is measuring your risk of skin cancer from a photograph of a skin lesion. Telemedicine is accelerating and a Qualcomm company is measuring heart rates using an iPhone. Cell phones that display your vital signs and take ultrasound images of your heart or abdomen are in the offing as well as genetic scans of malignant cells that match your cancer to the most effective treatment. Ear infection and skin rash pictures and more will all be mobile phone based, often supplemented by the kind of (fractal) analysis that Skin Scan does, and more than what the doctors naked eye could usually see.
The history of symptoms, illnesses, and test results could be accessed, processed, and assessed by a computer to see any correlation or trends with the patient’s past. You are the one providing the doctor with the symptoms anyway after all!
Any follow-up hunts for clues could again be done with mobile devices. Etc…
I have no doubt that better expert systems, high-power computing in smaller format devices, better graphics at lower costs, better understanding of what parameters count with better man-machine interfaces and fast access to expert answers could help replace 80% of physicians and could help more people be healthier and at a lower cost than the current healthcare system.
But they won’t. Not soon anyhow. There are a few bugs in Khoslas vision:
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- A VC doesn’t actually have to deliver great products with outstanding value to customers. He just has to make a 5-10x return on investment. Khosla is a VC hyping his portfolio companies. The time to exit might be as much as 10 years and that requires a lot of hype to keep funding flowing and deals coming. He doesn’t have to actually solve healthcare issues (because truth be told – there is an insanely cheap way to solve Americans’ health issues and that is for people to walk instead of driving and stop eating fried snickers for breakfast) – all he has to do is sell the startup to GE Healthcare and make his numbers.
- Over-optimistic time estimations. It won’t be 10 years. It might be 100 years. My anecdotal evidence for this is based on an expert system we worked on almost 40 years ago for diagnosing and treating female infertility. The computing technology has approved by over 4 million times (consider Moore’s Law) but the problem has still not been solved to the point where we can do without 80% of the physicians in this space. I won’t even go into the reasons for why a project we thought should take 3 years may take 100 years, because beyond the usual software project estimation error, problems of software implementation and algorithm development there are many factors that we probably don’t know yet or won’t be able to solve with technology – like compatibility between men and women.
- Confusing technology performance with the quality of algorithms and deep understanding of what parameters that we measure in order to analyze, diagnose and treat a particular clinical issue are really important. Yes – it is true. A mechanical algorithm with a well-tested and proven record is better than doctors relying on hind-sight and intuition in many many cases. The challenge is arriving at the algorithm. It may take another 100 years of basic science research before the female infertility problem is cracked and then we will be in the realm of software implementation risk and project overruns where things still take 2-4x as long as we thought. Yes it is true. Brute force computing is a great substitute for inefficient algorithms but being able to run a lot of fast iterations with a crappy algorithm doesn’t help a woman trying to get pregnant as she tires of needles and measurements after 5 years or more of trying.
- Big data for healthcare is not retail data. Khosla makes a big point about the promise of big-data for healthcare. This is arguable.The US consumer industry has been a major influence on the big data analytics space. If it works for sales transactions in retail, so the reasoning goes, it should work in healthcare as well.Actually no.Meaningful machine analysis of big EHR data sets could be possible but is probably currently infeasibleexcept in well-defined and well-controlled use cases. The reason for this is that unlike retail transactions that are well defined and deterministic, EHE data is unstructured, poorly codified and of low evidence value.
- Current EHR systems store large volumes of data about diseases and symptoms in unstructured text, codified using systems like SNOMED-CT.Even if the data was perfectly codified, it is impossible to achieve meaningful machine diagnosis of medical interview data that was uncertain to begin with and not collected nor validated using evidence-based methods.
- Big healthcare data is by its very nature retrospective data, which is of very low evidence value. Using retrospective data, or “experience”, is fraught with dangers, misleading us to draw the wrong conclusions about what is cause and effect. For this very reason, we replaced retrospective data with prospective, controlled, data when we dumped anecdotal medicine and replaced it with evidence based medicine, over 50 years ago. The US Preventive Services Task Force regards retrospective evidence as Level III, i.e. the worst possible. UK National Health Service regards this evidence as Level C or D on a scale of A through D.
One of the commenters (tcoyote) on the Khosla article said it well:
Big thinkers like Khosla have had an embarrassing track record investing in healthcare over the past twenty years. The Silicon Valley geninuses have squandered hundreds of millions of dollars of venture money in “molecule of the month” biotech companies, brought us “recreational genomics” from 23andme, etc., and $3000 a test single gene tests with debatable connections to future health risk. It’s been fun to watch. Glad not to be one of their limiteds.
The healthcare landscape sure looks different from 40 thousand feet than it does from the ground. I think Khosla’s thesis is abject bullshit. It’s not just diagnosis physicians do, but manage evolving and complex situations in real time. Knowing the patient and knowing how patients think and act is really important in being a good physician. We’re a hundred years away from knowing enough about human disease to do what he suggests. Sounds great as a TED talk though.
It could happen and the technologist in me is sure it will, just not in the lifetime of Khoslas portfolio companies.
