Great read. I see the potential benefit of this technology — helping to offload some of the onerous charting requirements physicians face in combination with providing support in clinical decision making. I have two concerns, one of which will probably be ameliorated over time. First, I imagine there will be a fair number of errors during early adoption. If Microsoft rolls this out too soon and the number of errors are significant (be it in quantity or, certainly, in quality), I think they risk losing physician buy-in very quickly. Nothing is more frustrating than using a scribe service only to find out that you have to go back at the end of the shift/day and correct multiple documentation errors made by the scribe. In these cases, I know from personal experience that many physicians will elect not to have a scribe at all and remain skeptical.
Second, I believe this technology cannot fall into the same trap as current EHRs. Many current electronic systems are created to meet insurance/government metrics, and, I would argue, all else is secondary (including measuring meaningful clinical metrics, streamlining physician workflow, and improving the patient experience). As a result, data entry has become a large part of physicians’ workflow, and the information collected has not led to the potent clinical insights that were hoped for or promised.
Great submission. I agree with the premise of your article — we have an unprecedented ability to collect individual patient-level data in the healthcare space. However, our ability to make use of this data is sadly lagging, and computer learning/analytics might be able to fill the gap. While the machines may not have to make actual decisions (as mentioned by Energy), I still think your Watson comparison is valid. Unlike most other fields, mistakes in healthcare often have catastrophic consequences. Even machines’ synthesis of this data must be thoroughly scrutinized and verified for accuracy. Moreover, recommendations made by machines will still rely on human input (programming, etc). The way forward should be a slow, methodical one.
You’ve done a good job of highlighting the opportunity of crowdsourcing to help speed the process of discovery and R&D. I have two concerns, however. First, I think there is a risk of opening the gates to all ideas, good and bad, which has the potential to create more work for Pfizer as it has to weed out good/credible ideas from a larger pool. Second, I think there are concerns about fair compensation for ideas that are used. I wonder if another “disruptive technology,” blockchain, could help in this regard.
Great analysis. My impression is, like most things medical, entrenched businesses are moving at a cautious (some might say glacial?) pace in terms of new technology. I would argue that the true benefit of additive manufacturing/personalized medicine in many of Medtronic’s successful product lines is yet unproven. However, if Medtronic wants to branch out from its traditional core business lines (pursuing areas like, say, tissue engineering and biomedical scaffolding), I agree with you that a delay in the exploration of the technology could put it at a disadvantage going forward.
I think you did a great job framing the challenge of transitioning to value-based care and the opportunities for machine-based learning in this space. Our ability to gather immense amounts of patient data is quickly outstripping our ability/time to meaningfully process and leverage this data. New technologies tout the ability to almost continually monitor heart rate, blood pressure, glucose, and any number of other physiologic metrics. Currently, however, this begs the question of: so what? Ten thousand heart rate measurements are meaningless unless they can be placed in the context of a patient’s baseline physiology and then used as a warning sign for impending medical disease or deterioration when the patient strays from that baseline. Few physicians have time in their clinical work lives to process such immense amounts of data, but machines can.