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Using Machine Learning to Optimize Hospital Operations
kw
Posted on November 13, 2018 at 3:06 pm
Many healthcare systems grapple with the paradox where assets are over-booked but under-utilized daily. LeanTaaS is a startup that is using machine learning to solve this problem.

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On November 15, 2018, kw commented on Machine Learning and Radiologists: Friends or Foes? :

Great article! Would love to learn more about the impact on physician workflow and clinical practices since the IBM Watson Health collaboration. As a consumer, I’m comfortable with my physician using technology/ML when arriving at a medical opinion. I agree with the comments above that machine learning will more likely augment the role of a physician, rather than replace them. For example, I imagine most radiologists see 99% of “no tumor” images and 1% of “possible/confirmed tumor” images; I’d think machine learning algorithms can easily identify the 99% “no tumor” images and flag the 1% “possible/confirmed tumor” images for the radiologist as a way to prioritize their time.

On November 15, 2018, kw commented on As 3D printing drugs in rural hospitals becomes a reality, what are the opportunities and threats for AstraZeneca? :

Great read! I’ve never heard about 3D-printing drugs before. The caution I have is with regards to quality control and in turn liability. At least in the US, there is a ton of quality control and testing in drug manufacturing to make sure that its API, formulation, etc. is precisely as is in the approved label. Without adequate quality control, one can imagine the safety and efficacy of a 3-D printed drug be very different and have costly consequences.

On November 15, 2018, kw commented on 3D Printing Better Surgery at the Mayo Clinic :

Interesting read! There have been recent advances in using 3D printing for organs and also preclinical research. I have a sense that 3D printing is still cost-prohibitive to most hospitals because of the limited near-term use case, which makes a centralized outsource model more viable. But as 3D-printing organs get approved and preclinical 3D models get validated in the future, it’s exciting to think that 3D printing should be more affordable and more widely implemented in clinical practice!

On November 15, 2018, kw commented on An Open Call for Help: Can Pfizer Find the Cure for Cancer… Outside the Walls of Pfizer? :

Great read! I think the competitive advantage of a pharma is inherently tied to its IP (e.g., gene therapy manufacturing expertise), so I am also curious about how patents are awarded and how contributors are compensated in a crowdsource model. Perhaps another form of open innovation (albeit a more traditional approach) is to give early-stage research grants to scientists in areas that NIH and other gov’t funds are not addressing. In the long-run, I think a big innovation challenge pharma continues to face is prioritizing its funnel of novel drugs from P1 to P3 more akin to a nimble biotech…

On November 15, 2018, kw commented on Using Open Innovation to Solve the Ocean Plastics Problem :

Interesting application of open innovation and blockchain to solve this problem! I wonder how much scale Plastics Bank has in Haiti and Philippines. From a “chicken and egg” network effect view, is it more important to reach critical mass for collectors, recyclers, or companies? I agree with the comment above that subsidizing the cost in the short-term may enable Plastics Bank to reach critical mass and reduce costs in the long-term.

On November 15, 2018, kw commented on Slack’s Battle Against Information Overload :

Great read! As a recent Slack user, I can certainly resonate with this pain of managing information both within the app and across apps. In response to the risks of missing information in the comments above, perhaps ML can be used to prioritize the messages based on a rank of importance, rather than filtering out messages.