Friday, May 23, 2014
Nascent biotech entrepreneurs
In between days of the Stanford Big Data in Biomedicine conference, on Thursday night I attended the finals of the Oxbridge Biotech Roundtable Onestart Americas business plan competition. I posted my thoughts over on my Engineering Entrepreneurship blog.
Thursday, May 22, 2014
Stone age EHR
Today was my first #bigdatamed conference data at Stanford Medical School, which is hosting a three day conference on Big Data in Biomedicine. (I wasn't able to come Wednesday but watched two sessions on the live webcast).
I first learned of the conference last year from Atul Butte (@ajbutte), who I met when he presented at the 2012 Open Science Summit. My impression from Atul (and watching the webcast last year) is this is a bunch of computational biologists who’ve replaced their wet labs with databases (or nowadays, cloud computing accounts), in search of the next great lead to be found on their computer screen.
Certainly the first two panels yesterday fit that pattern (the first moderated by Butte). So did the after-lunch keynote by former UCSD professor Phil Bourne, creator of the PDB (protein database): a few months ago, Bourne joined NIH as its first-ever associate director for data science, reporting directly to NIH Director Francis Collins.
Translating from Science to Practice
But today the conversation broadened (as one slide put it) from the "science of medicine (biomedical research)" to the "practice of medicine (healthcare)". In other words, from faculty to the clinicians, and from universities (few industry scientists were present) to hospitals and clinics.
Some of the differences were as expected. Drug discovery researchers are at the bleeding edge of the science, and then after 5 or 10 or 15 years of drug development (animal models, clinical trials, regulatory filings, manufacturing, marketing etc.) the product finally shows up in the hands of doctors. Similarly, researchers are hoping to add to their journal publications while providers are trying to improve clinical outcomes — and increasingly under pressure to do so at higher efficiency (of both time their time and the amount spent on tests and treatments).
For clinicians, HIPAA privacy rules limit dramatically what and how data can be used and shared. Researchers have institutional review boards, but also face HIPAA restrictions. The NIH helpfully makes available a brief (16-page) note on researchers should interpret the interaction of IRB and HIPAA privacy constraints. (As it turns out, both clinicians and non-clinical researchers at the conference complained that HIPAA places unrealistic limits on combining data from differing sources to render an assessment of a given patient's health).
Proprietary vs. Open Platforms
At some point, it was inevitable that participants would discuss where the patient’s clinical data resides. Ten years ago, it was in paper charts, but now the ACA has strong incentives and penalties to store it in an electronic health record or EHR. (The administration’s healthcare IT czar says don’t call it an “electronic medical record”).
It was also inevitable that someone would ask: if we are compiling personal genomic data for patients, how will that data be made available for the clinical benefit of that patient? By one estimate, a patient’s EHR runs less than 100 megabytes while whole genome data (I’m told) runs into the gigabytes. As David Watson (ex Kaiser CTO, now at Oracle) said on today’s opening panel, medical images (such as MRI scans) are stored external to the EHR; will that happen with genomic data?
More seriously, how will such data be phased into operational systems? On the same panel, Jim Davies (CTO for England’s 100K genome project) suggested that existing EHRs would need an abstraction layer that would allow new data types to be added on, i.e. the way that apps, plug-in modules and extensions are added to other modern software systems.
However, today the EHR vendors (except for VistA) as proprietary as mainframe platform companies of the 1960s. Even Kaiser — which in 2010 had the largest private EHR implementation to date — is highly dependent on a proprietary vendor (Epic).
Proprietary control of the platform means high switching costs and other proprietary control of the customer, and so (I predict) this is something that none will relinquish unless forced to. We have a technical solution, but not a market solution. And the ACA penalties for EHR non-compliance mean that no provider can credibly defer or set aside EHR adoption until one provides the necessary openness.
So we know where we need to go, but it’s not clear how we get there. Two Harvard researchers — Zak Kohane and Ken Mandl — have proposed a way forward, and the following year won $15 million from HHS to implement their Smart Platforms project.
However, the plan seems to think that either vendors will see openness as being in their own interests or that customers will organize to demand openness. As someone who’s studied IT openness for 15 years, I can say that openness is almost always instituted by the weakest player (e.g. a late entrant), and right now I don’t see an obvious candidate in the EHR market.
WIthout such openness, health care providers are stuck with healthcare IT systems without third party add-ons. This is not just pre-app store, but pre-IBM PC, pre-Apple II, vertically integrated platforms with little if any choice to extend or change their systems. In other words, EHR systems are stuck in the stone age (1960s) of the digital computer era, with little prospect for improvement.
I first learned of the conference last year from Atul Butte (@ajbutte), who I met when he presented at the 2012 Open Science Summit. My impression from Atul (and watching the webcast last year) is this is a bunch of computational biologists who’ve replaced their wet labs with databases (or nowadays, cloud computing accounts), in search of the next great lead to be found on their computer screen.
Certainly the first two panels yesterday fit that pattern (the first moderated by Butte). So did the after-lunch keynote by former UCSD professor Phil Bourne, creator of the PDB (protein database): a few months ago, Bourne joined NIH as its first-ever associate director for data science, reporting directly to NIH Director Francis Collins.
Translating from Science to Practice
But today the conversation broadened (as one slide put it) from the "science of medicine (biomedical research)" to the "practice of medicine (healthcare)". In other words, from faculty to the clinicians, and from universities (few industry scientists were present) to hospitals and clinics.
Some of the differences were as expected. Drug discovery researchers are at the bleeding edge of the science, and then after 5 or 10 or 15 years of drug development (animal models, clinical trials, regulatory filings, manufacturing, marketing etc.) the product finally shows up in the hands of doctors. Similarly, researchers are hoping to add to their journal publications while providers are trying to improve clinical outcomes — and increasingly under pressure to do so at higher efficiency (of both time their time and the amount spent on tests and treatments).
For clinicians, HIPAA privacy rules limit dramatically what and how data can be used and shared. Researchers have institutional review boards, but also face HIPAA restrictions. The NIH helpfully makes available a brief (16-page) note on researchers should interpret the interaction of IRB and HIPAA privacy constraints. (As it turns out, both clinicians and non-clinical researchers at the conference complained that HIPAA places unrealistic limits on combining data from differing sources to render an assessment of a given patient's health).
Proprietary vs. Open Platforms
At some point, it was inevitable that participants would discuss where the patient’s clinical data resides. Ten years ago, it was in paper charts, but now the ACA has strong incentives and penalties to store it in an electronic health record or EHR. (The administration’s healthcare IT czar says don’t call it an “electronic medical record”).
It was also inevitable that someone would ask: if we are compiling personal genomic data for patients, how will that data be made available for the clinical benefit of that patient? By one estimate, a patient’s EHR runs less than 100 megabytes while whole genome data (I’m told) runs into the gigabytes. As David Watson (ex Kaiser CTO, now at Oracle) said on today’s opening panel, medical images (such as MRI scans) are stored external to the EHR; will that happen with genomic data?
More seriously, how will such data be phased into operational systems? On the same panel, Jim Davies (CTO for England’s 100K genome project) suggested that existing EHRs would need an abstraction layer that would allow new data types to be added on, i.e. the way that apps, plug-in modules and extensions are added to other modern software systems.
However, today the EHR vendors (except for VistA) as proprietary as mainframe platform companies of the 1960s. Even Kaiser — which in 2010 had the largest private EHR implementation to date — is highly dependent on a proprietary vendor (Epic).
Proprietary control of the platform means high switching costs and other proprietary control of the customer, and so (I predict) this is something that none will relinquish unless forced to. We have a technical solution, but not a market solution. And the ACA penalties for EHR non-compliance mean that no provider can credibly defer or set aside EHR adoption until one provides the necessary openness.
So we know where we need to go, but it’s not clear how we get there. Two Harvard researchers — Zak Kohane and Ken Mandl — have proposed a way forward, and the following year won $15 million from HHS to implement their Smart Platforms project.
However, the plan seems to think that either vendors will see openness as being in their own interests or that customers will organize to demand openness. As someone who’s studied IT openness for 15 years, I can say that openness is almost always instituted by the weakest player (e.g. a late entrant), and right now I don’t see an obvious candidate in the EHR market.
WIthout such openness, health care providers are stuck with healthcare IT systems without third party add-ons. This is not just pre-app store, but pre-IBM PC, pre-Apple II, vertically integrated platforms with little if any choice to extend or change their systems. In other words, EHR systems are stuck in the stone age (1960s) of the digital computer era, with little prospect for improvement.
Sunday, May 4, 2014
The next wave of life science startups (and entrepreneurs)
On Wednesday, KGI held a major public event for the finals of our business plan competition. This year (as with last year), the competition was tied to our business plan class, which was first offered in Fall 2003, for our third graduating class of MBS students. Also like last year, the class was team taught by me (as the business guy and course coordinator) and Mark Brown, a KGI grad and senior scientist at a local KGI spinoff company.
This year we had 17 MBS students and 8 (PhD-educated) PPM students across seven teams. The students worked with four external sponsors (plus KGI) to develop detailed business plans — product, sales, marketing, operations and financing — for the patented invention provided by (in most cases) the university technology transfer office.
The teams included therapeutics, research tools/services and a medical device:
Entrepreneurship is the lifeblood of any high-tech industry, including biotechnology and the related life science industries that have arisen over the past 30 years. Being entrepreneurial — and applied — are two of KGI’s core values, that we try to embody in our programs, courses, events, faculty and students.
Mark and I want to thank all the judges, the university sponsors and of course our student entrepreneurs for all the hard work that made this event possible.
This year we had 17 MBS students and 8 (PhD-educated) PPM students across seven teams. The students worked with four external sponsors (plus KGI) to develop detailed business plans — product, sales, marketing, operations and financing — for the patented invention provided by (in most cases) the university technology transfer office.
The teams included therapeutics, research tools/services and a medical device:
- Elegans Therapeutics: a novel treatment for asthma (California Institute of Technology)
- Insituomics: improved visualization of RNA transcripts (California Institute of Technology)
- Click-Brains: software that analysis neurological MRI scans (Children’s Hospital Los Angeles)
- Klondike Therapeutics: improved therapy for anthrax (Keck Graduate Institute)
- Cardiovascular Cell Source: improved quality supply for endothelial cells (UC Merced)
- Mucotherapeutics: therapy to clear mucus for COPD (UC Merced)
- Innovfusion: improved epidural infusion pump (BioFactory Pte. Ltd)
- Robert Baltera, a director of the San Diego Venture Group, former CEO of Amira Pharmaceuticals until its acquisition by Bristol-Meyers Squib, a 17 year Amgen veteran (and a KGI trustee)
- Craig Brooks, angel investor, head of two current life science startups (BCN Biosciences, Biostruxs) and a 19 year Amgen veteran who formerly worked for Procter & Gamble
- Robert Curry, partner of Latterell Venture Partners, former general partner of Alliance Technology Ventures, former faculty member at the University of Delaware (and chair of the KGI trustees)
- Stephen Eck, vice president of Astellas who previously worked for Eli Lilly and Pfizer, a board-certified hematologist/ oncologist (and a member of the Board of Advisors of the KGI School of Pharmacy)
- James Widergren, a former senior VP, group vice president and treasurer of Beckman Coulter, angel investor (and a former KGI trustee)
 |
| Head judge Bob Curry with the winners of KGI’s 2014 business plan competition: Jagan Choudhary, Jixi He, Ashi Jain and Melanie Ufkin |
Entrepreneurship is the lifeblood of any high-tech industry, including biotechnology and the related life science industries that have arisen over the past 30 years. Being entrepreneurial — and applied — are two of KGI’s core values, that we try to embody in our programs, courses, events, faculty and students.
Mark and I want to thank all the judges, the university sponsors and of course our student entrepreneurs for all the hard work that made this event possible.
Wednesday, January 29, 2014
Someday putting doctors out of business
In the KGI business class today, a student idly asked “when will computer replacing doctors in diagnosing patients?” Another student said “never”.
It's pretty clear that technology will reduce the labor-intensity of medical care, and also shift some tasks from expensive high-skill people to inexpensive low-skill people. Look at how cars were made by Gottlieb Daimler, Henry Ford, Toyota in the 1970s and then today.
Computers will over the next 20-40 years replace some or all of the role of doctors in diagnosing conditions. It’s too labor intensive and expensive not to become a target. The question is not if, but when, where first and how fast.
The claim will be that it's intended to improve consistency and accuracy, but the real reason will be cost. Claimed improvements in quality — such as for patients who lack access to a specialist for diagnosis — could be used to overcome the opposition of highly educated, compensated and organized physicians.
The initial push thus will come from an organization that both has scale economies and a record of innovating to save pennies. My prediction is that the first major use in North America will fall in one of three categories:
It's pretty clear that technology will reduce the labor-intensity of medical care, and also shift some tasks from expensive high-skill people to inexpensive low-skill people. Look at how cars were made by Gottlieb Daimler, Henry Ford, Toyota in the 1970s and then today.
Computers will over the next 20-40 years replace some or all of the role of doctors in diagnosing conditions. It’s too labor intensive and expensive not to become a target. The question is not if, but when, where first and how fast.
The claim will be that it's intended to improve consistency and accuracy, but the real reason will be cost. Claimed improvements in quality — such as for patients who lack access to a specialist for diagnosis — could be used to overcome the opposition of highly educated, compensated and organized physicians.
The initial push thus will come from an organization that both has scale economies and a record of innovating to save pennies. My prediction is that the first major use in North America will fall in one of three categories:
- US government, probably the Veterans Health Administration
- An HMO, almost certainly Kaiser Permanente; or
- A provider serving rural areas, most likely the First Nations and Inuit Health Branch of Health Canada.
Subscribe to:
Posts (Atom)