Preparing the 21st century healthcare industry

Bob Curry, Ph.D.

As KGI prepares the strategic plan that will take it to its 25th anniversary, the members of the KGI community are being challenged to imagine where we need to be 2022 to continue to prepare cutting-edge graduates who will work across the healthcare value chain.

During a post-dinner exercise Wednesday night, we were encouraged to consider a map of imminent changes in healthcare suggested by Bob Curry, who is chairman of our Board of Trustees, a veteran VC, and now CEO of Perceptimed.

Based on this experience, Bob suggested four megatrends that KGI should consider:

1. Growth in the science of diagnostic, prognostic, and monitoring tools will be explosive and be increasing paired with drug and procedure usage.
2. Drugs will become every more customized to treat highly defined cohorts as characterized in the discussion point above. This will change both the nature of drug discovery and of clinical trail design.
3. Healthcare will be delivered by a broader, integrated team of professionals than has been the traditional norm. Pharmacists and clinical diagnosticians will be teamed with physicians and nurses in staffing the healthcare system.
4. Hospital systems and health insurance companies, as we currently recognize them, will disappear and will be replaced by 50-100 integrated care organizations to cover the entire U.S. (e.g. 50-100 Kaiser-like organizations).

The first two points appealed to our scientists in the room, who (since our 1997 founding) have been thinking about genomic and personalized medicine. The third point relates to those interested in clinical care delivery, particularly our pharmacy school which trains its PharmD students to work in teams with MDs and RNs. The final point ties to the business side — our group within KGI — and the changes brought by ACA (Obamacare), both pushed by the strong patient incentives for adverse selection and pulled by incentives for Affordable Care Organizations.

Based on this, the 70+ trustees, faculty, staff and students at nine tables generated a series of ideas. From their ideas — and my own observations — I see four important trends:

• The importance of big data and data analytics. This is not just for analyzing genomic data for personalized medicine, but for patterns of clinical and other bioinformatic data for efficacy, drug-drug interactions, and other healthcare outcomes.
• New types of healthcare providers and business models for funding them. 
• Increasing importance of healthcare economics. Whether it’s HMOs, ACOs, capitation models, bundled pricing, or other approaches, we are moving away from a fee-for-service and dollars-per-pill model toward outcomes-based compensation.
• New regulatory approaches to deal with these changes.

Some of these trends were building and accelerating over the past two decades. (I've been with one HMO for 30 years). Others were accelerated by the ACA. Still others (the destruction of insurance companies) were not among the announced goals of the ACA, but may be its inevitable outcome.

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.

Spurring innovation for a system that doesn't want it

This morning I watched the #bigdatamed conference at Stanford via webcast. (I was out of town earlier in the week, presenting my own talk, and couldn’t attend the conference in person.)

The session I watched was entitled “Big Data Opportunities for Healthcare Startups,” part of a larger conference on “Big Data in Biomedicine: Driving Innovation for a Healthier World.” The panel consisted of Ori Geva (Medial Research), Vicki Seyfert-Margolis (MyOwnMed), Risa Stack (GE Healthmagination), Warren Hogarth (Sequoia Capital) and moderator Chris Longhurst (Lucile Packard Children’s Hospital).

It’s no revelation to say that healthcare startups face huge regulatory barriers that drive up both costs and capital requirements. It’s hard to imagine — other than perhaps a defense company — a more regulated industry the USA. (And, as I showed in a 2008 study, aerospace communications allowed market entry by some underfunded entrepreneurs who later gave us Qualcomm).

It’s also no revelation that most stakeholders — patients, doctors, taxpayers, the government, insurance companies — would like better outcomes at a lower cost, but have different perspectives on how to do so.

Notably, former Kleiner Perkins exec Risa Stack cited last week’s published lament by healthcare IT entrepreneur Jonathan Roth about the difficult of bringing innovation into the healthcare system. She cited his three barriers:

1. Healthcare consumers don’t shop. … With hardly an exception, patients can’t shop for healthcare. Why compare prices if you have no choice? Caregivers can’t shop for healthcare either. Few doctors know the true cost of tests and treatments they refer patients to. The setup leaves caregivers with little incentive to differentiate themselves by embracing innovative care delivery practices.

2. The biggest buyer stifles innovation. The government is the biggest buyer in the healthcare market. In fact, it represents more than half of all healthcare buying in the U.S. Unfortunately, the big spender has not prioritized supporting innovation, but rather minimizing the scenario of maximum regret: the audit, the lawsuit, the death. This compliance burden breeds risk-averse behaviors among healthcare providers, not creativity. Furthermore, the government rewards caregivers for a narrowly defined set of activities, limiting their appetite for innovation and thus for entrepreneurial services and technology.

3. Service, quality, and competitive pricing aren’t rewarded. By and large, doctors continue to be paid based on how many services and tests they provide. Here and there, they are also paid for reporting some data. They are not commonly incentivized to compete on price and quality, and there’s no obvious way to display that information for shoppers.

While Stack thought high-deductible plans might make patient more cost-sensitive, she offered no hope on the other two.

In her talk, former FDA official (turned healthcare social media entrepreneur), Vicky Seyfert-Margolis, noted the pressures coming to the system for greater real world effectiveness. For example, right now oncology drugs are tested for late stage cancers that have failed chemo (with benefits measured in terms of 3 month survival) rather than earlier in the process. Since her husband helped get Obama re-elected, I was not surprised at her optimism about the effect of the Affordable Care Act and Accountable Care Organizations. However, Stack noted that ACOs would tend to increase patient switching costs and decrease choice (which by the way would exacerbate Roth’s concerns).

Still, there was one glimmer of hope. Given HIPAA, the one party that could actually do something to enable patient data availability for better outcomes is the patient him/herself. There are important technical and economic barriers to overcome, but also potential payoffs.

As VC Warren Hogarth noted, this would be an improvement over the current system, where patients (for example) give their tissue to a hospital for (say) a cancer test and then the hospital controls who and how that sample is used. The technical problem, noted Ori Geva, is how will the data be made available in a portable way so that the patient can share it on other platforms.

Several panelists noted the opportunity (and challenge) was what’s in it for the patient. On the one hand, patients [likely an unusually motivated subpopulation] opt in to sharing their data on Patients Like Me. Many patients also want to be treated as co-managers of their own data or (as Seyfert-Margolis noted), the household “chief medical officer” (i.e. mom) does so on behalf of the family.

One challenge is that (as noted) patients don't shop. Another is the (realistic) fear that medical groups, insurance companies or HMOs will use the data to reduce choices for patients in the name of cost containment. (I have no problem with Kaiser choosing cancer treatment A over cancer treatment B based on my genotype, but I start to get pissed when they tell me I can’t have a diagnostic because I’m “low risk”).

On the one hand, the normal way of getting something through the system is to appeal to payers. If entrepreneurs can find a way to directly appeal to patients — as have 23 & Me, Patients Like Me and the various quantified self efforts — perhaps this will create other opportunities for innovation that don’t require winning the end-to-end cooperation of the entire system.