Category Archives: research methodology

Gruen: A unified economic theory of privately provided public goods and social capital

Nicholas delivers a terrific presentation (1hr 7mins) to The Australian Centre for Social Innovation about the private provision of public goods and the subsequent generation of social capital.

It is as crisply considered as it is thought provoking:

  • after all this time, I think I finally understand that economists believe themselves to be the purveyors and arbiters of well-being, and ultimately health – no wonder they’re so suspicious of, and have so much trouble relating to medicine and health care
  • it would be interesting to apply this prism to health care – I don’t think it will present favourably

The presentation wraps with a 5 min video of the Family by Family program – a compelling sounding intervention that generates an abundance of social capital by developing and then using resources embedded in the community.

Many references to Adam Smith, Hayek and Robert Putnam.

Great to see something not confined to the sub-20min constraint.

Bravo Nicholas!!


http://clubtroppo.com.au/2013/12/20/public-goods-privately-provided-the-video/

 

 

Big Data supporting NZ diabetes policy

  • NZ is using big data to drive improvements in diabetes policy and planning
  • The  Virtual Diabetes Register (VDR) is aggregating data from 6 data sources:
  1. hospital admissions coded for diabetes
  2. outpatient attendees for diabetes
  3. diabetes retinal screening
  4. prescriptions of specific antidiabetic therapies
  5. laboratory orders for measuring diabetes management
  6. primary health (general practitioner) enrollments
  • the analytics showed that Indian and Pacific people have the highest diabetes prevalence rates

http://www.futuregov.asia/articles/2013/dec/13/new-zealand-health-improves-diabetes-policy-big-da/

NEW ZEALAND HEALTH IMPROVES DIABETES POLICY WITH BIG DATA ANALYTICS

By Kelly Ng | 13 December 2013 | Views: 2743

The Ministry of Health New Zealand uses big data analytics to accurately determine current and predict future diabetic population to improve diabetes policy planning.

In collaboration with experts from the New Zealand Society for the Study of Diabetes (NZSSD), the ministry created a Virtual Diabetes Register (VDR) that pulls and filters health data from six major databases.

The six data sources were: hospital admissions coded for diabetes, outpatient attendees for diabetes and diabetes retinal screening, prescriptions of specific antidiabetic therapies, laboratory orders for measuring diabetes management and primary health (general practitioner) enrollments.

According to Emmanuel Jo, Principal Technical Specialist at Health Workforce New Zealand, Ministry of Health, the previous way of measuring diabetes using national surveys was inefficient, expensive and had a high error rate.

The new analytical model, using SAS software, significantly improved the accuracy and robustness of the system, combining several data sources to generate greater insights.

Interestingly, analytics showed that Indian and Pacific people have the highest diabetes prevalence rate, said Dr. Paul Drury, Clinical Director of the Diabetes Auckland Centre and Medical Director of NZSSD. Health policies can therefore be focused on this group.

“We have 20 different District Health Boards, and the data can show them how many diabetic people are in their area,” Drury said.

“GPs should know already how many they have, but the VDR is also able to help them predict who may be at risk so they can be prepared. By knowing the populations where diabetes is more prevalent, more resources can be directed at them to provide clinical quality improvements,” he added

Patient privacy is protected by regulating access to data in the VDR.

Simply Statistics on scientific folly…

http://simplystatistics.org/2013/12/16/a-summary-of-the-evidence-that-most-published-research-is-false/

A summary of the evidence that most published research is false

One of the hottest topics in science has two main conclusions:

  • Most published research is false
  • There is a reproducibility crisis in science

The first claim is often stated in a slightly different way: that most results of scientific experiments do not replicate. I recently got caught up in this debate and I frequently get asked about it.

So I thought I’d do a very brief review of the reported evidence for the two perceived crises. An important point is all of the scientists below have made the best effort they can to tackle a fairly complicated problem and this is early days in the study of science-wise false discovery rates. But the take home message is that there is currently no definitive evidence one way or another about whether most results are false.

  1. Paper: Why most published research findings are falseMain idea: People use hypothesis testing to determine if specific scientific discoveries are significant. This significance calculation is used as a screening mechanism in the scientific literature. Under assumptions about the way people perform these tests and report them it is possible to construct a universe where most published findings are false positive results. Important drawback: The paper contains no real data, it is purely based on conjecture and simulation.
  2. Paper: Drug development: Raise standards for preclinical researchMain ideaMany drugs fail when they move through the development process. Amgen scientists tried to replicate 53 high-profile basic research findings in cancer and could only replicate 6. Important drawback: This is not a scientific paper. The study design, replication attempts, selected studies, and the statistical methods to define “replicate” are not defined. No data is available or provided.
  3. Paper: An estimate of the science-wise false discovery rate and application to the top medical literatureMain idea: The paper collects P-values from published abstracts of papers in the medical literature and uses a statistical method to estimate the false discovery rate proposed in paper 1 above. Important drawback: The paper only collected data from major medical journals and the abstracts. P-values can be manipulated in many ways that could call into question the statistical results in the paper.
  4. Paper: Revised standards for statistical evidenceMain idea: The P-value cutoff of 0.05 is used by many journals to determine statistical significance. This paper proposes an alternative method for screening hypotheses based on Bayes factors. Important drawback: The paper is a theoretical and philosophical argument for simple hypothesis tests. The data analysis recalculates Bayes factors for reported t-statistics and plots the Bayes factor versus the t-test then makes an argument for why one is better than the other.
  5. Paper: Contradicted and initially stronger effects in highly cited research Main idea: This paper looks at studies that attempted to answer the same scientific question where the second study had a larger sample size or more robust (e.g. randomized trial) study design. Some effects reported in the second study do not match the results exactly from the first. Important drawback: The title does not match the results. 16% of studies were contradicted (meaning effect in a different direction). 16% reported smaller effect size, 44% were replicated and 24% were unchallenged. So 44% + 24% + 16% = 86% were not contradicted. Lack of replication is also not proof of error.
  6. PaperModeling the effects of subjective and objective decision making in scientific peer reviewMain idea: This paper considers a theoretical model for how referees of scientific papers may behave socially. They use simulations to point out how an effect called “herding” (basically peer-mimicking) may lead to biases in the review process. Important drawback: The model makes major simplifying assumptions about human behavior and supports these conclusions entirely with simulation. No data is presented.
  7. Paper: Repeatability of published microarray gene expression analysesMain idea: This paper attempts to collect the data used in published papers and to repeat one randomly selected analysis from the paper. For many of the papers the data was either not available or available in a format that made it difficult/impossible to repeat the analysis performed in the original paper. The types of software used were also not clear. Important drawbackThis paper was written about 18 data sets in 2005-2006. This is both early in the era of reproducibility and not comprehensive in any way. This says nothing about the rate of false discoveries in the medical literature but does speak to the reproducibility of genomics experiments 10 years ago.
  8. Paper: Investigating variation in replicability: The “Many Labs” replication project. (not yet published) Main ideaThe idea is to take a bunch of published high-profile results and try to get multiple labs to replicate the results. They successfully replicated 10 out of 13 results and the distribution of results you see is about what you’d expect (see embedded figure below). Important drawback: The paper isn’t published yet and it only covers 13 experiments. That being said, this is by far the strongest, most comprehensive, and most reproducible analysis of replication among all the papers surveyed here.

I do think that the reviewed papers are important contributions because they draw attention to real concerns about the modern scientific process. Namely

  • We need more statistical literacy
  • We need more computational literacy
  • We need to require code be published
  • We need mechanisms of peer review that deal with code
  • We need a culture that doesn’t use reproducibility as a weapon
  • We need increased transparency in review and evaluation of papers

Some of these have simple fixes (more statistics courses, publishing code) some are much, much harder (changing publication/review culture).

The Many Labs project (Paper 8) points out that statistical research is proceeding in a fairly reasonable fashion. Some effects are overestimated in individual studies, some are underestimated, and some are just about right. Regardless, no single study should stand alone as the last word about an important scientific issue. It obviously won’t be possible to replicate every study as intensely as those in the Many Labs project, but this is a reassuring piece of evidence that things aren’t as bad as some paper titles and headlines may make it seem.

Many labs data. Blue x’s are original effect sizes. Other dots are effect sizes from replication experiments (http://rolfzwaan.blogspot.com/2013/11/what-can-we-learn-from-many-labs.html)

The Many Labs results suggest that the hype about the failures of science are, at the very least, premature. I think an equally important idea is that science has pretty much always worked with some number of false positive and irreplicable studies. This was beautifully described by Jared Horvath in this blog post from the Economist.  I think the take home message is that regardless of the rate of false discoveries, the scientific process has led to amazing and life-altering discoveries.

Forcing the prevention industry – a 10 year journey

Vision

  • The Future of Human API www.thehumanapi.com
  • Forcing the prevention industry into existence
  • Stage Zero disease detection and treatment

Critical trends:

  • lab-in-a-box diagnostics
  • quantified self
  • medical printing

When these trends converge, there’ll be an inflection point where a market is established.

Health data moves from system of record >> system of engagement.

Promoting the evolution from a Product mentality to a Market mentality

As treatment starts to focus on Stage Zero/pre-clinical disease,  it turns into prevention.

 

Video: http://www.youtube.com/watch?feature=player_embedded&v=gJHaoqeucX8

http://www.forbes.com/sites/johnnosta/2013/12/12/the-asymptotic-shift-from-disease-to-prevention-thoughts-for-digital-health

The Asymptotic Shift From Disease To Prevention–Thoughts For Digital Health

It’s been said that good artists borrow and great artist steal.  And I believe that Picasso was right.  So, I guess I’m somewhere between a thief and a artist and that suits me just fine.

I’ve stolen from two great thinkers, so let’s get that out of the way.  The first isDaniel Kraft, MD. Daniel Kraft is a Stanford and Harvard trained physician-scientist, inventor, entrepreneur, and innovator. He’s the founded and Executive Director of FutureMed, a program that explores convergent, rapidly developing technologies and their potential in biomedicine and healthcare. He’s also a go-to source on digital health. I’m stealing “zero stage disease” from Dr. Kraft. Simply put, it’s the concept of disease at its most early, sub-clinical stage.  It’s a point where interventions can halt or change a process and potentially eliminate any significant manifestation of disease.

The second source of inspiration is Richie Etwaru.  He is a brilliant and compelling speaker and a champion for global innovation, Mr. Etwaru, is responsible for defining and delivering the global next generation enterprise product suite for health and life sciences at Cegedim RelationshipManagement. His inspiring video, The Future of Human API really got me thinking.

At the heart of Mr. Etwaru’s discussion is the emergence of prevention–not treatment–as the “next big thing”.

EtwaruSlide

Ok, nothing new so far.  But the important changes seen in the digital health movement have given us a profound opportunity to move away from the conventional clinical identification of a that golf-ball sized tumor in your chest to a much more sophisticated and subtle observation. We are beginning to find a new disease stage–different from the numbers and letters seen in cancer staging.  The disease stage is getting closer and closer to zero.  It’s taking an asymptotic path that connects disease with prevention. The point here is that the holy grail of prevention isn’t born of health and wellness.  Prevention is born out of disease and our new-found ability to find it by looking closer and earlier.  Think quantified self and Google Calico.

And here lies the magic.

We all live in the era of disease.  And the vast majority of healthcare costs are spent after something happens. The simple reality is that prevention is difficult to fund and the health-economic model is so skewed to sickness and the end of life that it’s almost impossible to change. But if we can treat illness earlier and earlier–the concept of an asymptote–we build a model where prevention and disease share the very same border.  They become, in essence, the same. And it’s here that early, early, early disease stage recognition (Stage Zero) becomes prevention. The combination of passive (sensor mediated) observation and proactive life-style strategies for disease suppression can define a new era of health and wellness.

Keep Critical! Follow me on Twitter and stay healthy!

 

NeuroOn sleep tracking mask…

Polyphasic sleep looks like something I want to get into, though am not convinced this is the way to achieve it. Will see how the trials go….

http://www.kickstarter.com/projects/intelclinic/neuroon-worlds-first-sleep-mask-for-polyphasic-sle

The NeuroOn is for you!

The final prototype
The final prototype

What is polyphasic sleep?

It is a term referring to alternate sleep patterns that can reduce the required sleep time to just 2-6 hours daily. It involves breaking up your sleep into smaller parts throughout the day, which allows you to sleep less but feel as refreshed as if you slept for 8 hours or more.

Polyphasic sleep modes
Polyphasic sleep modes

Simply put, it’s a series of fine-tuned power naps that allow you to sleep effectively, rest better and perform at optimum energy levels during the day.

Additionally, NeuroOn monitored polyphasic sleep allows you to sync your body clock to very demanding schedules at whatever time is convenient or required.

In conclusion, through great sleep efficiency, Polyphasic sleep can give you an extra 4 hours of free time every day. That’s up to 28 hours (1 day+) a week, 1460 hours a year.

That’s right – Your year now has over 420 working days!

Trust the masters

So, you’ve heard of Leonardo? No, not the turtle!

Apparently Da Vinci, Tesla, Churchill and even Napoleon used polyphasic sleep to rest. It allowed them to fully regenerate, reducing sleep time to 6.5 hours or sometimes just 2 hours. And those guys got things done!

Famous polyphasic sleepers
Famous polyphasic sleepers

Hammerbacher, Sinai and Minerva…

Top piece on Sinai’s vision. Everything’s lined up there except the doctors – hmmm…. They’ll need some amazing insights to bust through the inertia, but expect they’ll glean them…

http://www.fastcoexist.com/3022050/futurist-forum/in-the-hospital-of-the-future-big-data-is-one-of-your-doctors

In The Hospital Of The Future, Big Data Is One Of Your Doctors

December 5, 2013 | 7:30 AM

From our genomes to Jawbones, the amount of data about health is exploding. Bringing on top Silicon Valley talent, one NYC hospital is preparing for a future where it can analyze and predict its patients’ health needs–and maybe change our understanding of disease.

The office of Jeff Hammerbacher at Mount Sinai’s Icahn School of Medicine sits in the middle of one of the most stark economic divides in the nation. To Hammerbacher’s south are New York City’s posh Upper East Side townhouses. To the north, the barrios of East Harlem.

What’s below is most interesting: Minerva, a humming supercomputer installed last year that’s named after the Roman goddess of wisdom and medicine.

It’s rare to find a supercomputer in a hospital, even a major research center and medical school like Mount Sinai. But it’s also rare to find people like Hammerbacher, a sort of human supercomputer who is best known for launching Facebook’s data science teamand, later, co-founding Cloudera, a top Silicon Valley “big data” software company where he is chief scientist today. After moving to New York this year to dive into a new role as a researcher at Sinai’s medical school, he is setting up a second powerful computing cluster based on Cloudera’s software (it’s called Demeter) and building tools to better store, process, mine, and build data models. “They generate a pretty good amount of data,” he says of the hospital’s existing electronic medical record system and its data warehouse that stored 300 million new “events” last year. “But I would say they are only scratching the surface.”

Could there actually be three types of Type 2 diabetes? A look at the health data of 30,000 volunteers hints that we know less than we realize. Credit: Li Li, Mount Sinai Icahn School of Medicine, and Ayasdi

Combined, the circumstances make for one of the most interesting experiments happening in hospitals right now–one that gives a peek into the future of health care in a world where the amount of data about our own health, from our genomes to ourJawbone tracking devices, is exploding.

“What we’re trying to build is a learning health care system,” says Joel Dudley, director of biomedical informatics for the medical school. “We first need to collect the data on a large population of people and connect that to outcomes.”

To imagine what the hospital of the future could look like at Mount Sinai, picture how companies like Netflix and Amazon and even Facebook work today. These companies gather data about their users, and then run that data through predictive models and recommendation systems they’ve developed–usually taking into account a person’s past history, maybe his or her history in other places on the web, and the history of “similar” users–to make a best guess about the future–to suggest what a person wants to buy or see, or what advertisement might entice them.

Through real-time data mining on a large scale–on massive computers like Minerva–hospitals could eventually operate in similar ways, both to improve health outcomes for individual patients who enter Mount Sinai’s doors as well as to make new discoveries about how to diagnose, treat, and prevent diseases at a broader, public health scale. “It’s almost like the Hadron Collider approach,” Dudley says. “Let’s throw in everything we think we know about biology and let’s just look at the raw measurements of how these things are moving within a large population. Eventually the data will tell us how biology is wired up.”

Dudley glances at his screen to show the very early inklings of this vision of what “big data” brought to the world of health care and medical research could mean.

On it (see the figure above) is a visualization of the health data of 30,000 Sinai patients who have volunteered to share their information with researchers. He points out, in color, three separate clusters of the people who have Type 2 diabetes. What we’re looking at could be an entirely new notion of a highly scrutinized disease. “Why this is interesting is we could really be looking at Type 2, Type 3, and Type 4 diabetes,” says Dudley. “Right now, we have very coarse definitions of disease which are not very data-driven.” (Patients on the map are grouped by how closely related their health data is, based on clinical readings like blood sugar and cholesterol.)

From this map and others like it, Dudley might be able to pinpoint genes that are unique to diabetes patients in the different clusters, giving new ways to understand how our genes and environments are linked to disease, symptoms, and treatments. In another configuration of the map, Dudley shows how racial and ethnic genetic differences may define different patterns of a disease like diabetes–and ultimately, require different treatments.

These are just a handful of small examples of what could be done with more data on patients in one location, combined with the power to process it. In the same way Facebook shows the social network, this data set is the clinical network. (The eventual goal is to enroll 100,000 patients in what’s called the BioMe platform to explore the possibilities in having access to massive amounts of data.) “There’s nothing like that right now–where we have a sort of predictive modeling engine that’s built into a health care system,” Dudley says. “Those methods exist. The technology exists, and why we’re not using that for health care right now is kind of crazy.”

While Sinai’s goal is to use these methods to bring about more personalized diagnoses and treatments for a wide variety of diseases, such as cancer or diabetes, and improve patient care in the hospital, there are basic challenges that need to be overcome in order to making this vision achievable.

Almost every web company was born swimming in easily harvested and mined data about users, but in health care, the struggle has for a long time been more simple: get health records digitized and keep them private, but make them available to individual doctors, insurers, billing departments, and patients when they need them. There’s not even a hospital’s version of a search engine for all its data yet, says Hammerbacher, and in the state the slow-moving world of health care is in today, making predictions that would prevent disease could be just the icing on the cake. “Simply centralizing the data and making it easily available to a broad base of researchers and clinicians will be a powerful tool for developing new models that help us understand and treat disease,” he says.

Sinai is starting to put some of these ideas into clinical practice at the hospital. For example, in a hint of more personalized medicine that could come one day, the FDA is beginning to issue labels for some medicines that dictate different doses for patients who have a specific genetic variant (or perhaps explain that they should avoid the medicine altogether). The “Clipmerge” software that the hospital is beginning to now use makes it easier for doctors to quickly search and be notified of these kinds of potential interactions on an electronic medical record form.

On the prediction side, the hospital has already implemented a predictive model called PACT into its electronic medical record system. It is used to predict the likelihood that a discharged patient will come back to the hospital within 90 days (the new health care law creates financial incentives for hospitals to reduce their 90-day readmission rate). Based on the prediction, a high-risk patient at the medical center now might actually receive different care, such as being assigned post-care coordinator.

Eventually, there will be new kinds of data that can be put in mineable formats and linked to electronic patient records, from patient satisfaction surveys and doctors’ clinical notes to imaging data from MRI scans, Dudley says.

Right now, for example, the growing volumes of data generated from people’s fitness and health trackers is interesting on the surface, but it’s hard to glean anything meaningful for individuals. But when the data from thousands of people are mined for signals and links to health outcomes, Dudley says, it’s likely to prove valuable in understanding new ways to prevent disease or detect it at the earliest signs.

A major limitation to this vision is the hospital’s access to all of these new kinds of data. There are strict federal laws that govern patient privacy, which can make doctors loathe to experiment with ways to gather it or unleash it. And there are many hoops today to transferring patient data from one hospital or doctor to another, let alone from all the fitness trackers floating around. If patients start demanding more control over their own health data and voluntarily provide it to doctors, as Dudley believes patients will start to do, privacy could become a concern in ways people don’t expect or foresee today–just as it has on the Internet.

One thing is clear: As the health care system comes under pressure to cut costs and implement more preventative care, these ideas will become more relevant. Says Dudley: “A lot of people do research on computers, but I think what we’re hoping for is that we’re going to build a health care system where complex models … are firing on an almost day-to-day basis. As patients are getting information about them put in the electronic medical record system there will be this engine in the background.”

 

JESSICA LEBER

Healthways…

http://www.healthways.com  || http://www.healthways.com.au

Christian Sellars from MSD put on a terrific dinner in Crows Nest, inviting a group of interesting people to come meet with his team, with no agenda:

  • Dr Paul Nicolarakis, former advisor to the Health Minister
  • Dr Linda Swan, CEO Healthways
  • Ian Corless, Business Development & Program Manager, Wentwest
  • Dr Kevin Cheng, Project Lead Diabetes Care Project
  • Dr Stephen Barnett, GP & University of Wollongong
  •  Warren Brooks, Customer Centricity Lead
  • Brendan Price, Pricing Manager
  • Wayne Sparks, I.T. Director
  • Greg Lyubomirsky, Director, New Commercial Initiatives
  • Christian Sellars, Director, Access 

MSD are doing interesting things in health. In Christian’s words, they are trying to uncouple their future from pills.

After some chair swapping, I managed to sit across from Linda Swan from Healthways. It was terrific. She’s a Stephen Leeder disciple, spent time at MSD, would have been an actuary if she didn’t do medicine, and has been on a search that sounds similar to mine.

Healthways do data-driven, full-body, full-community wellness.

They’re getting $100M multi-years contracts from PHIs.

Amazingly, they’ve incorporated social determinants of health into their framework.

And even more amazingly, they’ve been given Iowa to make healthier.

They terraform communities – the whole lot.

Linda believes their most powerful intervention is a 20min evidence-based phone questionnaire administered to patients on returning home, similar to what Shane Solomon was rolling out at the HKHA. But they also supplant junk food sponsorship of sport and lobby for improvements to footpaths etc.

Just terrific. We’re catching up for coffee in January.

Living on the edge with Farzad

  • It’s not as simple as you give people information and they change their behavior.  It’s information tools that build on that data and build on communities and a much more sophisticated understanding about how behavior changes. What TEDMED is also great at, is understanding the power of marketing. People think of marketing of being about advertising, but marketing is the best knowledge we have about how to change behavior and all those intangibles, those predictably irrational insights, of how and why we do what we do.
  • It’s harnessing those, instead of having them lead to worse health – like present value discounting that leads to people wanting to procrastinate and eat that doughnut now instead of going to the gym. Or the power of anchoring, where we fixate on the first thing we see and won’t think objectively about the true risks of things. Or the herd effect, our friend is overweight and so we are more likely to be overweight.
  • All those nudges that are possible can be delivered to us ubiquitously and continuously, and we can choose to have them. It’s not some big brother dystopic vision. It’s me saying, ‘I want to be healthier, so I will do something now that will help me overcome and use my irrationality to help me stay healthy.  To me, that’s the neat new edge between mobile cloud computing, personal healthcare, behavioral economics, healthcare IT, data science and visualization, design, and marketing. It’s that sphere that has so many possibilities to get us to better health.

http://blog.tedmed.com/?p=4153

 

The exit interview: Farzad Mostashari on imagination, building healthcare bridges and his biggest “aha” moments

Posted on  by Stacy Lu

Farzad Mostashari, MD, stepped down from his post as the National Coordinator for Health Information Technology at the U.S. Department of Health and Human Services (HHS), during the first week of October, which was also the first week of the Federal partial shutdown. During his tenure, Dr. Mostashari, who spoke at TEDMED 2011 with Aneesh Chopra, led the creation and definition of meaningful use incentives and tenaciously challenged health care leaders and patients to leverage data in ways to encourage partnerships with patients within the clinical health care team.

Whitney Zatzkin and Stacy Lu had the opportunity to speak with Dr. Mostashari during his last week in office.

WZ: Sometimes, a person will experience an “aha!” moment – a snapshot or event that reveals a new opportunity and challenges him/her to pursue something nontraditional. Was there a critical turning point when you figured out, ‘I’m the guy who should be doing this?’

Yeah, I’ve been fortunate to have a couple of those ‘aha’ moments in my life. One of them was when I was an epidemic intelligence service officer back in 1998, working for the CDC in New York City. I’ve always been interested in edge issues, border issues; things that are on the boundaries between different fields. I was there in public health, but I was interested in what was happening in the rest of the world around electronic transactions and using data in a more agile way.

In disease surveillance we often look back — the way we do claims data now – years later or months later you get the reports and you look for the outbreak, and often times the outbreak’s already come and gone by the time you pick it up. But I started thinking and imagining: What if the second something happens, you can start monitoring it? In New York City the fire department was monitoring ambulance calls. I said, ‘Wow, if we could just categorize those by the type of call, maybe we’ll see some sort of signal in the noise there.’

When I was first able to visualize the trends in the proportion of ambulance dispatches in NYC that were due to respiratory distress, what I saw was flu.  What jumped out at me was the sinusoidal curve. Wham! At different times of year, it could be a stutter process – it would go up and you would see this huge increase, followed two weeks later by an increase in deaths. It was like the sky opening up. The evidence was there all along, but I am the first human being on earth to see this. That was validation, for me, of the idea that electronic data opens up worlds. To bring that data to life, to be able to extract meaning from those zeros and ones — that’s life and death. That was my first ‘aha’ moment.

The second aha was after I joined New York City Department of Health, and I started a data shop to build our policy around smoking and tracking chronic diseases. What we realized was that healthcare was leaving lives on the table. There were a lot of lives we could save by doing basic stuff a third-year medical student should do, but we’re not doing it.  Related to that – Tom Frieden had a great TEDMED talk about everybody counts.

I said, ‘I want to take six months off and do a sabbatical, and see if there’s anything to using electronic health records to provide those insights, not to save lives by city level, but on the 10 to the 3 level – the 1,000 patient practice. That started the whole journey.  None of the vendors at the time had the vision we had, but we finally got someone to work with us and rolled this system out.  We called some doctors some 23 times, and did all the work to get to the starting line.  Finally, I took Tom on a field visit to see one of the first docs to get the program.

It was a very normal storefront in Harlem, and a nice physician, very caring, very typical.  I asked her what she thought of the program. She said, ‘It’s ok. I’m still getting used to it.’  I said, ‘Did you ever look at the registry tab on the right, where you can make a list of your patients? She said no.  I said, ok – how many of your elderly patients did you vaccinate for flu this year? She said, ‘I don’t know, about 80 to 85 percent.  I’m pretty good at that.’  I said, ‘o.k., let’s run a query.’  And it was actually something like 22 percent. And she said – this was the aha moment – ‘That’s not right.’

That’s generally the feeling the docs have when they get a quality measure report from the health plan. But that’s population health management — the ability to see for the first time ever that everybody counts. And being able to then think about decision support and care protocols to reduce your defect rate. That was the validation that we’re on to something. Without the tools to do this, all the payment changes in the world can’t make healthcare accountable for cost and quality if you can’t see it.

WZ: Everyone has that moment in life when they’re considering all of their career options. As you were considering medical school, what else was on the table?

I actually didn’t think I was going to go to medical school. I was at the Harvard School of Public Health. I was interested in making an impact in public health. I grew up in Iran, and thought I would do international public health work. And then my dad got sick; he had a cardiac issue. The contrast between the immediacy of the laying on of hands of healthcare, and the somewhat abstractness of international public health — the distance, the remove — tipped me into saying,  ‘You know, maybe I should go to medical school.’  I’ve been on that edge between healthcare and public health ever since, and always trying to drag the two closer to each other.

SL: Fast forward 20 years.  You’re giving another talk at TEDMED.  What’s the topic?

TEDMED and Jay Walker’s vision is more powerful in the futurescope, rather than in the retroscope. It’s more powerful to be where we are today and imagine a different future rather than look back and say, ‘Oh, yeah, we’ve done this.’  So what’s the future I would love to imagine?

The most exciting thing – as Jay Walker once mentioned in a talk comparing “medspeed” to “techspeed” – is to fully imagine what will happen if techspeed is brought to healthcare. Right now, there’s all this unrealized value that’s being given away for free that doesn’t show up on any GDP lists – what Tim O’Reilly called “the clothesline paradox.”  That kind of possibility brought to medicine, but where software costs $100,000 as opposed to free, and it evolves daily and is more powerful and quicker every day, and it’s beautiful and usable and intuitive, and that’s what people compete on.

And all of that is toward the goal of empowering people.  Someone said, maybe it was Jay at TEDMED, that a 14-year-old kid in Africa with a smart phone has more access to information than Bill Clinton did as President. Information is power, and it has changed everything but healthcare. For me the vision is breaking down that wall, so that patients can be empowered and can bind themselves to the mast to use what we’ve learned about how behavior changes.

It’s not as simple as you give people information and they change their behavior.  It’s information tools that build on that data and build on communities and a much more sophisticated understanding about how behavior changes. What TEDMED is also great at, is understanding the power of marketing. People think of marketing of being about advertising, but marketing is the best knowledge we have about how to change behavior and all those intangibles, those predictably irrational insights, of how and why we do what we do.

It’s harnessing those, instead of having them lead to worse health – like present value discounting that leads to people wanting to procrastinate and eat that doughnut now instead of going to the gym. Or the power of anchoring, where we fixate on the first thing we see and won’t think objectively about the true risks of things. Or the herd effect, our friend is overweight and so we are more likely to be overweight.

All those nudges that are possible can be delivered to us ubiquitously and continuously, and we can choose to have them. It’s not some big brother dystopic vision. It’s me saying, ‘I want to be healthier, so I will do something now that will help me overcome and use my irrationality to help me stay healthy.  To me, that’s the neat new edge between mobile cloud computing, personal healthcare, behavioral economics, healthcare IT, data science and visualization, design, and marketing. It’s that sphere that has so many possibilities to get us to better health.

The thing about the health is, we have a Persian saying: Health is a crown on the head of the healthy that only the sick can see. When you have it, you don’t appreciate it, but when you’re sick and someone you love is sick, there’s nothing better.  You would do anything to get that. We need to bring that vision of the crown to everyone and help each of us grab it when we can.

WZ: I noticed you closing your eyes while preparing to answer a question. How do you pursue being able to exercise your imagination, in particular while you’re sitting in a building that’s been marked for being the least imaginative?

Because the world, as it is, is too immediate and real and limiting, sometimes you have to close your eyes to see a different world.

What has been amazing has been to see that, contrary to what people expect, this building is filled with people with untapped, unbound, unfettered imaginations who are slogging through. They’re just trapped. You give them the opening, the smallest bit of daylight to exercise that, and they’re off and running.

I give a lot of credit to Todd Park as our “innovation fellow zero,” He saw the possibility that there are more than two kinds of people in the world, innovators and everybody else. For him, it was about going to create a space where outside innovators can be the catalyst or spark that elevates and permissions the innovation of the career civil servant at CMS in Baltimore. That’s been cool.

SL: What’s your bowtie going to do after you leave HHS?  Will we see it lounging on the beach in Boca?

I like the bowtie.  I think I’m going to keep it.  Perhaps the @FarzadsBowtie Twitter handle is going to go into hibernation, I don’t know.  I don’t control it. One of the things the bowtie does for me is help me remember not to get too comfortable.

I once said at the Consumer Health IT Summit – ‘You’re a bunch of misfits – glorious misfits. And I feel like I’m very well suited to be your leader. You know, I always felt American in Iran, and felt Iranian in America when I came here. I felt like a jock among my geeky friends, and like a geek among jocks. For crying out loud, I wear a bowtie!  I don’t have to tell you I’m a misfit.’

It’s that sense of not fitting into the world as it is. The world doesn’t fit me.  So instead of saying,  ‘I need to change,’ this group of people said, ‘The world needs to change.’ That’s the difference between a misfit and a glorious misfit.

The person who doesn’t fit into our healthcare system is the patient. The patient’s preferences don’t fit into the need to maximize revenue and do more procedures. The patient’s family doesn’t fit into the, ‘I want to do an eight-minute visit and get you out the door’ agenda. The patient asking questions doesn’t fit.  That’s the change we need to make. It’s not that we need to change. Healthcare needs to change to fit the patient.

Shortly following this interview, Dr. Mostashari left HHS and is now the a visiting fellow of the Engelberg Center for Health Care Reform at the Brookings Institution, where he aims to help clinicians improve care and patient health through health IT, focusing on small practices.

This interview was edited for length and readability.

Healthy life years is the key selling proposition for funding NCD interventions…

Non-communicable disease presents an as-yet, unresolved health research challenge. But they may also lie at the heart of a similarly unresolved intergenerational, macroeconomic challenge.

To date, governments and academics around the world have sat back and carefully observed the epidemic of overweight, obesity, metabolic syndrome and diabetes overtake their communities.

The food industry has aggressively defended its turf, understandably resisting any calls for regulation in the absence of definitive evidence that these interventions will work.

Only the most courageous of politicians would ever embark on the regulation of such a powerful sector in the absence of evidence supporting efforts such as restricting advertising to children, mandating processed food composition, food labeling and taxing macronutrients know to be harmful.

So we find ourselves at an impasse that no one seems particularly able to break.

An emerging theme related to this issue is the idea that while the health system has succeeded in delivering extended life, it has not yet extended healthy life years. As such, the population still shudders at the thought of raising the retirement age past 70, even though average life expectancy now surpasses 80.

Non-communicable disease is considered a major driver of this divergence. As such, preventing non-communicable disease may represent an important challenge, not only driven by a health/moral imperative, but also for important economic reasons.

There are significant macroeconomic consequences of people not living most of their lives in a productive state of health. Most significant of these is the capacity of societies to sustain pensions when boomer-driven demographic shifts result in an increasing ratio of pensioners to tax payers.

This places life insurers, governments and superannuation funds into the medium- to long-term frame as key beneficiaries of addressing non-communicable disease.

This in turn makes them key targets for attracting investment capital to a venture addressing this concern.

Imagine a world where people lived healthy, vital, productive lives well into the 70s.

Too much?

Google have spotted this opportunity by investing $100Ms in a new start up called the California Life Company (CaLiCo). Its initial focus is on “ageing” with an early emphasis on genomics, epigenetics and a pharmaceutical fix.

I starting to think the answer is much simpler: Eat food, not too much, mainly plants. Move.

It’s about less, not more.

Establishing the evidence for this inkling, and then commercialising the insights gained is the inspiration behind Riot Health.

Stand by.