October 19, 2013

It’s time to state the obvious. We are essentially a three party country now trying to pretend we are a two party country. We have the Democrats, largely unchanged since Bill Clinton revamped who they were to be in favor of economic growth. We have the “old Republicans” once defined by Ronald Reagan who were actually pro-science, pro-business, pro-immigration (in those days it was the Democrats egged on by labor unions who were against immigration) and against the government telling you what to do in private although a bit mealy mouthed about the latter. Perhaps most importantly, the Reagan Republicans were fully aware that intelligence mattered and that compromise was necessary to getting change made, for example getting the consensus required to make the most sweeping and helpful changes made to the US tax code in decades.  Full disclosure. I voted for Ronald Reagan. Lastly we have the Tea Party, reminiscent of the old No Nothing Party of the 19th century, anti-science, anti-business, anti-immigration (especially of anyone who looks different regardless of how much smarter or more talented they might be) and really anti everything except that they are in favor of having the government tell you what you can do in your private life. The irony of Paul Ryan being a Tea Party darling who calls himself a Libertarian is that he would be Ayn Rand’s worst nightmare. Most importantly though, (at least for those of us who create wealth in the country and real jobs) the Tea Party is essentially pro-stupid. Data means nothing to them. Strategy means nothing to them. Actually studying and understanding economics is foreign to them. Having a public tantrum about the fact they don’t agree with the rest of the country (only 30% of the country has a favorable view of them based on the polls this week) means everything. We should just admit we have three parties, let them have their own primaries and hold three party elections. Heck, Ted Cruz and Sarah Palin can duke it out for who runs as Presidential candidate for the Tea Party. God bless them.

Where is the decency?

April 18, 2013

I haven’t posted for a very long time because I’ve been busy turning a startup into a going concern and, frankly, occasionally what I’d like to say might annoy clients. However enough is enough.

Yesterday was one of the most shameful days I can recall in our nation in my 57 years. Despite the pleas of the bereaved parents from Sandy Hook 46 Senators chose not to make background checks mandatory on gun sales. Background checks!! It is like saying that it would be wrong to even try to keep these terrible weapons of destruction out of the hands of bad people. Have they no decency? Have they no children of their own? Do they truly think they are worthy of representing this great country when they cannot even agree to try and keep guns out of the hands of the wrong people?

I am ashamed of the Republicans who once were a great party and stood for competition and freedom, but also stood for building a great world for our children to  grow up in. Now they support the polluters who destroy the environment our children will inherit, the armed crazies who threaten the life and serenity of our children as they grow up, the health of our children fed increasingly terrible foods (read Salt, Sugar, Fat), and in general the welfare of those who are our future and deserve our help and protection as they grow up. But most of all, I simply cannot imagine how the people who represent this great country could look those parents of Sandy Hook in the eye and then vote to allow these weapons and to prevent background checks.

They are beneath our contempt for their total lack of decency, once an American hallmark, and frankly, if you support the NRA or these Senators in this vote so are you.

And on another front

December 21, 2009

This is a direct quote. James May, chief executive of the Air Transport Association, the industry’s largest trade group, said Monday that its members would comply with the new rule “even though we believe it will lead to unintended consequences — more canceled flights and greater passenger inconvenience.” He added that “the requirement of having planes return to the gates within a three-hour window or face significant fines is inconsistent with our goal of completing as many flights as possible. In  other words the Air Transport Association representing the airlines doesn’t care about us being stuck on the ground in a plane for more than 3 hours if they can fly more planes. They might as well tell us that they don’t care about us at all. Hello. We pay your bills. We are your customers.

It is always amazing to hear such organizations admit that the comfort and service to the customer is simply not a goal.

To fix health care, release our data

December 9, 2009

What is the future of health care? How will we actually lower the number of people who suffer or die needlessly?  How will we deliver care more effectively? Today, two ideas are competing for attention in this space:

  1. Personalized Medicine
  2. Personalized Wellness

Let’s talk first about Personalized Medicine. There is a lot of talk about the future of medicine being personalized medications. What is usually meant by this is that the blood and DNA of the patient is analyzed and then, using data gleaned from the EMR, a medicine precisely tailored to meet that patient’s need and their metabolism is prescribed. This is of course a wonderful vision—one that I would have loved to see realized a few years earlier. My mother was given several medicines for her recurrent ovarian cancer that were more or less ineffective.

Now it isn’t a pipe dream. There are blood and DNA tests run today for medicines like Warfarin or treatments for breast cancer. The best example is AIDS/HIV where DNA of the virus is used to determine which retrovirals will work. But in general, this is turning out to be very hard and very slow to do. It is hard just to figure out which medicines work for who based on their blood, DNA, and other phenotypic data. It works in some cases but fails in many. And even when a drug targeting a specific genetic profile is engineered, it is difficult and expensive to deliver to the right place in the body at the right time, in the right amount, and for the right duration. For example, we’ve known a lot about the genetics of cystic fibrosis —i.e., which proteins aren’t being generated properly in the lung cells due to mutations in a specific gene. Presently there are viruses that have been engineered that can generate the correct, functioning proteins, but the means to deploy an effective treatment has yet to be solved. Still there are clear examples of personalized therapies based on an individual’s DNA which help prolong life and have sufficient sales to warrant biotechnology/ pharmaceutical interest.  The clearest example of this is the drug from Genentech called trastuzumab (brand name Herceptin).  All in all, it is likely that it will be expensive and hard to change the DNA, but that the ability to produce solutions based on one’s DNA will be more viable.

Another issue is cost effectiveness in producing personalized medicines when such treatments serve a small market; the more specialized the medicine, the less likely it is to be developed. Thus, will we see a slew of highly personalized drugs targeting unique genomes or disease organisms? As was said in the movie “The Princess Bride,” when two magicians tried to bring the hero back to life and one magician asked another “think it will work?”, the reply was “it would take a miracle.” Of course in the movie he did come back to life, but life isn’t a movie.

Now let’s talk about Personalized Wellness. The leading causes of death relate to life style, lack of routine medical examination, and basic outages in care.  Put differently, it doesn’t require medical miracles to prevent far more disease and avoid far more suffering and deaths than all those caused by cancer (outside of lung cancer) each year. It requires personalized wellness and “good health incentives.” What is personalized wellness? It is personal advice to individuals about their health that takes into account their health data, their personalities, their goals, and their activities and what is the appropriate standard of care for them. It involves tracking their progress or lack thereof—what the Robert Wood Johnson Foundation has called ODLs or observations of daily living.

It’s possible for people who are at risk for diabetes or heart disease to avoid these diseases.  And for those who already suffer from them, it’s possible to cure them by clearing up their arteries or at least stop complications like blindness and renal failure. If they are living with asthma, get them the personalized help they need to minimize attacks and shorten episodes. If they are living with depression, give them support and tools like breathing calmly, meditation, regular exercise, and smart diets. This isn’t magic.  There is much scientific evidence about what works, and translations for healthy living are plentiful on the Web.  Think mint.com, a site that balances your budget, for health. The cost of building a site that empowers patients to manage their health is a tiny fraction of the cost of a single medicine being brought to market. Will DNA count in this space? Certainly. Some people have lower risks based on their genetic makeup, and others have higher risks. Certain nutritional interventions will benefit some people and may harm other.  But DNA testing can also inform intelligent prevention.

We want both personalized medicine and personalized wellness. But we can have the latter much sooner and it will probably do more good, at least in the next decade or two.

There is one thing making it very hard to deliver on this vision today. Much of personalized wellness advice depends on basic lab results like the lipid panel. The person with a total cholesterol of 150 may need different advice than the person with a total cholesterol of 250, for example. Today, if I go into a lab to get my blood drawn, say for my checkup, I cannot download the data into my personalized wellness tool of choice unless my doctor electronically approves it.  Not because the lab cannot support this—90% of labs performed outside hospitals are covered by Quest Diagnostics or LabCorp and both support electronic data transfer.  Rather, a doctor’s electronic approval is required to release the lab data to the patient, even when the patient wants this data. Well, most of the doctors aren’t using electronic systems and most of the ones who are don’t have the ability to approve these transfers, while some of the ones who do have the ability choose not to. The notable exception is Kaiser, which delivers labs to all of its patients online at the same time that the patients’ doctors get them. Three million patients use Kaiser’s PHR and the number one use is for viewing labs. Kudos, Kaiser!

But if you aren’t lucky enough to be a Kaiser member or want to use a different tool for this purpose, you are out of luck. (Actually, Kaiser may be integrating with Microsoft HealthVault and then one could use one’s own tools, but the timetable for rollout is unclear.) This is like not being able to use mint.com because your bank won’t allow the transfer of financial data to your account at that site.  It makes no sense, and is one more example of how the system foils patients’ attempts to take responsibility for their own health.  It clearly stifles innovation in an area that has the most potential to solve economic and personal health care issues in the U.S.

I call on DC and the State Legislatures to change these laws.  Learn from Kaiser.  Pass laws that specifically give the lab companies the obligation to deliver our data electronically directly to us – the people, if we want it. If you desire true health care reform that actually will lower costs and curb illness, unleash the power of the innovators to help consumers with personal wellness as mint.com does with financial wellness. Release our health data.

Engage with Grace Blog Rally

November 28, 2009

Alexandra Drane started a wonderful movement called Engage with Grace over a year ago and she asked me to join a Thanksgiving rally supporting this movement. I’m happy and proud to do so. As I wrote in one of my most contentious posts, once my mother was diagnosed as being terminal after a valiant 4 year battle with Ovarian cancer, the system totally failed us. Support turned to indifference. Every attempt was made to have my mother end her days in the hospital rather than spending her last 2 months at home. It was only because of my connections and resources that she was even able to end her days with dignity surrounded by those who loved her. Indeed just days before the end, she was able to be taken in a wheelchair to the library she had presided over for over 40 years at Saint Ann’s School and see it officially renamed to the Anne Bosworth library and hear the tributes of all who have known her and learned from her. All this would have been denied if the current “health system” had had its way. It is this indifference to the needs of those at this stage of life that the movement is dedicated to combating and I enthusiastically endorse it. Engage with Grace has 5 basic questions everyone should know.

We are supposed to ask more lighthearted questions on this Thanksgiving weekend, but I’ve been unable to get WordPress to accept this questionnaire and I think it is a sign. We need to change the system profoundly to take human needs into account first. We need a system that works to meet these needs, not to try every possible futile procedure leaving those poor souls to suffer their last weeks or days in pain and indignity against their will. This is a serious business for those of us who have lived through this, seen the suffering first hand. We give thanks for many things this weekend but we look forward to the day when we can give thanks for a caring health care system.

To learn more please go to http://www.engagewithgrace.org.

Looking for a leader – Keas is hiring

November 17, 2009

Keas has launched.  Keas is a place consumers come to when they want to take charge of their health or that of someone they love. They come to get the personalized advice and content that they need to understand their health and to know what they need to do and to be reminded/helped to do it. Keas delivers this personalized advice via Keas Care Plans. Think of each Care Plan as a set of great health experts giving you personalized interpretation and advice about your health and what you need to do based on your health data, your goals, and your progress to date. Not just once, but on an ongoing basis. But we at Keas don’t write these Care Plans in general. Great experts in health, whether in pediatric Asthma or dealing with H1N1 or with Diabetes do so.  You don’t need to be a programmer or have an IT department to build Keas Care Plans, but you do need to have great health experts,  great content people and usually (at least for your first one) help from what we have come to call Keas Producers.

We at Keas have been overwhelmed with astonishing potential partners in the health field who want to build great Keas Care Plans. We are humbled and gratified, but we are also urgently in need of someone to lead this effort for us. What sort of person do we need? We need someone with passion for the customer who will work with every partner to ensure that their care plans are engaging,  personalized, helpful and responsive and hire/manage the Keas producers we need to help the partners in this effort. We need someone who will be able to understand the health issues involved, but also the consumer passion and who can help our partners not just to deliver content personalized to the need, but video, twitter, great links, living discussions, polls, and everything else required to actually help the users of their Keas Care Plan to get the most out of it.

So, in short you need to be a leader, tireless,willing to get your fingernails dirty and lead by doing, passionate, unafraid of risk (this is a start up!), excited and knowledgeable about health, great at working with partners, with good business sense, experienced in building and leading teams that partner with others, and with an understanding of how the web is changing from a text world to an interactive and video world. If this is you and you want to help our partners produce the 100’s of care plans they now want to build, then let us know please at careers@keas.com.

Excellent Post

November 10, 2009

John Halamka put up a thoughtful piece today which I for one heartily endorse. I’ve worked with John off and on since starting Google Health and we have really traversed down this road together.

Talking to DC

October 29, 2009

Warning. This is a rare nerdy technical post more for. It is about Healthcare XML standards.

I’ve was kindly asked to testify at a meeting in DC this week about standards at an hour when I’m normally not awake. But despite a deep aversion to not getting enough sleep, I was up and on the phone. What made me do such a thing? Well, the discussion was about what actually will work in terms of making health data liquid. What standards should be used for the integration of such data?

Somewhat to my surprise and usually to my pain, I’ve been involved in several successful standards. One was used to exchange data between databases and consumer applications like spreadsheets and Access. It was called ODBC and worked surprisingly well after some initial hiccups. Another was the standard for what today is called AJAX, namely building complex interactive web pages like gmail’s. Perhaps most importantly there was XML. These are the successes. There were also some failures. One that stands in my memory is one called OLE DB which was an attempt to supplant/replace ODBC. One that comes close to being a failure was/is the XML Schema specification. From all these efforts, there were a few lessons learned and it is these that I shared with DC this Thursday. What are they?

  1. Keep the standard as simple and stupid as possible. The odds of failure are at least the square of the degrees of complexity of the standard. It may also be the square of the size of the committee writing the standard. Successful standards are generally simple and focused and easy to read. In the health care world, this means just focus first on that data which can be encoded unambiguously such as demographics, test results, medicines. Don’t focus on all types of health data for all types of health. Don’t focus on how to know if your partner should have access to what (see points 2,3, and 4 below).
  2. The data being exchanged should be human readable and easy to understand. Standards are adopted by engineers building code to implement them. They can only build if they can easily understand the standard (see above) and easily test it. This is why, in the last 15 years, text standards like HTTP, HTML, XML, and so on have won. The developers can open any edit editor, look at the data being sent/received, and see if it looks right. When Tim Berners Lee first did this on the internet, most of the “serious” networking people out there thought using text for HTTP was crazy. But it worked incredibly well. Obviously this worked well for XML too. This has implications. It isn’t enough to just say XML. The average engineer (who has to implement these standards) should be able to eyeball the format and understand it. When you see XML grammars that only a computer can understand, they tend not to get widespread adoption. There are several so-called XML grammars that layer an abstract knowledge model on top of XML like RDF and in my experience, they are much harder to read/understand and they don’t get used much.  In my opinion Hl7 suffers from this.
  3. Standards work best when they are focused. Don’t build an 18 wheeler to drive a city block. Standards often fail because committees with very different complex goals come together without actual working implementations to sanity check both the complexity (see point 1 above) and the intelligibility (see point 2 above). Part of the genius of the web was that Tim Berners-Lee correctly separated the protocol (HTTP) from the stuff the browser should display (HTML). It is like separating an envelope from the letter inside. It is basic. And necessary. Standards which include levels or layers all jammed into one big thing tend to fail because the poor engineers have to understand everything when all they need to understand is one thing. So they boycott it. In health care, this means don’t include in one standard how to encode health data and how to decide who gets it and how to manage security. If all I, as an engineer, want is to put together a list of medicines about a patient and send that to someone who needs it, then that’s all I should have to do. The resulting XML should look like a list of medicines to the me. Then, if it doesn’t work, I can get on the phone with my opposite number and usually figure out in 5 minutes what’s wrong. Also I can usually author this in a day or two because I don’t have to read/learn/understand a spec like a telephone book. I don’t have to have to understand the “abstract data model”. The heart of the initial XML spec was tiny. Intentionally so. I heard someone say indignantly about the push to simplify Health IT standards that we should be “raising the bar on standards” not lowering them. This is like arguing that we should insist that kids learn to drive an airplane to walk to the next door neighbor’s house. All successful standards are as simple as possible, not as hard as possible.
  4. Standards should have precise encodings. ODBC was precise about data types. Basic XML is a tiny standard except for the precise encodings about the characters of the text, Unicode. That is most of the spec, properly so, because it ensures that the encodings are precise. In health care this means that the standard should be precise about the encodings for medicines, test results, demographics, and conditions and make sure that the encodings can be used legally and without royalties by all parties. The government could play a role here by requiring NPI’s for all doctor related activities, SNOMED CT for all conditions, LOINC for all labs, and some encoding for all medicines (be it NDC, rxNorm, or FDB) and guaranteeing that use of these encodings is free for all use.
  5. Always have real implementations that are actually being used as part of design of any standard. It is hard to know whether something actually works or can be engineered in a practical sense until you actually do it. ODBC for example was built by many of us actually building it as we went along. In the health care world, a lot of us have built and used CCR as we go, learning what works and what doesn’t very practically and that has made it a good easy to use standard for bundling health data. And the real implementations should be supportable by a single engineer in a few weeks.
  6. Put in hysteresis for the unexpected. This is something that the net formats do particularly well. If there is something in HTTP that the receiver doesn’t understand it ignores it. It doesn’t break. If there is something in HTML that the browser doesn’t understand, it ignores it. It doesn’t break. See Postel’s law.  Assume the unexpected. False precision is the graveyard of successful standards. XML Schema did very badly in this regard. Again, CCR does fairly well here.
  7. Make the spec itself free, public on the web, and include lots of simple examples on the web site. Engineers are just humans. They learn best by example and if the standard adheres to the points above, then the examples will be clear and obvious. Usually you can tell if a standard is going to work if you go to a web site by the group and there is a clear definition and there are clear examples of the standard that anyone can understand. When you go to the HL7 site the generality and abstraction and complexity are totally daunting to the average joe. It certainly confuses me. And make no mistakes. Engineers are average joes with tight time deadlines. They are mostly not PhD’s.

Let’s be honest, a lot of standards are written for purposes other than promoting interoperability. Some exist to protect legacy advantages or to create an opportunity to profit from proprietary intellectual property. Others seem to take on a life of their own and seem to exist solely to justify the continued existence of the standards body itself or to create an opportunity for the authors to collect on juicy consultant fees explaining how the standard is meant to work to the poor saps who have to implement it. I think we can agree that,  whatever they are, those are usually not good standards. Health data interoperability is far too important an issue to let fall victim to such an approach.

Learning from customers

October 13, 2009

We have been truly blessed here at Keas. We have amazing partners in Quest Diagnostics, Healthwise, CVS MinuteClinic, Dr. Alan Greene and the DiabetesMine/Joslyn team of Amy Tenderich and Dr. Rich Jackson. We have a great team within Keas. And we received some extraordinarily supportive news reporting about Keas during the last week including The New York Times and Fox Business as we opened up a public beta for everybody. We are truly grateful.

For those of you who missed this news, Keas now has an open, free public beta at www.keas.com.

What is Keas? Keas brings you the best medical minds to deliver personalized help so that you can start to take charge of your health. These health experts build personalized expertise into a Keas Care Plan, based on the very same questions and feedback that occurs in person, during an office visit. In other words, these Care Plans look at or ask for your data just as health experts would. Given that data, Keas Care Plans can help you understand your health by charting the results that matter, indicating whether you are where you should be (in the green), have some risks (in the yellow), or clearly need serious attention (in the red). And because they are developed by health professionals who understand the nuances of health issues, Care Plans deliver “to-dos” for you to see at a glance what steps to take to get in the green and stay in the green.

We also announced a wonderful strategic alliance with Quest Diagnostics. If your doctor orders a blood test to be taken at a Quest Diagnostics Patient Service Center, when the results come in Quest and your doctor will help get your data into Keas. In addition, as part of the strategic alliance, Quest Diagnostics has worked with Keas to help interpret your data, based on your personal health status, as falling in the red, the yellow, or the green. It is another layer of expertise that offers you the best advice for taking charge of your health.

Thanks to the news coverage and our partnership with Quest Diagnostics, we are now getting large numbers of users each day. And that brings us yet another layer of expertise – you, the user.  As we develop communities based on individual Care Plans, your knowledge and wisdom will be invaluable to those who share your specific health concerns, and we’ll provide the tools for peer-to-peer support. In addition, we at Keas need your smart observations: we can only make our services great and truly useful with your help. We want to know from you what Keas Care Plans you need that we haven’t built. We want to know which Care Plans can be better and how. We want to know which “to-dos” need to be improved and expanded, and your preferred modes and frequency of messaging. We want to know what Keas should be doing for you that it isn’t already doing in order to provide the best personalized help from the best medical minds. So please keep your feedback coming.

Our commitment to you is that we will learn and work hard and steadily to fix the things that need fixing and add the things that need adding. Working together over the next few months, we can make Keas the tool you need to understand your health and take charge of it, with help from the best health experts and from each other. It is an exciting time.

If you are a health expert and want to join us in building Care Plans for your patients, please email us here.

Learning from data

October 5, 2009

In most fields of human endeavor, there has been a sea-change, a revolution in technology, over the last decade which has gone largely unrecognized or acknowledged outside of the IT industry. It has been in the area of what is known either a machine-learning or data-mining. These are different tactics for accomplishing the same goal – learning from data.

What makes Google such a formidable competitor in the ads space is machine learning. What makes my bank now able to do such a good job of warning me about possible fraud is machine learning. What makes travel companies so good at pricing is data mining and machine learning. If I were giving any aspiring student going to a university to study computer engineering advice, it would be to focus on this area. It is almost like magic. We see it in subtle ways like NetFlix movie recommendations, but this is just the tip of the iceberg. Beneath the waves, almost every field is moving in this direction. And, these systems are dynamic and rapid. They are constantly learning and constantly improving.

There has been one notable exception. Health care. Machine learning and data mining do require a lot of data. Since you aren’t able to do controlled double blind randomized experiments, you need enough data to make the conclusions statistically significant in a messy data world. But given enough data, learning can and does happen. We are poised at the beginning of a similar sea-change in health care. As vast amounts of personal health care data start to get collected we will start to learn what is actually effective and what isn’t for whom. This is really a prerequisite for personalized health. The term is used loosely to mean giving people the personalized advice/treatment that they need based on their data. But the only way to personalize is to know what’s effective for whom. Some of this will doubtless be based on genomic information. But far more will just be based on looking at what is working for whom based on their conditions, ongoing test results, and treatments.  And this is key. The human body varies tremendously based both on environment and on inheritance. One size doesn’t fit all.

Until recently, a lot of machine learning from health data has been still-born for 3 reasons:

  1. It has been too hard to translate what is known about personalized medicine from research into clinical practice. This is known as the “translation” problem. But online tools that do know these things are going to rapidly change this failure in translation in the decade to come.
  2. There hasn’t been nearly enough data because almost no data was automated and, even when it was, it wasn’t tracking the data over the individual and their treatment plan. Instead, it was tracking the order over the insurance number and the practice because that’s where the money was. Between ARRA’s meaningful use mandates which are going to force tracking against the patient and the burgeoning consumer movement to take charge of their own health as the system increasingly limits their access to continuous care from physicians, this lack of data is going to change at least as profoundly in the decade to come.
  3. There was no money in giving consumers personalized treatment and indeed movements against it, both the population studies (witness the debates right now about diabetics being told to lower their blood sugar) and because the doctor’s weren’t paid for outcomes. But consumers are going to demand the treatment for the best outcome. Also we’re learning that often, it will cost less. Often the standard care given is too much treatment, so brilliantly called out in the book “Overtreated” and, paradoxically, your outcomes are better as the cost goes down, not up.  Back surgery tends to be a post-child for this, also called out well in the book “Flatlined“. We are going to be forced to figure out how to be more cost-effective, and more effective in general in treating illness.

All the systems emerging to help consumers get personalized advice and information about their health are going to be incredible treasure troves of data about what works. And this will be a virtuous cycle. As the systems learn, they will encourage consumers to increasingly flow data into them for better more personalized advice and encourage physicians to do the same and then this data will help these systems to learn even more rapidly. I predict now that within a decade, no practicing physician will consider treating their patients without the support/advice of the expertise embodied in the machine learning that will have taken place. And finally, we will truly move to an evidence based health care system.