Category Archives: EF

complex adaptive systems, data saving lives, EF, health market quality, healthcare, healthy habits, nutrition, policy, politics, quantified self, rapid learning health systems

Creating a Market for Disease Prevention

 

http://thevitalityinstitute.org/news/focus-on-pharma-creating-a-market-for-disease-prevention/

Focus on Pharma: Creating a Market for Disease Prevention

SustainAbility Newsletter “Radar” | Oct 30, 2014

Should pharmaceutical companies be in the business of producing pills, or of making people well? The answer is both. Elvira Thissen argues that with diminishing returns in medicines it is time for pharma companies to move away from philosophical discussions on prevention and adapt to new realities instead.

[…]

The Business Case for Prevention

A recent report by The Vitality Institute – founded by South Africa’s largest health insurance company – estimates potential annual savings in the US of $217–303 billion on healthcare costs by 2023 if evidence-based approaches to NCD prevention are rolled out.

At an estimated global cost of illness of nearly US$1.4 trillion in 2010 for cardiovascular disease and diabetes alone, there is a market for prevention. In the UK, the NHS spends 10% of its budget on treating diabetes, 80% of which goes to managing (partly preventable) complications. Reducing disease incidence represents a considerable value to governments, insurance companies and employers.

Some sectors are already eyeing the value of this market.

[…]

For access to the full article and SustainAbility newsletter, click here.

complex adaptive systems, data saving lives, EF, health market quality, healthcare, healthy habits, meaning, nutrition, policy, politics, rapid learning health systems, storytelling

Dr Atul Gawande – 2014 Reith Lectures

Lecture 1: Why Do Doctors Fail?

Lecture 2: The Century of the System

Lecture 3: The Problem of Hubris

Lecture 4: The Idea of Wellbeing

http://www.bbc.co.uk/programmes/articles/6F2X8TpsxrJpnsq82hggHW/dr-atul-gawande-2014-reith-lectures

Dr Atul Gawande – 2014 Reith Lectures

Atul Gawande, MD, MPH is a practicing surgeon at Brigham and Women’s Hospital and Professor at both the Harvard School of Public Health and Harvard Medical School.

In his lecture series, The Future of Medicine, Dr Atul Gawande will examine the nature of progress and failure in medicine, a field defined by what he calls ‘the messy intersection of science and human fallibility’.

Known for both his clear analysis and vivid storytelling, he will explore the growing importance of systems in medicine and argue that the future role of the medical profession in our lives should be bigger than simply assuring health and survival.

The 2014 Reith Lectures

The first lecture, Why do Doctors Fail?, will explore the nature of imperfection in medicine. In particular, Gawande will examine how much of failure in medicine remains due to ignorance (lack of knowledge) and how much is due to ineptitude (failure to use existing knowledge) and what that means for where medical progress will come from in the future.

In the second lecture, The Century of the System, Gawande will focus on the impact that the development of systems has had – and should have in the future – on medicine and overcoming failures of ineptitude. He will dissect systems of all kinds, from simple checklists to complex mechanisms of many parts. And he will argue for how they can be better designed to transform care from the richest parts of the world to the poorest.

The third lecture, The Problem of Hubris, will examine the great unfixable problems in life and healthcare – aging and death. Gawande will argue that the reluctance of society and medical institutions to recognise the limits of what professionals can do is producing widespread suffering. But research is revealing how this can change.

The fourth and final lecture, The Idea of Wellbeing, will argue that medicine must shift from a focus on health and survival to a focus on wellbeing – on protecting, insofar as possible, people’s abilities to pursue their highest priorities in life. And, as he will suggest from the story of his father’s life and death from cancer, those priorities are nearly always more complex than simply to live longer.

Five things to know about Dr Atul Gawande

Find out about Atul Gawande ahead of his 2014 Reith Lectures…

1.

In 2010, Time Magazine named him as one of the world’s most influential thinkers.

2.

His 2009 New Yorker article – The Cost Conundrum – made waves when it compared the health care of two towns in Texas and suggested that more expensive care is often worse care. Barack Obama cited the article during his attempt to get Obamacare passed by the US Congress.

3.

Atul Gawande’s 2012 TED talk – How do we heal medicine? – has been watched over 1m times.

4.

Atul Gawande has written three bestselling books: Complications, Better and The Checklist Manifesto.

The Checklist Manifesto is about the importance of having a process for whatever you are doing. Better focuses on the drive for better medicine and health care systems. Complications was based on his training as a surgeon.

5.

In 2013, Atul launched Ariadne Labs – a new health care innovation lab aiming ‘to provide scalable solutions that produce better care at the most critical moments in people’s lives everywhere’.

EF, health market quality, healthcare, policy, rapid learning health systems

 

Professor Guy Maddern’s tips on protecting yourself in surgery

1. If you are away from a major hospital, get yourself to one. A particular problem, Professor Maddern says, exists when rural patients resist transfers to major hospitals because they don’t want to leave their families.

2. Lose weight and don’t smoke.The proportion of deaths where obesity was a factor increased slightly this year. “An operation done on a thin person relative to a fat person can have a completely different outcome,” Professor Maddern says. This is particularly important for older people, who have the most operations.

3. Go to a hospital that performs a lot of the type of surgery you are going to have, particularly if it is complex. Remember, practice makes perfect.

http://www.canberratimes.com.au/national/health/one-in-10-surgery-deaths-due-to-flawed-care-or-injury-caused-by-treatment-20141203-11z5y1.html

One in 10 surgery deaths due to flawed care or injury caused by treatment

Date December 3, 2014

Health Editor, Sydney Morning Herald

View more articles from Amy Corderoy

Dangerous: Surgery risks can outweigh benefits.

Dangerous: Surgery risks can outweigh benefits. Photo: Nic Walker

More than one in 10 deaths during or after surgery involved flawed care or serious injury caused by the treatment, a national audit has found.

The Australian and New Zealand Audits of Surgical Mortality shows delays in treatment or decisions by surgeons to perform futile surgeries are still the most common problems linked to surgical deaths.

But surgery also appears to be getting a little safer, with the audit, which covers almost every surgery death in Australia, finding fewer faults with the medical care provided to patients than it has in the past.

Audit chair Guy Maddern said of the deaths where there were concerns, about 5 per cent involved serious adverse events that were likely to have contributed to the person’s death.

In about 8 per cent of cases, the audit found some area of care could have been delivered better.

“These are the sorts of deaths where it was a difficult surgery, and instead of going straight to an operation, maybe additional X-rays and imaging should have been pursued, or maybe the skill set of the team that was operating could have been more appropriate,” he said.

“Sometimes, of course, the result would have been exactly the same.”

Professor Maddern said some surgeons, particularly in general surgery, orthopaedics, and, to a lesser extent, neurosurgery, still needed to work on deciding not to proceed with surgeries where the risks outweighed the benefits.

“People are thinking a little bit longer and harder about whether an operation is really going to alter the outcome,” he said. “These are the types of cases where you know before you begin that it is not going to end well.”

However, in some areas with many patients with complex conditions, things were just more likely to go wrong.

The report, which includes data from nearly 18,600 deaths over five years, found in 2013 the decision to operate was the most common reason a death was reviewed.

Overall, delays in treatment, linked to issues such as patients needing to be transferred or surgeons delaying the decision to operate, were still the most common problem, and in about 26 per cent of the deaths no surgery was performed.

Between 2009 and 2013, the report shows a decrease in the proportion of patients who died with serious infection causing sepsis from 12 per cent to 9 per cent, while significant post-operative bleeding decreased from 12 per cent to 11 per cent. Serious adverse events halved from 6 per cent of deaths in 2009 to 3 per cent in 2013.

Every public hospital now participates in the audit, along with all private hospitals in every state except NSW. However, Professor Maddern said he was pleased NSW private hospitals had agreed to participate in future.

Doctors are now provided with regular case studies from the audit, in which de-identified information about the death is provided, so they can learn from any mistakes.

“What we are seeing is an overall decrease in deaths associated with surgical care, which may be due to many things, and we think the audit is helping,” he said. “It’s making people think twice.”

Professor Guy Maddern’s tips on protecting yourself in surgery

1. If you are away from a major hospital, get yourself to one. A particular problem, Professor Maddern says, exists when rural patients resist transfers to major hospitals because they don’t want to leave their families.

2. Lose weight and don’t smoke.The proportion of deaths where obesity was a factor increased slightly this year. “An operation done on a thin person relative to a fat person can have a completely different outcome,” Professor Maddern says. This is particularly important for older people, who have the most operations.

3. Go to a hospital that performs a lot of the type of surgery you are going to have, particularly if it is complex. Remember, practice makes perfect.

data saving lives, EF, health market quality, policy, quantified self, rapid learning health systems

Creepy data

 

http://www.theguardian.com/technology/2014/dec/05/when-data-gets-creepy-secrets-were-giving-away

When data gets creepy: the secrets we don’t realise we’re giving away

We all worry about digital spies stealing our data – but now even the things we thought we were happy to share are being used in ways we don’t like. Why aren’t we making more of a fuss?
ben goldacre illustration data security
We have few sound intuitions into what is safe and what is flimsy when it comes to securing our digital lives – let alone what is ethical and what is creepy. Photograph: Darrel Rees/Heart Agency for the Guardian

Ben Goldacre

But these are straightforward failures of security. At the same time, something much more interesting has been happening. Information we have happily shared in public is increasingly being used in ways that make us queasy, because our intuitions about security and privacy have failed to keep up with technology. Nuggets of personal information that seem trivial, individually, can now be aggregated, indexed and processed. When this happens, simple pieces of computer code can produce insights and intrusions that creep us out, or even do us harm. But most of us haven’t noticed yet: for a lack of nerd skills, we are exposing ourselves.

At the simplest level, even the act of putting lots of data in one place – and making it searchable – can change its accessibility. As a doctor, I have been to the house ofa newspaper hoarder; as a researcher, I have been to the British Library newspaper archive. The difference between the two is not the amount of information, but rather the index. I recently found myself in the quiet coach on a train, near a stranger shouting into her phone. Between London and York she shared her (unusual) name, her plan to move jobs, her plan to steal a client list, and her wish that she’d snogged her boss. Her entire sense of privacy was predicated on an outdated model: none of what she said had any special interest to the people in coach H. One tweet with her name in would have changed that, and been searchable for ever.

An interesting side-effect of public data being indexed and searchable is that you only have to be sloppy once, for your privacy to be compromised. The computer program Creepy makes good fodder for panic. Put in someone’s username from Twitter, or Flickr, and Creepy will churn through every photo hosting service it knows, trying to find every picture they’ve ever posted. Cameras – especially phone cameras – often store the location where the picture was taken in the picture data. Creepy grabs all this geo-location data and puts pins on a map for you. Most of the time, you probably remember to get the privacy settings right. But if you get it wrong just once – maybe the first time you used a new app, maybe before your friend showed you how to change the settings – Creepy will find it, and your home is marked on a map. All because you tweeted a photo of something funny your cat did, in your kitchen.

medical records

Pinterest
Many people will soon be able to access their full medical records online – but some might get some nasty surprises. Photograph: Sean Justice/Getty

Some of these services are specifically created to scare people about their leakiness, and nudge us back to common sense: PleaseRobMe.com, for example,checks to see if you’re sharing your location publicly on Twitter and FourSquare (with sadistic section headings such as “recent empty homes” and “new opportunities”).

Some are less benevolent. The Girls Around Me app took freely shared social data – intended to help friends get together – and repurposed it for ruthless, data-driven sleaziness. Using FourSquare and Facebook data, it drew neat maps with the faces of nearby women pasted on. With your Facebook profile linked, I could research your interests before approaching you. Are all the women visible on Girls Around Me willingly consenting to having their faces mapped across bars or workplaces or at home – with links to their social media profiles – just by accepting the default privacy settings? Are they foolish to not foresee that someone might process this data and present them like products in a store?

But beyond mere indexing comes an even bigger new horizon. Once aggregated, these individual fragments of information can be processed and combined, and the resulting data can give away more about our character than our intuitions are able to spot.

Last month the Samaritans launched a suicide app. The idea was simple: they monitor the tweets of people you follow, analyse them, and alert you if your friends seem to be making comments suggestive of very low mood, or worse. A brief psychodrama ensued. One camp were up in arms: this is intrusive, they said. You’re monitoring mood, you need to ask permission before you send alerts about me to strangers. Worse, they said, it will be misused. People with bad intentions will monitor vulnerable people, and attack when their enemies are at their lowest ebb. And anyway, it’s just creepy. On the other side, plenty of people couldn’t even conceive of any misuse. This is clearly a beneficent idea, they said. And anyway, your tweets are public property, so any analysis of your mood is fair game. The Samaritans sided with the second team and said, to those worried about the intrusion: tough. Two weeks later they listened, and pulled the app, but the squabble illustrates how much we can disagree on the rights and wrongs around this kind of processing.

The Samaritans app, to be fair, was crude, as many of these sites currently are:analyzewords.com, for example, claims to spot personality characteristics by analysing your tweets, but the results are unimpressive. This may not last. Many people are guarded about their sexuality: but a paper from 2013 [pdf donwload] looked at the Facebook likes of 58,000 volunteers and found that, after generating algorithms by looking at the patterns in this dataset, they were able to correctly discriminate between homosexual and heterosexual men 88% of the time. Liking “Colbert” and “Science” were, incidentally, among the best predictors of high IQ.

Sometimes, even when people have good intentions and clear permission, data analysis can throw up odd ethical quandaries. Recently, for example, the government has asked family GPs to produce a list of people they think are likely to die in the next year. In itself, this is a good idea: a flag appears on the system reminding the doctor to have a conversation, at the next consultation, about planning “end of life care”. In my day job, I spend a lot of time working on interesting uses of health data. My boss suggested that we could look at automatically analysing medical records in order to instantly identify people who are soon to die. This is also a good idea.

But add in one final ingredient and the conclusion isn’t so clear. We are entering an age – which we should welcome with open arms – when patients will finally have access to their own full medical records online. So suddenly we have a new problem. One day, you log in to your medical records, and there’s a new entry on your file: “Likely to die in the next year.” We spend a lot of time teaching medical students to be skilful around breaking bad news. A box ticked on your medical records is not empathic communication. Would we hide the box? Is that ethical? Or are “derived variables” such as these, on a medical record, something doctors should share like anything else? Here, again, different people have different intuitions.

shopping centre

Pinterest
Many shopping centres can now use your mobile data to track you as you walk from shop to shop. Photograph: Christian Sinibaldi/Guardian

Then there’s the information you didn’t know you were leaking. Every device with Wi-Fi has a unique “MAC address”, which is broadcast constantly as long as wireless networking is switched on. It’s a boring technical aspect of the way Wi-Fi works, and you wouldn’t really care if anyone saw your MAC address on the airwaves as you walk past their router. But again, the issue is not the leakiness of one piece of information, but rather the ability to connect together a thread. Many shops and shopping centres, for example, now use multiple Wi-Fi sensors, monitoring the strength of connections, to triangulate your position, and track how you walk around the shop. By matching the signal to the security video, they get to know what you look like. If you give an email address in order to use the free in-store Wi-Fi, they have that too.

In some respects, this is no different to an online retailer such as Amazon tracking your movement around their website. The difference, perhaps, is that it feels creepier to be tracked when you walk around in physical space. Maybe you don’t care. Or maybe you didn’t know. But crucially: I doubt that everyone you know agrees about what is right or wrong here, let alone what is obvious or surprising, creepy or friendly.

It’s also interesting to see how peoples’ limits shift. I felt OK about in-store tracking, for example, but my intuitions shifted when I realised that I’m traced over much wider spaces. Turnstyle, for example, stretches right across Toronto – a city I love – tracing individuals as they move from one part of town to another. For businesses, this is great intelligence: if your lunchtime coffeeshop customers also visit a Whole Foods store near home after work, you should offer more salads. For the individual, I’m suddenly starting to think: can you stop following me, please? Half of Turnstyle’s infrastructure is outside Canada. They know what country I’m in. This crosses my own, personal creepiness threshold. Maybe you think I’m being precious.

There is an extraordinary textbook written by Ross Anderson, professor of computer security at University of Cambridge. It’s called Security Engineering, and despite being more than 1,000 pages long, it’s one of the most readable pop-science slogs of the decade. Firstly, Anderson sets out the basic truisms of security. You could, after all, make your house incredibly secure by fitting reinforced metal shutters over every window, and 10 locks on a single reinforced front door; but it would take a very long time to get in and out, or see the sunshine in the morning.

Digital security is the same: we all make a trade-off between security and convenience, but there is a crucial difference between security in the old-fashioned physical domain, and security today. You can kick a door and feel the weight. You can wiggle a lock, and marvel at the detail on the key. But as you wade through the examples in Anderson’s book – learning about the mechanics of passwords, simple electronic garage door keys, and then banks, encryption, medical records and more – the reality gradually dawns on you that for almost everything we do today that requires security, that security is done digitally. And yet to most of us, this entire world is opaque, like a series of black boxes into which we entrust our money, our privacy and everything else we might hope to have under lock and key. We have no clear sight into this world, and we have few sound intuitions into what is safe and what is flimsy – let alone what is ethical and what is creepy. We are left operating on blind, ignorant, misplaced trust; meanwhile, all around us, without our even noticing, choices are being made.

• Ben Goldacre’s new book, I Think You’ll Find It’s a Bit More Complicated Than That, is published by Fourth Estate. Buy it for £11.99 at bookshop.theguardian.com

Topics

data saving lives, EF, health market quality, healthy habits, nutrition, research methodology

Jeffrey Braithwaite on Microlifes and Micromorts

Punchy.

http://www.jeffreybraithwaite.com/new-blog/2014/11/20/youll-be-dying-to-hear-about-this

You’ll be dying to hear about this

There’s lots of death in the world. Transport is risky, for instance—planes, automobiles, trains and ships can crash, maiming or killing passengers. You don’t have to go much further than seeing the road toll, or hearing about Malaysian Airlines Flight MH17 shot down over the Ukraine, or watching the TV scenes of the Costa Concordia, run aground just off Isola del Giglio near the coast of Italy, to appreciate that death is never far away.

Then there’s infectious diseases. You can all-too-readily catch a cold, or the flu, or TB, or lately, the Ebola virus. And there seem to be never-ending wars and skirmishes in the Middle East; and terror, spread by fundamentalists.

Each of these, depending on fate, can hasten someone’s demise. Wrong place, wrong time, wrong circumstances.

Lifestyle issues can cause problems for your risk profile too—but these are slower, and more stealthy. Think of smoking, drinking too much, eating yourself into a coma or just gross obesity, or the more insidious dangers of sitting at a computer for years on end with little exercise. These can translate over time into heart or lung disease, diabetes, and cancer.

Whether you are active or passive, things you do or don’t do can shorten your lifespan, or kill you a little or a lot faster than you would otherwise last. So what levels of risk do you actually, quantitatively, face in your own life?

*****

Stanford University decision scientist Ron Howard in the 1970s presented a novel way to calculate this risk. He introduced the idea of the micromort, defined as a one-in-a-million likelihood of death.  This is such an evocative unit of measurement that it deserves a little further attention.

If you live in the US or another relatively rich, OECD-style country, with good law and order, legislation that keeps society relatively risk free (such as with environmental and public health issues sorted out, effective building codes, and so forth), a well-educated population, access to health care, and a buoyant GDP, you can expect a micromort of one on any particular day. Another way of saying this is that’s the standard expected death rate for any individual today in any one 24 hour period: a microprobability of one in a million is your index of baseline risk.

These are great odds for you, today, as you read this; you are very likely to get through it. Congratulations if you do.

What circumstances lead to an elevated risk? Say if you do dangerous things or even just live life to the full? How does your micromort level get upgraded?

In the United States, you accumulate an extra 16 micromorts each time you ride a motorcycle 100 miles, for instance. Or 0.7 micromorts are added for each day you go skiing; so go for a week and you’ve added five more.

Or you might decide to do something a little more strenuous. With hangliding, the additional risk of dying equates to eight micromorts per flight; or skydiving, nine per freefall.

They are relatively benign compared to moving up to base-jumping. Do so, and you rapidly earn many more risk points: 430 micromorts per jump, in fact.

Marathon running, anyone? That will be seven micromorts to your debit account for each run. Even walking 17 miles adds one micromort, as does a 230 mile car trip, and add another one for every 6,000 mile train trip. But the puzzle is, it’s not always clear how to treat these: the walking introduces an element of risk (you could be out and about and get run over, or be struck by lightning) but it’s also beneficial (it contributes to improved health).

Perhaps even more interesting, there are microprobabilities associated with accumulated chronic risks in contrast to these other single-shot event risks. These are lifestyle choices and behaviors that incrementally add a little more risk through exposure. They won’t kill you if you have bad luck on a given day, but will slowly have an effect—and may claim you in the end.

Every half a liter of wine exposes you to a micromort because it can accrue into cirrhosis of the liver. Each one and a half cigarettes does the same, but the menace here is cancer or heart disease. Even eating 100 char-broiled steaks, 40 tablespoons of peanut butter or 1,000 bananas sneaks up on you in the form, respectively, of cancer risk from benzopyrene, liver cancer risk from aflatoxin B or cancer risk from radioactive potassium-40.

*****

Hang on though. I doubt I’ve done much to help anyone.

Because a clear problem is that people aren’t very good at doing these kinds of statistics, or applying them to their own lives—and are even less capable of acting on them. We can readily appreciate that skiing or motorcycling add some risk for the time you are doing them compared to the everyday activities of being at work or hanging out at home, yet many people are undeterred. People even cheerfully find ways of taking on more risk, such as by climbing Everest, driving fast cars, or having unsafe sex.

Everyone knows about that steadily accumulated risk, too: not too many of us are blind to the fact that drinking too much alcohol can lead to liver disease or smoking to lung cancer over time. And although both have been falling for decades, this hasn’t stopped millions of people indulging. There’s 42.1 million US smokers at last count, or 18.1% of the population, and on average each adult US citizen consumes 8.6 liters of alcohol annually.

This is not the best performance internationally but is by no means high by international standards, and Eastern Europeans smoke more heavily, and really give hard booze like vodka a nudge.  Nevertheless, both activities contribute to what public health people quaintly call excess deaths and the rest of us know by “their drinking or smoking (or both) killed them eventually.”

But what does it actually mean that you expose yourself to increased risk if you go out walking regularly or eat bananas?  We need another way of looking at this, because it’s too hard to do the sums.

*****

Enter the University of Cambridge medical statistician David Spiegelhalter and his colleague Alejandro Leiva who invented the idea of a microlife. This is another unit of risk which has the calculation built in for you. It is half an hour of your life.

If you increase your risk by one micromort, then this shortens your life by half an hour. These calculations apply to people on average, and work out for entire populations, but any one of us might be lucky or unlucky, depending on our individual characteristics. Any particular risk doesn’t convert exactly to the specific individual. But with enough people in the US (beyond 316 million now) and on the planet (7 billion and rising), there’s a relentlessness accuracy about the statistics.

So now let’s do some life expectancy math with Spiegelhalter. Smoke a pack a day? You lose up to five hours a day. Accumulated, that’s up to eight years off your life. Have six drinks a day and that binge costs you one half hour allocation—a shortened life by ten months or so. Stay eleven pounds overweight and you sacrifice half an hour every day you do so (another ten months across your lifespan), as you do if you watch TV for two hours. Your coffee habit at 2-3 cups daily takes away another half hour lot. So does every portion of red meat each day. Another ten months each time.

It’s not all negative. There’s good news. Eat five serves of fruit and vegetables every day and you gain up to a couple of hours each time. You get three years back. Exercise and the first 20 minutes per day earns you a surprising hour (there’s a good investment—a year and a half), and each subsequent 40 minutes adds up to one more half hour bonus to your credit (a bit more work but that seems a pretty good deal, too, to get a ten month return).

If you have a hobby, activity or diet and it’s not been dealt with so far, you can fill in some of the gaps with some good guesstimates. Do you have passive pursuits, akin to watching TV? This is a net deficit. Do you do active, exercise-oriented activities, such as weekly amateur netball, soccer, bowling or basketball—or just walking regularly? Add some lifespan.

These half hour allocations alter somewhat depending on your genetics of course (you can have lucky or unlucky genes) or your socioeconomic status (wealthy people typically live longer than poorer folks) or your gender (women on the whole live longer than men). That said, with this idea you are now able to alter your risk profile by changing your behavior with a tangible, calculable return.

*****

There’s a punchline to this, and it may be already occurring to you as you reflect on your own lifestyle and lifespan. There are a million microlives in fifty seven years of existence. That, for many of us, is roughly the adult allocation.

Let’s call that your life expectancy baseline. We can assume that you have had a reasonably healthy childhood (not so for everyone, of course, but true for many US children, and true for most readers). Then, from that point on, a large part of your healthy adult life is now measureable.

So: come out of your teens, reach your 21st birthday, and as the “jolly good fellow” and “happy birthday to you” songs subside, imagine you then have 57 years to go. That is, you have an allocation of 78 years in total, maybe a little longer, maybe a little shorter.

Yes, all sorts of unexpected things might happen along the way, but to some degree your lifespan is now no longer vague, but quantifiable. The actual life expectancy in the US indeed hovers around this: it’s 79.8 years overall, 77.4 for males and 82.2 for females. (It’s higher in some northern European countries and Japan, but that’s a story for another day).

However, you might be reading this thinking: Yikes. I’m not 21: I’m a bit older than that. In this case, you’ve already used up a proportion of your time left. Console yourself. At least you got through the riskiest stage of all: being a baby, up to one year of age, and childhood, up to six or so, when many things can go wrong.

But have you used what you were given so far, well? Or do you have a fair bit of regret?

To make an obvious point, however, this isn’t Doctor Who. You don’t have a Tardis to go back in time and fix the past. So stop any lamentations. Look forward.

By now, if you’ve come to value more readily each half hour and especially the cumulative effect of your lifestyle choices to date, don’t listen to me preaching. Feel completely empowered. You know what to do and how to alter your own numbers.

Now, all that’s left is to do the math. You’ll have a much clearer picture of your life and potential death than ever before. It’s your move: what’s next?

Further reading

Blastland, Michael and Spiegelhalter, David (2014). The Norm Chronicles: Stories and Numbers About Danger and Death. New York: Basic Books.

Howard, Ronald (1984). On fates comparable to death. Management Science 30 (4): 407–422.

Spiegelhalter, David (2012). Using speed of ageing and “microlives” to communicate the effects of lifetime habits and environment. British Medical Journal 345: e8223.

Spiegelhalter, David (2014). The power of the MicroMort. BJOG: An International Journal of Obstetrics & Gynaecology 121 (6): 662–663.

EF, health market quality, policy

FBI employing analytics in healthcare fraud investigations

 

http://www.fiercehealthpayer.com/antifraud/story/data-analysis-adds-new-dimension-old-school-fraud-investigations/2015-01-13

Data analysis adds new dimension to old-school fraud investigations

Billing data has become a useful tool in detecting hints of healthcare fraud, and then leading investigators in the right direction
January 13, 2015 | By

In 2010, the FBI organized an undercover sting of a Brooklyn medical clinic that was suspected of Medicare fraud. Agents installed a hidden camera in an air conditioning vent and watched employees pay kickbacks to patients in exchange for Medicare identification numbers, which they used to bill Medicare $50 million in fraudulent claims.

Agents eventually arrested 16 people in connection with the scheme and used the video evidence, along with audio and video from wired elderly clients, in their prosecution. However, it was data analytics that led them to the Brooklyn clinic in the first place, according to The Times.

Data analytics has helped investigators build cases and uncover fraud faster and easier, particularly in areas such as Detroit and Miami that have been hotspots for fraud schemes. In some cases, data mining has helped stop fraud even before criminal charges come to light.

“The idea of using real-time data to generate fraud cases is unique,” Leslie Caldwell, chief of the Department of Justice (DOJ) criminal division, told the newspaper.  “We have the ability to suspend–[when] there’s reasonable suspicion–[those] who are engaged in fraud even before they are prosecuted and indicted.”

The article points to the recruitment of Kirk Ogrosky, who spent time as a federal prosecutor in Miami. In 2006, the DOJ asked him to head the healthcare unit. Ogrosky accepted on the condition that the agency would “rethink the way they prosecute healthcare fraud, with an emphasis on real-time prosecutions.” Ogrosky began by searching for postal codes in which patient spending was three or four times the national average, and then employing old-school detective tactics to further the investigation.

“Most times, those zip codes would help generate a list of providers that had what I would call ‘medically impossible’ claims,” he told The Times. “[It was] like peeling an onion ring by ring–and yes, it always burnt my eyes at some point.”

Data analytics have since been used to uncover schemes related to chemotherapy drugs, home healthcare, and durable medical equipment. In Indiana, data-driven investigations have saved the state $85 million. FierceHealthPayer: AntiFraud previously reported on predictive models and algorithms such as the government’s Fraud Prevention System (FPS), which has led to more than $50 million in actual and projected savings in two years.

For more:
– read The Financial Times article

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Health Analytics Intrapreneurial JV

Teams building analytics technology for healthcare organizations find themselves jointly holding intellectual property and equity in new arrangements not seen before in healthcare.

Extrapreneurial energy turns intrapreneurial analytics initiatives into companies in which healthcare enterprises retain some equity, remain customers, and benefit other healthcare enterprises who wish to purchase analytics technology and services.

Involves a definitive ten-year agreement valued at more than $100 million, to combine technologies, some of which Allina developed since becoming the first customer of Health Catalyst technology in 2008.

“We have a lot of confidence in our partner in Health Catalyst. Eighty percent of that [$100 million] is standard, but 20% of it is at risk, based on how we perform on key indicators, like how well the tools perform, for example, on reducing readmissions or unnecessary admissions for people who can spend nights in their own bed.

Wheeler says use of Health Catalyst technology has permitted Allina clinicians to significantly reduce readmissions, elective inductions of labor, time required to diagnose breast concerns, and sepsis rates.

http://www.healthleadersmedia.com/print/TEC-312328/Allina-Health-and-Health-Catalysts-Unusual-Deal

Allina Health and Health Catalyst’s Unusual Deal

Scott Mace, for HealthLeaders Media , January 20, 2015

Teams building analytics technology for healthcare organizations find themselves jointly holding intellectual property and equity in new arrangements not seen before in healthcare.

Follow the money, they say. It’s not always easy. “Terms of the transaction were not disclosed” is the common coin of many a deal. But despite this, some deals are harbingers of bigger things.

To make my point, I will appropriate a word: extrapreneur. It’s a word that you won’t find in most dictionaries. In 1992, the American Heritage Dictionary defined intrapreneur as “a person within a large corporation who takes direct responsibility for turning an idea into a profitable finished product through assertive risk-taking and innovation.”

So what’s an extrapreneur? One suggestion from England: Someone who shares information among organizations that they wouldn’t share among themselves.

That’s a good place to start when trying to understand what is occurring at Cleveland Clinic, Geisinger Health System, and, most recently, Allina Health, where teams building analytics technology for healthcare organizations find themselves jointly holding intellectual property and equity in new arrangements not seen before in healthcare.

Extrapreneurial energy turns intrapreneurial analytics initiatives into companies in which healthcare enterprises retain some equity, remain customers, and benefit other healthcare enterprises who wish to purchase analytics technology and services.

The term extrapreneurial also reminds me of extranets, the early e-commerce concept that extended intranets (internal TCP/IP-based corporate networks) to business partners as supply chains started being built when the World Wide Web was young.

In 2009, Cleveland Clinic’s extrapreneurial initiative spawned Explorys, an analytics platform which now counts numerous large healthcare systems among its clients. Yet, for quite some time, Explorys remained located on the Cleveland Clinic campus. And Cleveland Clinic remains an investor.

Geisinger created xG Health “to bring Geisinger’s expertise in healthcare delivery transformation to organizations nationwide,” according to xG’s Web site. xG describes itself as the primary provider of Geisinger’s health performance improvement intellectual property.

Launched in 2013 with $40 million of financing from venture capital partner Oak Investment Partners, and located in Columbia, Maryland, xG is not far from Geisinger’s Pennsylvania base of operations.

Allina and Health Catalyst
Then, on January 6, Allina Health joined the extrapreneurial ranks. A few terms of the agreement are intriguing the entire analytics industry. Allina took an undisclosed stake in analytics firm Health Catalyst.

Health Catalyst had just come off an impressive year, having raised $41 million in funding in January 2014, and convening a conference of its own rapidly-growing healthcare system analysts last fall in Salt Lake City, where the company is located.

But back to those interesting terms between Allina and Health Catalyst. It’s a definitive ten-year agreement valued at more than $100 million, to combine technologies, some of which Allina developed since becoming the first customer of Health Catalyst technology in 2008.

Once a year, a governing committee of the Allina / Health Catalyst partnership will identify a prioritized list of improvement projects, each designed to provide measurable care improvement and financial value to Allina. As the partnership achieves each goal, both partners will share in the benefits of that success.

The deal also means that Allina is outsourcing its data warehousing, analytics, performance improvement technology, content, and personnel to Health Catalyst to accelerate advances. Beginning this month, in phases, Allina employees working in these areas—some 60 in all—will become onsite Health Catalyst team members.

When you have a partnership of this magnitude, extrapreneurial forces also allow each partner to remain agile rather than locked into an arrangement that has the possibility of souring due to the changing vicissitudes of technology and healthcare.

The $100 million represents the cost of what the staff and tools were costing Allina, says Penny Wheeler, MD, president and CEO of Allina Health, a $3.7 billion not-for-profit organization whose more than 90 clinics, 12 hospitals, and related healthcare services provide care for nearly 1 million people across Minnesota and western Wisconsin.

Use the Best Tool
“We weren’t falling back on hope as a strategy,” Wheeler says. “We have a lot of confidence in our partner in Health Catalyst. Eighty percent of that [$100 million] is standard, but 20% of it is at risk, based on how we perform on key indicators, like how well the tools perform, for example, on reducing readmissions or unnecessary admissions for people who can spend nights in their own bed.

Wheeler says use of Health Catalyst technology has permitted Allina clinicians to significantly reduce readmissions, elective inductions of labor, time required to diagnose breast concerns, and sepsis rates.

“Our agreement with Health Catalyst says that if we find a better tool out there, we can use it,” she says. “So, for example, if [Epic analytics software] Cogito excels at the capabilities that we work with, then we use that,” she says.

“So it’s more about what can you use the best to improve care than any exclusivity. That just speaks to the confidence level we both have in our ability to partner and make things better, despite what else is out in the market.

“I’m pretty confident that we’re going to have a ten-year agreement and beyond,” Wheeler says.

“The margins in healthcare right now are so razor-thin, and that’s pretty apparent at Allina, given some of their recent financials. But they want to be able to create a little bit of a for-profit business around this core competency they’ve built in terms of managing their clinical performance with IT, which is what’s going on here,” notes Judy Hanover, research director of provider IT transformation at IDC Health Insights.

In the era of extrapreneurs, it’s all part of doing business.

Scott Mace is senior technology editor at HealthLeaders Media.

complex adaptive systems, data saving lives, EF, health market quality, healthcare, healthy habits, rapid learning health systems

Population Health: A riddle wrapped in an enigma

PN: The health sector is very happy to take full responsibility for the health of the population for as long as substantial monies are tied to that claim. The moment the health sector is asked to account for it, they get nervous.

Tying funding to value is a terrifying prospect for the health sector as having to account for the benefit they deliver would inevitably lead to a diminution in income and status.

“Because so many factors lie outside clinicians’ control, we need to understand what factors the healthcare system can reasonably be expected to act on, given professionals’ training, infrastructure and scope of practice,” they said. “We also need to determine the appropriate levels of health system accountability for population health outcomes.”

http://www.modernhealthcare.com/article/20150108/BLOG/301089997/population-health-improvement-still-a-riddle-wrapped-in-an-enigma

Population health improvement still a riddle wrapped in an enigma

By Melanie Evans  | January 8, 2015
Population Health
New England Journal of Medicine
The push to invest more of the healthcare industry’s time and money into promoting good health is, so far, uneven and uncertain in terms of effectiveness. Perhaps nowhere is that more apparent than in federal initiatives to broadly improve health by extending care beyond clinics and pharmacies into neighborhoods and homes.Federal funding for population-health efforts—the management of health and medical care for an entire group of patients or a community—has expanded under the Affordable Care Act. It’s included financing for states and providers to experiment with ways to better coordinate healthcare and other needs that affect health, such as housing and transportation. But the initiatives are not without risk or challenges, a point three federal officials underscored in the latest issue of the New England Journal of Medicine.

Efforts are still underway to identify what works and how to make widespread use of the most effective strategies, write Dr. William Kassler, Naomi Tomoyasu and Dr. Patrick Conway of the agency that oversees Medicare and Medicaid. The CMS Innovation Center, in a report to Congress last month, also said results were largely not yet available for nearly two dozen initiatives to bolster population health, improve quality and increase efficiency in healthcare, financed with $2.6 billion through last year.

Calculating a dividend from those investments presents another challenge, the trio wrote. Kassler is one of the CMS’ chief medical officers; Tomoyasu is deputy director of the prevention and population health care models group within the CMS Innovation Center; and Conway is the CMS’ deputy administrator for innovation and quality.

The return on any investment in prevention will necessarily take time, raising the risk that “current actuarial methods used to evaluate return on investment may underestimate potential savings,” they warned.

Investment at the federal level is not small. Medicare and Medicaid—which combined account for $1 of every $3 the nation spends on healthcare—have increasingly poured money into strategies for disease prevention and health promotion.

Those strategies extend the reach of healthcare beyond hospitals, clinics and pharmacies into neighborhoods, homes and schools. Such extended investment can include help with housing, transportation, literacy, day care and groceries, the officials wrote.

But with that expanded reach comes a debate “regarding the specific population-based activities that fall within healthcare providers’ scope of practice,” wrote the CMS officials. “Because so many factors lie outside clinicians’ control, we need to understand what factors the healthcare system can reasonably be expected to act on, given professionals’ training, infrastructure and scope of practice,” they said. “We also need to determine the appropriate levels of health system accountability for population health outcomes.”

Follow Melanie Evans on Twitter: @MHmevans

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WSJ: Can a Smartphone Tell if You’re Depressed?

 

http://www.wsj.com/articles/can-a-smartphone-tell-if-youre-depressed-1420499238

TECHNOLOGY

Can a Smartphone Tell if You’re Depressed?

Apps, Other Tools Help Doctors, Insurers Measure Psychological Well-Being

By

JOSEPH WALKER

HUNTERSVILLE, N.C.—Toward the end of Janisse Flowers’s pregnancy, a nurse at her gynecologist’s office asked her to download an iPhone app that would track how often she text messaged with friends, how long she talked on the phone and how far she traveled each day.

The app was part of an effort by Ms. Flowers’s health-care provider to test whether smartphone data could help detect symptoms of postpartum depression, an underdiagnosed condition affecting women after they give birth. The app’s developer, San Francisco-based…

complex adaptive systems, data saving lives, EF, facts & data points, health market quality, healthcare, policy, politics, quantified self, rapid learning health systems

Yach: Changing the Landscape for Prevention and Health Promotion

 

http://www.huffingtonpost.com/dr-derek-yach/changing-the-landscape-fo_1_b_6439328.html

Become a fanExecutive Director of the Vitality Institute

Changing the Landscape for Prevention and Health Promotion

Posted: Updated:

By Bridget B. Kelly and Derek Yach*

Chronic diseases like heart disease, diabetes, and cancer are major contributors to poor health and rising health care costs in the U.S. The cost of treating these conditions is estimated to account for 80 percent of annual health care expenditures. More and more, experts agree on the great potential for preventing or delaying many cases of costly chronic diseases by focusing on environmental, social, and behavioral root influences on health. Yet the U.S. has been slow to complement its considerable spending on biomedical treatments with investments in population-based and non-clinical prevention interventions.

What is getting in the way of strengthening our investments in prevention and health promotion? A few consistent themes emerged across multiple expert consensus studies conducted by the Institute of Medicine (IOM), which were summarized in the report Improving Support for Heath Promotion and Chronic Disease Prevention — developed in support of the recent Vitality Institute Commission on Health Promotion and Prevention of Chronic Disease in Working-Age Americans.

First, prevention is challenging — chronic health problems are complex, and so are the solutions. Second, decision-makers who allocate resources have tough choices to make among many competing pressures and priorities; prevention and promotion can be at a disadvantage because their benefits are delayed. Third, there is a need for better, more usable evidence related to the effectiveness, the implementation at scale, and the economics of prevention interventions. Decision-makers need information that makes it easier to understand, identify, and successfully implement prevention strategies and policies. As noted in a recent opinion piece in the Journal of the American Medical Association (JAMA), limited investment in prevention research has resulted in an inaccurate perception that investing in preventive measures is of limited value. This has profound implications for federal funding allocations.

The mismatch in funding allocations is seen right at the source of our nation’s major investment in new health-related knowledge: the National Institutes of Health (NIH). A new paper in the American Journal of Preventive Medicine found that less than 10 percent of the NIH annual budget for chronic diseases is allocated to improving our knowledge base for effective behavioral interventions to prevent chronic diseases. This means that despite the immense potential for prevention science to reduce the burden of chronic diseases in the U.S., it is woefully underfunded compared to what we invest in researching biomedical treatment interventions for these conditions. NIH investments affect what evidence is ultimately available to those who decide how to allocate resources to improve the health of our nation, and they also affect the kinds of health experts we train as a country. By not investing in prevention science and in a future generation of scientists capable of doing high quality research in prevention, we are perpetually caught in the same vicious cycle where prevention continues to lag behind in our knowledge and therefore our actions.

There is hope that the landscape is slowly changing. Initiatives such as the NIH Office of Disease Prevention‘s Strategic Plan for 2014-2018 and the Affordable Care Act’s mandated Patient-Centered Outcomes Research Institute (PCORI) have the potential to strengthen prevention science and build the evidence-base for effective prevention interventions. Innovations in personalized health technologies and advances in behavioral economics also show great promise in improving health behaviors for chronic disease prevention.

The Vitality Institute Commission’s report emphasized the need for faster and more powerful research and development cycles for prevention interventions through increased federal funding for prevention science as well as the fostering of stronger public-private partnerships. It is essential to generate and communicate evidence in a way that enables decision-makers to understand the value of investing in prevention while taking into account their priorities, interests and constituencies. This will lead us to more balanced investments, make prevention a national priority, and boost the health of the nation.

*The authors are responsible for the content of this article, which does not necessarily represent the views of the Institute of Medicine.