Category Archives: quantified self

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

Peter Hinssen: The Tiger and The Rock

EDxBrussels – Peter Hinssen – The TIGER & the ROCK

Why Extrapolating WON’T WORK & What it means for HEALTH http://www.tedxbrussels.eu About TEDx, x = independently organized event In the spirit of ideas worth…

http://wn.com/tedxbrussels_-_peter_hinssen_-_the_tiger_&_the_rock

8:20 – The Contiguous United States – macdonald’s proximity to people in the US

9:10 The Flip: Pharma moving to Health as a Service (no longer a product)

Institutions > Communities (trust)

Reactive > Proactive (attitude)

Hinssen_HealthMatrix

http://www.datapointed.net/visualizations/maps/distance-to-nearest-mcdonalds-sept-2010/

 

distance_to_mcdonalds_2010_l

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

Should we pay people to look after their health?

 

http://theconversation.com/should-we-pay-people-to-look-after-their-health-24012

Should we pay people to look after their health?

The key to using incentives may be to do so with a high enough frequency to create healthy habits. Health Gauge/Flickr, CC BY-SA

With the Tony Abbott government expressing concern about the growing health budget and emphasising personal responsibility, perhaps it’s time to consider some creative ways of curbing what Australia spends on ill health. One solution is to pay people to either get well or avoid becoming unwell in the first instance.

The United Kingdom is already doing this kind of thing with a current trial of giving mothers from disadvantaged suburbs A$340 worth of food vouchers for breastfeeding newborn babies. And from January 1 this year, employers in the United Statescan provide increasingly significant rewards to employees for having better health outcomes, as part of the Affordable Care Act.

But should people really be paid to make healthy choices? Shouldn’t they be motivated to improve their health on their own anyway?

Encouraging right decisions

People don’t do what’s in their best interest in the long term for many reasons. When making decisions we tend to take mental short cuts; we allow the desires and distractions of the moment get in the way of pursuing what’s best.

One such “irrationality” is our tendency to focus on the immediate benefits or costs of a situation while undervaluing future consequences. Known as present bias, this is evident every time you hit the snooze button instead of going for a morning jog.

Researchers have found effective incentive programs can offset present bias by providing rewards that make it more attractive to make the healthy choice in the present.

Research conducted in US workplaces, for instance, found people who were given US$750 to quit smoking were three times more successful than those who weren’t given any incentives. Even after the incentive was removed for six months, there was still a quit rate ratio of 2.6 between the incentive and control groups – 9.4% of the incentive group stayed cigarette-free versus only 3.6% of the control group.

A refined approach

Still, while research on using financial incentives to encourage healthy behaviours is promising, it isn’t as straightforward as doling out cash in exchange for good behaviour.

Standard economic theory posits that the higher the reward, the bigger the impact – but this is only one ingredient to success. Behavioural economics shows that when and how you distribute incentives can determine the success of the program.

Here are a few basic principles to consider. First, small rewards can have a big impact on behaviour if they’re provided frequently and soon after the healthy choice is made. We have found this to be true in the context of weight-loss programs, medication adherence, and even to quit the use of drugs such as cocaine.

Games of chance are an effective way of distributing rewards as research has found people tend to focus on the value of the reward rather than their chance of winning the prize. Many people think that a 0.0001 and a 0.0000001 chance of winning a prize are roughly equivalent even though in reality they are vastly different probabilities.

Finally, people are more influenced by the prospect of losses than by gains. Studies show people put much greater weight on losing something than gaining something of a similar value.

In one weight-loss experiment, for instance, participants were asked to place money into a deposit account. If they didn’t achieve their weight goals, the money would be forfeited, but if they were successful, the initial deposit would be doubled and theirs to keep.

Reluctant to lose their deposits, participants in the deposit group lost over three times more weight than the control group, who were simply weighed each month.

Creating good habits

Incentives are particularly effective at changing one-time behaviours, such as encouraging vaccination or attendance at health screenings. But with increasing rates of obesity and other lifestyle-related diseases, we need to focus on how incentives can be used to achieve habit formation and long-term sustained weight loss.

We know financial incentives can increase gym usage and positively impact weight, waist size and pulse rate, but how to sustain gym use after the incentive is removed? The key may be to use incentives to achieve a high frequency of attendance for long enough to create a healthy habit.

We also need to consider how we can leverage social incentives, such as peer support and recognition, together with new technologies to maximise the impact of incentive-based programs.

Innovative solutions, like paying people to encourage the right health choices, may help to reduce both the health and economic impact of Australia’s growing burden of disease.

business, complex adaptive systems, cool, data saving lives, EF, entrepreneurship, file note, health market quality, healthcare, healthy habits, meaning, musings, nutrition, policy, politics, quantified self, rapid learning health systems, research methodology, startup, storytelling, technology

Establishing markets in prevention and wellness – 3 examples

1. AIA Vitality Life Insurance

  • https://www.aiavitality.com.au/vmp-au/
  • Wendy Brown – University of Queensland wbrown@hms.uq.edu.au
  • Tracy Kolbe-Alexander – University of Queensland

2. Data Driven Healthcare Quality Markets

3. Abu Dhabi Health Authority – Weqaya

 

 

Vitality Institute Commission – Recommendation 3 http://thevitalityinstitute.org/commission/create-markets-for-health/

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.

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, entrepreneurship, healthcare, healthy habits, quantified self, rapid learning health systems

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.

data saving lives, EF, entrepreneurship, healthy habits, nutrition, policy, quantified self, rapid learning health systems

Bloomberg: Omada Health Pitch

  • Digital Therapeutics — “Prevent”
  • Digitally-mediated behavioural change
  • Business Model: Charge on success
  • Enterprise Customers

http://www.bloomberg.com/video/take-face-to-face-medicine-to-digital-omada-health-ceo-luSxUqctQcqbjUMc6Wf41g.html

Transcript:

Thanks for joining us on “bottom line.” tell me what your company does.

What is digital therapeutics?

Digital therapeutics is the idea that medicine in the past was conducted in a face-to-face setting.

On the web and social and mobile on the way we can create digital expenses is allowing us to be done digitally.

We take proven lifestyle and behavioral medicine interventions from face-to-face to digital.

That is what we do.

This could help me — well, i don’t smoke, but if i did, it could help me quit and eat healthier, which i don’t do.

Is that the idea — lose weight, quit smoking?

Matt, we can help you with that, and if you want a free pass to our program, let me know . our program helps people with high risk of type two diabetes lose weight and make lifestyle changes over the course of 16 weeks and it is conducted entirely digitally.

I use my iphone or ipad and this will actually work?

Is that the case?

That is the idea.

It can help people proven at risk for type two.

If you help them in a high-tech fashion, our program is digital, a small group environment, where you are paired with others like you and you see how others are doing and we get android and iphone apps and we have a whole bunch of things to make you successful.

Every time i want a delicious cherry coke at lunch, you suggest something that won’t give me diabetes?

The idea is that that moment you want that delicious cherry coke, you think of your health coach and your groups going on with you and maybe you will get a water instead of something better for you.

Very smart man , mark andreessen, is a big backer of you guys.

What is the future of this company?

What does he see there as far as growth is concerned?

You know, i think the interesting bit is what is happening from the company landscape is that you get folks like me with tech and health care backgrounds will bring companies.

I studied neuroscience and i worked at google for a well and went to harvard medical school.

My passion has always been tech plus health care.

I think andreessen horowitz saw a consumer grade, rich product and experience, but to an enterprise customer set with a unique business well behind it that got them excited and that is what led them to pull the trigger on the deal.

$23 million?

What’s next?

Next for us is working with customers.

We have an innovative business model and that we only charge our employer and health plan customers if we are successful with members . because of that model, we have had a lot of demand coming in and it is just scale, scale, scale.

You sold me with harvard med school and you are a neuroscientist with an nba paper you have competition out there — but you’d have competition out there.

What are the barriers?

We do have competition.

The biggest barrier is for entrepreneurs and companies like myself is figuring out health care.

It is incredibly complex.

But so far, so good.

We want competition.

This is a space where there is a lot of people at me.

One third of the adult population has prediabetes, the latest stats from the cdc.

Let’s have a lot of people take a bite.

I wonder about results.

How can you prove that your programs give people the results they want in order to pay money up front and center for your courses — sign up for your courses?

The first is in the world of behavioral medicine.

There are a lot of published studies that show you what you need to achieve from the results standpoint, and then because of the element in our program like the digital scale, the cell phone chip, we can determine if people are successful and show the results in a very transparent and authentic way to our enterprise partners.

Diabetes is obviously a huge and growing problem.

I am certainly at risk for it.

But the weight loss thing is where i guess you will make the big money.

Type 2 diabetes is correlated to being overweight but it is not the only thing good genetics comes into play as well.

As a country, if we are to avoid the stats the cdc put out, 40% of adults of finding out at some point in their life that they are thank you, there needs to be weight loss and lifestyle intervention programs.

I’m just saying that if your marketing materials show that i lost 10 pounds in weeks with this outcome everyone will sign up.

It’s fascinating, what happens when we work with a self-interested employer is that employees who go through a program and become successful rave about it and tell their colleagues and they get colleagues to sign up.

Thanks very much.

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

McKinsey on Digital Health

Good observations on mega-trends in healthcare…

Patients arm themselves with information about product safety and efficacy gleaned from websites and online communities such as PatientsLikeMe, pore over cost and quality indicators from healthcare start-ups such as Castlight Health or HealthGrades, and comparison shop using information synthesized by their insurance providers.

PDF: A digital prescription for pharma companies McKinsey

http://www.mckinsey.com/Insights/Health_systems_and_services/A_digital_prescription_for_pharma_companies

A digital prescription for pharma companies

Pharmaceutical and medical-device companies have been slow to adopt digitization. Here are five reasons they should get moving.

November 2014 | bySastry Chilukuri, Rena Rosenberg, and Steve Van Kuiken

The US healthcare industry is undergoing a major transformation as healthcare reform encourages consumers to play a far more active decision-making role. Yet despite this traditionally business-to-business industry moving quickly to a business-to-consumer model, companies have been slow to join the digital movement. Unlike successful B2C companies in other industries—which offer mobile solutions, provide personalized product recommendations, and empower customer-service agents with a 360-degree view of the customer—most healthcare providers and payors are lagging, as are pharmaceutical companies and medical-device manufacturers. That’s problematic when customers are increasingly expecting a better, more personalized experience from companies taking advantage of the host of digital tools and analytics at their disposal.

Healthcare is not immune to this reality. The sudden increase in the individual market1through the creation of exchanges and growth in Medicare Advantage2 has forced US payors to adopt some of these digital tools, while the growing cost burden for healthcare absorbed by consumers inspires many would-be patients to jump on the web or social networks to conduct research. So why, with a few exceptions, are pharmaceutical and device companies taking a “wait and watch” approach? Government agencies, payors, disease advocates, and disrupters are launching digital solutions that threaten product sales and take advantage of the opportunity to respond to patient needs. This role should be a natural extension for pharmaceutical and medical-device companies, and we have identified five compelling reasons they must get moving before it is too late.

1. Patient behavior is changing

As with many other industries, consumers in the healthcare sector are becoming more informed, empowered, and demanding. The vast majority of connected patients are using an array of digital tools to take control of their health and the healthcare services they access and buy: more than 70 percent of patients who are online in the United States use the Internet to find healthcare information, and more than 40 percent of people who diagnosed their condition through online research had it confirmed by a physician.3Patients arm themselves with information about product safety and efficacy gleaned from websites and online communities such as PatientsLikeMe, pore over cost and quality indicators from healthcare start-ups such as Castlight Health or HealthGrades, and comparison shop using information synthesized by their insurance providers.

The more that healthcare data becomes digitally accessible, the more patients will use it to weigh—and potentially reject—expensive healthcare treatments. This is particularly true in the United States, where patients pay a greater percentage of the cost of their drug therapies (25 percent is not unusual) than they do for other healthcare expenses such as inpatient services. Not surprisingly, these consumers are demanding more information so they can apply the same cost-benefit analysis and research techniques they use to purchase cars or phones when they purchase healthcare; they are also making more informed, rational choices about where they put their money. Data and information about insurance plans, pharmaceutical products, and manufacturers are discussed in a variety of virtual forums. If companies do not join the digital dialogue and influence the conversation, they will lose an opportunity to shape it, and they may be put on the defensive trying to refute the statements made by those that do take part.

2. Government agencies are moving surprisingly quickly

As patient and consumer demand for information grows, the government is beginning to supply healthcare data either directly, through the release of information, or indirectly, by providing incentives for collection and aggregation of relevant clinical data. A recent McKinsey Global Institute report4 found that healthcare is one of seven sectors that could generate billions of dollars of value per year as companies use open data—machine-readable information made available to others, often free of charge—to develop new products and improve the efficiency and effectiveness of operations.

Government health agencies, from national health services in Asia and Europe to government organizations in the United States, are already harnessing the power of big data to figure out what’s working and what isn’t and encouraging others to do the same. The Health Data Initiative launched in 2010 by the US Department of Health & Human Services (HHS) was one of the first and is still among the most prominent examples. In June 2011, former HHS chief technology officer Todd Park described an ambition to make HHS the “NOAA of health data.”5 It appears that his vision is becoming reality, as HHS reported that more than 1,000 data sets were available on healthdata.gov at the end of 2013,6 and the agency’s catalog continues to expand.

The hope is that greater “data liquidity” will both enable more collaborative research among academics and inspire healthcare innovation. Greater access to data is already driving changes in care protocols, allowing the benchmarking of physicians, aiding the identification of clinical best practices, informing the adjustment of benefits and reimbursement structures, and resulting in actual behavioral change. At the federal level in the United States, for example, the recent release by the Centers for Medicare & Medicaid Services of Medicare reimbursements to providers put some physicians on the defensive to explain billing perceived as excessive, and the organization also proposed rescinding the prohibition against releasing prescriber, pharmacy, and plan identifiers related to Medicare Part D payments.

In another example, the new openFDA application-programming-interface initiative for drug-adverse events allows researchers to synthesize, interrogate, and generate insights from a decade (2004–13) of adverse-event reports—an effort that is almost certain to stir conversation. And at the US state level, Arkansas and Tennessee are examining treatment protocols and zeroing in on the relatively small number of care episodes that comprise the majority of medical costs. The states’ shared goal is cutting waste and revising reimbursement policies to encourage high-quality and efficient care.

These efforts mean that providers and manufacturers of drugs and devices only control a small fraction of the data relevant to their work or products. If healthcare follows the path of other consumer-oriented sectors that compete on data analytics, such as high tech and retailing, winners and losers will be determined in part by who makes the best use of the data available and the strongest case for change. Government agencies across the globe are leading the way, and entrepreneurs are taking advantage of government’s interest in facilitating data exchange. However, pharmaceutical and medical-device companies are on the sidelines, leaving others to dictate how information related to their products is used.

3. Trial data is necessary but no longer sufficient

Pharmaceutical companies have used data generated from long-running randomized controlled trials as the gold standard to demonstrate the efficacy and safety of products and gain regulatory approval or formulary listings. Yet many of their customers—payors, increasingly providers, and even patients—are looking for real-world evidence. Both access to and quality of real-world data are increasing exponentially, spanning everything from patient electronic health records to social platforms, healthcare claims, demographic trends, and genomic insights.

The difference in emphasis by certain stakeholders creates pressure on pharmaceutical companies to respond. As data integration and analyses take precedence over data ownership or sponsorship, competitive advantage will rest with those organizations that innovatively use multiple data sources to uncover true insights. Meeting long-standing requirements regarding clinical-trial data continues to be necessary for approval, but it is no longer enough for other stakeholders when more and more targeted and timely data are available. Consider this: Thomson Reuters found that the number of observational research studies tripled from roughly 80,000 between 1990 to 2000 to more than 263,000 in the following decade from 2001 through 2011.7

There is a concerted effort to facilitate collaboration by making more real-world data available at a fairly low cost. Initiatives such as PCORnet, a distributed research network, were launched to advance researchers’ ability to conduct comparative-effectiveness and clinical-outcomes research more efficiently. Aggregating data across “networks of networks” dramatically reduces the cost of observational studies and more quickly generates insights about patient care. Innovative methods enable randomization using real-world data to improve the quality of findings.

Pharmaceutical companies can’t discount observational data because such data already affect product pricing and reimbursement levels. European markets are using real-world evidence to limit reimbursements on new drugs to the competitor’s level until real-world evidence is provided to demonstrate that the new therapy is better. The International Society for Pharmacoeconomics and Outcomes Research reported in 2007 that countries were using reference pricing for new treatments assessed to add little incremental medical value, and real-world data was part of that effectiveness assessment.8 In short, pharmaceutical companies need a data strategy that reflects the shift in how data are shared and analyzed, as well as a plan to manage all types of data that affect product sales, pricing, and reimbursement.

4. Care is evolving

Healthcare is moving from a focus on addressing point-in-time issues toward coordinated, continuous health management. The need to provide ongoing management of chronic diseases and to predict and prevent severe episodes and events offers new opportunities and places new communication demands on every member of the healthcare team, including pharmaceutical companies. Sensor technology, such as that produced by Proteus Digital Health, allows continuous collection of physiological data (for example, electroencephalograph, electrocardiogram, movement, heart rate, and glucose levels), which could vastly improve disease management by providing real-time status reports that can alert providers to impending patient problems. When scaled broadly, these innovations also may reduce the need for many courses of treatment. Pharmaceutical companies need to be at the forefront of developing “beyond the pill” services that deliver value to patients and evolve from a mind-set that measures success based largely on the number of prescriptions written.

Some innovators already are combining technology-enabled monitoring and insight to deliver new solutions to patients. Propeller Health inserted GPS technology in inhalers to identify environmental triggers that caused asthma sufferers to use their device, thus allowing consumers to head off severe attacks. Similarly, a pharmaceutical company that made a pain medication equipped patients with Jawbone devices to continuously capture patient mobility. This showed that patients experienced greater relief that allowed them to increase their movement, even if they did not report lower pain scores. The evidence was used to convince payors to relist the pain medication on formularies.

Not all wraparound services rely on new technology. Telemedicine outreach and coaching efforts by nurses at one of the largest government hospital systems in the United States dramatically reduced the risk of complications from conditions such as diabetes.

Whether low or high tech, patient services aimed at preventing acute episodes or supporting compliance deliver significant benefits to patients. Pharmaceutical companies that remain fixated solely on prescription volume, rather than on sustaining relationships between a brand and patients, risk ceding the role of trusted provider to others. For industry participants to thrive in the digital era, they must build a broader menu of service offerings instead of merely using technology solutions to increase prescriptions.

5. Competition is faster and fiercer

Technology cycles are getting shorter and the cost of experimentation cheaper. The run-up to the passage of the Health Information Technology for Economic and Clinical Health Act in 2009 and Affordable Care Act in 2010 saw significant investment in companies developing systems, solutions, or applications to support electronic health records. From 2010 to the end of 2013, seed and Series A–stage healthcare investments continued to grow, multiplying fivefold in the United States in that time. In the first half of 2014, investors spent $2.3 billion, with more than 140 digital companies each raising more than $2 million,9 as the investment focus shifted from providers of electronic-health-records solutions to developers of consumer-oriented applications, makers of wearable health technology, and health data and analytics. There are thousands of healthcare-related apps available from the US Apple App Store, but only a fraction are patient facing with genuine health content, according to a new study from the IMS Institute for Healthcare Informatics. The recent announcement of the Apple Watch and the company’s release of its HealthKit developer tool are likely to increase the variety of functions and number of health-related apps that are available.

Google Glass is the most high-profile wearable being tested for numerous healthcare applications—for example, surgeons are using it to facilitate and record operations, office physicians are reducing interruptions in patient engagement by retrieving and sending information to electronic medical records through the device, and emergency-medicine physicians are getting specialist consults by transmitting video or images taken by Glass.10 Beyond Google, Intel acquired BASIS Science, MC10 raised a $41.9 million investment, and Proteus raised $183.4 million to develop its line of sensor-based products. Services or applications that facilitate consumer communication with doctors such as Doctor on Demand and HealthTap+ also secured financing.

These new entrants to the healthcare sector have different ways of thinking about solving healthcare problems and using proven agile iterative techniques to bring products to market rapidly and in iterations as improvements are made. Pharmaceutical companies need to recognize the value and impact of these disrupters and learn from them.

Digitally enabled healthcare is here, and most pharmaceutical companies aren’t ready. Despite access to unprecedented data and technologies that can be used to drive better health outcomes by influencing customer behavior, few are truly exploring digital-engagement models. The opportunity to learn more about consumers and develop better, more targeted products and services far outweighs the threat digitization presents companies—for now. Unless incumbent pharmaceutical companies move quickly, innovative competitors may grab a greater share of benefits and stronger customer loyalty.

About the authors

Sastry Chilukuri and Rena Rosenberg are principals in McKinsey’s New Jersey office, where Steve Van Kuiken is a director.

The authors wish to thank Elizabeth Doshi for her contribution to this article.