Category Archives: data saving lives

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

Cth Fund Country Comparisons

 

http://www.commonwealthfund.org/publications/fund-reports/2014/jun/mirror-mirror

PPT: Exhibit_ES1_CthFund

Mirror, Mirror on the Wall, 2014 Update: How the U.S. Health Care System Compares Internationally

Executive Summary

The United States health care system is the most expensive in the world, but this report and prior editions consistently show the U.S. underperforms relative to other countries on most dimensions of performance. Among the 11 nations studied in this report—Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States—the U.S. ranks last, as it did in the 2010, 2007, 2006, and 2004 editions of Mirror, Mirror. Most troubling, the U.S. fails to achieve better health outcomes than the other countries, and as shown in the earlier editions, the U.S. is last or near last on dimensions of access, efficiency, and equity. In this edition of Mirror, Mirror, the United Kingdom ranks first, followed closely by Switzerland (Exhibit ES-1).

Expanding from the seven countries included in 2010, the 2014 edition includes data from 11 countries. It incorporates patients’ and physicians’ survey results on care experiences and ratings on various dimensions of care. It includes information from the most recent three Commonwealth Fund international surveys of patients and primary care physicians about medical practices and views of their countries’ health systems (2011–2013). It also includes information on health care outcomes featured in The Commonwealth Fund’s most recent (2011) national health system scorecard, and from the World Health Organization (WHO) and the Organization for Economic Cooperation and Development (OECD).

Overall health care rankingClick to download Powerpoint chart.

The most notable way the U.S. differs from other industrialized countries is the absence of universal health insurance coverage.5 Other nations ensure the accessibility of care through universal health systems and through better ties between patients and the physician practices that serve as their medical homes. The Affordable Care Act is increasing the number of Americans with coverage and improving access to care, though the data in this report are from years prior to the full implementation of the law. Thus, it is not surprising that the U.S. underperforms on measures of access and equity between populations with above- average and below-average incomes.

The U.S. also ranks behind most countries on many measures of health outcomes, quality, and efficiency. U.S. physicians face particular difficulties receiving timely information, coordinating care, and dealing with administrative hassles. Other countries have led in the adoption of modern health information systems, but U.S. physicians and hospitals are catching up as they respond to significant financial incentives to adopt and make meaningful use of health information technology systems. Additional provisions in the Affordable Care Act will further encourage the efficient organization and delivery of health care, as well as investment in important preventive and population health measures.

For all countries, responses indicate room for improvement. Yet, the other 10 countries spend considerably less on health care per person and as a percent of gross domestic product than does the United States. These findings indicate that, from the perspectives of both physicians and patients, the U.S. health care system could do much better in achieving value for the nation’s substantial investment in health.

Major Findings

  • Quality: The indicators of quality were grouped into four categories: effective care, safe care, coordinated care, and patient-centered care. Compared with the other 10 countries, the U.S. fares best on provision and receipt of preventive and patient-centered care. While there has been some improvement in recent years, lower scores on safe and coordinated care pull the overall U.S. quality score down. Continued adoption of health information technology should enhance the ability of U.S. physicians to identify, monitor, and coordinate care for their patients, particularly those with chronic conditions.
  • Access: Not surprisingly—given the absence of universal coverage—people in the U.S. go without needed health care because of cost more often than people do in the other countries. Americans were the most likely to say they had access problems related to cost. Patients in the U.S. have rapid access to specialized health care services; however, they are less likely to report rapid access to primary care than people in leading countries in the study. In other countries, like Canada, patients have little to no financial burden, but experience wait times for such specialized services. There is a frequent misperception that trade-offs between universal coverage and timely access to specialized services are inevitable; however, the Netherlands, U.K., and Germany provide universal coverage with low out-of-pocket costs while maintaining quick access to specialty services.
  • Efficiency: On indicators of efficiency, the U.S. ranks last among the 11 countries, with the U.K. and Sweden ranking first and second, respectively. The U.S. has poor performance on measures of national health expenditures and administrative costs as well as on measures of administrative hassles, avoidable emergency room use, and duplicative medical testing. Sicker survey respondents in the U.K. and France are less likely to visit the emergency room for a condition that could have been treated by a regular doctor, had one been available.
  • Equity: The U.S. ranks a clear last on measures of equity. Americans with below-average incomes were much more likely than their counterparts in other countries to report not visiting a physician when sick; not getting a recommended test, treatment, or follow-up care; or not filling a prescription or skipping doses when needed because of costs. On each of these indicators, one-third or more lower-income adults in the U.S. said they went without needed care because of costs in the past year.
  • Healthy lives: The U.S. ranks last overall with poor scores on all three indicators of healthy lives—mortality amenable to medical care, infant mortality, and healthy life expectancy at age 60. The U.S. and U.K. had much higher death rates in 2007 from conditions amenable to medical care than some of the other countries, e.g., rates 25 percent to 50 percent higher than Australia and Sweden. Overall, France, Sweden, and Switzerland rank highest on healthy lives.

Summary and Implications

The U.S. ranks last of 11 nations overall. Findings in this report confirm many of those in the earlier four editions of Mirror, Mirror, with the U.S. still ranking last on indicators of efficiency, equity, and outcomes. The U.K. continues to demonstrate strong performance and ranked first overall, though lagging notably on health outcomes. Switzerland, which was included for the first time in this edition, ranked second overall. In the subcategories, the U.S. ranks higher on preventive care, and is strong on waiting times for specialist care, but weak on access to needed services and ability to obtain prompt attention from primary care physicians. Any attempt to assess the relative performance of countries has inherent limitations. These rankings summarize evidence on measures of high performance based on national mortality data and the perceptions and experiences of patients and physicians. They do not capture important dimensions of effectiveness or efficiency that might be obtained from medical records or administrative data. Patients’ and physicians’ assessments might be affected by their experiences and expectations, which could differ by country and culture.

Disparities in access to services signal the need to expand insurance to cover the uninsured and to ensure that all Americans have an accessible medical home. Under the Affordable Care Act, low- to moderate-income families are now eligible for financial assistance in obtaining coverage. Meanwhile, the U.S. has significantly accelerated the adoption of health information technology following the enactment of the American Recovery and Reinvestment Act, and is beginning to close the gap with other countries that have led on adoption of health information technology. Significant incentives now encourage U.S. providers to utilize integrated medical records and information systems that are accessible to providers and patients. Those efforts will likely help clinicians deliver more effective and efficient care.

Many U.S. hospitals and health systems are dedicated to improving the process of care to achieve better safety and quality, but the U.S. can also learn from innovations in other countries—including public reporting of quality data, payment systems that reward high-quality care, and a team approach to management of chronic conditions. Based on these patient and physician reports, and with the enactment of health reform, the United States should be able to make significant strides in improving the delivery, coordination, and equity of the health care system in coming years.

us health care ranks last

complex adaptive systems, data saving lives, health market quality, healthcare, policy, politics, rapid learning health systems

Selecting health insurance based on value of care covered…

A solid idea.

Allowing consumers to pick how “fruity” they want their cover to be. This takes self-serving autonomy from the clinicians and places it back with the patients, who no longer have to cross-subsidise silly, exorbitant care.

 

http://www.nytimes.com/2014/06/10/upshot/how-to-pay-for-only-the-health-care-you-want.html

How to Pay for Only the Health Care You Want

Magoz

By Austin Frakt andAmitabh Chandra

One reason health insurance is expensive is that most plans cover just about every medical technology — not just the ones that work, or the ones that are worth the price. This not only drives up costs, but also forces many Americans into purchasing coverage for therapies they may not value. But there’s no reason things couldn’t be different, and better for consumers.

Consider the latest technology for treating prostate cancer: the proton beam. It’s delivered with a football field-size machine costing well over $100 million. Per treatment, this therapy costs at least twice as much as alternative approaches, but is no more effective. Many health plans cover it and other therapies of low or uncertain value because they pay for anything that physicians deem medically necessary even when evidence suggests otherwise. And, without even knowing it, Americans pay for it in higher premiums.

It doesn’t have to be this way. If plans could compete on the basis of the therapies they cover, consumers could decide what they wish to pay for. This sounds complicated, but it need not be.

Health plans could define themselves at least in part by the value of technologies they cover, an idea proposed by Professor Russell Korobkin of the U.C.L.A. School of Law. For example, a bronze plan could cover hospitalizations and visits to doctors for emergencies and accidents; genetic diseases; and prescription drugs that keep people out of hospitals. A silver plan could cover what bronze plans do but also include treatments a large majority of physicians find useful. A gold plan could be more inclusive still, adding coverage, for instance, for every cancer therapy shown to improve patient outcomes (no matter the cost) as long as it was delivered at a leading cancer center. Finally, a platinum plan could cover experimental and unproven cancer therapies, including, for example, that proton beam.

This way, nothing would be concealed or withheld from consumers. Someone who wanted proton-beam cancer treatment coverage could have it by selecting a platinum policy and paying its higher premiums. Someone who did not want to pay higher premiums for lower-value care, in turn, could choose a bronze or silver plan. This gives a different, but more useful, meaning to the terms “gold,” “silver” and “bronze” than they have in the new insurance exchanges today.

A second concern is that as people become sick, they will prefer plans that cover more treatments, including experimental ones. As sick people disproportionately choose more generous plans, their expenses and premiums will have to rise. This phenomenon, known as adverse selection, is familiar in most health insurance markets, including those for employer-sponsored plans, private plans that participate in Medicare and in the Affordable Care Act’s new marketplaces. One common way to address it is to permit individuals to switch plans only once per year, during an open enrollment period. This locks people into their choice for some time, so they can’t suddenly upgrade their plan after getting sick. If a once-per-year enrollment period proves insufficient in this case, a longer period could be imposed.

Structuring health plans according to value would give Americans the ability to buy whatever health care technologies they choose — including, if they want it, unproven and expensive care — without forcing others to pay for that choice. This would help address the key, though under-recognized, problem in American health care today: not that Americans spend a lot on health care, but that they spend a lot without always getting good value for the money.

Austin Frakt is a health economist with several governmental and academic affiliations. He blogs at The Incidental Economist, and you can follow him on Twitter at @afrakt.

Amitabh Chandra is a professor of public policy at Harvard’s John F Kennedy School of Government. His government affiliations and financial disclosures are listed on hisHarvard website. Follow him on Twitter at @amitabhchandra2.

The Upshot provides news, analysis and graphics about politics, policy and everyday life. Follow us on Facebook and Twitter.

complex adaptive systems, data saving lives, health market quality, healthcare, healthy habits, musings, policy, politics, rapid learning health systems

A chat with Terry

An excerpt of a conversation with Terry Hannan on the business and bureaucracy of health and clinical care…

 

Terry, thank you for sharing those terrific papers by John Wennberg and Brent James… inspiring and affirming thinking.

 

Regarding your request for me to expand on how “true” priorities of the system are expressed:

My overarching thesis for eHealth and its myriad follies is that the systems built often correctly reflect the “true” priorities of the system. The only glitch is that these priorities are often so radically divorced from those stated by the system’s leaders and in turn expected by clinicians and/or the public.[I would like you to expand this # as I am not sure I entirely grasp your focus here.]

 

Different stakeholders expect different returns from their investments. Roughly speaking (and apologies in advance for some of the generalisations that follow):

– politicians want to maximise votes in return for policy announcements

– bureaucrats want to maximise budget, status and power, and minimise risk in return to turning up to work

– public sector doctors want to maximise the health of their patients and status in return for turning up to work and working hard

– private sector doctors want to maximise income and status and minimise legal exposures in return for doing as much work as possible

– private hospitals want to maximise revenue in return for getting as many patients through their doors as possible

– nurses unions want to maximise members in return for negotiating improved work conditions

– not-for-profit (mutual) private health insurers want to maximise their perks by keeping doctors and private hospitals happy

– for-profit private health insurers want to maximise their profit margins by minimising doctor and hospital payments and maximising membership

– health researchers want to maximise their research capacity in return for increased publications

 

(Patients don’t even make my list of stakeholders, because they are not truly involved at present. An interesting remedy for this is citizen juries, a discussion for another time – did you ever engage with Prof Gavin Mooney before his untimely death?)

 

This suggests that each of these tribes wants a different “currency” in exchange for the “value” they deliver to the health system. They all use “patient interest” as the public justification for their claims on the system, but most of them are not actually remunerated in a currency that relates to the patient’s interest.

 

Indeed, in true “rent seeking” fashion, most of these stakeholders would rather not have to justify their remuneration to anyone – see this recent HLM news article.

 

At present, the easiest way to see what the system actually values is by looking at what it invests in. One “tell” that belies the health system’s “true” priorities is what it goes to the effort to properly records in electronic form i.e. billing data. This therefore suggests that money is the priority, and so it is what is tracked carefully.

 

If “patient interest” was truly the priority of the system, then far more effort and expense would be put into tracking patient outcomes, and in time, paying for them. On this, I am encouraged by the early shoots starting to sprout in the US around the development of ACOs, though I’m sure there are a lot more warts on it when seen up close.

 

My favourite “tale” of how to get there relates to how I’m told traditional chinese medical practitioners used to be paid. Everyone in the village would pay the practitioner as long as they were well, but stopped paying them whenever they ever got sick. This tight pecuniary alignment between patient and practitioner interest excites me, and makes me think there is still hope for ACO-style reform here. Indeed, my current health policy horizon doesn’t even involve hospitals and doctors, but rather looks at prevention efforts as the focus, as separate system with separate funding and separate participants.

 

I suspect this is best discussed over a long lunch or dinner, which I look forward to when the opportunity next arises.

 

Best regards, Paul

 

 

 

 

 

 

 

From: Hannan, Terry J (DHHS) [mailto:Terry.Hannan@dhhs.tas.gov.au]
Sent: Tuesday, 17 June 2014 9:07 PM
To: Paul Nicolarakis
Subject: RE: contact

 

See my inserted notes.

 

From: Paul Nicolarakis [mailto:pnicolarakis@cmcrc.com]
Sent: Tuesday, 17 June 2014 5:45 PM
To: Hannan, Terry J (DHHS)
Subject: RE: contact

 

Thanks for your forbearance Terry.

 

I’m inspired by your resilience and enthusiasm for the area, and quite certain that this particular eHealth conversation is going to yield some interesting insights. I present these ideas (which tend towards the political more than technical or clinical) to you in confidence, with a view to sharpening up the thinking. In light of my political experience, I would not want to offend any past masters as they were operating within some diabolical constraints.

 

My overarching thesis for eHealth and its myriad follies is that the systems built often correctly reflect the “true” priorities of the system. The only glitch is that these priorities are often so radically divorced from those stated by the system’s leaders and in turn expected by clinicians and/or the public.

 

Applying this analysis, it makes sense that an EMR purpose built to support HIV treatment in Africa would work because the only people involved in its development are dedicated clinicians, developers and minimal funding from similarly aligned entities with the specific purpose of improving the quality of care.

 

It also makes sense that physician led, integrated health systems (e.g. Regenstrief, Intermountain) that understand the “triple aim” nexus between high quality care and reduced costs would employ these systems successfully.

 

But finally, it also follows that systems built for governments in modern western democratic economies will never work because they are being built to get their political leaders re-elected, and make their vendors lots of money, but not really serve the community. The failure of these systems is ultimately guaranteed when the transparency they risk introducing into a system starts to threaten vested interests such as private medical providers and their associated institutions.

 

As per your slide from Blum, the red tail wags the yellow and blue dog because this is actually what matters in modern health care.

 

Microsoft learned this the hard way with their health solutions group efforts that I was involved in for a few years. The analytics software (Amalga) was quite impressive, initially developed by a group of keen, inquisitive (“data curious”) emergency physicians. They used the solution to monitor all sorts of clinical quality metrics across the business Washington Hospital Center service. Microsoft executives saw it, were impressed and acquired it. They then tried for 4 years to sell it to the world, only to discover that the “world” was not as interested in “clinical quality” as they were in bottom line revenues. What emerged from this experiment was the realisation that Microsoft had found itself ambushed by the gross conceit of modern healthcare i.e. stating that it was all about patient care, when in actual fact it was all about cash. Hence the highly administrative focus of most EMRs?

 

What has been terrific is to see US policy makers respond to this realisation by establishing “business models” around meaningful use and clinical outcomes. This is what seriously excites me now, though I suspect Australia is a decade away from adopting anything like what’s going on in the US at the moment.

 

One of the mantras we have here at the CRC (born in part out of our academic finance roots) is: “Healthcare is not a system, it’s a series of highly dysfunctional markets”. Applying this prism to healthcare really does start to clarify things, especially on the private side. On the public side, the currencies are sometimes different, but no less predictable.

 

I’ll pause here for fear of triggering some sort of global terrorist alert and/or offending you? Needless to say, I look forward to seeing where this conversation goes!

 

Best regards, Paul

 

 

 

From: Hannan, Terry J (DHHS) [mailto:Terry.Hannan@dhhs.tas.gov.au]
Sent: Tuesday, 17 June 2014 2:15 PM
To: Paul Nicolarakis
Subject: Re: contact

 

Take your time you just spark my enthusiasm. The fact that you are interested is such joy. Terry

Sent from my iPhone Terry Hannan
On 17 Jun 2014, at 1:40 pm, “Paul Nicolarakis” <pnicolarakis@cmcrc.com> wrote:

Please bear with me Terry… I’ve got lots on at work… will respond soon… Paul

 

From: Hannan, Terry J (DHHS) [mailto:Terry.Hannan@dhhs.tas.gov.au]
Sent: Monday, 16 June 2014 2:12 PM
To: Paul Nicolarakis
Subject: RE: contact

 

Paul, thank you for the taking the time to write to me and if you think about it this is the first time in our long association where we have done a bit of eHealth “together”.

Based on your enthusiasm in the text I will now send you some materials which should further extend our discussions.

 

Firstly I have attached nan short slide set that I had prepared for the Sydney meeting-just in case.

The next slide is explained in the text flowing it.

<image001.png>

 

This slide is taken from B. Blum’s Clinical Information Systems and you can see the small RED Administrativebox in the top left which is where most HIS funding and management comes from and they try to meet the needs of the most important cost generator Clinical Decision Making.

This is confirmed by the work in cost reduction in CDSS as shown in the slide set attached by Tierney in Regenstrief.

Also in the references below.

1.         Slack WV. Cybermedicine, How Computing Empowers Doctors and Patients for Better Health Care. 2nd ed. San Francisco: Jossey-Bass; 2001 2001.

2.         Tierney WM, Fitzgerald JF, Miller ME, James MK, McDonald CJ. Predicting inpatient costs with admitting clinical data. Med Care. 1995;33(1):1-14. Epub 1995/01/01.

3.         Tierney WM, Overhage JM, Takesue BY, Harris LE, Murray MD, Vargo DL, et al. Computerizing guidelines to improve care and patient outcomes: the example of heart failure. J Am Med Inform Assoc. 1995;2(5):316-22. Epub 1995/09/01.

 

In addition these results from institutions such as Regenstrief, Intermountain Health (HELP System), Brigham’ and Women’s Hospital and Beth Israel Deaconess Hospitals confirm these findings and show that the current funding models by governments are incorrect.

 

I am attaching two summary papers from the Kenyan project.

I hope I have not burdened you.

 

Terry

Dr Terry J. Hannan MBBS;FRACP;FACHI;FACMI
Consultant Physician
Clinical Associate Professor  School of Human Health Sciences, University of Tasmania Department of Medicine, Launceston General Hospital
Charles Street Launceston 7250

Moderator: http://www.ghdonline.org/

Ph. 61 3 6348 7578
Mob. 0417 144 881
Fax 61 3 6348 7577
Email terry.hannan@dhhs.tas.gov.au

Skype: thehannans

 

From: Paul Nicolarakis [mailto:pnicolarakis@cmcrc.com]
Sent: Monday, 16 June 2014 1:33 PM
To: Hannan, Terry J (DHHS)
Subject: RE: contact

Paper (PDF): Are docs the weakness in the ehealth building

Dear Terry,

Thank you for sharing the paper and referring me to ghdonline.org – I’ve just signed up.

The paper touches on many issues close to my heart, but two that I am particularly interested in is the exploration of “healthcare as business” vs “the business of clinical care”.

I won’t commit my dismal views to this email for fear of offending due to lack of context, but would welcome an opportunity to a vigorous discussion with you when we next have an opportunity? To the discourse I would like to add “healthcare as a bureaucracy” and “the bureaucracy of clinical care” as I believe this frame paired with “business” frame are particularly explanatory of most things that happen (or in the case of e-health, don’t happen) in the sector. Needless to say, the clinical and information systems you helped to establish in Africa represent something of an ideal in my mind for an end-goal of a “lite”, modern, effective health system following the “less is more” maxim.

Looking forward to continuing the conversation.

Best regards, Paul

I’ve now seen the Australian health system laid bare while working for the Minister, and many other health systems up close while working internationally at Microsoft. I’ve concluded that with rare exceptions, health care represents “just another unremarkable business” or “just another unremarkable

bureaucracy” depending on the type of funding system that is used.

data saving lives, healthcare

Wired: AI telling doctors how to treat…

 

 

http://www.wired.com/2014/06/ai-healthcare/

Artificial Intelligence Is Now Telling Doctors How to Treat You

  • BY DANIELA HERNANDEZ, KAISER HEALTH NEWS
  •   |

Image: Courtesy of Modernizing Medicine

Long Island dermatologist Kavita Mariwalla knows how to treat acne, burns, and rashes. But when a patient came in with a potentially disfiguring case of bullous pemphigoid–a rare skin condition that causes large, watery blisters–she was stumped. The medication doctors usually prescribe for the autoimmune disorder wasn’t available. So she logged in to Modernizing Medicine, a web-based repository of medical information and insights.

Within seconds, she had the name of another drug that had worked in comparable cases. “It gives you access to data, and data is king,” Mariwalla says of Modernizing Medicine. “It’s been very helpful, especially in clinically challenging situations.”

The system, one of a growing number of similar tools around the country, lets Mariwalla tap the collective knowledge gathered from roughly 3,700 providers and more than 14 million patient visits, as well as data on treatments other doctors have provided to patients with similar profiles. Using the same kind of artificial intelligence that underpins some of the web’s largest sites, it instantly mines this data and spits out recommendations. It’s a bit like Amazon.com recommending purchases based on its massive trove of data about what people have bought in the past.

Using the same kind of artificial intelligence that underpins some of the web’s largest sites, it instantly mines this data and spits out recommendations.

Tech titans like Google, Amazon, Microsoft, and Apple already have made huge investments in artificial intelligence to deliver tailored search results and build virtual personal assistants. Now, that approach is starting to trickle down into health care, thanks in part to the push under the health reform law to leverage new technologies to improve outcomes and reduce costs–and to the availability of cheaper and more powerful computers. In an effort to better treat their patients, doctors are now exploring the use of everything from IBM’s Watson supercomputer, the machine that won at Jeopardy, to iPhone-like pop-up notifications that appear in your online medical records.

Artificial intelligence is still in the very early stages of development–in so many ways, it can’t match our own intelligence–and computers certainly can’t replace doctors at the bedside. But today’s machines are capable of crunching vast amounts of data and identifying patterns that humans can’t. Artificial intelligence–essentially the complex algorithms that analyze this data–can be a tool to take full advantage of electronic medical records, transforming them from mere e-filing cabinets into full-fledged doctors’ aides that can deliver clinically relevant, high-quality data in real time. “Electronic health records [are] like large quarries where there’s lots of gold, and we’re just beginning to mine them,” said Dr. Eric Horvitz, who is the managing director of Microsoft Research and specializes in applying artificial intelligence in health care settings.

Increasingly, physician practices and hospitals around the country are using supercomputers and homegrown systems to identify patients who might be at risk for kidney failure, cardiac disease, or postoperative infections, and to prevent hospital re-admissions, another key focus of health reform. And they’re starting to combine patients’ individual health data–including genetic information–with the wealth of material available in public databases, textbooks, and journals to help come up with more personalized treatments.

For now, the recommendations from Modernizing Medicine are largely based on what is most popular among fellow professionals–say, how often doctors on the platform prescribe a given drug or order a particular lab test. But this month, the system will display data on patient outcomes that the company has collected from its subscribers over the past year. Doctors will also be able to double-check the information against the latest clinical research by querying Watson, IBM’s artificially intelligent supercomputer. “What happens in the real world should be informed by what’s happening in the medical journals,” said Daniel Cane, CEO of Florida-based Modernizing Medicine. “That information needs to get to the provider at the point of care.”

‘Quick and Seamless’

Using homegrown systems, doctors at Vanderbilt University Medical Center in Nashville and St. Jude’s Medical Center in Memphis are getting pop-up notifications within individual patients’ electronic medical records. The alerts tell them, for instance, when a drug might not work for a patient with certain genetic traits. It shows up in bright yellow at the top of a doctor’s computer screen–hard to miss. “With a single click, the doctor can prescribe another medication. It’s a very quick and seamless process,” says Vanderbilt’s Dr. Joshua Denny, one of the researchers who developed the system there.

‘Computers are notoriously bad at understanding English. It’s a slow haul, but I’m still optimistic.’

Denny and others used e-medical records on 16,000 patients to help computers predict which patients were likely to need certain medications in the future. Take the anti-blood clot medication Plavix. Some people can’t break it down. The Vanderbilt system warns doctors to give patients likely to need the medication a genetic test to see whether they can. If not, it gives physicians suggestions on alternative drugs.

Doctors heed the computer’s advice about two-thirds of the time, figuring in, for example, the risks associated with the alternative medication. “The algorithm is pretty good,” says Denny, referring to its ability to predict who’s going to need a certain drug. “It was smarter than my intuition.”

So far, computers have gotten really good at parsing so-called structured data—information that can easily fit in buckets, or categories. In health care, this data is often stored as billing codes or lab test values. But this data doesn’t capture patients’ full-range of symptoms or even their treatments. Images, radiology reports, and the notes doctors write about each patient can be more useful. That’s unstructured data, and computers are less savvy at handling it because it requires making inferences and a certain understanding of context and intent.

That’s the stuff humans are really good at doing–and it’s what scientists are trying to teach machines to do better. “Computers are notoriously bad at understanding English,” said Peter Szolovits, the director of MIT’s Clinical Decision Making Group. “It’s a slow haul, but I’m still optimistic.”

The Challenge Ahead

Computers are getting better at reading unstructured information. Suppose a patient says he doesn’t smoke. His doctor checks ‘no’ in a box–structured data, easily captured by a machine. But then the doctor notes that the patient’s teeth are discolored or that there are nicotine stains on his fingers–a clue that the patient in fact does smoke. Soon a computer may be able to highlight such discrepancies, bringing to the fore information that otherwise might have been overlooked.

In recent years, universities, tech companies, and venture capital firms have invested millions into making computers better at analyzing images and words. Companies are popping up to capitalize on findings in studies suggesting that artificial intelligence can be used to improve care. “Artificial intelligence–ultimately that’s where the biggest quality improvements will be made,” says Euan Thomson, a partner at venture capital firm Khosla Ventures.

The data is often stored in servers at individual clinics or hospitals, making it difficult to build a comprehensive reservoir of medical information.

But many challenges remain, experts say. Among them is the tremendous expense and difficulty of gaining access to high-quality data and of developing smart models and training them to pick up patterns. Most electronic medical record-keeping systems aren’t compatible with each other. The data is often stored in servers at individual clinics or hospitals, making it difficult to build a comprehensive reservoir of medical information.

Moreover, the systems often aren’t hooked up to the internet and therefore can’t be widely distributed or accessed like other information in the cloud. So, unlike the vast amount of data on Google and Facebook, the information can’t be mined from anywhere by those interested in analyzing it. From the perspective of privacy advocates, this makes some good sense: A researcher’s treasure trove is a hacker’s playground. “It’s not the greatest time to talk about” health records on the web, given security scandals such as the Edward Snowden leaks and the Heartbleed bug, says Dr. Russ Altman, the director of Stanford University’s biomedical informatics training program.

Drawing the Line

Also standing in the way are concerns about how far computers should encroach on doctors’ turf. As artificial intelligence systems get smarter, experts say, the line between making recommendations and making decisions could become more murky. That could cause regulators to view the systems as a medical devices, subject to the review of the U.S. Food and Drug Administration.

Wary of the time and expense required for FDA approval, companies engineering the systems–at least for now–are careful not to describe them as diagnostic tools but rather as information banks. “The FDA would be down on them like a ton of bricks because then they would be claiming to practice medicine,” says MIT’s Szolovits.

At the moment, he said, the technology isn’t good enough to tell doctors with 100 percent certainty what the best course of treatment for a patient may be. Others agree. “It’s going to be a long road,” says Michael Matheny, a biostatistician at the Vanderbilt School of Medicine.

Back at her clinic in Long Island, Dr. Mariwalla is thankful for the information that the artificial intelligence system can provide. For the patient with that blistering skin condition, she took the machine’s suggestion for an alternative medication. The patient has recovered, Mariwalla says, but she’s careful to add that she made the call herself—based in part on her conversation with her patient. “That’s where medical judgment comes in,” she says. “You can’t [just] rely on a system to tell you what to do.”

Kaiser Health News is an editorially independent program of the Henry J. Kaiser Family Foundation, a nonprofit, nonpartisan health policy research and communication organization not affiliated with Kaiser Permanente.

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McKinsey: Feeding consumer decisions…

Will be useful to plug this into our health market quality explorations…

PDF: Digitizing the consumer decision journey McKinsey

http://www.mckinsey.com/Insights/Marketing_Sales/Digitizing_the_consumer_decision_journey?cid=DigitalEdge-eml-alt-mip-mck-oth-1406

Digitizing the consumer decision journey

In a world where physical and virtual environments are rapidly converging, companies need to meet customer needs anytime, anywhere. Here’s how.

June 2014 | byEdwin van Bommel, David Edelman, and Kelly Ungerman

Many of the executives we speak with in banking, retail, and other sectors are still struggling to devise the perfect cross-channel experiences for their customers—experiences that take advantage of digitization to provide customers with targeted, just-in-time product or service information in an effective and seamless way.

Video

How consumer behavior keeps changing

McKinsey’s David Edelman explains how purchasing decisions are made in a digital world.

This quest for marketing perfection is not in vain—during the next five years or so, we’re likely to see a radical integration of the consumer experience across physical and virtual environments. Already, the consumer decision journey has been altered by the ubiquity of big data, the Internet of Things, and advances in web coding and design.1 Customers now have endless online and off-line options for researching and buying new products and services, all at their fingertips 24/7. Under this scenario, digital channels no longer just represent “a cheaper way” to interact with customers; they are critical for executing promotions, stimulating sales, and increasing market share. By 2016, the web will influence more than half of all retail transactions, representing a potential sales opportunity of almost $2 trillion.2

Companies can be lulled into thinking they’re already doing everything right. Most know how to think through customer search needs or have ramped up their use of social media. Some are even “engineering” advocacy—creating easy, automatic ways for consumers to post reviews or otherwise characterize their engagement with a brand.

Yet tools and standards are changing faster than companies can react. Customers will soon be able to search for products by image, voice, and gesture; automatically participate in others’ transactions; and find new opportunities via devices that augment their reality (think Google Glass). How companies engage customers in these digital channels matters profoundly—not just because of the immediate opportunities to convert interest to sales but because two-thirds of the decisions customers make are informed by the quality of their experiences all along their journey, according to research by our colleagues.3

To keep up with rapid technology cycles and improve their multiplatform marketing efforts, companies need to take a different approach to managing the consumer decision journey—one that embraces the speed that digitization brings and focuses on capabilities in three areas:

  • Discover. Many of the executives we’ve spoken with admit they are still more facile with data capture than data crunching. Companies must apply advanced analytics to the large amount of structured and unstructured data at their disposal to gain a 360-degree view of their customers. Their engagement strategies should be based on an empirical analysis of customers’ recent behaviors and past experiences with the company, as well as the signals embedded in customers’ mobile or social-media data.
  • Design. Consumers now have much more control over where they will focus their attention, so companies need to craft a compelling customer experience in which all interactions are expressly tailored to a customer’s stage in his or her decision journey.
  • Deliver. “Always on” marketing programs, in which companies engage with customers in exactly the right way at any contact point along the journey, require agile teams of experts in analytics and information technologies, marketing, and experience design. These cross-functional teams need strong collaborative and communication skills and a relentless commitment to iterative testing, learning, and scaling—at a pace that many companies may find challenging.

Let’s consider what an optimized cross-channel experience could look like when companies target improved capabilities in these three areas.

A new normal …

Imagine that a couple has just bought its first home and is now looking to purchase a washer and a dryer. Mike and Linda start their journey by visiting several big-box retailers’ websites. At one store’s site, they identify three models they are interested in and save them to a “wish list.” Because space in their starter home is limited—and because it is a relatively big purchase in their eyes—they decide they need to see the items in person.

Under an optimized cross-channel experience, the couple could find the nearest physical outlet on the retailer’s website, get directions using Google Maps, and drive over to view the desired products. Even before they walk through the doors, a transmitter mounted at the retailer’s entrance identifies Mike and Linda and sends a push alert to their cell phones welcoming them and providing them with personalized offers and recommendations based on their history with the store. In this case, they receive quick links to the wish list they created, as well as updated specs and prices for the washers and dryers that they had shown interest in (captured in their click trails on the store’s website). Additionally, they receive notification of a sale—“15 percent off selected brand appliances, today only”—that applies to two of the items they had added to their wish list.

When they tap on the wish list, the app provides a store map directing Mike and Linda to the appliances section and a “call button” to speak with an expert. They meet with the salesperson, ask some questions, take some measurements, and close in on a particular model and brand of washer and dryer. Because the store employs sophisticated tagging technologies, information about the washer and dryer has automatically been synced with other applications on the couple’s mobile phones—they can scan reviews using their Consumer Reports app, text their parents for advice, ask Facebook friends to weigh in on the purchase, and compare the retailer’s prices against others. Mike and Linda can also take advantage of a “virtual designer” function on the retailer’s mobile app that, with the entry of just a few key pieces of information about room size and decor, allows them to preview how the washer and dryer might look in their home.

All the input is favorable, so the couple decides to take advantage of the 15 percent offer and buy the appliances. They use Mike’s “smartwatch” to authenticate payment. They walk out of the store with a date and time for delivery; a week later, on the designated day, they receive confirmation that a truck is in their area and that they will be texted within a half hour of arrival time—no need to cancel other plans just to wait for the washer and dryer to arrive. Three weeks after that, the couple gets a message from the retailer with offers for other appliances and home-improvement services tailored toward first-year home owners. And the cycle begins again.

… requires new capabilities

As this example makes clear, the forces enabling consumers to expect real-time engagement are unstoppable. Across the entire customer journey, every touchpoint is a brand experience and an opportunity to engage the consumer—and digital touchpoints just keep multiplying. To maximize digital channels, companies need to focus on improving their “3-D” capabilities.

Discover: Build an analytic engine

Even in this era of big data and widespread digitization of customer information, some companies still lack a 360-degree view of the people who buy their products and services. They typically measure the performance of direct sales activities such as product pitches and encourage downloads using “last-action attribution” analyses, which assess campaigns in isolation rather than in the context of the entire cross-channel consumer decision journey. Usually these data will have been stored in disparate locations and legacy systems rather than in a central server. Complicating matters further is the range and quantity of unstructured data out there—information about consumers’ behaviors and preferences that is, for instance, captured in online reviews and social-media posts. In our experience, this type of data is usually the least understood and therefore the least utilized by companies.

To get the full customer portrait rather than just a series of snapshots, companies need a central data mart that combines all the contacts a customer has with a brand: basic consumer data plus information about transactions, browsing history, and customer-service interactions (for an illustrative example of how companies can lose potential customers by failing to optimize digital channels, see exhibit). Tools like Clickfox and Teradata can help marketers gather these data and begin to pinpoint opportunities to engage more effectively with consumers across the decision journey. This collection effort requires input from people across multiple functions—a complex undertaking, to be sure, but the payoff can be big. Our work in this area suggests that the growth rate of earnings before interest, tax, depreciation, and amortization of grocers that focus on customer analytics is 11 percent, compared with just 3 percent on average for their main competitors. For big-box retailers, the difference is 10 percent compared with 2 percent.4

Exhibit

Failure to optimize digital channels may result in underperformance.

With a comprehensive data set in hand, companies can undertake the sort of quick-hit “shop diagnostics” that many tell us is lacking in their marketing and e-commerce programs. Using analytic applications such as SAS and R, and by applying various algorithms and models to longitudinal data, companies can better model the cost of their marketing efforts, find the most effective journey patterns, spot potential dropout points, and identify new customer segments. Based on its analysis of click-through behaviors, for instance, one regional retailer saw that a particular set of customers preferred digital shopping over physical and always read e-mail on Saturdays, and so the retailer altered its e-mail campaign to send this cohort online offers only on Saturdays.

Additionally, by using business-process software and services from vendors such as Adobe Systems, ExactTarget, Pegasystems, and Responsys, companies can identify in real time the basic “triggers” for what individual customers need and value—regardless of the product or service—and personalize their approach when making cross- or up-sell offers. They can also use these tools to generate automated reports that track customer trends and key performance indicators. For instance, the regional retailer’s analytics suggested that two of the customers who read their e-mail only on Saturdays were in the midst of a career change; both had revised their profiles on LinkedIn within the past three days. Based on its analytics efforts, the company was able to create targeted offers for each—one received information about laptop bags (based on her previous purchases) while the other received information about suits (based on his previous purchases).

Already, the companies employing these types of advanced analytics have seen significantly improved click-through rates and higher conversion rates (between three and ten times the average). Additionally, McKinsey analysis shows that using data to make better marketing decisions can increase marketing productivity by between 15 and 20 percent—that’s as much as $200 billion given the average annual global marketing spend of $1 trillion.5

Design: Create frictionless experiences

Careful orchestration of the consumer decision journey is incredibly complex given the varying expectations, messages, and capabilities associated with each channel. According to published reports, 48 percent of US consumers believe companies need to do a better job of integrating their online and off-line experiences. There is a premium for getting this right. One major bank unlocked more than $300 million in additional margins by making better use of digital channels. It tapped into underutilized customer data and delivered targeted marketing messages at various points in the purchase-decision process. The bank used the data, plus various personalization and testing tools, to inform changes in marketing campaigns for certain product lines; every next step for every customer was progressively tailored to help the customer take the best action.

Digital natives such as Amazon, eBay, and Google have been leading the pack in resetting consumers’ expectations for cross-channel convenience. (Think of eBay’s Now mobile app, which provides one-touch ordering from any of eBay’s retail partners and same-day delivery in some US cities, or Amazon’s recent incorporation of a help button in the company’s latest-generation Kindle Fire tablet, linking users to a live help-desk representative.) These players have perfected the ability to test new user experiences and constantly evolve their offers—often for segments of one.

This lean, start-up approach might sound counterintuitive to large, entrenched marketing organizations in which decisions are made at a snail’s pace, but test-and-learn methods can help companies decide how best to optimize (and customize) critical design attributes of the consumer decision journey at various points along the way. In the appliances example discussed earlier, the retailer’s customer analytics allowed it to design an experience for the couple that was completely customized to their context—from their initial online searches to their physical and virtual interactions at the store and to their follow-up with the company postpurchase. Rather than push what could be construed as intrusive (even creepy) messaging, the retailer provided Mike and Linda with the most useful information at every point in their decision journey and offered the easiest possible path to purchase and delivery.

To create similarly frictionless experiences, some companies have created 24/7 digital “window shops” to test product ideas and customer interactions and collect rapid feedback without the need for additional labor or inventory. Several companies that offer inherently complex products or services have incorporated “gaming” elements into their experiences—tweaking the navigation, content architecture, and visual presentation to allow consumers to trade off and test various options and prices associated with a product before making a decision. One financial-services firm redesigned its mobile app for collecting credit-card applications to incorporate the customer context. Previously it had a one-size-fits-all interface; in the redesigned version, various elements of the mobile app’s interface—such as pricing, stage of process, and designated credit limits—are dynamically generated based on existing customer information. And the app’s page layout and navigation are rendered simply, allowing for easy completion within just a few clicks. The result has been a significant uptick in online applications.

Deliver: Build a more agile organization

In our experience, too many companies are afraid to launch “good enough” campaigns—ones that are continually refined as customers’ purchase behaviors and stated preferences change. Under the direction of conservative senior leaders, teams tend to launch campaigns that take too long to get off the ground and end up revealing few new insights. Instead, they must be willing to conduct lots of small-scale experiments with cloud or proxy website services to pilot new designs and prove their value for investment.

These types of agile, data-driven activities must be supported by an organization that has the right people, tools, and processes. Many companies will have some of the talent required, but not all, and executives will inevitably face resistance when it comes to introducing lean tools and techniques into their sales, marketing, and IT processes. The most successful omnichannel marketers we’ve seen have established centers of excellence in both analytics and digital marketing, and they practice end-to-end management of microcampaigns. Their campaign-building processes typically include systematic calendaring, brainstorming, and evaluation sessions to allow for one-week and two-week turnaround times. And roles and responsibilities are clearly defined. Far from creating a rigid, hierarchical process, this model frees up individuals to iterate quickly—what is sometimes called “failing fast forward” in the world of high tech.

At one bank, for instance, business-unit leaders gather each month to talk about their progress in improving different consumer journeys. As new products and campaigns are launched, the team places a laminated card illustrating the journey at the center of the conference-room table and discusses its assumptions about the flow of the experience for different segments and about how the various functional groups need to contribute: Where does customer data need to be captured and reused later? How will the design of the campaign flow from mass media to social media and then on to the website? What is the follow-up experience once a customer sets up an account? The team has also appointed dedicated mobile and social-media executives to become evangelists for strengthening the omnichannel experience, helping business units raise their game along a range of consumer interactions. The company’s first wave of fixes and new programs generated tens of millions of dollars in the first six months, and the team expects it to continue scaling beyond $100 million in added annual margins.

Building an agile marketing organization will take time, of course. Companies should start by assembling a “scrum team” that will bring the right people together to test, learn, and scale. The team should incorporate cross-functional perspectives (marketing, e-commerce, IT, channel management, finance, and legal), and its members must adopt a war-room mentality—for instance, making tough calls about which campaigns are working and which aren’t, and which messages should take priority for which segments; launching new tests every week rather than every six months; and mustering the IT and design resources to create content for every possible type of interaction.

Companies likely will need to hire people with skills that differ from the ones they rely on now. Some organizations have developed innovative, venture capital–like strategies for finding and recruiting the people they need. Staples, for instance, has built an e-commerce innovation center in Cambridge, Massachusetts, to better recruit technology talent from nearby Harvard University and MIT, and it recently bought conversion-marketing start-up Runa to act as a talent hub on the West Coast.

New types of information systems may also be required. The best technology solutions will vary according to a company’s starting point and objectives. Generally, though, companies will get the best results from tools that enable large-scale data management and the integration of databases; the generation of next-best-action and other types of advanced analyses; and simpler campaign testing, execution, and metrics.

Companies need to make strategic decisions about the best pathways to build customer value. Many cite digital as one of their top three priorities in this regard, but few have taken the time to measure the level of digital maturity their organization has achieved. A company’s digital quotient (DQ) is a function of how well defined its long-term digital strategy is, its effectiveness in implementing that strategy, and the strength of its organizational infrastructure and information technologies. The companies that incorporate the notion of DQ into their short list of performance metrics can more effectively monitor their progress across the digital capabilities we’ve outlined here, enabling more targeted investments and accelerated rates of digital growth.

Indeed, the companies that ultimately succeed in omnichannel marketing and sales will likely resemble tech companies and, interestingly, publishers—effectively using big data and digital touchpoints to drive growth and reduce costs, while producing and managing a variety of content (catalogs, coupons, web pages, mobile apps, and user-generated content) in real time across multiple platforms to create breakthrough customer experiences. This means rethinking the analytics that inform their segmentation strategies, the flow of the experiences they design, and the way they set up their internal operations for faster iteration and delivery of service.

About the authors

Edwin van Bommel is a principal in McKinsey’s Amsterdam office, David Edelman is a principal in the Boston office, and Kelly Ungerman is a principal in the Dallas office. They are leaders in McKinsey’s revenue enhancement through digital (RED) initiative, which redesigns the consumer decision journey to encompass all commercial levers, across all channels and touchpoints, thereby creating growth in revenue and profits.

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The Hospital of the Future is not a Hospital

Great insights into where capital is being invested in US healthcare…

http://www.healthleadersmedia.com/print/LED-305089/The-Hospital-of-the-Future-is-Not-a-Hospital

The Hospital of the Future is Not a Hospital

Philip Betbeze, for HealthLeaders Media , May 30, 2014

Pursuing expensive inpatient volume in the traditional sense is a strategic dead end. Any new construction undertaken by hospitals and health systems should be based on adaptability, patient flow, and efficiency gains—not bed count.

I’ve spent a good deal of time the past several weeks interviewing senior healthcare leaders for my story in the May issue of HealthLeaders magazine about the hospital of the future. But in truth, that headline might be a bit of a tease.

As it turns out, the hospital of the future doesn’t look much like a hospital at all. Instead, it’s a cohesive amalgamation of plenty of outpatient modalities that represent growth in healthcare. Inpatient care, increasingly, represents stagnation and shrinkage, in the business sense.

In the past, a story about the hospital of the future has meant investigating healthcare organizations’ access to capital, and their ability to fund expensive new patient bed towers with all-private rooms and top technologies, in a race to grab volume from competitors.

Under that operating scenario, the sky was the limit, in terms of what organizations were willing to do to attract volume.

That calculus has changed drastically.

In a recent survey on healthcare design trends conducted by Minneapolis-based Mortenson Construction, 95% of the healthcare organizations surveyed said most of the projects they are undertaking are predominantly ambulatory in nature.

“If, in theory, the [Patient Protection and Affordable Care Act] has now got 7 million people engaged in healthcare insurance who didn’t have that previously, the inrush of patients will be outpatient-based,” says Larry Arndt, general manager of healthcare in the company’s Chicago offices. “What’s not needed is bed space or heavy procedural space.”

A Strategic Dead End
The PPACA, employers, and commercial health plans have made clear that pursuing expensive inpatient volume in the traditional sense is a strategic dead end. That doesn’t mean new patient towers won’t go up, but it does mean their construction will be based on adaptability, patient flow, and efficiency gains, not bed count.

As few as five to seven years ago, says Arndt, a healthcare leadership team would take a capital improvement project through a planning and programming phase in which they followed a traditional approach. The team would utilize widely standardized metrics and program their building based on what they’re doing now, with no consideration of the future, Arndt says.

By contrast, within the last five years, more leaders have been embracing the concept of lean operational improvement.

In order to be competitive in a limited amount of reimbursements, they have had to become more efficient. So instead of the traditional approach of programming new construction based on how the organization operates today, instead, it attempts to map out its current patient flows and discover how to become more efficient. Only then will the team look at how to build around that improved and more efficient model.

Indeed, a whopping 22% of respondents to Mortenson’s February survey said they were “doing nothing” construction-related right now, and only 5% were planning for a traditional replacement hospital.

Instead, a majority said they are focusing new construction on building clinics that can feature just about any outpatient modality except surgery, Arndt says.

Healthcare Shifts to Outside
They’re focusing on combining dialysis, radiology and other treatments that can be provided in one location. And they’re funneling more of their capital budget to items that are outside the realm of new construction, like home health and what Arndt calls e-home healthcare—in other words, technological solutions that help patients access their caregivers outside of any facility.

“Our customer understands that healthcare is moving more toward healthcare outside a facility,” says Arndt. “That means more money is being invested in health information technology. Also, you see more constellation or satellite projects, for example, a small 15,000-20,000 square-foot clinic in a neighborhood. That allows patients to travel a shorter distance to a less congested environment, but yet allows connection to the bigger facility if needed.”

Modular construction is a trend that Arndt sees developing quickly. It’s in the process of designing a clinic for a client that will feature modular walls, to make it more flexible for the changes in care protocols that are assured, but that healthcare’s leaders aren’t sure how will ultimately affect their competitive offerings.

In one clinic, doctors want to be able to meet with patients in groups, for example. Modular walls mean physicians can occasionally meet with groups of patients instead of individually, or vice-versa. Their space is less limiting.

“The clinic can adapt,” says Arndt.

Prefabricating buildings is also gaining steam in healthcare, he says.

“Money is being invested much more wisely than it has been in the past,” he says. “For the design/construction field, we have to be more lean too.”

Part of that lean attitude means offering customers 3-D modeling that starts with design partners, such as the people who will be staffing the building, to optimize work flow.

Adapting Takes Time
“We can prefab things we couldn’t years ago,” he says. An example might be a bathroom “pod” that can be built offsite and installed on site. Full exam rooms can be prepared the same way, and models can be constructed to test care protocols with the team that will be working there.

Arndt’s customers, he says, can be categorized two ways. Either they’re thinking broadly about adapting to the future without knowing exactly what it’s going to bring, or they’re standing idly on the sideline until they understand better how the PPACA and other drastic changes in how healthcare is provided and paid for will affect their bottom lines.

Neither approach is necessarily better than the other, but waiting just puts off the action that needs to be taken. It can be a prudent approach, but even in healthcare, what works can change quickly. Designing, building, and adapting still takes time.

Don’t wait too long.

Philip Betbeze is senior leadership editor with HealthLeaders Media. 

data saving lives, facts & data points, healthcare, healthy habits, musings, nutrition, policy, quantified self, rapid learning health systems

Apples cocks up HealthKit slide at WWDC…

It’s already starting to seem a lot like HSG, except less credible…!

 

http://rockhealth.com/2014/06/digital-health-entrepreneurs-thoughts-healthkit/

Embedded image permalink

A digital health entrepreneur’s thoughts on HealthKit

Guest Contributor
June 03, 2014

Tags: 

This morning, Apple made its much-anticipated move into healthcare with HealthKit (aka, the formerly rumored HealthBook.) With a typically dissonant and ever-growing ecosystem of health apps, devices and data, digital health needs a major player to enter to integrate these products and tools. We’re excited about what the largest company in the world is capable of doing for digital health. Here’s some perspective on what a seasoned digital health entrepreneur had to say about today.

Aaron Rowe
HealthKit is really exciting. Putting all of this information in one place, in a gorgeous app that will reach a ton of people, could do wonders for public health. But it won’t do much good if the on-screen content is designed without input from people who deeply understand health metrics. It looks like Apple or one of its partners made some technical mistakes on a slide that was shown during the big reveal of their new health app.

The slide, which appeared toward the end of the HealthKit segment of today’s WWDC keynote, neatly displays four key metrics for diabetes management: glucose, carbs, walking, and diabetes medication adherence. The numbers and units that Apple used as examples to illustrate their vision don’t make sense. When you measure your glucose with a personal blood sugar meter, it is measured in mg/dL— but the example shown by Apple displayed these numbers in mL/dL. Whoops!

What’s worse, the app screen features an SMS-style message from a particularly photogenic doctor who says, “You’re making great progress with your diet and exercise. Keep it up.” While the graph above this message shows a steady and very unhealthy looking uptrend in the users glucose readings. The current reading shown on the app is 122 “mL/dL”.

“People with a fasting glucose level of 100 to 125 mg/dL have impaired fasting glucose (IFG), or prediabetes,” according to a National Institute of Diabetes and Digestive and Kidney Diseases website. “A level of 126 mg/dL or above, confirmed by repeating the test on another day, means a person has diabetes.

It strikes me as particularly unusual that Apple would make these mistakes, since they are known for their intense attention to detail. Perhaps this kerfuffle happened because none of the folks who were involved with the WWDC keynote know what medical details should look like—is there some disconnect within the group that is building HealthKit?  Have the designers who worked on this screen had enough contact with Apple’s partners at the Mayo Clinic or recently hired health experts? Not long ago, the Cupertino-based company onboarded several noninvasive glucose-monitoring experts from the wearable Raman spectrometer company C8 MediSensors and an early employee of Rock Health’s own Sano Intelligence.

I hope HealthKit will help patients understand and react to the results of every common blood test that is done in the home and medical labs–from cholesterol to creatinine. This could be one of the greatest ways in which Apple can make the world a better place. But they may need to sync internally to refine their understanding of these numbers, before they release this potentially lifesaving product into the wild.

Aaron Rowe is a research director at Integrated Plasmonics, a San Francisco startup that has developed a new class of spectrometer and surface plasmon resonance sensor chips. He and his colleagues are exploring ways to expand the scope of chronic disease management programs, enhance the success of new medications, and increase the usefulness of telemedicine by bringing a wide variety of in vitro diagnostics devices into the home and workplace. You can follow him on Twitter at @soychemist

 

cool, data saving lives, health market quality, healthcare, healthy habits, policy, rapid learning health systems

Russian bank rewards customer exercise…

Now we’re talking… very Russian, no mucking around… even if it was developed by an ad agency. Go team…

http://www.springwise.com/russian-bank-rewards-customers-sweat-higher-interest-rates/

Russian bank rewards customers’ sweat with higher interest rates

The Alfa-Bank in Russia has created a new account with a high interest rate of 6% per annum, which users can only pay into by exercising with a fitness tracker.

Russia 4 Jun 2014alttext

There are countless initiatives designed to get the public fitter and healthier, but (perhaps unsurprisingly), it’s often those that offer a financial incentive that prove the most effective. We’ve already seen gym classes which become cheaper the more the user works out, and Nike’s Facebook app which enables runners to pay for products with kilometers they have run. Taking the link between financial savings and health benefits to an even more literal level, we’ve now come across a Russian bank offering a new account which rewards customers for every step they take.

To take advantage of Alfa-Bank’s fitness account, and it’s high interest rate of 6% per annum, users first need to sync their Jawbone, RunKeeper or Fitbit fitness tracker to the bank. Then, using the new Activity™ software, the user decides how much their activity is worth. They can select for every step or meter they walk or run to transfer between 1 to 50 cents into the fitness savings account to enjoy the high interest rate. In essence, the more the user walks, sweats, and exercises, the more they’ll save. The video below shows the initiative in action:

Created with Moscow-based advertising agency and marketing consultancy 42 Agency, the idea is already proving a hit with beta testers. How else could banks take a greater role in their customers’ lives for the better?

Website: www.activity.alfabank.ru/Activity/
Contact: activity@alfabank.ru

data saving lives, entrepreneurship, facts & data points, health market quality, healthcare, healthy habits, policy, politics, rapid learning health systems

Wired Health – Proteus Digital Pill Presentation

Proteus occupy an interesting position… ingestibles are the ultimate in wearables. It’s smart also to be backed a big flailing incumbent player. It will be interesting to see if this stuff works.

http://www.proteus.com/andrew-thompson-on-transforming-healthcare-at-wired-health-2014/

Andrew Thompson on transforming healthcare at Wired Health 2014

Published On: May 5, 2014

Watch Proteus CEO Andrew Thompson present at Wired Health 2014 on transforming healthcare through digital medicines:  http://bit.ly/1lS7RLe 

WIRED Health is a one-day summit designed to introduce, explain and predict the coming trends facing the medical and personal healthcare industries. The inaugural event was held on Tuesday April 29, at the new home of the Royal College of General Practitioners, 30 Euston Square, London.Andrew Thompson at Wired