Human Computation

On the things that computers can’t do but humans can, and vice versa…

http://bigthink.com/think-tank/luis-von-ahn-on-recaptcha

Why Humans Can Solve Some Problems Better Than Computers, with Luis von Ahn

NOVEMBER 18, 2014, 12:00 PM
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Back at the beginning of the century, a 21-year-old Luis von Ahn helped invent CAPTCHA, which is that familiar internet thing you see above this post. Commonly used as a security mechanism, CAPTCHA is a way for a website to determine if someone trying to obtain access is actually human and not a computer. In his recent Big Think interview, von Ahn describes how the idea behind CAPTCHA formed the essence of reCAPTCHA, which he invented in 2007. ReCAPTCHA relies on what is known as human computation, which harnesses the unique abilities of both humans and computers to accomplish difficult tasks:

 

Video Link: http://bcove.me/ghm5j3n2

In describing human computation, von Ahn explains that both computers and humans have their own sets of advantages and disadvantages when it comes to problem solving:

“There are problems that computers cannot yet solve. It’s funny because some of these problems are very simple problems seemingly. For example, a computer cannot tell you what’s inside an image. They can tell you somethings but it can’t really quite tell you there’s a cat next to a dog and they’re both running. A computer can’t do that. Well humans, we can do it super easily.”

Simple enough in concept, right? There may soon come a day when computer cognition takes a huge step forward and current limitations vanish. But until then, image identification and thematic analysis are the stuff of human expertise. Way to go, fellow humans.

On the flip side though…

“There are also things that computers can do that humans can’t do. I mean computers can multiply humongous numbers, humans may be able to do it but very slowly and we’re error-prone.”

Alas, we dumb humans exhibit our own limitations, particularly when it comes to the scale of a certain task. Any one person could memorize a poem yet no human being could memorize every piece of poetry written since Antiquity. Computers can and do. In a way, we and computers form a Yin to each other’s Yang. Our abilities match up with computers’ weaknesses like corresponding puzzle pieces.

This is where human computation comes in.

So the essential idea is that there are certain tasks that require both a human’s attention to detail and a computer’s ability to store vast quantities of information. These are problems neither side can solve alone. Human computation therefore harnesses the talents of both. This is how reCAPTCHA works:

“The Idea with reCAPTCHA is that we take a problem that neither humans nor computers can solve by themselves, which is fully digitizing books. The idea there is we would like to digitize books. And the way this process works is you start with a book and then you scan it. The next step in the process is that the computer needs to be able to decipher all of the words in this picture. It’s a picture of words. The computer needs to be able to decipher all of those words. The problem is that sometimes the computer cannot decipher these words because for older books the ink has faded a little or the pages have turned yellow so the computer cannot decipher all of the words. But, humans can.”

You may, at this point, be able to identify where von Ahn is heading here. Just like he explains in his interview about Duolingo, von Ahn has created a piece of technology that serves multiple purposes. ReCAPTCHA is partly a security device and partly a tool of crowdsourcing brilliance. It’s still the same idea as CAPTCHA, except with one added component:

“So what we’re doing with reCAPTCHA… the idea is that some of these [squiggl CAPTCHA] words, nowadays some of these words are words that are actually coming from books that the computer could not recognize in this process and we’re using what people enter to help us digitize the books.”

Von Ahn sold reCAPTCHA to Google in 2009. Since its inception, over 1.1 billion people worldwide have contributed by way of reCAPTCHA to the digitization of old books. Google is now digitizing 2 million per years utilizing the respective powers of humans and computers.

And that’s how human computation works.

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.

McKinsey’s Plan to fight obesity…

http://www.mckinsey.com/Insights/Economic_Studies/How_the_world_could_better_fight_obesity

Executive Summary: Innovation vs Obesity_McKinsey

MGI Obesity_Full report_November 2014

Sensible stuff. Possibly the most sensible stuff I’ve seen on this. Good for them…

How the world could better fight obesity

November 2014 | byRichard Dobbs, Corinne Sawers, Fraser Thompson, James Manyika, Jonathan Woetzel, Peter Child, Sorcha McKenna, and Angela Spatharou

Obesity is a critical global issue that requires a comprehensive, international intervention strategy. More than 2.1 billion people—nearly 30 percent of the global population—are overweight or obese.1 That’s almost two and a half times the number of adults and children who are undernourished. Obesity is responsible for about 5 percent of all deaths a year worldwide, and its global economic impact amounts to roughly $2 trillion annually, or 2.8 percent of global GDP—nearly equivalent to the global impact of smoking or of armed violence, war, and terrorism.

Podcast

Implementing an Obesity Abatement Program

MGI’s Richard Dobbs and Corinne Sawers discuss how a holistic strategy, using a number of interventions, could reverse rising rates of obesity around the world.

And the problem—which is preventable—is rapidly getting worse. If the prevalence of obesity continues on its current trajectory, almost half of the world’s adult population will be overweight or obese by 2030.

Much of the global debate on this issue has become polarized and sometimes deeply antagonistic. Obesity is a complex, systemic issue with no single or simple solution. The global discord surrounding how to move forward underscores the need for integrated assessments of potential solutions. Lack of progress on these fronts is obstructing efforts to address rising rates of obesity.

A new McKinsey Global Institute (MGI) discussion paper,Overcoming obesity: An initial economic analysis, seeks to overcome these hurdles by offering an independent view on the components of a potential strategy. MGI has studied 74 interventions (in 18 areas) that are being discussed or piloted somewhere around the world to address obesity, including subsidized school meals for all, calorie and nutrition labeling, restrictions on advertising high-calorie food and drinks, and public-health campaigns. We found sufficient data on 44 of these interventions, in 16 areas.

Although the research offers an initial economic analysis of obesity, our analysis is by no means complete. Rather, we see our work on a potential program to address obesity as the equivalent of the maps used by 16th-century navigators. Some islands were missing and some continents misshapen in these maps, but they were still helpful to the sailors of that era. We are sure that we have missed some interventions and over- or underestimated the impact of others. But we hope that our work will be a useful guide and a starting point for efforts in the years to come, as we and others develop this analysis and gradually compile a more comprehensive evidence base on this topic.

We have focused on understanding what it takes to address obesity by changing the energy balance of individuals through adjustments in eating habits or physical activity. However, some important questions we have not yet addressed require considerable further research. These questions include the role of different nutrients in affecting satiety hormones and metabolism, as well as the relationship between the gut microbiome and obesity. As more clarity develops in these research areas, we look forward to the emergence of important insights about which interventions are likely to work and how to integrate them into an antiobesity drive.

The main findings of this discussion paper include:

  • Existing evidence indicates that no single intervention is likely to have a significant overall impact. A systemic, sustained portfolio of initiatives, delivered at scale, is needed to reverse the health burden. Almost all the identified interventions (exhibit) are cost effective for society—savings on healthcare costs and higher productivity could outweigh the direct investment required by the intervention when assessed over the full lifetime of the target population. In the United Kingdom, for instance, such a program could reverse rising obesity, saving the National Health Service about $1.2 billion a year.
  • Education and personal responsibility are critical elements of any program aiming to reduce obesity, but they are not sufficient on their own. Other required interventions rely less on conscious choices by individuals and more on changes to the environment and societal norms. They include reducing default portion sizes, changing marketing practices, and restructuring urban and education environments to facilitate physical activities.
  • No individual sector in society can address obesity on its own—not governments, retailers, consumer-goods companies, restaurants, employers, media organizations, educators, healthcare providers, or individuals. Capturing the full potential impact requires engagement from as many sectors as possible. Successful precedents suggest that a combination of top-down corporate and government interventions, together with bottom-up community-led ones, will be required to change public-health outcomes. Moreover, some kind of coordination will probably be required to capture potentially high-impact industry interventions, since any first mover faces market-share risks.
  • Implementing an obesity-abatement program on the required scale will not be easy. We see four imperatives: (1) as many interventions as possible should be deployed at scale and delivered effectively by the full range of sectors in society; (2) understanding how to align incentives and build cooperation will be critical to success; (3) there should not be an undue focus on prioritizing interventions, as this can hamper constructive action; and (4) while investment in research should continue, society should also engage in trial and error, particularly where risks are low.

Exhibit

Cost-effective interventions to reduce obesity in the United Kingdom include controlling portion sizes and reducing the availability of high-calorie foods.

The evidence base on the clinical and behavioral interventions to reduce obesity is far from complete, and ongoing investment in research is an imperative. However, in many cases this requirement is proving a barrier to action. It need not be so. Rather than wait for perfect proof of what works, we should experiment with solutions, especially in the many areas where interventions are low risk. We have enough knowledge to do more.

About the authors

Richard Dobbs, James Manyika, and Jonathan Woetzel are directors of the McKinsey Global Institute, where Corinne Sawers is a fellow and Fraser Thompson is a senior fellow; Peter Child is a director in McKinsey’s London office; Sorcha McKenna is a principal in the Dublin office; and Angela Spatharou is a principal in the Mexico City office.

 

MGI_Implementing_an_Obesity_Abatement_Program_Exibit18 MGI_Implementing_an_Obesity_Abatement_Program_Exibit3 MGI_Implementing_an_Obesity_Abatement_Program_ExibitE3 MGI_Implementing_an_Obesity_Abatement_Program_Exibit1

NEJM: Everything that kills us in one morbid chart

 

 

Image: The New England Journal of Medicine
Here’s everything that kills us in one morbid chart

Researchers have reviewed all the causes of death recorded in the US in 1900 and 2010 to find out just how much society has changed over the past century. The results are fascinating.

BEC CREW   21 NOV 2014
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A team of researchers from the New England Journal of Medicine have done some investigating to discover how much things can change in 100 years. While the year 1900 brought with it many different causes of death, from bacterial infections to severe problems with the gut, now most of us pretty much just have heart disease and cancer to fear.

The authors note that in many respects, the medical systems of today are best suited to the killer diseases of the past, which is kind of a worry. “Disease is a complex domain of human experience, involving explanation, expectation, and meaning,” they write. “Doctors must acknowledge this complexity and formulate theories, practices, and systems that fully address the breadth and subtlety of disease.”

“There’s reason to temper optimism,” Julia Belluz adds at Vox. “What kills us will continue to change – and medical advancements may not keep up.”

Sources: Vox, New England Journal of Medicine