health

Improving patient care by bridging the divide between doctors and data scientists

Curated on May 26, 2016August 3, 2018 by Stefaan Verhulst

Leo Anthony Celi at the Conversation: “While wonderful new medical discoveries and innovations are in the news every day, doctors struggle daily with using information and techniques available right now while carefully adopting new concepts and treatments. As a practicing doctor, I deal with uncertainties and unanswered clinical questions all the time….At the moment, a report from the National Academy of Medicine tells us, most doctors base most of their everyday decisions on guidelines from (sometimes biased) expert opinions or small clinical trials. It would be better if they were from multicenter, large, randomized controlled studies, with tightly controlled conditions ensuring the results are as reliable as possible. However, those are expensive and difficult to perform, and even then often exclude a number of important patient groups on the basis of age, disease and sociological factors.

Part of the problem is that health records are traditionally kept on paper, making them hard to analyze en masse. As a result, most of what medical professionals might have learned from experiences was lost – or at least was inaccessible to another doctor meeting with a similar patient.

A digital system would collect and store as much clinical data as possible from as many patients as possible. It could then use information from the past – such as blood pressure, blood sugar levels, heart rate and other measurements of patients’ body functions – to guide future doctors to the best diagnosis and treatment of similar patients.

Industrial giants such as Google, IBM, SAP and Hewlett-Packard have also recognized the potential for this kind of approach, and are now working on how to leverage population data for the precise medical care of individuals.

Collaborating on data and medicine

At the Laboratory of Computational Physiology at the Massachusetts Institute of Technology, we have begun to collect large amounts of detailed patient data in the Medical Information Mart in Intensive Care (MIMIC). It is a database containing information from 60,000 patient admissions to the intensive care units of the Beth Israel Deaconess Medical Center, a Boston teaching hospital affiliated with Harvard Medical School. The data in MIMIC has been meticulously scoured so individual patients cannot be recognized, and is freely shared online with the research community.

But the database itself is not enough. We bring together front-line clinicians (such as nurses, pharmacists and doctors) to identify questions they want to investigate, and data scientists to conduct the appropriate analyses of the MIMIC records. This gives caregivers and patients the best individualized treatment options in the absence of a randomized controlled trial.

Bringing data analysis to the world

At the same time we are working to bring these data-enabled systems to assist with medical decisions to countries with limited health care resources, where research is considered an expensive luxury. Often these countries have few or no medical records – even on paper – to analyze. We can help them collect health data digitally, creating the potential to significantly improve medical care for their populations.

This task is the focus of Sana, a collection of technical, medical and community experts from across the globe that is also based in our group at MIT. Sana has designed a digital health information system specifically for use by health providers and patients in rural and underserved areas.

At its core is an open-source system that uses cellphones – common even in poor and rural nations – to collect, transmit and store all sorts of medical data. It can handle not only basic patient data such as height and weight, but also photos and X-rays, ultrasound videos, and electrical signals from a patient’s brain (EEG) and heart (ECG).

Partnering with universities and health organizations, Sana organizes training sessions (which we call “bootcamps”) and collaborative workshops (called “hackathons”) to connect nurses, doctors and community health workers at the front lines of care with technology experts in or near their communities. In 2015, we held bootcamps and hackathons in Colombia, Uganda, Greece and Mexico. The bootcamps teach students in technical fields like computer science and engineering how to design and develop health apps that can run on cellphones. Immediately following the bootcamp, the medical providers join the group and the hackathon begins…At the end of the day, though, the purpose is not the apps….(More)

Nudging – Possibilities, Limitations and Applications in European Law and Economics

Curated on May 24, 2016August 3, 2018 by Stefaan Verhulst

Book edited by Mathis, Klaus and Tor, Avishalom: “This anthology provides an in-depth analysis and discusses the issues surrounding nudging and its use in legislation, regulation, and policy making more generally. The 17 essays in this anthology provide startling insights into the multifaceted debate surrounding the use of nudges in European Law and Economics.

Nudging is a tool aimed at altering people’s behaviour in a predictable way without forbidding any option or significantly changing economic incentives. It can be used to help people make better decisions to influence human behaviour without forcing them because they can opt out. Its use has sparked lively debates in academia as well as in the public sphere. This book explores who decides which behaviour is desired. It looks at whether or not the state has sufficient information for debiasing, and if there are clear-cut boundaries between paternalism, manipulation and indoctrination. The first part of this anthology discusses the foundations of nudging theory and the problems associated, as well as outlining possible solutions to the problems raised. The second part is devoted to the wide scope of applications of nudges from contract law, tax law and health claim regulations, among others.

This volume is a result of the flourishing annual Law and Economics Conference held at the law faculty of the University of Lucerne. The conferences have been instrumental in establishing a strong and ever-growing Law and Economics movement in Europe, providing unique insights in the challenges faced by Law and Economics when applied in European legal traditions….(More)”

Health care data as a public utility: how do we get there?

Curated on May 23, 2016May 29, 2019 by Stefaan Verhulst

Mohit Kaushal and Margaret Darling at Brookings: “Forty-six million Americans use mobile fitness and health apps. Over half of providers serving Medicare or Medicaid patients are using electronic health records (EHRs). Despite such advances and proliferation of health data and its collection, we are not yet on an inevitable path to unleashing the often-promised “power of data” because data remain proprietary and fragmented among insurers, providers, health record companies, government agencies, and researchers.

Despite the technological integration seen in banking and other industries, health care data has remained scattered and inaccessible. EHRs remain fragmented among 861 distinct ambulatory vendors and 277 inpatient vendors as of 2013. Similarly, insurance claims are stored in the databases of insurers, and information about public health—including information about the social determinants of health, such as housing, food security, safety, and education—is often kept in databases belonging to various governmental agencies. These silos wouldn’t necessarily be a problem, except for the lack of interoperability that has long plagued the health care industry.

For this reason, many are reconsidering if health care data is a public good, provided to all members of the public without profit. This idea is not new. In fact, the Institute of Medicine established the Roundtable on Value and Science-Driven Healthcare, citing that:

“A significant challenge to progress resides in the barriers and restrictions that derive from the treatment of medical care data as a proprietary commodity by the organizations involved. Even clinical research and medical care data developed with public funds are often not available for broader analysis and insights. Broader access and use of healthcare data for new insights require not only fostering data system reliability and interoperability but also addressing the matter of individual data ownership and the extent to which data central to progress in health and health care should constitute a public good.”

Indeed, publicly available health care data holds the potential to unlock many innovations, much like what public goods have done in other industries. As publicly available weather data has shown, the public utility of open access information is not only good for consumers, itis good for businesses…(More)”

Moneyballing Criminal Justice

Curated on May 22, 2016November 14, 2018 by Stefaan Verhulst

Anne Milgram in the Atlantic: “…One area in which the potential of data analysis is still not adequately realized,however, is criminal justice. This is somewhat surprising given the success of CompStat, a law enforcement management tool that uses data to figure out how police resources can be used to reduce crime and hold law enforcement officials accountable for results. CompStat is widely credited with contributing to New York City’s dramatic reduction in serious crime over the past two decades. Yet data-driven decision-making has not expanded to the whole of the criminal justice system.

But it could. And, in this respect, the front end of the system — the part of the process that runs from arrest through sentencing — is particularly important. Atthis stage, police, prosecutors, defenders, and courts make key choices about how to deal with offenders — choices that, taken together, have an enormous impact on crime. Yet most jurisdictions do not collect or analyze the data necessary to know whether these decisions are being made in a way that accomplishes the most important goals of the criminal justice system: increased public safety,decreased recidivism, reduced cost, and the fair, efficient administration of justice.

Even in jurisdictions where good data exists, a lack of technology is often an obstacle to using it effectively. Police, jails, courts, district attorneys, and public defenders each keep separate information systems, the data from which is almost never pulled together and analyzed in a way that could answer the questions that matter most: Who is in our criminal justice system? What crimes have been charged? What risks do individual offenders pose? And which option would best protect the public and make the best use of our limited resources?

While debates about prison over-crowding, three strikes laws, and mandatory minimum sentences have captured public attention, the importance of what happens between arrest and sentencing has gone largely unnoticed. Even though I ran the criminal justice system in New Jersey, one of the largest states in the country, I had not realized the magnitude of the pretrial issues until I was tasked by theLaura and John Arnold Foundation with figuring out which aspects of criminal justice had the most need and presented the greatest opportunity for reform….

Technology could help us leverage data to identify offenders who will pose unacceptable risks to society if they are not behind bars and distinguish them from those defendants who will have lower recidivism rates if they are supervised in the community or given alternatives to incarceration before trial. Likewise, it could help us figure out which terms of imprisonment, alternatives to incarceration, and other interventions work best–and for whom. And the list does not end there.

The truth is our criminal justice system already makes these decisions every day.But it makes them without knowing whether they’re the right ones. That needs to change. If data is powerful enough to transform baseball, health care, and education, it can do the same for criminal justice….(More)”

…(More).

Twelve principles for open innovation 2.0

Curated on May 18, 2016August 3, 2018 by Stefaan Verhulst

Martin Curley in Nature: “A new mode of innovation is emerging that blurs the lines between universities, industry, governments and communities. It exploits disruptive technologies — such as cloud computing, the Internet of Things and big data — to solve societal challenges sustainably and profitably, and more quickly and ably than before. It is called open innovation 2.0 (ref. 1).

Such innovations are being tested in ‘living labs’ in hundreds of cities. In Dublin, for example, the city council has partnered with my company, the technology firm Intel (of which I am a vice-president), to install a pilot network of sensors to improve flood management by measuring local rain fall and river levels, and detecting blocked drains. Eindhoven in the Netherlands is working with electronics firm Philips and others to develop intelligent street lighting. Communications-technology firm Ericsson, the KTH Royal Institute of Technology, IBM and others are collaborating to test self-driving buses in Kista, Sweden.

Yet many institutions and companies remain unaware of this radical shift. They often confuse invention and innovation. Invention is the creation of a technology or method. Innovation concerns the use of that technology or method to create value. The agile approaches needed for open innovation 2.0 conflict with the ‘command and control’ organizations of the industrial age (see ‘How innovation modes have evolved’). Institutional or societal cultures can inhibit user and citizen involvement. Intellectual-property (IP) models may inhibit collaboration. Government funders can stifle the emergence of ideas by requiring that detailed descriptions of proposed work are specified before research can begin. Measures of success, such as citations, discount innovation and impact. Policymaking lags behind the market place….

Keys to collaborative innovation

Purpose. Efforts and intellects aligned through commitment rather than compliance deliver an impact greater than the sum of their parts. A great example is former US President John F. Kennedy’s vision of putting a man on the Moon. Articulating a shared value that can be created is important. A win–win scenario is more sustainable than a win–lose outcome.
Partner. The ‘quadruple helix’ of government, industry, academia and citizens joining forces aligns goals, amplifies resources, attenuates risk and accelerates progress. A collaboration between Intel, University College London, Imperial College London and Innovate UK’s Future Cities Catapult is working in the Intel Collaborative Research Institute to improve people’s well-being in cities, for example to enable reduction of air pollution.
Platform. An environment for collaboration is a basic requirement. Platforms should be integrated and modular, allowing a plug-and-play approach. They must be open to ensure low barriers to use, catalysing the evolution of a community. Challenges in security, standards, trust and privacy need to be addressed. For example, the Open Connectivity Foundation is securing interoperability for the Internet of Things.
Possibilities. Returns may not come from a product but from the business model that enabled it, a better process or a new user experience. Strategic tools are available, such as industrial designer Larry Keeley’s breakdown of innovations into ten types in four categories: finance, process, offerings and delivery.
Plan. Adoption and scale should be the focus of innovation efforts, not product creation. Around 20% of value is created when an innovation is established; more than 80% comes when it is widely adopted⁷. Focus on the ‘four Us’: utility (value to the user); usability; user experience; and ubiquity (designing in network effects).
Pyramid. Enable users to drive innovation. They inspired two-thirds of innovations in semiconductors and printed circuit boards, for example. Lego Ideas encourages children and others to submit product proposals — submitters must get 10,000 supporters for their idea to be reviewed. Successful inventors get 1% of royalties.
Problem. Most innovations come from a stated need. Ethnographic research with users, customers or the environment can identify problems and support brainstorming of solutions. Create a road map to ensure the shortest path to a solution.
Prototype. Solutions need to be tested and improved through rapid experimentation with users and citizens. Prototyping shows how applicable a solution is, reduces the risks of failures and can reveal pain points. ‘Hackathons’, where developers come together to rapidly try things, are increasingly common.
Pilot. Projects need to be implemented in the real world on small scales first. The Intel Collaborative Research Institute runs research projects in London’s parks, neighbourhoods and schools. Barcelona’s Laboratori — which involves the quadruple helix — is pioneering open ‘living lab’ methods in the city to boost culture, knowledge, creativity and innovation.
Product. Prototypes need to be converted into viable commercial products or services through scaling up and new infrastructure globally. Cloud computing allows even small start-ups to scale with volume, velocity and resilience.
Product service systems. Organizations need to move from just delivering products to also delivering related services that improve sustainability as well as profitability. Rolls-Royce sells ‘power by the hour’ — hours of flight time rather than jet engines — enabled by advanced telemetry. The ultimate goal of open innovation 2.0 is a circular or performance economy, focused on services and reuse rather than consumption and waste.
Process. Innovation is a team sport. Organizations, ecosystems and communities should measure, manage and improve their innovation processes to deliver results that are predictable, probable and profitable. Agile methods supported by automation shorten the time from idea to implementation….(More)”

Robot Regulators Could Eliminate Human Error

Curated on May 17, 2016August 3, 2018 by Stefaan Verhulst

Cary Coglianese in the San Francisco Chronicle and Regblog: “Long a fixture of science fiction, artificial intelligence is now part of our daily lives, even if we do not realize it. Through the use of sophisticated machine learning algorithms, for example, computers now work to filter out spam messages automatically from our email. Algorithms also identify us by our photos on Facebook, match us with new friends on online dating sites, and suggest movies to watch on Netflix.

These uses of artificial intelligence hardly seem very troublesome. But should we worry if government agencies start to use machine learning?

Complaints abound even today about the uncaring “bureaucratic machinery” of government. Yet seeing how machine learning is starting to replace jobs in the private sector, we can easily fathom a literal machinery of government in which decisions made by human public servants increasingly become made by machines.

Technologists warn of an impending “singularity,” when artificial intelligence surpasses human intelligence. Entrepreneur Elon Musk cautions that artificial intelligence poses one of our “biggest existential threats.” Renowned physicist Stephen Hawking eerily forecasts that artificial intelligence might even “spell the end of the human race.”

Are we ready for a world of regulation by robot? Such a world is closer than we think—and it could actually be worth welcoming.

Already government agencies rely on machine learning for a variety of routine functions. The Postal Service uses learning algorithms to sort mail, and cities such as Los Angeles use them to time their traffic lights. But while uses like these seem relatively benign, consider that machine learning could also be used to make more consequential decisions. Disability claims might one day be processed automatically with the aid of artificial intelligence. Licenses could be awarded to airplane pilots based on what kinds of safety risks complex algorithms predict each applicant poses.

Learning algorithms are already being explored by the Environmental Protection Agency to help make regulatory decisions about what toxic chemicals to control. Faced with tens of thousands of new chemicals that could potentially be harmful to human health, federal regulators have supported the development of a program to prioritize which of the many chemicals in production should undergo the more in-depth testing. By some estimates, machine learning could save the EPA up to $980,000 per toxic chemical positively identified.

It’s not hard then to imagine a day in which even more regulatory decisions are automated. Researchers have shown that machine learning can lead to better outcomes when determining whether parolees ought to be released or domestic violence orders should be imposed. Could the imposition of regulatory fines one day be determined by a computer instead of a human inspector or judge? Quite possibly so, and this would be a good thing if machine learning could improve accuracy, eliminate bias and prejudice, and reduce human error, all while saving money.

But can we trust a government that bungled the initial rollout of Healthcare.gov to deploy artificial intelligence responsibly? In some circumstances we should….(More)”

Crowdsourcing corruption in India’s maternal health services

Curated on May 17, 2016August 3, 2018 by Stefaan Verhulst

Joan Okitoi-Heisig at DW Akademie: “…The Mera Swasthya Meri Aawaz (MSMA) project is the first of its kind in India to track illicit maternal fees demanded in government hospitals located in the northern state of Uttar Pradesh.

MSMA (“My Health, My Voice”) is part of SAHAYOG, a non-governmental umbrella organization that helped launch the project. MSMA uses an Ushahidi platform to map and collect data on unofficial fees that plague India’ ostensibly “free” maternal health services. It is one of the many projects showcased in DW Akademie’s recently launched Digital Innovation Library. SAHAYOG works closely with grassroots organizations to promote gender equality and women’s health issues from a human rights perspective…

SAYAHOG sees women’s maternal health as a human rights issue. Key to the MSMA project is exposing government facilities that extort bribes from among the poorest and most vulnerable in society.

Sandhya and her colleagues are convinced that promoting transparency and accountability through the data collected can empower the women. If they’re aware of their entitlements, she says, they can demand their rights and in the process hold leaders accountable.

“Information is power,” Sandhya explains. Without this information, she says, “they aren’t in a position to demand what is rightly theirs.”

Health care providers hold a certain degree of power when entrusted with taking care of expectant mothers. Many give into bribes for fear of being otherwise neglected or abused.

With the MSMA project, however, poor rural women have technology that is easy to use and accessible on their mobile phones, and that empowers them to make complaints and report bribes for services that are supposed to be free.

MSMA is an innovative data-driven platform that combines a toll free number, an interactive voice response system (IVRS) and a website that contains accessible reports. In addition to enabling poor women to air their frustrations anonymously, the project aggregates actionable data which can then be used by the NGO as well as the government to work towards improving the situation for mothers in India….(More)”

Is behavioural economics ready to save the world?

Curated on May 10, 2016October 10, 2018 by Stefaan Verhulst

Book review by Trenton G Smith of Behavioral Economics and Public Health : “Modern medicine has long doled out helpful advice to ailing patients about not only drug treatments, but also diet, exercise, alcohol abuse, and many other lifestyle decisions. And for just as long, patients have been failing to follow doctors’ orders. Many of today’s most pressing public health problems would disappear if people would just make better choices.

Enter behavioural economics. A fairly recent offshoot of the dismal science, behavioural economics aims to take the coldly rational decision makers who normally populate economic theories, and instil in them a host of human foibles. Neoclassical (ie, conventional) economics, after all is the study of optimising behaviour in the presence of material constraints—why not add constraints on cognitive capacity, or self-control, or susceptibility to the formation of bad habits? The hope is that by incorporating insights from other behavioural sciences (most notably cognitive psychology and neuroscience) while retaining the methodological rigour of neoclassical economics, behavioural economics will yield a more richly descriptive theory of human behaviour, and generate new and important insights to better inform public policy.

Policy makers have taken notice. In an era in which free-market rhetoric dominates the political landscape, the idea that small changes to public health policies might serve to nudge consumers towards healthier behaviours holds great appeal. Even though some (irrational) consumers might be better off, the argument goes, if certain unhealthy food products were banned (or worse, taxed), this approach would infringe on the rights of the many consumers who want to indulge occasionally, and fully understand the consequences. If governments could instead use evidence from consumer science to make food labels more effective, or to improve the way that healthy foods are presented in school cafeterias, more politically unpalatable interventions in the marketplace might not be needed. This idea, dubbed “libertarian paternalism” by Richard Thaler and Cass Sunstein, has been pursued with gusto in both the UK (David Cameron’s Government formed the Behavioural Insights Team—unofficially described as the Nudge Unit) and the USA (where Sunstein spent time in the Obama administration’s Office of Information and Regulatory Affairs).

Whatever public health practitioners might think about these developments—or indeed, of economics as a discipline—this turn of events has rather suddenly given scholars at the cutting edge of consumer science an influential voice in the regulatory process, and some of the best and brightest have stepped up to contribute. Behavioral Economics & Public Health (edited by Christina Roberto and Ichiro Kawachi) is the product of a 2014 Harvard University exploratory workshop on applying social science insights to public health. As might be expected in a volume that aims to bring together two such inherently multidisciplinary fields, the book’s 11 chapters offer an eclectic mix of perspectives. The editors begin with an excellent overview of the field of behavioural economics and its applications to public health, and an economic perspective can also be found in four of the other chapters: Justin White and William Dow write about intertemporal choice, Kristina Lewis and Jason Block review the use of incentives to promote health, Michael Sanders and Michael Hallsworth describe their experience working within the UK’s Behavioural Insights Team, and Frederick Zimmerman concludes with a thoughtful critique of the field of behavioural economics. The other contributions are largely from the perspectives of psychology and marketing: Dennis Runger and Wendy Wood discuss habit formation, Rebecca Ferrer and colleagues emphasise the importance of emotion in decision making, Brent McFerran discusses social norms in the context of obesity, Jason Riis and Rebecca Ratner explain why some public health communication strategies are more effective than others, and Zoe Chance and colleagues and Brian Wansink offer frameworks for designing environments (eg, in schools and workplaces) that are conducive to healthy choices.

This collection of essays holds many hidden gems, but the one that surprised me the most was the attention given (by Runger and Wood briefly, and Zimmerman extensively) to a dirty little secret that behavioural economists rarely mention: once it is acknowledged that sometimes-irrational consumers can be manipulated into making healthy choices, it does not require much of a leap to conclude that business interests can—and do—use the same methods to push back in the other direction. This conclusion leads Zimmerman to a discussion of power in the marketplace and in our collective political economy, and to a call to action on these larger structural issues in society that neoclassical theory has long neglected….(More; Book)

Can An Online Game Help Create A Better Test For TB?

Curated on May 3, 2016May 29, 2019 by Stefaan Verhulst

Esther Landhuis at NPR: “Though it’s the world’s top infectious killer, tuberculosis is surprisingly tricky to diagnose. Scientists think that video gamers can help them create a better diagnostic test.

An online puzzle released Monday will see whether the researchers are right. Players of a Web-based game called EteRNA will try to design a sensor molecule that could potentially make diagnosing TB as easy as taking a home pregnancy test. The TB puzzle marks the launch of “EteRNA Medicine.”

The idea of rallying gamers to fight TB arose as two young Stanford University professors chatted over dinner at a conference last May. Rhiju Das, a biochemist who helped create EteRNA, told bioinformatician Purvesh Khatri about the game, which challenges nonexperts to design RNA molecules that fold into target shapes.

RNA molecules play key roles in biology and disease. Some brain disorders can be traced to problems with RNA folding. Viruses such as H1N1 flu and HIV depend on RNA elements to replicate and infect cells.

Das wants to “fight fire with fire” — that is, to disrupt the RNA involved in a disease or virus by crafting new tools that are themselves made of RNA molecules. EteRNA players learn RNA design principles with each puzzle they solve.

Khatri was intrigued by the notion of engaging the public to solve problems. His lab develops novel diagnostics using publicly available data sets. The team had just published a paper on a set of genes that could help diagnose sepsis and had other papers under review on influenza and TB.

In an “Aha!” moment during their dinner chat, Khatri says, he and Das realized “how awesome it would be to sequentially merge our two approaches — to use public data to find a diagnostic marker for a disease, and then use the public’s help to develop the test.”

TB seemed opportune as it has a simple diagnostic signature — a set of three human genes that turn up or down predictably after TB infection. When checked across gene data on thousands of blood samples from 14 groups of people around the globe, the behavior of the three-gene set readily identified people with active TB, distinguishing them from individuals who had latent TB or other diseases.

Those findings, published in February, have gotten serious attention — not only from curious patients and doctors but also from humanitarian groups eager to help bring a better TB test to market. It can currently take several tests to tell whether a person has active TB, including a chest X-ray and sputum test. The Bill & Melinda Gates Foundation has started sending data to help the Stanford team validate a test based on the newly identified TB gene signature, says study leader Khatri, who works at the university’s Center for Biomedical Informatics Research….(More)”

Army looks to outsmart soldiers’ bad habits

Curated on May 3, 2016August 3, 2018 by Stefaan Verhulst

Kyle Jahner at the Army Times: “You wouldn’t think that moving the salad bar to the front of the chow hall and moving the dessert bar back 10 feet would make the Army healthier. But at Fort Campbell, Kentucky, that bumped up salad sales about 24 percent and dessert sales down 10 percent, a nudge toward goals of soldiers eating, exercising and sleeping healthier.

That’s just an example of the kind of change Army Medical Command hopes to inspire and successes it hopes to share across installations through its first annual Health of the Force report.

“I’m pretty proud of what we’ve been able to accomplish with this inaugural report,” said Col. Deydre Teyhen during a recent roundtable at Defense Health Agency headquarters in Falls Church, Virginia. “I think we can’t get to a better state of health unless we inform people of what’s working out there in the field.”

The Army hopes to reduce the figure of 17 percent of soldiers not medically deployable within 72 hours. …The overarching philosophy of these recent MEDCOM efforts is to improve overall health rather than play whack-a-mole with problems as they arise. Teyhen pointed out that the average soldier is a patient at a health care facility for about 100 minutes per year, and the trick is to influence soldier health choices over the other 525,500 minutes, extending influence outside of brick-and-mortar health facilities.It dovetails with the Army’s Performance Triad, the plan to improve readiness through sleep, nutrition and exercise….(More)”