Ada’s AI-powered health companion helps individuals understand and manage their health. In practice, Ada works much like a GP in your pocket, asking relevant, personalized questions, enabling people to self-assess their symptoms and identify possible causes and appropriate next steps. Ada is supported by a sophisticated AI engine and curated medical knowledge base, which also augments doctors by providing earlier health information and decision support. Ada’s deep medical foundation and our approach is really what distinguishes us from similar applications. Our AI platform has been built over six years of research and development, enabling us to refine our reasoning technology and build up a very robust medical knowledge base covering many thousands of real medical cases, conditions, symptoms and findings. Doctors have also played a key role in Ada’s development and continue to contribute to Ada’s learning. Our bespoke AI uses a probabilistic reasoning technology, reviewing multiple pieces of data to output probabilities. This means that Ada can consider a variety of diseases to make a health assessment, providing the most likely explanations for a user’s symptoms. We continue to update our medical knowledge base with the latest information, and we’ve integrated machine learning techniques that enable Ada to learn from every interaction. Ada doesn’t exclusively rely on information from one type of source. Over 2M assessments have been completed on Ada, and as Ada’s user base continues to grow and incorporate different data sources (i.e. medical history, sensors, lab tests, etc.), Ada is continuing to become more intelligent, putting us ahead in the market. Ada’s Foundation: Grounded in Medical Precision & Human Insight Ada’s technology was initially designed to support medical specialists with diagnosis, but early testing and subsequent healthcare data quickly made it clear that there was a real need for patient support before individuals even stepped into the doctor’s office. We chose to start with medical experts, because we wanted to make sure that Ada’s reasoning and knowledge was intuitive and accurate, and then continued to build on that, expanding Ada’s reasoning system to GP’s, and finally translating our core content, knowledge base and technology into a tool that would also be accessible to patients, and culminating in the consumer-facing personal companion app that you see today. Data played an important role in helping to inform the design of our health companion app. We conducted an extensive market research study on health care attitudes and the perception of e-health tools, along with surveying doctors, family and friends. What we learned was that there was a real demand for easily accessible, credible health information and something more personalized than a broad web search. So when developing our health companion app, we took this into account in the design. We really wanted Ada to be more personal and work like a human doctor with no time pressure- friendly, conversational and underpinned by medical precision. We are very excited about Ada’s future and are continuing to evolve our product to incorporate the feedback from the people who use Ada on a daily basis. Their feedback remains an integral part of how we write content, design solutions and the inspiration for how we’ll continue to evolve Ada’s capabilities in the future. Diagnosis Support and Population Health Benefits Already, we have learned much from collaborating with research institutes. First, we have gathered medical knowledge and insights about individual conditions which has been incorporated into our service, even before the medical research is published. Second, we have also been able to verify just how useful Ada is in terms of furthering the capabilities of doctors for diagnosis, particularly for rare diseases. We’ve done retroactive studies with specialized research institutions, and have found that in many cases, if the patient had used Ada at the onset of initial symptoms, the patient could’ve saved years of suffering and been directed to the appropriate specialist for treatment. There’s a lot more potential for Ada in this area, and as Ada continues to build its user base, the possibilities of its population health benefits will grow substantially – including the potential to identify and track health trends and symptoms, patterns of disease and flag potential health risks early on. The Future of Healthcare: Increased Digitization and Patient Empowerment In the future, Ada will continue to provide increasingly more sophisticated and personalized health assessments and will also become more of an ongoing health companion, allowing individuals to have more responsibility and control over their data and their health, a lot of it even from home. Consider this: a woman with one or more long term conditions might use Ada to monitor symptoms on a daily basis and to continuously track a range of data points such as heart rate, blood pressure, body temperature and activity levels. In addition she might choose to enable regular automated updates of her Ada profile with lab results and other clinical findings following interactions with her doctors. By gathering data over time, Ada effectively ‘gets to know’ the patient, a bit like a regular GP does, and in doing so is able to provide increasingly relevant and personalized insights and suggestions for how individuals can manage their symptoms and long-term health more effectively. Eden Duthie is the Head of Data Science at Ada Health in Berlin and is originally from Melbourne, Australia. He has had an extensive career applying machine learning to disrupt a wide variety of industries such as vehicle manufacturing, drug discovery, weather forecasting, and sports betting. He was also the original developer of the Kaggle competition platform. Eden leads the data team at Ada where they apply machine learning to primary care patient-focused medicine, developing conversational health assessment services. He believes that healthcare is in the initial stages of world-changing advances based on the availability and flow of data. Source : http://dataconomy.com/2017/11/look-adas-ai-powered-health-companion/

2017 was an election year in two major European countries: in Germany, Angela Merkel was elected for the third time in September; and in France, Emmanuel Macron took office in May. It is too early to tell whether these elections will lead to significant reforms in health care in these countries. Unlike the United States, where abolishing Obamacare was a key pillar of the new president’s pre-election platform and a big focus of his first year in office, Germany and France seem to agree that they would, in general, like to keep the systems they have. After all, they perform relatively well in terms of outcomes, life expectancy, and other critical indicators in comparison to resources consumed as a percentage of GDP. President Macron said he stands for “une Europe qui protège”—a Europe that protects its values while addressing everyday challenges and the large-scale disruption of globalization. This protection—in the sense of keeping established frameworks while learning from each other—might describe the French-German axis at its best. Both systems are based on old cultures and established values that permit nearly universal coverage at much lower costs than those of the United States health care systems while achieving equal or better outcomes. What can we learn nevertheless from each other across the pond, taking the newly established European axis as one unit and the evolving United States system as the example from a new world. Silver surfers who turn into a tsunami France, Germany, and the United States are facing some of the same issues: demographic shifts paired with the rise of chronic disease and medical advances that, while raising costs, also make the impossible possible. Historically, the nations’ population pyramids have taken on the classical “pear shape,” with large segments of younger and middle-aged individuals contributing to the care and support of a smaller population of older people. But this will soon turn into a nicely shaped “potato”, where the remaining workforce will have to generate resources for retirees, children, and other non-working parts of the population. In the European context, the idea of solidarity is kept alive. In general, people can afford to retire, receive healthcare benefits, and guaranteed pensions while poverty rates at old age are low (in comparison to the United States). To be sure, this picture might change and governments might have to kick in some money to fund rising costs of care (or decide to ration—which is contradictory to the right of free choice described below). Yet, such a change is not explicitly addressed in any European efforts at health care reform. Cutting back pensions and/or social benefits is a no-go at least in Germany. This social spending or rather redistribution of income is generally considered to be an investment in a strong middle class. And a strong middle class represents the main pillar of a European society and its political system. This picture looks differently in the United States. Solidarity and Healthcare for All Many European health care systems go back to the idea of Bismarck (roughly 150 years ago) that when individuals’ incomes and ability to pay for care are unequal, we carry a social responsibility as citizens to redistribute resources. Under this redistribution, administered by the government, the rich pay for the poor, smaller families pay for larger families, and the younger pay for the older. Most Europeans willingly accept this notion—it is deeply ingrained in their culture. What is more, Europeans accept that we have to kick in some money, from time to time, to prevent the failure of an important market—such as the health care market— that has not found its own equilibrium. On the other hand, large parts of the United States society have elected leaders who favor a smaller government and believe that market forces require only limited regulation. Instead of fearing market failure, these leaders seem to relish a Darwinian view of health care markets where only the wealthy have true access and wide choice in the care they receive. Free Choice - No Gated Healthcare Community A major right of both French and German health care systems is the right to choose your doctor freely and have those services he or she deems necessary covered with co-pays that are as little as €10 in Germany and more nuanced in France where a visit without referral to any specialist might reduce reimbursement rates to 30% of total costs, but is covered with referral. How can Europe afford this luxury of free choice and (nearly) full coverage? The first answer is to look at prices: If you compare services in Europe with those in the United States, you will realize that prices for regular lab, x-ray, and doctor visits are a fraction of what they amount to in the United States. As in many other markets, high costs are attributable in part to medical inflation and supplier-induced demand which are aggravated by price effects in the United States. Second, European medicine does not have the same level of provider dominance as the United States where clinicians, making all diagnostic and treatment decisions, have the greatest influence on costs. In European systems, governmental regulation frequently caps expenses. This does not mean that services are declined, but deficits for sicker populations are settled among insurances under a regulated mandate. In response, United States health care executives have embraced the idea that, to paraphrase Oscar Wilde, if you wish to limit you have to define. Developing closed networks to keep costs in check, the United States system has essentially rationed access while requiring additional resources to manage and coordinate these networks. On the bride side, this management awareness in the United States has facilitated advances in population health management and created a better mindset for actually managing care in pathways across sectors. More broadly this approach has led to many accomplishments: aligning incentives, a focus on performance management, cross-sectoral patient flows (within the defined network though) and the related, and necessary IT implementation that creates connectivity in many role-model United States health care systems. These capabilities are less advanced in Europe—due to the historical separation of sectors and regions that impact the exchange of patient data in general. The Comparative Paradox And here lies the cross-pond health care system paradox: European systems—like Germany and France—pursue a strictly regulated ideological framework that is based on solidarity, equality, and social responsibility but once people have been socialized into this framework they can eat à la carte—choosing to utilize care freely and as much as they want. The United States represents the opposite: a free market system as a framework in which participants can (or cannot, due to limited financial resources) choose freely from the “prix fixe formule” (as one says in France). In this scenario, one can buy insurance at various levels, pay co-pays and deductibles, eventually having to find help to understand all these options, and then once they are “in”—though not necessarily fully covered—they must live with limited choices in a gated system. What can we learn from each other? This question puts Europe in a much better position because, even though the United States might benefit from learnings from Europe at a macro-level (including those social care reforms that improve living conditions and are known to reduce health care costs as a consequence), these ideas are unlikely to find fertile ground in the United States where ideology makes many policymakers less receptive to such lessons. Europe in turn can learn a lot from the United States system on a micro level—how to manage populations and connect sectors. Nobody really coordinates or manages care in many European systems, so there is room for improvement. As a caveat, one might say that the European systems work well and one should never change a system that is already running well. That may not continue to be the case, however, when the population pyramid’s “pear” turns into a “potato” and the silver tsunami hits the shore. Crisis Management Although the deep impact of demographics is not openly discussed in European health care systems, they must nevertheless prepare for the changes to come. The United States has—over the last 40 years—developed management capacity to confront similar challenges. To be sure, one could ask whether medical care is provided or rather managed nowadays, taking into account that the medical workforce has grown marginally whereas medical administrators have increased by more than 3000%. In addition, wages for managers in United States health care are a multiple of those in Europe (it’s the prices again!). In Germany, administrative costs in the statutory system are below 10% and non-medical professions, including consultants and others, represent less than 15% of the workforce in health care. It remains to be evaluated whether a further investment in health care management contributes to better outcomes as a result of coordinated care pathways. And the assessment to what extent these outcomes are “better” than with less management will most likely lie in the eye of the beholder. Opportunities to learn exist and it will help us to address common challenges before crisis unfolds. As any consumption of menu items, cross-national learnings should also be consumed with moderation. Source : https://www.healthaffairs.org/do/10.1377/hblog20171214.835155/full/ Dr Katharina Janus Katharina Janus, a professor of healthcare management in Germany and at Columbia University New York, is the founder and managing director of the Center for Healthcare Management and the president of the consulting network ENJOY STRATEGY. She has twenty years of global healthcare management experience in science and corporate practice. Starting her career in managed care at one of the largest hospital chains in the United States, she has learned about challenges on the shop floor before turning to academia where she continued to pursue applied research. Her global consulting network ENJOY STRATEGY supports many of the Center’s projects on the implementation side. She also serves as a member of the board at Allianz health insurance, Munich, Germany. She has been invited frequently as a speaker and moderator to contribute her global domain expertise and in-depth knowledge of healthcare markets and trends. In this respect, she has helped major multinational companies with market access strategy and business development to facilitate on-site implementation in various cultural environments. Dr. Janus was a 2006-07 Harkness Fellow in Health Care Policy at The Commonwealth Fund, a Rockefeller Foundation academic fellow in 2012 and a Brocher Foundation resident in 2014. She was also one of the youngest tenured professors and female board members of a DAX company appointed in Germany. Etienne Minvielle Etienne Minvielle is Professor of Management at the French School of Public Health (EHESP), University of Sorbonne Paris Cité. He holds the Chair of Management and is the Director of the Research unit, Management of HealthCare Organizations (MOS). He received his MD from the Paris V School of medicine where he did his residency in Public Health. He also received a PhD in Management Science from the Polytechnique School, and also be graduated from the French business school, ESSEC. He spent one year at the Leonard Davis Institute, University of Pennsylvania, during his post-doctoral period. He recently became the director of the Master of Science in Health Care Management (EHESP, AP-HP, Ecole du Santé du Val-de-Grace, Paris VII). He serves on numerous expert panels advising policy makers and government officials on the use of quality improvement incentives in health care. In addition to his research, Etienne Minvielle is also advisor at Gustave Roussy, a leading European Cancer center, for implementing innovative patient pathways. He works closely with the CEO and the board of directors to help design innovative patient pathways.

If you find it hard to remember to take your medicine, you are not alone – and you are doing yourself no favours. Research in the United States has shown that half of all medication issued for chronic conditions isn’t taken as prescribed, leading to about 125,000 deaths, 10 per cent of all hospitalisations and a bill for the US health system of as much as US$290 billion (Dh1.07 trillion) every year. Small wonder, then, that healthcare firms are scrambling to bring high-tech solutions to market. But if you are still coming to terms with the world of “wearables” – healthcare apps and sensors designed to monitor exercise routines and vital signs, including heart rate, temperature and blood pressure – brace yourself for the next big thing: “swallowables”. The Digital Pill Last month, the US Food and Drug Administration approved the first “digital pill”, a tablet embedded with a tiny sensor that tracks when and if patients have ingested their medication. The technology also has the potential to measure activity, such as the number of steps a patient takes and their heart rate, in some future applications, which means that swallowables could actually replace wearables. Proteus Discover, the technology at the heart of the Abilify MyCite system, has been developed by Californian company Proteus Digital Health. It comprises an ingestible sensor, a wearable sensor patch, a mobile-device app and an internet portal. The sensor, a silicon circuit sandwiched between thin films of copper and magnesium (all elements found in food), is about the size of a grain of sand. Activated and powered up by contact with stomach fluid – “like a potato battery”, the company says – it transmits a message to the smart patch, worn by the patient, which in turn forwards it via Bluetooth to a smartphone app. With the patient’s permission, caregivers and doctors can also access the information online. Job done, the sensor is digested and passed from the body. The Abilify MyCite system has so far been approved for use only with aripiprazole, an antipsychotic drug used to treat schizophrenia, bipolar I disorder and, as a secondary treatment to other drugs, major depressive disorders. Clearly, it is a breakthrough for such patients, for whom compliance with medication regimes is crucial, but can often be a struggle. The system, as Proteus says, “has been designed for the individual with serious mental illness to allow them to record their daily medication intake and have a more informed dialogue with their healthcare team”. But it won’t be long before patients with a wide range of chronic conditions will be swallowing high-tech pills. Abilify MyCite is a joint venture between Proteus and Japanese pharmaceutical company Otsuka. Robert McQuade, Otsuka America Pharmaceutical’s US executive vice president and chief strategic officer, says the company sees plenty of potential for the technology in other clinical fields. Mental Health “People ask us: ‘Why mental health first?’ And it’s because adherence is such a major issue in the treatment of serious mental illness,” he says. “But adherence to treatment is a problem around the world in many diseases. We do think it has potential with other products and we are looking at some that are still in the development stage.” For years, research has found that significant numbers of patients with serious conditions, including cancer, diabetes, chronic obstructive pulmonary disease and heart disease, fail to take their medicine as prescribed, despite potentially fatal consequences. Proteus, which developed the digital-pill technology, is already targeting some of these clinical areas in trials. “Digital medicines, medications that communicate that you’ve taken them, have the potential to help people with many health issues,” says David O’Reilly, Proteus’s chief platform officer. Trials with patients being treated for conditions including raised cholesterol levels and high blood pressure have already found that combining medication with Proteus Discover technology improves outcomes. Interim findings presented at the annual meeting of the American College of Cardiology last year showed that patients taking digital pills saw greater reductions in blood pressure and cholesterol. In a 12-week trial involving 96 patients, 85 per cent of those taking digital pills achieved a lowered blood pressure, against 33 per cent of those on normal medication. In addition to hypertension and cholesterol, Proteus envisages a role for digital medicines in the treatment of diabetes, hepatitis C, HIV and more. “In the future, we see digital medicines becoming mainstream to help patients achieve their health goals who have many different health issues, such as cancer or opioid addiction,” O’Reilly says. Some experts caution that simple forgetfulness is not the only reason patients fail to take their medicine, and that digital pills should not be seen as a golden-bullet solution to non-compliance. “For conditions where there’s a threat to public health that can be mitigated with high adherence – for example, easily transmissible diseases with a high burden of illness, but with an effective and safe drug – such technologies might be useful,” says Meera Viswanathan, director of the RTI-UNC Evidence-based Practice Centre, an independent non-profit research institute based in North Carolina. But, she says, “non-adherence arises from many issues [and] the nature and consequences of non-adherence can vary by clinical condition, so addressing the problem will require many types of solutions”. Chronic Conditions In health systems that are not free at the point of delivery, cost is likely to be the main barrier, she says. “The premise for these technologies is that patients have affordable access to healthcare and to the drug. For a range of chronic conditions, that may not be the case. In those instances, reducing the costs of medications should serve as the starting point for improving adherence, although more interventions to address other constraints or behaviours might also be needed.” In an age of widespread fears about digital security and privacy, concerns have also been voiced about connecting patients so intimately to the internet. When the New York Times reported the FDA decision last month, its headline focused on “worries about biomedical Big Brother”, and it quoted experts who feared the potential for coercing vulnerable patients into being remotely monitored. Otsuka says that it recognises such concerns as entirely valid, which is why it took the “extraordinary” step of assembling an independent team of bioethicists to monitor development of the technology. “We wanted to make sure we fully understood the potential for any ethical concerns well in advance of even making it through the FDA process,” says John Bardi, vice-president of digital medicines business development for Otsuka America Pharmaceutical. “They worked very closely with the team as they went through different elements of what this product system was going to be.” Everything about the product, McQuade says, “is driven by the consent of the patient, so if the patient doesn’t want it, they don’t get it – they get a normal tablet without the sensor in it. This is all about establishing a therapeutic relationship between a caregiver and a patient, and they both have to want to participate in that partnership.” Digital pills are here to stay, and Proteus is not the only company in the field. The Veloce Corporation of Denver, Colorado, is developing an ingestible wireless capsule that can be activated by a remote trigger or a monitored condition to release drugs at specific times or sites inside the body. The SmartTab Drug Delivery and Monitoring System, Veloce says, will “optimise patient outcomes, significantly increase prescription drug adherence, and drastically reduce healthcare costs”. Though novel, in a sense there is nothing new about the digital pill, which has its roots in space-age technology developed almost 30 years ago by Nasa, in partnership with Johns Hopkins University. The Ingestible Thermal Monitoring System was a small capsule, loaded with wireless signal transmission telemetry, micro-miniaturised circuitry, sensors and batteries, designed to monitor the body temperature of astronauts. The device came down to Earth and became commercially available in 1988; it has since been used to monitor core temperature and prevent heat stroke in people taking part in a range of specialised, heat-sensitive activities, including sport, firefighting and the military. Abilify MyCite will be brought to market next year, at a price yet to be fixed and, at first, within a limited number of US health plans. But global roll-out will follow quickly and many patients, with many conditions, could soon find themselves faced with the choice of taking their medicine with a side order of wireless technology. Source : https://www.thenational.ae/lifestyle/a-spoonful-of-medicine-with-a-side-of-tech-why-swallowables-are-the-next-big-thing-1.685673

The internet, Apps and AI have affected many aspects of our lives. Shopping, entertainment, communicating with social groups, booking travel, and banking are dramatically different experienced for my teenage children than they were for me. In spite of the many conferences, blogs, and £Billions of private equity funding, ‘Digital’ has had by comparison relatively limited effect to date on how we as citizens obtain healthcare, and how doctors and clinicians deliver healthcare. There are many reasons why healthcare may be a slow adopter including clinical governance, legacy systems, and vested interests. Babylon’s recent launch of their GP App, and NHS England’s announcement of £45M to digitise GP surgeries, have dramatically increased the public awareness of digital health, and we may be at a tipping point. Over the past eight years, I have spent significant time working in pathology. This is a heterogenous discipline which includes highly automated blood science laboratories processing up to 20,000 samples per day, many low volume sub specialist tests, and some areas which remain highly manual, such as histology, where samples are manually cut, processed into paraffin blocks, sliced thinly onto glass slides (sometimes with stains to detect cellular abnormalities), and interpreted under a microscope by a consultant, who may then attend MDT meetings in person to discuss the results. The NHS has 200 consultant vacancies out of c2200 posts, and new training posts have not been filled for several years. District General Hospitals in remote areas in particular have struggled to fill posts. Histology reporting is often on the critical path to achieving cancer access times, and the NHS is spending c£60M per annum on backlog services and additional payments to consultants. Whilst the process has made developments over the years, most 2017 histology labs would be recognisable to colleagues who retired 30 years ago. This may be about to change. A number of companies have been developing digital histology solutions for several years, but until recently the business cases didn’t stack up: scanners were slow and expensive, a single slide can be a 500MB file even with compression (c 100 MRI scans), and digital reading was slower than a microscope. Within the past twelve months, technology seems to have broken through, although many histopathology consultants remain reluctant to switch from microscopes. I was recently invited to visit one of the early large scale adopters of digital histology. Not in the UK – I flew three hours to Bucharest to visit Synevo, the diagnostics division of Medicover. Many of my generation in the UK still associate the old Soviet nations with Trabants and crumbling concrete communist blocks. Romania has invested massively in technology infrastructure (my hotel broadband was 4x faster than any London hotel) and education. The Synevo lab was a futuristic building, with well designed process flows, and better than most I have seen in Western Europe. Dr Stoicea, the lead histologist spent two hours talking passionately about quality, digital histology their investment in the Philips system, the journey to implement it, and his plans for future development. Whilst some histologists see digital technology as a threat, it clearly could be used to improve outcomes and turnaround times: Digital slides can be sent anywhere. Histologists can sub-specialise, which should result in greater accuracy of reporting. Slides can be sent to where capacity is waiting. Why employ histopathology consultants? An Uber type system would allow the next available sub-specialist anywhere in the world to report. Many slides are negative, but still need reporting. Many positive slides are stained, and the proportion of slides taking the stain are manually counted. Philips and others are developing AI systems which will look at slides and cells and undertake interpretation. Some manual checking will probably be mandated for several years after these systems are developed, but they should reduce time per slide by guiding consultants to anomalous cells, and doing routine counting. BD’s focal point technology already offers this in gynaecological cytology. Quality control can be improved.The system can track what the consultant looked at, potentially prompt them if they missed something, and allow them to annotate and send to other global experts for a second opinion. Slides can be reported anywhere.Staff who would otherwise take career breaks could report from home. MDT meetings can be virtual, again with a global histology expert attending for the part of the meeting where he is needed. There are very substantial discrepancies between the salaries of histologists around the world – probably ranging from £30K to >£300K within Europe. The efficiency of histologists in terms of comparable cases reported also varies by a factor of 3-4 between the least efficient public sector services and the most efficient private ones. The correlation between efficiency and salary is also far from perfect. It is difficult to see how this economic disparity can be sustained, once slides can move around the globe at a click. If China were to train 10,000 English speaking histology consultants, they could dominate then world in reporting, as they have done in manufacturing, and as they seem intent upon doing in genetic testing, based upon their investment in genetic sequencing labs. Hugh Risebrow CEO, Latchmore Associates http://www.latchmoreassociates.co.uk Founded in 2003, Latchmore Associates supports public, private and third sector health and social care organisations, with particular expertise in the following areas: • Supporting overseas/ new entrants to the UK market • NHS private patients, commercialisation and revenue generation • Public/private partnerships from market sounding through to procurement and implementation • Pathology partnerships/ consolidation • Bid and transaction negotiation support • Interim management

Apple reveals 2017’s most popular apps, music, movies and more Apple has unveiled its 2017 charts and trends, celebrating the most popular apps, music, movies, TV shows, books and podcasts across the App Store, Apple Music, iTunes, iBooks and Apple Podcasts. Editors and curators from Apple Music, App Store and iTunes highlight great content from indie artists and developers from around the globe. App Store editors identify four breakout trends in app culture for 2017: the introduction of AR (augmented reality) apps and games, the rise of real-time competitive gaming, apps focused on mental health and mindfulness and apps transforming storytelling and reading. Calm is the 2017 iPhone App of the Year Calm is the #1 app for mindfulness and meditation to bring more clarity, joy and peace to your daily life. Join the millions experiencing less anxiety and better sleep with our guided meditations, Sleep Stories, breathing programs and relaxing music. Recommended by top psychologists and mental health experts to help you de-stress. Calm is the perfect meditation app for beginners, but also includes hundreds of programs for intermediate and advanced users. Guided meditation sessions are available in lengths of 3, 5, 10, 15, 20 or 25 minutes so you can choose the perfect length to fit with your schedule. Topics include: * Calming Anxiety * Managing Stress * Deep Sleep * Focus and Concentration * Relationships * Breaking Habits etc... Source : https://www.apple.com/newsroom/2017/12/apple-reveals-2017-most-popular-apps-music-and-more/

Computational techniques illuminate such illnesses as depression and schizophrenia, forging new paradigms for classifying and treating the thorniest psychiatric disorders. Each year, before their annual meeting on machine learning, a group of electrical engineers who work with huge sets of data dream up a problem for their peers to solve. Most of the themes have dealt with arcane engineering topics, but three years ago organizers wanted to tackle something a bit more practical. Could artificial intelligence be used to solve problems in medicine? One of the competition's organizers, Rogers Silva, a postdoctoral researcher at the Mind Research Network in Albuquerque, had been experimenting with data from brain scans. He wanted to know how to extract information from brain-imaging data sets "to make more accurate inferences about diseases, causes, and what features would be good for a diagnostic," he says. A skilled physician can determine a lot from a brain scan, but Silva was looking for something more—subtle patterns invisible to the naked eye. Silva posed this question to his colleagues: Can experts in big data look at brain scans from patients with schizophrenia and healthy controls and determine which is which? The brains of people with schizophrenia are different in countless subtle ways from those of people without the disease. The differences are likely due to multiple genetic variants in the brain, many of which might not be harmful by themselves. For the competition, Silva collected the brain scans of 144 people, about half of whom had schizophrenia. The data from the scans were given to some 245 competing teams, which produced 2,000 entries (teams were allowed to submit more than one). The patient scans were divided into two groups. The competing teams were told which patients among the first group did and did not have schizophrenia. Based on that data, the teams looked for differences between patients in the cohort for whom no diagnostic information was shared, those with schizophrenia and those without. Using that information, the teams were able to predict which of the patients from the second unlabeled cohort had schizophrenia. Silva and his colleagues were impressed with the results. The best submissions distinguished between patients and controls with only about a 10 percent rate of error—far better than chance alone. Could these techniques be used to diagnose patients routinely? "That's conceivable," Silva says. "The work we did is a first step." The goal is not to replace psychiatrists with circuits, but to help psychiatrists do what they are already doing. Silva expects that to happen in less than five years. In this form of applied artificial intelligence, "Machines have the ability to guide their own learning, if you will," says Hugh Garavan, an associate professor of psychiatry at the University of Vermont. Machine learning is the term for allowing a computer program to wander through data and attempt to identify what's important, without any instructions about where to look or what is likely to be crucial. It's a way of finding unexpected connections. Machine learning is being used in a study he is conducting on teens and binge drinking. There are a lot of data on dopamine and its reward system. Yet, he says, "That system might be blunted in kids, and if alcohol activates that system, these kids are more likely to try it again." One could design a study pursuing that idea, Garavan says, but it would have shortcomings, as "you are essentially confirming what you already know." With machine learning, there is no presumption that the dopamine system, or anything else, is involved in binge drinking. "We can tell the algorithm: Sample the brain. Look at 1,000 brain regions. See which region at a certain age correlates best with binge drinking at a later age." With a second pass, the first associations can be confirmed. "Then you can repeat this multiple times." The algorithm the machine uses is designed by the machine, not by humans, who couldn't possibly grapple with all the data that machine learning can. Some of that information is useful and relevant to understanding disease, some is not. Computational psychiatry The approach is about a decade old. In 2006, Read Montague, a computational neuroscientist at the Virginia Tech Carilion Research Institute, was among the first to start a computational psychiatry unit. He and a colleague, Peter Dayan, of University College London, worked on computer models that could mimic the action of the neurotransmitter dopamine when looking at diseases and disorders such as addiction, Parkinson's, and some forms of psychosis. They can now use their models to ask what happens when dopamine systems are overactive or get changed, to understand components of addiction or obsessive-compulsive disorder, for instance. This is just one way of using computation in psychiatry. The schizophrenia challenge and the binge-drinking study were data-driven. That is, they were intended to draw useful correlations from data. But Montague and Dayan's work starts with a theory and tests it. Their computer models can show how dopamine systems might work, and researchers can then determine to what extent the theoretical models apply to humans. Computational psychiatry was slow to take off, Montague says, partly because of the success of biological approaches. Prozac and similar drugs that followed it, known collectively as SSRIs, have been quite effective at treating depression. They "can take people from suicidal to functional," he says, "but that's not an explanation" of what's wrong with the individual. Developing models of dopamine systems and other systems in the brain can help connect the dots between molecules and mental illness, as can the modeling done in machine learning. One obvious difficulty is that poking around in the brains of living people is not welcomed by those individuals. Montague got around this problem, however, by working with neurosurgeons. He asked conscious patients to engage in certain games, such as betting, to monitor dopamine release while surgeons were performing surgery. "The model tells us what to look for in the brain," he says. Some patients were asked to play Go, the complex Asian strategy game. "We thought Go was profoundly deep and complicated," he says. But by tracking brain activity and making computer models of what was observed, "we could discover things that the best players didn't see." It was yet another example of how computational psychiatry can be used to identify brain activity that couldn't be spotted by even the most expert psychiatrist. The same idea—extracting important information from noisy data—can be used to study Parkinson's disease, a disorder of movement and balance caused by a shortage of dopamine-producing neurons. Parkinson's disease is unusual because it can take many different forms. Montague can look at the various systems affected by dopamine and determine which are affected in a given patient. Once again, the new techniques can find clarity in complicated data, and they are not limited to diagnosable conditions. "You're going to be able to collect people with traditional psychotherapies and subclassify them using these approaches," he says. Machine learning to study adolescent drinking behavior Robert Whelan, a psychologist at the Trinity Institute of Neurosciences in Dublin, is using machine learning to study adolescent drinking behavior. He wanted to answer an important question about adolescents: Could information from 14-year-olds enable him to predict who would become a binge drinker at age 16? Machine learning was suited for this, because Whelan didn't know what the answers were likely to be. He had a lot of data to work with—the brain scans of 2,500 adolescents at age 14. "We asked the machine to look through all of the data at 14 to pick out which are important, which could indicate who would become a binge drinker at age 16." He and his colleagues ran a number of tests. "We looked at the brain structure, did cognitive tests, behavioral tests, life histories, asked whether their parents were divorced." After all the computational work was complete, Whelan reported partial success. The prediction was 70 to 75 percent accurate. "We found that it was possible to some extent to predict who was going to become a drinker," he says. "The accuracy wasn't great, but it was probably real." But the experiment did more than show limited success at predicting binge drinking. It also revealed things that researchers hadn't expected—a virtue of machine learning. One was that "people who were more extroverted at age 14 had a higher probability of becoming binge drinkers," Whelan says. But the best predictor was that people who were more conscientious about their jobs and considerate of others were also more likely to binge drink at 16. So were adolescents who entered puberty early. These findings were slightly mysterious and unexpected, which is partly why they are so important. Whelan worked on the binge-drinking study as a post-doctoral fellow with Hugh Garavan at the University of Vermont. One interesting finding, he says, was that drinking was more likely in adolescents who had bigger brains—not the kind of hypothesis that researchers would likely develop on their own. But once they discovered this, it made sense. As the brain develops during adolescence, it tends to shrink. Neurons and the connections between them get smaller as unnecessary synapses are pruned. Kids with bigger brains are less mature than those whose brains are further along in the pruning process, and kids with immature brains are more likely to drink. One of the things that made the study possible was the large set of brain scans made available to Whelan and Garavan. To arrive at meaningful findings, researchers need huge data sets. Until recently, they didn't exist. In any given study, Garavan says, "you might have 20 kids," far fewer than the thousands needed. There are two problems with that, he says. One is that even the best computational work cannot uncover small abnormalities without a big collection of data. And without a lot of data, spurious connections can seem real: If the 20 kids in a pilot study just happen to be taller than usual, one could conclude that the taller an adolescent is the more likely she is to be depressed, let's say. A random group of 2,000 students makes it much less likely that they will be taller on average than the population overall. Big Data Collection The National Institutes of Health is spending $300 million to collect data on 11,000 kids, starting at age 9—including brain scans, genetic tests, hormone analysis, and psychological assessments, according to Garavan. The plan is to follow up with the group periodically until they reach age 20. For an upcoming project, Whelan is trying to use the same techniques to predict which patients will respond best to a given treatment. "When a person visits a psychiatrist, he's given treatment, then is sent away for eight weeks to see if the treatment will work." Whelan is trying to devise a way to increase the chances that patients will get the treatments that work best for them from the outset. Such an indicator of effectiveness would save money and time, and get patients feeling better much more quickly. He's excited about the future of these studies. The strategy of the binge-drinking study is now being adopted by other researchers to look for more correlations. And that highlights an important caveat of these studies. "Our research is always correlational," he says. "We don't know what causes kids' drinking or is a consequence of their drinking—or whether they're related somehow." But the research does give experts strong leads in their search for causes. While the work is exciting, what does it mean for doctors who want to apply the findings, or for patients who might benefit from them? Are there specific brain regions that make you more likely to commit suicide? Or to become depressed? "If we find a brain marker that is predictive, we might be able to come up with some sort of pencil-and-paper test to take advantage of that," Whelan says. The results of the binge-drinking study, for example, could conceivably generate a 10-item questionnaire that might identify kids likely to become binge drinkers, giving schools and parents a chance to intervene. It's not magic, but it's rational and scientific—unlike a high-school vice principal making guesses about kids who might be at risk. Computational techniques are also providing insights in areas that have already attracted a lot of attention in conventional studies. Arjun Krishnan is a computational biologist at Michigan State University doing pioneering work in the study of autism. While he studied genetics and majored in biology, he has also had an interest in computer science. As he continued his studies, he realized he could bridge the two fields. "Biologists used to say to me, 'You don't know enough biology,' and computer scientists would say, 'You don't know enough computer science.' " But that turned out to be ideal. "I knew more computer science than biologists, and I know more biology than computer scientists. The two camps couldn't talk to each other, but both could talk to me." Krishnan has used his cross-domain straddle to examine a fundamental problem in genetics: How do genes specialize and communicate with each other? "Cells in our body can do very different things even though they have the same set of genes," he says. "A brain cell has the same genes as a heart cell." But brain cells can't contract like heart muscles, and heart cells can't make your skin crawl when Dr. Frankenstein shouts, "It's alive! It's alive!" This has immediate practical implications, because "every disease is associated with a very specific cell type in our bodies," Krishnan says. "If genes 'break' and cause autism, the effect is through brain-related tissues." Computational models But genes don't work in isolation. Genes are social creatures, and they participate in social networks in the body. Likewise, Facebook uses computational models to decide who to recommend to you as friends—people with whom you should connect because they're in your social network. Krishnan has used a similar idea to study biological networks—looking through the networks of known disease-related genes to find hitherto unknown ones also related to disease. When Krishnan turned his focus to autism, only a few culprit genes had been identified. The big data he is using comes from the Simons Foundation Autism Research Initiative. It's a collection of genetic samples from 2,600 so-called "simplex" families, meaning that each family has only one individual with an autism spectrum disorder—siblings and parents are unaffected. Krishnan uses his computational methods to look for mutations that are present only in the child with autism. When he began his work five years ago, only 15 or 16 genes had been linked to autism. That figure has grown to about 65, Krishnan says. Researchers thought there had to be more; 10 years ago they were predicting that 600 to 1,000 genes might ultimately be found. "We thought we'd take known autism genes and see if they interact as part of networks in the brain," he says. The human genome contains 25,000 to 30,000 genes. Krishnan and his colleagues decided to give every one of those genes an autism score, ranked from the most highly predictive to the least predictive. "Our ranking contains many genes at the top that have never been studied before." In August 2016, he and his colleagues published their research in Nature Neuroscience, raising the number of candidate autism-linked genes from about 65 to 2,500. It's a potentially significant advance in understanding the genetics of autism. The question now is whether that can help clinicians discover the cause, or causes, of autism, and then treat it. The key is that Krishnan doesn't need to understand every gene. "That would be like saying I need to know about every individual buyer to form a marketing campaign," he says. Genes form networks, just like grocery-store shoppers, and if he learns what some of them do, he can draw conclusions about the others. The discovery also underscores the idea that autism "is not a single disorder at all. It's one of the most diverse disorders," he says. Autism can have a variety of symptoms: problems with sleep, digestive difficulties, issues with sensory perception, and others. Yet, too often, all get put in one box. Krishnan adds, "We don't yet understand which sets of genes contribute to which parts of autism." It's the same with other conditions, such as obesity and heart disease, which can also have varying symptoms and multiple causes. He plans to turn his attention next to Alzheimer's disease and heart disease, using the same computational techniques. The problem with cutting-edge computational research is that the tale usually ends with a promise of an exciting future and a reminder that more research is needed before any of the work will help patients. While that is true of this research, some findings are already reaching patients with depression. Adam Chekroud, a doctoral student at Yale, founded a company called Spring, to take advantage of his findings on depression. He notes that the care Americans get for depression is generally less than perfect. "Physicians probably detect only half of those who have a depressive episode. Of those who are identified, 30 percent don't return for treatment. Of those who do come back, 70 percent don't recover. Only a handful of people get optimal care all the way through." Chekroud has employed computational methods to brighten that picture. He used his data analyses to develop a questionnaire for patients that can not only help diagnose them but also predict what is likely the best treatment. In a paper earlier this year in The Lancet, Chekroud reported that some 30 to 40 percent of patients who recover from depression after treatment will relapse. About 30 percent of those who relapse recover with one of the SSRI antidepressants. But he was able to identify the 60 percent of that group likely to respond specifically to the SSRI citalopram, or Celexa. That could spare patients hit-or-miss trials of drugs and get them directly to the one that can help. "We showed [the prediction] was also statistically reliable when we tested it in a completely independent clinical trial," he says. Machine Learning People believe that childhood trauma is one factor associated with a higher risk of depression, Chekroud says. So is being female. "Machine learning combines all of these small effects and looks at overall symptoms." Chekroud, eager to expand the research, created Spring with two Yale computer science graduates. The company is now working with a clinic in the Bronx, in New York, to evaluate Spring's diagnostic questionnaire. Patients who walk into the clinic, most of them Spanish-speaking and on Medicare or Medicaid, are handed an iPad with the questionnaire. It takes about two minutes. When they are called, they take the iPad with them and give it to the doctor. The diagnostics on the iPad tell the doctor which treatments might work best, provide a list of options, and warn about potential side effects. Having started a few months ago, Chekroud and his colleagues are revising the protocol at least once a week in response to feedback from doctors and nurses at the clinic. The system can now diagnose depression with about 70 percent accuracy—before the patient even sees the doctor. The information-technology director at the clinic is pleased with the results of the pilot project. "We're about halfway through the pilot, and we've seen acceptance rates rise dramatically." That is, patients are welcoming the questionnaire. They are eager to reach for the iPad when they come in. "It's definitely an incremental step, but I think it's leading toward a revolution in health care. Big data is something we can't get away from and we won't be able to do without." Chekroud sees a bright future, and so does Microsoft, which sponsors his research and gives him free access to its cloud computing resources. He plans to incorporate more treatments into the protocol, including such things as exercise and psychotherapy. Read Montague bubbles with enthusiasm when asked about the future of computational research in psychiatry and neuroscience. He's excited about it and so are the students he's training. "It's going to be the big application for computer science," he says. "I'm making a bet, but I think it's a good bet—mental illness and neurological disease have touched nearly everyone on the planet." With further use of computational techniques, depression will no longer be simply depression. People with traditional psychopathologies can be subclassified into different categories, and that can lead to much more careful treatment—and ease the symptoms of mental illness more rapidly, and more effectively. Source : https://www.psychologytoday.com/articles/201709/will-big-data-save-psychiatry?collection=1105674

Several tech founders are trying to break into the NHS. How will their smartphone-powered ideas get on in an institution sceptical of change, underpinned by a notorious tangle of IT and bureaucracy? A near-sacred institution, the NHS has spent little time out of the public psyche since its inception in 1948. Today, far from being a celebrated feat of public welfare, each day brings a barrage of stories of closing hospital A&Es, cuts in funding, huge debts and an ageing population. Faced by a heavy funding deficit, demands vastly different to when the service was conceived, and shifts in politics and ideology, many believe the NHS is itself strapped to a life support machine. Yet several enterprising individuals see something quite different in the NHS: a massive client with a £116bn annual budget, and the platform from which to unleash smartphone-powered medical products and services that could spread all over the world. The barriers are high, but if they can be overcome, there’s potential for the NHS to spawn an entire economy in the way that modern-day Silicon Valley sprawled out of Intel, or London’s creative economy was accelerated by the BBC. In the last couple of years, millions of pounds from VCs and angel investors have been pouring into early stage British ‘medtech’ companies that are already, or have the potential to, sell into the NHS. Around 211 UK-based medical related companies have raised a combined £883m in the last five years, according to research firm Beauhurst. The NHS is, after all, a platform and client like no other in the world. It is unique in its size, scope and reputation, making it the ideal place to launch a health startup and prove a product’s effectiveness. Time travelling If successful, these startups could also achieve something which lavishly paid management consultants and IT firms have failed to do: modernise the NHS, propelling it into the digital age to transform how it operates and provides care, saving billions of pounds along the way. To the startup world, the state of healthcare provision in 2016, and especially within the NHS, is like time-travelling back to the 1980s; a tangle of paperwork and IT systems that should be consigned to museums combined with an unnecessary level of bureaucracy. It’s an especially striking contrast at a time when enormous leaps have been made in less vital services, from how current accounts are used through to ordering a burger. But there are already signs of startups at the gates of the NHS. It’s not quite an all-out digital revolution yet, but pilot schemes with smartphones and smartwatches where patients can book appointments, be reminded to take their medication and have their progress tracked by a doctor without visiting a hospital are going on across the NHS. A smaller number of forward-looking trusts have already invested in such startup-created technology. Accelerator programmes and startup initiatives meanwhile are getting rubber stamped by powerful figures within the NHS. Faster, cheaper, easier The cheerleaders for the digital transformation of the NHS are quick to illustrate the potential, with utopian descriptions of the future of UK healthcare. A multitude of unsynchronised IT systems and paper files could be replaced with a seamless platform that allows X-rays, medical records, medication, and the vitals of any patient to be available to a doctor at the tap of a tablet screen; no shuffling through papers or post, no lost emails; everything made faster, cheaper, easier and more accurate. Not only would such services make life better for staff and patients, and even save lives, but the cost savings through boosted efficiency could help ensure the NHS’s longevity for many decades. VC clamour Early success stories have built confidence. Patients Know Best, a platform that stores digital medical records for care givers, while maintaining patient privacy, has been taken up by 200 sites across the NHS. Another, Zesty, enables patient and referral bookings to be made through a smartphone. It had focused on private healthcare and dentists, until an NHS sexual health clinic approached founder James Balmain. It’s now used by 30 clinics across London and three large hospitals are set to add the service, ditching call centres and appointments sent by post. Zesty’s main competitor, Dr Doctor, is used by six trusts, reaching four million patients. Co-founder Tom Whicher says the app is saving each of the trusts over £1m a year by reducing missed and rescheduled appointments, as well as postal costs. Patients Know Best and Zesty are two of many firms to have benefited from the VC clamour around medtech. Patients Know Best passed through Seedcamp and has since raised £6m, while Zesty has raised over £7m. Another is Network Locum, a platform used to find roaming doctors, which raised £5.3m in summer 2016. NHS accelerator The NHS is changing too. After a period of seeming institutional resistance to change and innovation, the NHS is, on the surface, awash with schemes to embrace whizzy new startup ideas. Funds from various NHS digital schemes have been flowing for the last five years. A snappily named set of ‘Academic Health Science Networks’ were created across the country in 2013, with the sole focus of adopting innovation in the NHS. So-called ‘vanguard’ hospitals have been given the freedom to spend money on trying out the services startups are coming up with. There’s even an NHS tech accelerator and a new, faster way for hospitals to be reimbursed for spending on new technology (read more: How to get hospitals interested in tech). Enthusiastic noises The NHS has appointed a clinical director of innovation, Tony Young, an energetic doctor who’s launched four startups. ‘If there’s a place in the planet where you can innovate at scale in healthcare, the NHS is it. The size means we can do things no one else can do,’ he passionately tells Courier. Similarly enthusiastic noises are being made by Mahiben Maruthappu, who set up the NHS’s accelerator programme in 2015. He says: ‘Two thirds of the UK population have smartphones, yet only a fraction use them to interact with the NHS. We need to use this as people already have them, so the NHS doesn’t have to pay.’ Advocates like Young and Maruthappu are certainly leading the charge. Pressure to find ways to save money is also a powerful motivation behind adopting new tech. The government reckons tech can greatly contribute to a planned £22bn in savings by 2021, and has earmarked £4bn for making the NHS paperless by 2020. Hospitals in the red Melissa Morris worked as a management consultant on healthcare projects before launching Network Locum and sees a new willingness in hospitals to talk to startups. ‘Before, there was more of an option to stay in their comfort zone, but now that all the hospitals are in the red, they’re so indebted, they have no choice. It’s desperate times calling for desperate measures,’ she says. But this debt, amounting to £2.45bn across the country’s trusts, along with the government’s consistent statements that no more money will be given to the NHS, is as much a barrier as it is an opportunity. It means there’s little extra funding or resources for overworked staff to try things out, let alone fail. Alluding to the problem, Young says: ‘There’s a real passion and desire [in the NHS]. But a lot of it comes down to the fact that the NHS is given a set amount of money from the elected government, then the leadership has the difficult [task] of deciding how that pot of money is spent.’ Saving money is something startups working with the NHS are acutely aware of. Founders talk of having to provide a saving within a year or risk being dropped from contracts. ‘You have to show the savings you can make right from the start. [When writing a proposal] we clearly show what the savings can be in year one, in year two,’ says Zesty founder Balmain. Bruce Hellman, founder of patient monitoring app uMotif, adds that he actually helps trusts identify where money can be found to fund the use of the app’s service. The NHS badge The power of the NHS stamp around the world is a key attraction for startups. Ivana Schnur, co-founder of Silicon Valley-based AI nursing app Sensly, has placed a large amount of her focus on the NHS, asserting that it’s ‘the best system in the world to prove a product’. After successful deployments in the NHS, Patients Know Best has launched in 19 other countries. ‘The UK is a lot better than it gets credit for,’ says founder Mohammad Al-Ubaydli. ‘Intros from the NHS go across the world, and if you’ve been marked as a secure platform by the NHS, it makes things a lot easier abroad.’ For AliveCor, a US company that makes hand-held, patient-operated electrocardiograms, selling into the NHS has actually boosted sales back home, triggering meetings with large hospital operators in the US that were previously reluctant to see the product. ‘The big providers had been a tough nut, but since going back to them and saying the NHS loves it and it’s saving money, it’s opened doors,’ says Francis White, who heads up AliveCor’s UK business. Beyond the obvious places for startups to expand (such as the Gulf, Australia and the US), developing countries – where healthcare is of low quality, but smartphone use high – are also increasingly attractive to these startups. Globally, the digital healthcare market is expected to reach £162bn by 2020. Loosely structured There is, however, a big caveat to all the positivity. The barriers to breaking through with a healthcare startup, especially into the NHS, are manifold, and often so onerous many have been scared off. With no standard road map for selling into hospitals, every startup talks of the stultifying effects of navigating the labyrinthine arteries of the NHS. It’s complicated by the fact that far from being one organisation, the NHS is a decentralised collection of over 200 trusts, akin to individual businesses that decide what services they buy, and from whom. This loosely structured nature means that just because a business has sold into one trust, it doesn’t follow that any other trust will be interested. With most startups devoid of a massive sales force shuttling the length of the M1, progress is slow going. ‘You have to find a champion in a hospital, a human that appreciates what you’re doing and can see the benefit and go from there,’ comments Rich Khatib, co-founder of Medopad, which produces a series of connected health apps. ‘There are so many hurdles to jump through that, as a small startup team, it’s too difficult to target more than one at a time.’ Culture clash Bruce Hellman of uMotif adds: ‘The NHS is very difficult to sell into, and it takes longer than it should.’ More recently, Hellman has begun to place greater focus on selling data analysing software to pharma and research companies, noting that the NHS is ‘hard going’. A recurring issue faced by those who have tried to get a business off the ground in the NHS is a clash of culture between startups and hospital workers, as well as a reluctance to adapt. It’s an issue identified by those from outside the medical profession and so called ‘doctorpreneurs’ alike. ‘Healthcare is built to minimise risk, while entrepreneurs have an inherent risk-taking nature,’ says one doctor. Maxims from the startup world such as ‘fail fast, fail often’ are anathema in an industry where failure simply isn’t possible. But Tony Young is now keen to instil an attitude of learning from failure in the NHS. To help change attitudes – as well as make the NHS a place where new innovations are developed – he’s signed up 100 doctors to a new entrepreneurial programme. The problem with a funky app But changing culture in a disjointed organisation the size of the NHS is never going to be easy. And while the size is appealing to investors, and to startups at the ‘big vision’ stage, it presents extensive challenges with adoption. There are 150,000 doctors, over 300,000 nurses, 150,000 scientists and technical staff, plus 18,000 ambulance workers, 25,000 midwives and 30,000 managers: the NHS is the fifth biggest employer in the world with more staff than India’s railways and almost as many as McDonald’s globally. Mahiben Maruthappu, who works part-time as an A&E doctor, along with advising the NHS on innovation and running his own health startup, sympathises with the inertia among NHS staff towards new technology. ‘The NHS is under such pressure and people are used to working in a certain way, [so that] with everything else going on, innovation has become a nice-to-have rather than a must-have,’ he says. ‘When I’ve got 40 patients waiting, a funky app is not the first thing I’m going to be thinking about, unless it’s shown that it can make things easier, which is what we must now do.’ It’s a point that further highlights the barrier that a lack of funds and resources presents to innovation. One NHS employee commented that using new software on old machines that constantly crash leads many to quickly lose faith in technology, as it means things can actually be slowed down in comparison to using good old paper; off-putting when staff in their department are allocated just 12 minutes per patient. Spend smartly Lord Darzi is widely credited with starting the process of opening up the NHS to innovation after releasing a report into the future of the service in 2008. Now taking a back seat, having resigned his role as an under secretary in the Department of Health in 2009, he argues that there is enough money in the NHS; but more of the budget needs to be spent on new ways of doing things. ‘The NHS doesn’t need more money, it needs to spend it better,’ he tells Courier. ‘Commissioners need more power to drive innovation. More outdated services can be decommissioned, getting rid of what doesn’t work, rather than the new service needing to be adopted on top of the old as an extra cost.’ Some have found the NHS to be simply an unsuitable client for a startup. After taking the decision to move the headquarters of Big Health (the company that makes mindfulness app Sleepio) to the US in 2015, co-founder Peter Hames told Bloomberg: ‘We assessed [the NHS] and it was just not feasible. We could waste years with no impact.’ Looking overseas It’s a sentiment shared by former NHS doctor Jamie Wilson. After being unsuccessful in finding a market for a previous digital health startup, he launched home carer hiring platform Home Touch, which explicitly avoids selling to the NHS by going direct to users which receive council grants for care. ‘I know of people for whom it’s taken six or seven years to sell into the NHS,’ he says. ‘I just don’t have that amount of time to persuade people in the NHS to purchase the product.’ Mike Casey, the founder of Futurenova, has decided to focus on exporting his tablet cases, which are designed for hospital use and made in Manchester. He is winning customers in the US and Germany, where thanks to the corporate environment, products can be simply bought online with a business credit card, unlike the complex trail of purchase orders and approvals needed in NHS hospitals. Despite Sensly’s Ivana Schnur’s enthusiasm for the NHS as a place to prove a product works, she cautions that the commercial case ‘is a little bit more complicated’. Getting noticed But despite such challenges, there has undoubtedly been progress, and startups such as Network Locum and Dr Doctor are examples of companies which have won big contracts. ‘It’s become more sophisticated over the last five to 10 years. Before 2003 there was no way of getting your product in front of the top level,’ says NHS veteran Peter Young. He created his first patented medical device 20 years ago, and as a long-term proponent of reform to enable innovation, has had varying success in having his inventions adopted. There are also now numerous schemes for those inside and outside the NHS to get an idea or product noticed, including MedCity, an organisation created by London’s Academic Health Science Centres and the Mayor of London, that helps startups set up trials within hospitals. MedCity’s CEO Sarah Haywood argues that, with world-leading NHS hospitals connected to some of the best universities in the world, and London’s already excellent medical research and existing tech scene, the capital is ripe to become the world’s medtech centre. Indeed, if these startups can successfully marry technical innovation, outstanding product execution and savvy negotiation of the NHS system, as well as get politicians, hospital managers and doctors on side, this wave of innovators could well be at the vanguard of a glut of new products that could transform healthcare around the world. And it’s not beyond fantasy to believe that, just as the idea of universal healthcare at the point of access was revolutionary when it was introduced in the UK in 1948, innovation in the British health service could well be pioneering and radical once again some 70 years later. This story is taken from Courier Dec 2016/Jan 2017.

Squeezed budgets, political opposition and complex bureaucracy have made it difficult to bring the NHS into the digital age. But the life sciences industrial strategy report published on Monday called for a “new philosophy of collaboration and trust”between the health industry’s public and private sectors to speed up adoption of innovative technologies and make the system more efficient. Technology companies are already transforming the health service, making it simpler for patients to book appointments and order prescriptions and allowing doctors to monitor their patients’ health and diagnose conditions more effectively. Why is the NHS under so much pressure? An ageing population. There are one million more people over the age of 65 than five years ago. This has caused a surge in demand for medical care Cuts to budgets for social care. While the NHS budget has been protected, social services for home helps and other care have fallen by 11 per cent in five years. This has caused record levels of “bedblocking”; people with no medical need to be in hospital are stuck there because they can’t be supported at home Staff shortages. While hospital doctor and nurse numbers have risen over the last decade, they have not kept pace with the rise in demand. Meanwhile 2016 saw record numbers of GP practices close, displacing patients on to A&E departments as they seek medical advice Lifestyle factors. Drinking too much alcohol, smoking, a poor diet with not enough fruit and vegetables and not doing enough exercise are all major reasons for becoming unwell and needing to rely on our health services. Growing numbers of overweight children show this problem is currently set to continue Babylon One of the most high-profile of these is Babylon Health, which runs GP at Hand, a service launched last month that allows individuals to book an online video call appointment with an NHS doctor within two hours. Rather than having to take the morning off work to traipse into their local surgery, patients can consult with a GP on their smartphone in a quiet corner of their office or beside the side of the road. It is currently just available in London but patients have already signed up “in their thousands”. Another perennial problem for patients is remembering to re-order repeat prescriptions. Echo is an app that keeps track of what medication you’re taking and then allows you to order a refill, which is delivered within a few days. It’s free to use except for the standard NHS prescription charge (currently £8.60 in England) and makes its money by partnering with pharmacies, who charge the health service for the medicine. Then there are those companies improving healthcare delivery. Oxehealth Oxehealth, which was spun out of Oxford University, has developed software that can turn simple video cameras into health monitors capable of detecting a patient’s breathing, heartbeat and whether they have risen out of bed. Though it might seem like a bureaucratic nightmare from the outside, many of these companies have found the NHS to be welcoming of their attempts to improve it. “There are clinicians who really want to look at fresh ways to give themselves better information, to support their decision making, to help their staff cope with the demands of the work,” says Hugh Lloyd-Jukes, Oxehealth’s chief executive But some have faced a backlash. While it has spent a long time winning over health bosses and the Government, GP at Hand has faced opposition from the British Medical Association for supposedly weakening the link between patients and their local doctor. It was also criticised for discouraging, at the NHS’s advice, certain vulnerable people including pregnant women, drug addicts and those with severe mental health problems, leading some doctors to call for a judicial review. “There are a lot of vested interests in healthcare that lobby against what we do, that lobby against any innovation in healthcare,” says Babylon Health’s founder Ali Parsa. Most acknowledge there is room for improvement. Zesty James Balmain is chief executive and co-founder of Zesty, which lets its users schedule appointments with an NHS dentist or sexual health clinic. He suggests while the NHS is understandably risk-averse, it needs to be more daring and be able to countenance failure. Echo Echo co-founder Stephen Bourke suggests start-ups need more help “to figure out who you need to talk to, to get stuff done”. Collaboration between industry and the NHS won’t be straightforward but the mood amongst those who are already involved seems upbeat. Bourke says it’s an exciting time for British health technology companies but “we won’t see changes overnight”. Source : http://www.telegraph.co.uk/business/2017/12/02/firms-mission-drag-slumbering-nhs-digital-age/

Better Data Needed Demand for healthcare is exploding, costs are spiralling, while the true quality picture –the outcomes patients are getting - is very variable and poorly understood. Healthcare quality is typically reported in terms of processes like waiting times, length of stay, and discharge rates, or the success of hospitals in avoiding unintended harm, like hospital acquired infections and even death. These factors are clearly important and need to be managed and minimised, but they do not help patients, clinicians, managers or payers understand the actual results of care being provided from the perspective of the people that matter most – patients. The International Consortium of Health Outcomes Measurement (ICHOM)[1] define ‘outcomes’ as "the results people care about most when seeking treatment, including functional improvement and the ability to live normal, productive lives”. ICHOM was founded in 2014 by Harvard Business School, Boston Consulting Group and the Karolinska. They have developed Standard Sets of outcome measures for different clinical conditions that detail the questions (Patient Reported Outcome Measures or ‘PROMs’) that patients should be asked to ensure a fair comparison between different patients and groups of patients with the same condition. This allows clinicians and hospitals that treat patients with lung or breast cancer, coronary artery disease, hip and knee osteoarthritis, and many more, to collect the same patient outcomes and use it to compare themselves with others and identify opportunities to improve. Without outcomes data, clinicians are in the dark about the true quality of care they are providing, hospitals struggle to provide care as efficiently and effectively as possible and patients lack information to make truly informed decisions. Digital Outcomes Measurement Digital systems for collecting PROMs that wrap seamlessly around clinical workflows and integrate with existing electronic medical records are now available. They are highly configurable to local needs to overcome implementation and process barriers to adoption and deliver real value for patients, clinicians and hospital managers alike. Recently the private sector in the UK has introduced an outcomes measurement programme through the Private Healthcare Information Network (PHIN)[2], and from 2018, outcomes data from patients who have undergone any of fourteen mandated procedures in the sector will be made available online to help new patients make more informed choices about their care. Some private providers, such as Spire Healthcare, are already investing in these digital approaches to meet this new agenda and go further to capture outcomes data long-term, not just the immediate results, and to give patients and clinicians access to data in real-time to enhance clinical care. In June 2017, evidence of the survival benefit of digital PROMs measurement for patients with advanced solid tumour cancer was presented at the conference of the American Society of Clinical Oncology[3]. In the trial, 766 patients with advanced cancer were randomised to digital PROMs monitoring during routine chemotherapy or to a comparison group undergoing usual care (without PROMs). The study group lived an average of 5 months longer than the usual care group. To put that in context, this benefit is greater than for all but one of seven drugs approved by the FDA for advanced cancer in 2016, and in a context where drugs that increase life expectancy by weeks can cost tens or hundreds of thousands of dollars this approach clearly presents a highly cost-effective additional tool in the fight against cancer. Let’s Get On With It! The benefits of systematically collecting and analysing outcomes data in a many conditions are now clear. Jane Maher, Chief Medical Officer of Macmillan, one of the largest cancer charities Tweeted in response to the research: "Routine collection of patient reported outcomes improves survival of patients with advanced cancer - so let's get on with it.” We believe that very soon clinicians and hospitals treating all patients will have the outcomes data and tools they need to truly understand the quality of care they are delivering from the perspective of patients, and will therefore be able to deliver care that is higher quality, more patient-centred and greater value for all patients in the system. Ultimately, when that happens, the aims of Boston surgeon, Ernest Codman will be realised when he wrote over a century ago: “Every hospital should follow every patient it treats, long enough to determine whether or not the treatment has been successful, and then to inquire, ‘if not, why not?’ with a view to preventing similar failures in the future.” Biography Dr Tim Williams is the Co-founder and CEO of My Clinical Outcomes[4], a technology platform that automates the collection and analysis of Patient Reported Outcome Measures (PROMs) in clinical practice. MCO is an ICHOM TechHub affiliate and is accredited in the UK by PHIN. Tim Williams on Twitter https://twitter.com/t1mwilliams A list of suppliers of digital outcomes tools certified by ICHOM can be found at www.techhub.ichom.org. My Clinical Outcomes helps healthcare organisations collect and analyse the outcomes that matter to patients. Here's how ... References [1] http://www.ichom.org/who-we-are/ [2] https://www.phin.org.uk [3] https://jamanetwork.com/journals/jama/article-abstract/2630810 [4] https://www.myclinicaloutcomes.com

Most clinicians, whether in the UK or US, will tell you about the benefits of using instant messaging to coordinate care and self-organise as a team. At the same time the UK NHS has been trying to rein in the use of WhatsApp and other consumer messaging apps. They are right to place a premium on sharing data in a legal and secure manner, ensuring that information is available to support direct care without jeopardising patient confidentiality. And they are right to warn about the risks of creating new ‘silos’ of information that cannot be recorded as part of the official patient record. But the horse has truly bolted – as the numerous reports into the widespread use of WhatsApp, carried by the likes of the BMJ (British Medical Journal) , HSJ (Health Service Journal), the BBC, national newspapers and specialist technology media, testify. Try persuading a clinician who uses instant messaging to co-ordinate and organise their private life, that they can’t use a consumer messaging app like WhatsApp in their professional life when the alternative is the pager. So far, there have been no significant fines or reprimands from the regulators – whether it is the Information Commissioner’s Office (in its ruling on the Royal Free and Google DeepMind, for example) or the GMC (in warnings issued following 28 separate investigations), but surely it’s only a matter of time. What’s the alternative to WhatsApp? It’s important that the NHS can provide an alternative compliant service and it needs to be prepared to pay for it. It is true that there is no direct cost to the NHS in using WhatsApp – its parent company Facebook’s deep pockets give it the luxury of looking beyond monetary values, to the value of data, for example. But the NHS can’t have it all ways. There are a growing number of start-ups entering the UK market with secure messaging products and extensive social media conversations about their merits - clear evidence of the communication problem waiting to be solved. Not all of these will be able to gain traction. Those that do will need to meet some important prerequisites and may look to how the US market is evolving. The essentials: security, scalability and interoperability Base requirements are a solution that is hosted in the UK, encrypts data at rest and in transit, identifies patients and interoperates with NHS IT systems – in other words, a secure and compliant system specifically designed for health and social care staff, which supports team working and where messages can form part of the health and social care record. What is also clear is that any secure messaging platform needs to scale – it needs to be cloud-based and resilient, and so ultimately must be provided by organisations that can reliably deliver scalable enterprise health IT. The real secure messaging traction in the UK is currently coming where additional value is added for clinicians and the NHS. This means interoperability with EPRs and shared care records through open APIs and providing additional care coordination features including patient list management, handover, task management and alerting. Joining up clinicians and cross-organisational care teams At the System C & Graphnet Care Alliance, we have built a messaging and care coordination platform called CareFlow. It attaches to either a hospital master patient index or a care community index helping clinicians coordinate care across health economies. The platform includes secure messaging, but adoption is driven by providing key team based clinical workflow. The result is transformational. Particularly important is the fact that these solutions should not be restricted to hospitals. We are all now looking to Accountable Care Systems to help break down organisational and cultural boundaries and bring staff together to meet an individual’s needs, whether they are in primary care, hospitals, nursing homes, community services or social care. For the promise of ACS’ to become a reality, we will need joined up team-based communication, operating across wider care communities and where the information is secure and can be incorporated in the patient record. That is what health and social care agencies should insist on and that is what the market needs to deliver. Jonathan Bloor Director of Clinical Information at System C & Graphnet Care Alliance Hospital Doctor & Founder of CareFlow Connect https://twitter.com/DrJonathanBloor

Deepmind, the digital brain foundry owned by Google's parent company, Alphabet, wants to use artificial intelligence to solve… well, everything. Last year, its software taught itself to play the strategy game Go better than any human on the planet. For its next trick, it wants to move beyond games to a very real-world problem: health care. The London company has a fast-growing division – now 100 strong – dedicated to health. And while DeepMind’s research on Go may be years away from yielding practical applications, its health-care work is affecting people’s lives today through projects with the U.K.'s National Health Service. These include a mobile app to alert doctors and nurses to changes in a patient’s condition and efforts to research whether computers can analyze various kinds of medical imagery as well as experienced doctors. The company believes AI has the power to save lives. But DeepMind’s maiden voyage into the field has also run smack into an iceberg of privacy and ethical concerns – and the resulting controversy has threatened to sink its ambitions of using AI to transform health care. In July, after a year-long investigation, U.K. regulators ruled that London's Royal Free Hospital had illegally provided DeepMind access to 1.6 million patient records going back five years. DeepMind said it needed the records to conduct safety testing of its first product, a mobile app that gives doctors and nurses instant access to medical records and can alert them to patients at risk of deterioration. The first potentially fatal condition DeepMind built an alert for was acute kidney injury (AKI). The Royal Free said it accepts the decision, but disagrees it could have tested the mobile app, called Streams, another way. Regulators took no action against DeepMind, ruling that it acted on the Royal Free's instructions. But the company acknowledges it made mistakes. “In our determination to achieve quick impact when this work started in 2015, we underestimated the complexity of the NHS and of the rules around patient data, as well as the potential fears about a well-known tech company working in health,” Mustafa Suleyman, DeepMind's co-founder and head of its health projects, and Dominic King, the former surgeon who serves as the company's top clinician, said in a statement. DeepMind's stumble has implications far beyond one AI research firm. Tech companies are flooding into health care. IBM claims its Watson artificial intelligence software can help doctors find the best treatments for cancer. Genome pioneer J. Craig Venter's latest startup, Human Longevity Inc., wants to customize treatments for each patient's DNA. Even DeepMind Health is just one of three big health-care bets Alphabet is making. It also owns Verily, which creates medical device software, and Calico, which is trying to stretch human lifespans. Success or failure matters to more than just corporate bottom lines: the U.K.'s NHS is counting on technology to cope with an aging population and shrinking budgets that threaten to bankrupt the entire system. But if AI is going to fulfill the optimists' hopes, the companies behind it must prove they are trustworthy. And when it comes to Big Tech, trust is in increasingly short supply. From revelations about Russian meddling in the U.S. presidential election to new disclosures about how Apple and Google have avoided paying taxes, tech companies are no longer seen as benign — or even neutral — entities. DeepMind, founded in 2012, may still see itself as a startup, but it is a part of Alphabet, a huge conglomerate. And while Silicon Valley's clichéd "move fast, break stuff" ethos might have worked in the past, DeepMind is discovering that sometimes it really isn't better to ask for forgiveness than permission. The driving force behind DeepMind's push into medicine is Suleyman,who everyone at DeepMind calls "Moose" (an abbreviation of his first name). Suleyman's mother was a nurse. After Google bought DeepMind in 2014 for a reported 400 million pounds, he quickly homed in on health care. “There is no other area where we invest so much money in technology and get so little back,” Suleyman said in an interview in mid-August. Press coverage of DeepMind’s health-care efforts sometimes makes it seem as if the company is developing a software version of Hugh Laurie’s character in “House," a diagnostic genius able to deduce the solution to any medical mystery. But Suleyman said this is "total nonsense." “We are going to be solving all kinds of other magical problems in the world before we get to that sort of general diagnostician," he said. Hints of what DeepMind Health does want to do can be gleaned from a project at London's Moorfields Eye Hospital. Here, in an office cluttered with thick medical tomes, Pearse Keane is staring at an image on his laptop. Keane is a senior ophthalmologist and clinical researcher. The picture is a patient’s retina imaged with optical coherence tomography, or OCT. “It allows us to see things like bleeding and leakage into your retina and diagnose the most common causes of blindness," Keane said. Moorfields and DeepMind are trying to see if a computer, using AI, can read OCT scans as well as Keane. The project has achieved “impressive” results, Keane said, but he wasn't ready to talk about them yet. He and DeepMind hope to publish their research in the coming months. The company has also announced research projects with two London universities to see if AI software can learn to read head and neck scans and mammography scans as well as or better than doctors. Still, DeepMind said a commercial product using AI is a ways off. Streams, the only product DeepMind has actually deployed, uses no AI. While DeepMind originally set out to use machine learning to improve an existing NHS algorithm to detect AKI, it said it never carried out that research. When DeepMind visited the Royal Free, it found the existing algorithm— which wasn't half bad— was the least of the problem. Of far more concern were antiquated technology and Byzantine workflows that meant it took too long for doctors and nurses to act on blood test results. The real problems in medicine “are much more gritty and practical,” Suleyman said. Those pragmatic concerns are front-and-center on the ninth floor of the Royal Free hospital in early August, when a patient's kidneys suddenly start struggling after a liver transplant. Within seconds of a lab pathologist entering blood test results into a computer database, they are analyzed by a formula the NHS developed, and an alert sounds on nurse Sarah Stanley phone. Opening the Streams app, she sees a graph showing spiking indicators from the blood tests. Using the app, she messages a colleague to check on the patient. “We have just triaged that patient in less than 30 seconds,” she said. In the past, the process would have taken up to four hours. A few hours delay, Stanley said, can be critical: patients with AKI can deteriorate rapidly. DeepMind said it wants to move on from the controversy over Streams' development. In November 2016, it replaced its original information sharing agreement with the Royal Free with a new five-year contract designed to address the initial deal’s failings. Since then, the company has published, with a few redactions, copies of its contracts with hospitals. It set up and funded a panel of outside reviewers to investigate its work and report publicly each year. The company also announced it will create a digital ledger system – similar to the blockchain technology that underpins the cryptocurrency bitcoin – that would give NHS hospitals a tamper-proof audit trail of who has accessed patient data. But none of this has assuaged the company's detractors. Julia Powles is a law professor at the University of Cambridge who has written critically of DeepMind’s initial agreement with the Royal Free. She said DeepMind’s new contract does not explicitly prevent it from transferring patient data to sister company Google. DeepMind said it has not and never would give any data to Google. “If they can’t put that in writing I find it hard to believe them,” Powles said. She questions whether DeepMind was the best choice given that Streams doesn’t use AI, and thinks that other companies should have been allowed to bid on the project. And while DeepMind has been offering Streams to hospitals for free, Powles wonders if the company has an unfair advantage because it is backed by Alphabet and can afford to lose money on Streams. She said there was a danger hospitals will get locked into technology that they won’t be able to afford if DeepMind eventually decides to charge a market rate. The Royal Free is not entitled to any money DeepMind makes from Streams. But John Bell, a doctor who chairs the U.K.’s Office of Strategic Coordination of Health Research, recently recommended that the NHS retain an economic interest in any artificial intelligence developed using its data. “Our projects so far have assumed the right way to give value back to the public is through initially providing our resources and technologies to our NHS partners for free,” DeepMind said in an emailed response, adding that it was open to discussion of other ways of valuing its services. The Royal Free said it in a statement that it was “happy with the terms of the agreement" with DeepMind. Controversy hasn't stopped DeepMind from signing agreements to deploy Streams to additional NHS hospitals. Suleyman said the company has received interest from U.S. doctors, too. But privacy concerns may have dented DeepMind's hopes of integrating AI into its health-care offerings anytime soon. Taunton & Somerset NHS Foundation Trust, one of the hospitals now adopting Streams, explicitly ruled out doing anything with artificial intelligence, Tom Edwards, the hospital's joint clinical information officer said. Nicola Perrin, the head of Understanding Patient Data, a project run by the British health charity Wellcome Trust, worries that what happened to the Royal Free might deter U.K. hospitals from adopting potentially life-saving technology. "I think it is very important that we don't get so hung up on the concerns and the risks that we miss some of the potential opportunities of having a company with such amazing expertise and resources wanting to be involved in health care," she said. Health care, Suleyman said, “is incredibly valuable, it is incredibly broken and there is a massive opportunity to transform it with AI at some point in the future.” But not today. Suleyman describes DeepMind as “getting in early” with products like Streams, so that it is well-positioned to use AI later. DeepMind is “years away” from generating reliable revenue from health care, Suleyman said. The company earned just 40 million pounds of revenue in 2016 – none of it from health work – and reported a loss of 94 million pounds, according to accounts filed with UK business registry Companies House. As DeepMind is learning, changing the way people experience health care-- and turning a profit-- makes beating the world's best Go players look easy. Source : https://www.bloomberg.com/news/articles/2017-11-28/alphabet-s-deepmind-is-trying-to-transform-health-care-but-should-an-ai-company-have-your-health-records

How efficient is the Chinese healthcare system? Milcent examines the medication market in China against the global picture of healthcare organization, and how public healthcare insurance plans have been implemented in recent years, as well as reforms to tackle hospital inefficiency. Healthcare reforms, demographic changes and an increase in wealth inequity have altered healthcare preferences, which need to be addressed. Significantly, the patient–medical staff relationship is analysed, with new proposals for different lines of communication. Milcent puts forward digital healthcare in China as a tool to solve inefficiency and rising tensions, and generate profit. Where China is leading in the digitalization of healthcare, other countries can learn important lessons. Chinese social models are also put into context with respect to current reforms and experimentation. Carine Milcent is Professor of Health Economics and Econometrics at the Paris School of Economics, France, and French Center for Contemporary China, Hong Kong. She is Editor of China Perspectives and has published widely in journals and books. Publication Date - 26 January 2018 eBook ISBN - 978-3-319-69736-9 Hardcover ISBN - 978-3-319-69735-2 Source : http://www.palgrave.com/de/book/9783319697352