swiss re - center for global dialogue report: healthcare revolution: big data and smart analytics

Healthcare revolution: Big data and smart analyticsConference report

Swiss Re Conference report: Healthcare revolution: Big data and smart analytics 1

Table of contents

Introduction 3

From diagnosis to personalised prognosis: Will better information lead to better decisions? 4

Sensor innovations driving the digital health revolution 6

Reducing the burden of chronic disease through remote monitoring and management 8

Using common data to make uncommon predictions 10

The wireless future of medicine: How digital revolution will create better healthcare 12

Healthcare analytics: From reactive to proactive decision-making 14

Designing digital interventions using behavioural economics 15

Using predictive analytics to create pre-approved life insurance 16

Panel discussion on healthcare revolution: What is next? 17

Organisers 19

2 Swiss Re Conference report: Healthcare revolution: Big data and smart analytics


Introduction

Science fiction often moves one step ahead of innovation. In Star Trek, medic Dr. McCoy, carried a medical tricorder – a simple looking device which could instantly diagnose any condition. We are not quite at this futuristic level of technology; but we are approaching it. Increasingly ubiquitous mobile devices are becoming loaded with sensors to monitor health and well-being. They create large volumes of data which can provide new insights and lead to better diagnoses and behavioural suggestions. The day when we send our ‘well-being’ records, as tracked on our smartphone, to our doctor prior to an appointment will soon be upon us. As Karen Frick describes, increasing weight will be given to prognosis – finding the signal of a condition before it manifests itself as an illness – rather than to diagnosis, the ex-post identification of a condition only when the symptoms are visible.

The interests of the insurance industry in these trends are numerous. Many consumers fear more knowledge of their health may be used by insurers to increase premiums or refuse coverage. Insurers have no such agenda. They already collect copious amounts of health data on their customers and are very sensitive to issues of data privacy and its accompanying regulation. However, the new healthcare revolution may change the life and health underwriting landscape in a number of ways:

Big data: More data makes models more detailed, more specific and more prescriptive. Data manipulation, as demonstrated by Ben Reis, can lead to surprising insights. Insurers can use this in life and health risk analysis, allowing them to price and underwrite with greater confidence. Better data can allow new products to be created and insurance cover to be extended. Moreover, data can be used in marketing, as shown by William Trump and Edward Leigh, to provide pre-approved life insurance products.

Nudge: Sensors and data platforms can monitor how you exercise, eat and sleep. Using this information, platforms such as Peter Ohnemus’ dacadoo, already claim success in nudging people towards healthier behaviour. Dominic King believes the subtle approaches of behavioural economics can be used in public policy to encourage better living. If new nudging technologies promote greater general health, there should be a reduction in aggregate healthcare costs and an increase in life expectancy, both of which will impact current insurer models. Moreover, insurers themselves could become nudgers – for example, offering premium discounts to customers achieving a certain health score, as monitored on their smart device.

Next steps: Carrying around big handsets to monitor your health may seem passé in a few years. Zero-power transmitters will become smaller and more personalised – without necessarily being invasive – according to Adrian Ionescu. This will allow an increased ability to measure the chemical traces of chronic diseases, such as cancer and Alzheimer’s. IBM is already developing an oncology platform, as related by Boaz Carmeli, to align physician treatment with recommended guidelines and past experience. Again, if we are standing at the edge of a data-driven paradigm shift in healthcare, the effects on aggregate health, and therefore on insurance models, will be considerable.

The convergence of readily portable sensors, easily shared digital health platforms, and the subsequent creation of big new data sets may not yet have resulted in an unequivocal healthcare revolution; we do know it has the potential to do so. All players in the health industry – and not least insurers – need to carefully follow the opportunities and threats this digital shift will create.

Chris Singleton Christoph NabholzHead Medical Insurance Europe Head Business DevelopmentSwiss Re Swiss Re Centre for Global Dialogue


From diagnosis to personalised prognosis: Will better information lead to better decisions?

Karin Frick, Head of Think Tank, and Member of the Executive Board, GDI Gottlieb Duttweiler Institute

Prediction was once the realm of priests and magicians. In today’s society it is increasingly a tool of planning. Where once we had post-illness diagnosis of a condition, the future promises pre-illness prognosis.

Our increasing faith in prediction is based on technology. A body of data-driven tools is capable of discovering and analysing patterns so that past correlations can be used to forecast likely future outcomes. Predictive technologies, which include data mining, neural networks, and system modelling and simulation, have been applied to the study of weather systems, traffic patterns, stock markets, epidemiology, consumer behaviour, terrorist activity, and many other areas of study where there can be a significant number of variables.

Technology and our ability to record data is expanding exponentially; and healthcare is seen as one of the primary beneficiaries. Entrepreneurs are creating apps that can run on smart phones claiming to be capable of predicting conditions from depression to sport injury. These metrics not only benefit the individual, but also the wider healthcare industry. With the data gleaned from these devices, insurance companies, employers and healthcare providers can view a comprehensive picture of an individual and a population‘s health – one that is more accurate and trustworthy than a first-person narrative.

This increase in predictive ability should be a force for good; but human reactions may be unpredictable. Prognosis could provoke change; equally it could provoke over- or underestimation of the risk, or – if the prognosis is not positive – to ignore it altogether. We already know that humans have a tendency to drift towards hyperbolic discounting. The risk of a terrorist attack, which statistics tell us is highly unlikely, is seen by most respondents as being far higher than diabetes – which statistics suggest is a much likelier fate. Moreover, most people have an inability to imagine how they will age and how their preferences and personalities will change.

The most effective way for humans to take on and realise information is through feedback loops. The first stage is evidence, the actual data; then comes the prognosis based on the data; the relevance of the prognosis has to be realized in its social and physical context; the consequences of the prognosis are understood; and the individual finally acts according to the previous four steps. The circle is started again.

Prognosis

Evidence Relevance

Action Consequences

Source: GDI Gottlieb Duttweiler Institute

Figure 1: The power of feedback


These stages can be identified within the use of fitness armbands that record physical movement. Evidence (steps) leads to prognosis (expected gains in fitness); leads to relevance (frequently in a gaming context, which encourages participation); leads to consequences (feel better, weight loss); and finally action (take more steps in a day).

This does not mean there are not questions about predictive technology. Human happiness is not a universal defined quantity. Some may feel uncomfortable in the knowledge that our future is already defined. Moreover, it is difficult to know what to do with prognoses that may not be beneficial; such as a predisposition to criminal behaviour or a degenerative condition. Predictions can be self-fulfilling. As W.I. Thomas and D.S. Thomas suggested in 1928, “If men define their situations as real, they are real in their consequence.”


Sensor innovations driving the digital health revolution

Peter Ohnemus, President and CEO, dacadoo

The quantified self – the ability to record how we move, eat and sleep – is already upon us. It stands at the convergence of a number of societal trends. The first is that of the smart device. Smartphones now have the computing power of 1980s main frame computers. They are ubiquitous and the industry is keen to push sensor technology which records our daily activity. There is an explosion in the production of sensory devices currently. It comes at a time when social networking is increasingly forming a platform of interaction; and where the phenomenon of gaming, either against the computer or against social networks, is growing.

Source: iStock

Perhaps of greatest relevance is that mobile health is gaining traction as overall healthcare costs spiral; the world spends about USD 6 trillion on healthcare, a factor of four compared to what it spends on food. Healthcare as a percentage of GDP is rising towards 20% in the US; with predictions that, with an elderly society and more chronic disease, it could rise to 30% in coming decades. These healthcare costs are currently a liability for insurance companies and the government; increasingly there will be a trend to transfer healthcare costs towards employers. Studies suggest that using mobile health to contribute to ‘right living’ could save US healthcare costs of USD 70–100 billion per year.

In order for mobile health to be effective, it has to be easy to access – via a mobile device – it has to be fun and it has to be relevant. Relevance can be achieved by making data easy to understand; and platforms such as dacadoo can formulate a single health score from an array of data points. Some consumers will have concerns about data privacy; however, data privacy is as much an issue with analogue records as it is with digital. Many mobile health providers take the question of data extremely seriously.

Figure 2: Smart devices can measure heart rate, blood oxygen levels, temperature and movement recognition. They will only grow in sophistication.


Source: dacadoo

Mobile health will be a massive growth industry; according to a report from BCC Research it will be worth USD 50 billion by 2020 and the sector is gaining from large inflows of venture capital. The Obamacare programme will influence this development further. As of 2014, employers may use up to 30% of health insurance premiums to provide outcome based wellness initiatives. While it is still likely that the insurance model will stay social in its broad construction, health indicators will increasingly drive individual premiums, most probably initially in the form of rebates for verifiably healthier customers.

Figure 3: The dacadoo health score – a number from 1 (low) to 1000 (high) represents your current health and fitness status in real-time. When tracked over time, the dacadoo health score offers a good directional relative indicator of how your health and fitness is improving or deteriorating.


Reducing the burden of chronic disease through remote monitoring and management

Laurence Jacobs, Senior Research Scientist, University of Zurich Medical School

The traditional approach to the management of chronic diseases is not optimal from a medical perspective, and it is extremely expensive. Moreover, the worldwide growth in cases of chronic disease continues to increase at a very fast pace. Reasonable cost estimates place the total financial burden caused by chronic disease in the several hundreds of billions of dollars annually. This situation is untenable in the long run. Left unattended, this problem is such that in the not too distant future, no society will be able to afford the cost of caring for its ailing population. The traditional approach to this problem simply does not scale well.

Fortunately, there are alternatives to the traditional approach. These alternatives, at present mostly in the development and testing phases, are not only much cheaper, but they have the potential of being also better for the patient from a medical perspective.

The current opportunity was born not only of necessity, though that has played an important role, but also from the confluence of the general population’s interest in health. Companies have developed small, accurate and inexpensive biosensors. These have led to a growing availability of good quality data that can be used to derive accurate models that can generate alerts, or even trigger devices to react to critical changes in the one or more parameters being monitored.

Diabetes is a prominent example. As far as growth, it is estimated that there will be around 250 million sufferers worldwide by 2030, more than double the amount estimated in 2005. A key component of the process of managing diabetes is to measure the level of glucose in the blood several times a day. With the technology of a few years ago, this process is painful, expensive and cumbersome, requiring the extraction of blood and the use of portable meters. However, current technology already allows for a reasonably practical way to measure glucose continuously using a sensor that is implanted subcutaneously. Even better, several start-ups are announcing systems to measure glucose continuously without the need to extract any blood at all. These sensors, several using light, or estimating the levels of blood glucose by analysing tears or saliva, will soon become commercially viable. These systems will not only be simpler and cheaper, but they will also lead to better methods of treatment.

There are currently many clinical trials underway that aim to test integrated platforms, running on smartphones, that measure, analyse, and report on multiple continuous measurements of a potentially large number of important biometrics that promise to optimise the treatment of several chronic diseases. Patients and their doctors can be informed in real time on effective treatment change, and alert on critical risk factors. This would have been impossible only a few years ago, and it will eventually revolutionise the management of chronic disease.


Using common data to make uncommon predictions

Ben Reis, Director of the Predictive Medicine Group at Harvard Medical School and the Children‘s Hospital Informatics Program

We are living in the age of many unknowns. In two major respects, we are entering new territory for human evolution. One of those is age. Evolution has focused on keeping us healthy through reproduction and child-rearing years, but many people now live well beyond this stage. Those extra years bring co-morbidities and chronic conditions. Another novelty is urban living. There have been town dwellers for thousands of years; but never have we had more city dwellers than rural farmers. That implies different environments and different patterns of physical activity. The more we know about the effects of these new trends in human existence, the more we can seek to positively influence our health.

Previous healthcare revolutions have not proved to be quite as revolutionary as initially hoped. Although decoding the genome was a ground-breaking achievement, rather than providing a clear blueprint of our genetic selves, it revealed how much we did not know about the interaction of genes in determining human traits and the existence of epigenetics, the interaction of genes with their environment. Similarly, whether the potential treasure trove of data that accompanies the smartphone revolution will enhance our capacity to predict health status remains to be seen.

We already have, however, rich sources of data available to us. One source is claims submitted from providers to insurers to cover medical costs. These data provide information on the frequency of individual visits to the doctor, the conditions the individual suffers from, the geographic placement of claimants, the prescriptions or treatments undertaken by the doctors, and the effectiveness of these treatments as registered by the need of the individual to return to the doctor subsequently. On the whole, insurance claims have produced some remarkable insights:

Behavioural models: Health records provided a surprisingly accurate prediction as to those individuals who might be more susceptible to being victims of domestic violence.

Epidemiological models: Insurance claims have proved a good proxy in the identification of disease clusters and outbreaks.

Predictive drug affects: The safety of drugs is generally measured in comparison to a reference drug. Using network models, adverse drug effects can be detected and even predicted years in advance.


More predictive Less predictive

Alcohol- and substance-related mental disorders

Anxiety-Somatoform-Dissociative- and Personality-Disorders

Superficial injury; contusion

Sprains and strains

Residual codes; unclassified

Other injuries from external causes

Poisoning Open wounds

Burns

Other mental conditions

Affective disorders

History/screening mental disorder

Headache, including migraine

Epilepsy, con-vulsions

Asthma Diseases of female genital organs

Schizo-phrenia and related disorders

Factors influencing health care

Back problems

Tooth, jaw disorders

Liver disease

Other psychoses

Viral infection

Source: Reis B Y et al. BMJ 2009; 339: bmj.b3677

There are other rich sources of existing public data. One of these is search engine query data. One study suggests an inverse relationship between the availability of abortion services in a particular US state, and Google searches for abortion services. This may suggest that demand for abortion is relatively constant across different states, it is the supply side that accounts for different abortion search rates.

Figure 4: Some risk factors for domestic violence in women, as indicated by insurance claims. This is one of a number of innovative means of capturing and visualising data enabled by modern data analysis software.


The wireless future of medicine: How digital revolution will create better healthcare

Adrian Ionescu, Professor of Nanoelectronics, École Polytechnique Fédérale de Lausanne (EPFL), and Chair, Guardian Angels Initiative

Mobile health is not just an interface between individual and device. It will soon become part of a much more expansive and wider Internet-of-Things (IoT). This Internet-of-Things will not only capture health data; it will capture pollutant data; will monitor the way we drive and we live; will measure stress levels, with the final goal of proposing personalised safe and secure services for a better quality of life. Eventually it will allow the creation of smart cities and of a smarter society. The foundation of the IoT will be trillions of tiny wireless devices. Not only will it allow for smart care, it will allow for smart energy use and smart interaction. It will form part of our strategy to tackle problems caused by population ageing; the spread of chronic disease; and the associated healthcare costs.

Creating an Internet-of-Things represents a major logistical challenge. Smartphone handsets will eventually come to be seen as cumbersome relics. The IoT relies on multiparameter sensing technology being tiny (mm3) and power free. Guardian Angels are future zero-power smart autonomous systems with sensing, computation communication and energy harvesting features. Over three years the Guardian Angels research partnership aims to reduce current energy device consumption by a factor of 100; over a ten year period, the partnership wants to reduce device energy consumption by a factor of a thousand.

This can be done through combinations of advances in biochemical technology; through the application of stacked nanowire sensors; through the use of emerging 1D and 2D nanomaterials; through nanoelectronics and nanomechanics; enabling single molecule sensing and novel functions in forgetabble devices. These sensors will be able to function by use and storage of energy from an adjacent energy source, eg solar, thermal or vibration. All these technologies must be able to broadcast signals to some form of receptor and ultimately processor.


Source: www.ga-project.eu

Such sensors are already being built into wearable technology, such as armbands, smart garments and smart patches. They will be able to be transferred to lenses for the eyes and onto the skin in the form of skin tattoos. They should aid users in ultimately sensing and diagnosing conditions; allowing autonomy of decisions; security and privacy of the personal data and providing the platform for better decisions. Multiple sensor technologies have the ability to provide a more holistic health picture; which can be complemented by sensors monitoring physical manifestations of the individual’s emotional state. The final goal is a paradigm shift from prescription to prevention, enabling new solutions for more sustainable healthcare models.

Figure 5: The Guardian Angels European-wide partnership is teaming up to develop ever more compact and streamlined technology that slips seamlessly into our daily lives.


Healthcare analytics: From reactive to proactive decision-making

Boaz Carmeli, Manager, BioMedical Informatics, IBM Haifa Research Lab

Advances in data collection technology are not only featuring in improving fitness and general well-being, they are also being used in chronic conditions with complicated patterns of diagnosis and treatment, such as, in the case of IBM, the study of oncology. The goal is to improve care quality and patient experience while reducing the treatment cost. This is done through feedback loops in which the physician works in concordance with technology to contribute to collaborative decision-making.

© 2014 IBM Corporation

Figure 7

1 From Reactive to Proactive Decision Making4/17/2014

Source: IBM

IBM enables this through three complementary applications:

IBM Oncology Care Edit, providing clinical guidelines from experts. This begins by assessing the patient and the development of the illness. It then suggests a treatment programme based on guideline recommendation. A physician’s medical decision is said to adhere to the guidelines if it complies with a particular guideline recommendation. This is perhaps most valuable in oncology, where a particular tumour can have up to 350 unique clinical presentations and more than 1000 unique treatment options.

IBM Oncology Care Guide, aimed at creating a platform for collaborative decision making for the physician. It seeks similar disease instances for physicians as benchmarks. It assesses alternative treatments and related outcome based on patient similarity, comparing predictive results for each treatment based on outcome and cost for similar patients.

IBM Oncology Care View, which allows retrospective reviews of past care decisions for the ward manager. Text analytics are used to compare documented presentation with performed treatment; and a gap analysis is undertaken with diagnosis and recommended treatment.

These processes are designed to provide evidence-based guidelines for rule-based decision support. They collect data from past decisions to assess how well it can be used in guidelines and treatments. Analysing and learning from this past performance can improve future performance and allow learning from mistakes. IBM works closely with the Istituto Nazionale dei Tumori in Milan towards developing its oncology care platforms.

Figure 6: IBM‘s positive feedback loop


Designing digital interventions using behavioural economics

Dominic King, Clinical Lecturer in Surgery and Behavioural Economist, Imperial College London

Behavioural economics is attracting policy makers’ attention in countries including the UK, France, Australia and the USA. By incorporating insights from psychology with the laws of economics, behavioural economists have demonstrated how people’s behaviour can be strongly influenced by small changes in the context or environment in which choices are made. The basic insight of behavioural economics is that human behaviour is not guided by logic of a supercomputer, but is determined rather by our fallible and very human brains. Psychologists and neuroscientists have recently converged on a dual process model of decision-making. On one side the automatic system (System 1) provides fast, unconscious, intuitive, decision-making often based on mood or emotion. On the other, the reflective system (System 2) is slower, conscious, reflective, and rational. Behavioural economists are now consulted at the highest levels of government and industry, in an effort to target automatic decision-making and move away from targeting rational processes through information and price signals. Approaches targeting automatic processes are popularly called nudges after the influential book Nudge by Richard Thaler and Cass Sunstein.

The mnemonic MINDSPACE (messenger, incentives, norms, defaults, salience, priming, affect, commitment, and ego) seeks to capture effects acting largely but not exclusively on automatic processes. Norms, for example, suggests we are strongly influenced by what others do; while default suggests individuals go with pre-set options (prompting the use of ‚opt out‘ rather than ‚opt in’ defaults for choices such as organ donor cards). MINDSPACE was developed by the UK Cabinet Office and is widely used across the private and public sector in the UK.

Cue BehaviourMessenger We are heavily influenced by who communicates information to us.Incentives Our responses to incentives are shaped by predictable mental shortcuts

such as strongly avoiding losses. Norms We are strongly influenced by what others do. Defaults We ‘go with the flow’ of pre-set options.Salience Our attention is drawn to what is novel and seems relevant to us.Priming Our acts are often influenced by sub-conscious cues.Affect Our emotional associations can powerfully shape our actions.Commitments We seek to be consistent with our public promises, and reciprocate acts.Ego We act in ways that make us feel better about ourselves.

Source: Institute for Government and UK Cabinet Office

There is huge interest in applying insights from behavioural economics to influencing health-related behaviours. This is because the consequences of suboptimal decision-making is so substantial both in terms of morbidity and mortality and also financial costs. Delivering behaviour change interventions on a population scale may be aided by the increasing ubiquity of smartphones and tablet devices. The majority of people in developed countries (and increasingly low- and middle-income countries) now have access to these devices and they are usually with people at work and at home. These devices can be used to both monitor health and deliver healthy nudges. Interventions may be as simple as a text message or something much more complex. Smartphone apps are now being widely used to improve outcomes in areas including medication adherence, diabetes and smoking cessation.

At Imperial College London we have shown how MINDSPACE interventions delivered over mobile phones can help adolescents maintain weight loss after attending residential weight loss camps. We have also shown that a simple change in the text of an SMS message can significantly decrease the number of people who miss appointments in public hospitals in London. There is an opportunity to do much more.

Figure 7: MINDSPACE

The nine most robust effects that operate largely, but not exclusively, on the automatic system affecting human behaviour.


Using predictive analytics to create pre-approved life insurance

Edward Leigh, Innovation Lab Lead, Protection, Aviva Life & Pensions UK Limited William Trump, Predictive Underwriting Consultant, L&H Products, Swiss Re

There have been fears that the use of predictive analytics, combined with the resources now provided by big data, could be used by insurers to restrict or select their risk pool. That has not been the case; but insurers have been using predictive analytics on the marketing side, most notably with an application to life insurance.

Life insurance is an opaque product that frequently puts consumers off. They do not want to think of the consequences – death – and do not want to be faced with the hassle of form filling and underwriting. Yet most life insurance applicants are healthy. Predictive analytics is being used to pre-approve life insurance products for certain individuals and provide them with greater clarity and ease of process in acquiring life insurance.

“Life insurance is

complicated”

“It’ll take ages to

apply”

“Life insurance

isn’t for me”

“It’ll be really

expensive”

“I don’t like

declining

customers”

“There are lots of

complicated

words”

These insights can be used to

reduce the amount of traditional underwriting

(where there is data)

Predictive underwriting: the use of non-medical

data held on customers to reach a view

on their health

And we are in a data rich environment

Similar challenge faced by banks with loans

and credit cards

But most customers are in good health

Current sales process for life insurance is a

barrier for customers and staff

Easy and

straightforward

Quicker and

slicker

Personalised

communications

Actual price, not

indicative price

Pre-approved

customers to

boost confidence

No medical

terminology

Source: Leigh, E., Trump, W. (2014)

The data being used in the predictive modelling comes from Aviva‘s banking partners. The data includes over 150 variables attached to bank accounts; for example: age, occupation, house owner, direct debits, credit card payments and bank ATM withdrawals. Together, these predictors form the algorithm that can score any banking customer in terms of “likelihood to be given standard rates at underwriting” – that is, that they are healthy.

The result is a one question questionnaire (whether the respondent has cancer or diabetes). It helps front line colleagues, with consultations falling from as long as 90 to as quick as 15 minutes, as well as being simpler. It helps customers engage with life insurance quickly and easily. The success of life products could see similar pre-approved techniques being rolled over to lines such as critical illness.

Figure 8: A case study for pre-approved life insurance: the problem (red), the logic (grey), the aim (green)


Panel discussion and concluding remarks

Martin Denz, Head and Medical Director, sante24 – santémed Health Centres Switzerland, and President, Swiss Association for Telemedicine and eHealthAdrian Ionescu, Professor of Nanoelectronics, École Polytechnique Fédérale de Lausanne (EPFL), and Chair, Guardian Angels InitiativeDaniel Ryan, Head Population Risk & Data Analytics R&D, Swiss ReEffy Vayena, Senior Fellow, Institute of Biomedical Ethics, University of Zurich

Moderated by Christoph Nabholz, Head Business Development, Swiss Re Centre for Global Dialogue

We know, and increasingly the public is more aware, of the ability of technology to capture increasing amounts of our health data. Maybe that can be turned into a simple-to-understand health score that few would challenge. What, however, if the data concerns potentially serious underlying health issues? Can the data analysis be trusted?

In order to win trust, patients and doctors have to be consulted from the earliest stages of data monitoring, providing input and gaining trust in the system. Even for those with a big online presence, health data is one of the most sensitive subjects among consumers. Security will be a key concern. Increasingly it will not only be software security but hardware security. Trust is equally important on the side of insurers. Consumers are in large part fearful for their health data because they worry that insurers will use it against them. Insurers have a large job on their hands to persuade their customers that their medical data can be a force for good.

There is a further danger of patients being overwhelmed by data. Were an individual to discover the chances of them having a fatal heart attack, how would they deal with this information? Patients need a navigator, someone with a holistic view of their health who can guide them around the data maze. That could easily be the family general practitioner (GP); and the digital age could see us drifting back towards the GP model. Ironically, one of the current difficulties facing GPs is that in inputting electronic data they are sacrificing patient interaction. Another drawback to the GP model will be the increasing incidents of co-morbidity among older patients. This trend may require multiple specialists who must be coordinated in their output.

Data will provide cold analytics and objectivity; but human beings are not always coldly analytical. Learning better health behaviour, for example, has an emotional component. Individuals need to feel emotionally engaged before they act. One interesting recent development has been the website ‘Patients Like Me’, where individuals share their own subjective responses to various treatments.

The potential for biomonitoring to forward medicine is considerable. Evidence-based medicine is not based on population findings, but a narrow cohort of study subjects. Those subjects are analysed at a specific time point, normally in hospital. Dynamic data on whole populations, provided by biomonitors, could turn the medical approval process on its head; and in so doing, win greater public trust. Insurance companies could reward those who are willing to provide their structural health data, particularly if the data is endorsed by a doctor. New data bases need to be properly incentivised, such as Living Labs and other schemes.


Panel discussion and concluding remarks

Concerns for data security could lead to the danger of data becoming placed in silos and not shared to provide a more holistic picture of an individual’s or a population’s health. One possible means to better sharing would be to entrust an individual’s data in their own databanks – allowing the individual to share their data as they wish.

However, it would be equally unwise to suggest that data offers a healthcare panacea. Risk will still be entailed. Equally, it would be wrong to think that medical treatments can be reduced to a set of standardised steps. Every patient is different; and there is a little art in the way a doctor prescribes treatments, not just science. Data is a powerful tool, but needs to be used alongside a human touch.

The day began by suggesting that data would allow prognosis rather than diagnosis in the field of health. The conclusion has to be that the potential is there. New technologies exist in abundance; while new data modelling techniques can take advantage of existing data sources. However, concerns over the appropriate use of data and the appropriate approvals for the use of data clearly exist. Trust will be key to both of these factors. The balance must rest with the consumer: they must feel the benefit; equally they increasingly may feel responsibility with regards to data sharing.


Organisers

The Swiss Re Group is a leading wholesale provider of reinsurance, insurance and other insurance-based forms of risk transfer. Dealing direct and working through brokers, its global client base consists of insurance companies, mid-to-large-sized corporations and public sector clients. From standard products to tailor-made coverage across all lines of business, Swiss Re deploys its capital strength, expertise and innovation power to enable the risk taking upon which enterprise and progress in society depend.

The GDI Gottlieb Duttweiler Institute is a non-profit organisation conducting scientific research in the social and economic fields. The institute studies mega-trends and countertrends and draws up scenarios for the future. The GDI is also a meeting place: it hosts leading thinkers and decision-makers at its conferences, and it makes its spaces and infrastructure available for corporate or private events.

The Swiss Re Centre for Global Dialogue is a platform for the exploration of key global issues and trends from a risk transfer and financial services perspective. Founded by Swiss Re, one of the world’s largest and most diversified reinsurers, in 2000, this state-of-the-art conference facility positions Swiss Re as a global leader at the forefront of industry thinking, innovation and worldwide risk research. The Centre facilitates dialogue between Swiss Re, its clients and others from the areas of business, science, academia, and politics.


© 2014 Swiss Re. All rights reserved.

Publisher: Swiss Re Centre for Global Dialogue

Author: Simon Woodward

Photography: Wolfgang Jastrowski, iStock

Unless clearly stated as a view of Swiss Re, the comments and conclusions made in this report are those of the authors and are for information purposes only. Swiss Re, as editor, does not guarantee the accuracy and completeness of the content provided. All liability for the accuracy and completeness of or any break of confidentiality undertakings by this publication or for any damage resulting from the use of the information contained herein is expressly excluded. Under no circumstances shall Swiss Re or the entities of the Swiss Re Group be liable for any financial or consequential loss relating to this publication.

[email protected]

www.swissre.com/cgd

Swiss Re Centre for Global Dialogue Gheistrasse 37 8803 Rüschlikon Switzerland

Telephone +41 43 285 8100 Fax +41 43 282 8101 [email protected] www.swissre.com/cgd

© 2014 Swiss Re. All rights reserved.

05/14, 250 en

swiss re - center for global dialogue report: healthcare revolution: big data and smart analytics

Healthcare

big data

better data

better healthcare

data platforms

healthcare analytics

new healthcare revolution

digital health revolution

common data