Kant health report


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Kant health report

  1. 1. KANT BerliN HealtH The reporT
  2. 2. TABLE OF CONTENTS 1. Introduction How to read this report 2. Trends Key developments Software and mobile applications. Hardware and self tracking gadgets. Data sharing Telemedicine Augmentation 3. The changing environment of health care Tracking Personal Data Self-managing your illness Managing medical service providers 4. The Patient Experience Changing behavior through motivation Dealing with sensitive data 5. Software Apps Fitness Tracking & The Quantified Self Health management Symptom analysis & background information Big Data changes how medical software works Software for medical service providers Electronic Health Records Open-source Electronic Health Records Patient-controlled EHR's Developing software in a regulated environment 6. Hardware Personal Tracking Shaping Outcomes Easing the Burden 7. Key Challenges and Benefits Challenges Privacy and Confidentiality Security Benefits A more complete picture Efficiency & autonomy Better Research Data 8. Outlook / What to expect 9. KANT 10. About the Authors Alper Cugun Chris Eidhof Martin Spindler Matt Patterson Peter Bihr
  3. 3. INTRODUCTION This report aims to cover (primarily technology-related) macro trends in the larger health space. As "health" is a vast space or industry, our mental model – and the terminology we used – when approaching the field is this: Health: The traditional health industry as dominated by insurance and government-based health system; the heavily regulated end of the spectrum. Fitness/lifestyle/sports: The largely unregulated, more consumer-driven part of the industry that includes a wide range from the dietary to the brand-related (Nike+, etc.) to personal analytics and the Quantified Self. This is the much more vague and permeable area, and we approached it as such. The health market is huge. In Germany alone, the health industry spending (including health, prevention and treatment) contributes close to 300 billion Euros to the German GDP. This does not include the fitness & lifestyle markets. What we see is that the lines between health and fitness/lifestyle markets are increasingly blurred. This is particularly true where technology empowers individuals by giving them access to information about themselves – their body and behavioral data – and the tools to analyze that data. This is information that used to belong firmly in the hands of healthcare professionals. The umbrella terms Quantified Self and personal analytics, driven by increasingly cheap and ubiquitous sensors, are just one set of examples of where individuals take more responsibility and get more active in their health management. This goes from tracking heart rates and the speed of a run all the way to personal genome analysis. When these well-informed, active users seek out a doctor, they expect that encounter to be on eye-level. As patients, their expectations of their doctors differ wildly from former generations. The relationship between patient, medical professionals and the healthcare system is changing dramatically. This means doctors needs to reassess their positions and find ways of incorporating empowered patients into the treatment process. It also means that new business models emerge and are evaluated in a marketplace of ideas. New key players entering this market range from sports brands (Nike+) to technology startups (FitBit, Withings) to small teams of software developers who produce smartphone or mobile apps (Massive Health, etc.). And even established vendors of medical equipment are re-evaluating their strategies. The landscape is changing. With this report, we attempt to provide a map to navigate this rapidly changing space.
  4. 4. HOW TO READ THIS REPORT This report is the result of a so-called topic sprint, based vaguely on the book sprint methodology: Five authors writing for one full day in a highly collaborative fashion. We wrote it as an introduction and overview into the macrotrends shaping the health and fitness (as well as related) industries. If you work in these industries – be it on the insurance side, at a technology company, at a publisher within the sector or at a brand in the field – this is for you. We aimed at giving you an easy-to-read, quick introduction to what we see happening in the space from our specific perspectives. To learn more about these perspectives, see the author profiles at the end of this book or our website KANTBerlin.com.
  5. 5. TRENDS Two of the macro trends we see driving these developments are: (1) Data - the massive amount of data available through ubiquitous sensors (in smartphones or dedicated devices) and other connected devices (Internet of Things), made actionable by apps and web services and (2) Empowered users (rather than the more passive notion of “patients”) that are in a position to actively manage their health based on better information available from external sources (professional and academic research, experts, peers) as well as through tracking their own vital signs and behavior. These drivers both influenced and are influenced by more granular trends and influences on a technological level (both through advances in research and through adaption of emerging technologies), as well as through a changing self- perception of users and healthcare professionals. KEY DEVELOPMENTS Let’s highlight some of these trends and implementations. SOFTWARE AND MOBILE APPLICATIONS. According to a Gartner study from August 2013, global sales for smart phones exceeded sales of feature phones for the first time ever. This makes the smartphones of today the predominant computing platforms and we can expect that vendors for medical software will adapt to this trend. We also see a growing interest in web-based consumer-focussed health platforms and fully expect this trend to continue. (See the Software chapter for more detail.) HARDWARE AND SELF TRACKING GADGETS. Smartphones serve as personal computing hubs that connect sensors and fitness devices with users, each other, and the internet. The smartphone has become the backbone of the Quantified Self. (With their array of built-in sensors, smartphones also are one of the primary tools of capturing data.) It is likely that we will see the role of the smartphone increase from the fitness & lifestyle area into a tool that helps manage the patient-doctor relationship as well. On the other hand, there is a growing array of dedicated health devices, many of them connected (via smartphone or directly to the internet). (See the Hardware chapter for more detail.)
  6. 6. DATA SHARING Among users of data-intensive services awareness is growing that ownership of data is an issue worth paying attention to. Who can access data, who can share it, with whom and under which circumstances? Can the service provider share user data with third parties for commercial and marketing purposes? Can users download their data in useful formats? Are services built with data exchange and compatibility with other services in mind and offer an API? TELEMEDICINE Remote, tech-supported diagnostics ranging from “remote visits to the doctor” to sending vital signs data to the clinic for analysis. A prominent and promising example is Molly, an experimental avatar-based research project by the San Mateo Medical Center that aims to replace visits to the physical therapist for patients living remotely or unable to visit the doctor. Molly is able to watch patients’ movements through Kinect, a 3D-capable movement sensor originally developed for a game console. AUGMENTATION Augmentation of physical capabilities is certainly the yet least developed area, but also one with tremendous potential. With massive developments in the dexterity of artificial limbs and the fidelity of cochlear implant hearing aids to more subtle augmentations like insulin sensors embedded in the body, we can expect a big wave of innovation here.
  7. 7. THE CHANGING ENVIRONMENT OF HEALTH CARE The relationship between the health care provider and the patient is changing. Patients are taking matters into their own hands, and are better informed than ever before. They track personal data, have access to the vast resources on the internet, and self-manage their illnesses. While dealing with empowered patients might take some getting used to for traditional medical professionals, there are good reasons for doctors to embrace their users’ favorite health apps. TRACKING PERSONAL DATA With the advances in technology, personal data tracking is nearing ubiquity. Many individuals already track heart rate, sleep patterns, blood pressure, food intake and many more variables. They know how their bodies react to changes, and have this data ready before going to a health care professional. SELF-MANAGING YOUR ILLNESS With the vast information available online, many patients already started to take matters into their own hands. People with chronic illnesses use their own tracking devices to manage their illnesses. With smartphones, there's an app for tracking almost any variable, and patients use that to gain insight into their condition. For example, diabetes patients track their food intake and correlate that with their blood sugar levels to find out what causes spikes. MANAGING MEDICAL SERVICE PROVIDERS When individuals become chronically ill, it is also common that they manage their own team of service providers, such as doctors, insurance companies and other advisors. In case of serious illness, it is also common that the family gets involved in managing the teams. Individuals and their families use the internet to search for possible treatments and other healthcare professionals.
  8. 8. THE PATIENT EXPERIENCE The patient experience is a key part of the health ecosystem, and experiences from the traditional field of user experience are increasingly being translated into the health space. When designing services in this area, it is essential to design towards concrete goals and behaviors to sustainably influence behavior in positive ways. CHANGING BEHAVIOR THROUGH MOTIVATION Changing behaviour is often desired either by a person themselves or third parties. In both cases it is rather hard to achieve due to all manner of psychological factors. Self-Determination Theory (SDT) is a psychological framework which puts the needs and desires of the user at its centre and pro- vides a good framework to ask the right questions to have a chance of creating behaviour change. The basic needs for intrinsic motivation according to SDT are: Autonomy, allowing people to control their own directions Competence, helping people become better in things and giving them feed- back on it Relatedness, situating people’s actions in a social context Autonomy is essential because people will tend to resist doing anything based purely on external pressure. Being able to choose goals to work towards creates the necessary intrinsic motivation that is required to create behaviour change. Competence shows people how far they are into creating the change they want. Because change is hard to achieve people will have to start with small steps to reach the goal. Feedback on the progress towards the ultimate goal helps break down the task into manageable steps. Relatedness finally is a way to help people relate their actions to others and to figure out how they are doing individually or as a group. Social software in particular has a lot to add to this part of SDT. Surfacing their own and their peer groups activity and performance can work wonders. Social pressure and competition are powerful motivators. DEALING WITH SENSITIVE DATA Digital healthcare means the collection of large amounts of data. Data yields great benefits but it also carries with it a lot of issues that commissioning parties would rather ignore. The idea of having a turn-key solutions that work without hesitation is too seductive. Some issues to consider when dealing with collection and management of data:
  9. 9. Data applications in their current state often do not yield any more insight than we already have. Many applications of data yield patterns that we already knew. This is caused by a limitation in the explanatory power of data applications. Often data is processed and presented in the wrong way to be able to draw novel conclusions from it. New presentations or visualisations of data are created in a supervised fashion and the creator applies their interpretation to reach a certain goal. This interpretation guides the data collection and presentation towards a known entity and is the reason why we often get things we already knew. Data needs to be interpreted and translated into actionable options. Even though it may appear that adding more knowledge and modeling to the data processing will give us better results this is not necessarily true. Adding more factors to a model improves the results only to a certain point. After this point added information actually reduces the explanatory power because of over-modeling. Throwing more data after something therefore is not a solution to all the problems. The key is to find the right balance between quantity and quality. Reliability and incentives: Anytime the data or the results from the data are output to people who have a certain interest in the outcome or who are the subject to a power relation, they might have an incentive to massage the data in such a way that it will provide them with an advantageous outcome. This is a phenomenon that is known as ‘juking the stats’ and it has destroyed many well- intentioned projects from bearing fruit. To prevent this, service designers should be very careful – if not outright reluctant – to automate consequences from data inference and to use data in way that support power relations. Again, balancing incentives and data capturing/analysis is essential. Data often flows through complex, winding systems, in and out of institutions based on organically grown processes translated from a paper-based into the digital world. In many cases, any one institution does not have an in-depth understanding of the extent or scope of the whole system. (They might count their blessings if the system just works.) Sometimes parts of the system even change in ways that aren’t necessarily transparent to some other parties – most often the patients – who might not have consented. There are plenty scenarios in which a patient’s data could be used in ways that were previously unexpected or even undesirable for a patient. As it is nigh impossible to fully understand these systems, all the related terms of services involved and the potential implications of how their data might be used in the future, trust is essential. Trust between patient and medical institutions as well as private service providers needs to be earned and fostered. Patients should never be forced to adopt a defensive stance. The most promising way to create a favorable outcome is to legally guarantee basic rights. The data should be ultimately owned by the person it’s about. The consensus between patient and health provider should be for patients to allow use of their data in exchange for being able to take the data out and to other services whenever they choose to, in standardized formats.
  10. 10. SOFTWARE The software field in health is extremely varied. At one end we have the personal fitness and healthcare apps for the home and for personal devices: software apps with a hardware component like the Withings connected scale and even Wii Fit and software-only apps like Massive Health’s Eatery. At the other end are the patient record management systems used by primary care providers like family doctors, the similar but vastly different in scale systems used by health insurance companies, like Kaiser Permanente HealthConnect, or the NHS in the UK, and clinical software for providing information to assist diagnosis or drug prescription. To try and make sense of this, we’ll cover the two ends of the spectrum differently. At the personal end we'll talk about the specifics of developing apps for personal fitness and health at the non-clinical end of the spectrum: the kinds of apps that are not subject to regulatory approval or constraint, which is to say the kind of apps that you can use but your doctor can't. At the large-scale clinical end we'll talk about the general challenges and look at some of the successes and failures in that space. In the in-between space, developers build software for use in applications like clinical research trials, and primary care doctors buy in software for their own practices. We'll look at what it means to develop software under a complex regulatory framework, and what some of the particular needs of those users are. APPS Apps, short for applications, is a catch-all phrase software running on mobile devices. All leading mobile vendors allow for third-party applications to be installed on their platforms. Most current mobile phones are equipped with a vast array of sensors, for tracking location, motion, voice and cameras on the front and the back, and these sensors are available to software developers. Currently, there are about 25,000 apps on the Apple App Store in the category "Health & Fitness". Most of these apps are targeted at prevention and staying healthy, by tracking certain of the users' variables. A quick glance at the top lists show that the apps can be divided into the following categories: FITNESS There’s a multitude of apps that aim to assist in fitness programs, either by capturing data during the workout and analyzing it afterwards, or by giving instructions during training sessions. Differences also exist in how workouts are tracked, that is, whether the user is supposed to enter training sessions manually, or whether the smartphone app will monitor activity in the background, as it is usual in most bike or running apps. One recent development is apps that track what users do on a permanent basis.
  11. 11. A prime example of these apps is Moves, which analyzes the type of movement (biking, running, walking, or other forms of transportation) and gives their users feedback about their day and shows the extent of their baseline activity. The other big theme in fitness-related apps are instructional apps that show how to perform specific workouts. The advantage of having these instructions in an app (compared to traditional forms such as DVDs or books) is the accessibility of the instructions. TRACKING & THE QUANTIFIED SELF Also referred to as personal analytics, Quantified Self or tracking are terms described to capture the increasing ability to track body and behavioral data (pulse, heart pressure, steps taken, food intake, etc.) through sensors embedded in smart phones or dedicated devices (Jawbone Up, FitBit, etc.) or through manual data entry (for nutrition tracking apps like The Eatery). By tracking data over time, individuals get insight into their personal history with minimal effort. To learn more about the Quantified Self, we recommend the site quantifiedself.com One app that helps people track their food intake is Foodzy with which people can easily keep a food journal on their phones while other efforts have focused on people taking pictures of their meals for either others to validate or for the system to determine automatically based on image processing and statistical inference. There are tools and websites to track more or less anything imaginable, from mood and exercises to stool and sex – there is hardly a bodily function that is not subject to digital tracking. HEALTH MANAGEMENT For diabetics, tracking their body functions is nothing new, but getting automatic measurements of their insulin levels sent to their phone or even automatic injections can make their lives a little bit easier. For individuals that use regular medication, apps and devices like the GlowCap help users manage their taking of medicine by setting up reminders and alarms. Women can track their periods via apps and connected thermometers and can use that software in helping them determine their fertility. SYMPTOM ANALYSIS & BACKGROUND INFORMATION Apps can also enable patients to self-diagnose, or to support them in their evaluation of potential health issues as well as to better inform themselves. Ranging from apps helping with diagnosing sources of pain or identifying skin diseases all the way to guiding young parents-to-be through all stages of
  12. 12. pregnancy, there exists a plethora of applications on any particular health issue. The methods used are manifold. Some require the user to answer questions, others automate the process further by, for instance, analyzing photos of the skin to determine chances of skin cancer or other less critical diseases. BIG DATA CHANGES HOW MEDICAL SOFTWARE WORKS For finding trends on a larger scale, social networks and big data analysis allow for unprecedented prediction of trends. This includes analyzing flu outbreaks based on twitter data or Google searches, or analyzing drugs experiences based on crawling websites. Cloud services like Amazon Web Services make it possible to rent a lot of computing power for any desired amount of time, thus allowing analysis of large datasets without having to build and maintain expensive hardware infrastructure. Startups like 23andme offer genome sequencing to individuals and thereby increase the analytic base for genetic research and thus make it much easier to identify potentially harmful variations in the human genome. SOFTWARE FOR MEDICAL SERVICE PROVIDERS The legal, regulatory, and structural differences between different countries, even within the EU, mean that there are a lot of smaller, local providers of Electronic Health Records (EHR) and similar software for primary care providers like family doctors, and even small hospital groups. There are too many players to cover here, but for more information you may well find that local news media which cover healthcare will have a decent overview, for example The Guardian's UK healthcare coverage. What's important to note is that many of these companies share similar origins: spun off from software built in-house by healthcare professionals for a specific practice. They also seem, as a general rule, to be modelled after traditional enterprise-y software: powerful, but complex and hard to understand and use. ELECTRONIC HEALTH RECORDS The core part of these systems are concerned with capturing, storing, and presenting patient data. The aggregated data for a patient is generally called an Electronic Health Record (EHR). EHR’s consist of everything from notes taken by doctors, data logged by heart rate monitors, to X-Ray images. Many governments are aggressively lobbying for the adoption of EHRs. The US government has offered incentives to early adopters working for Medicare, but will penalise providers after 2015 if they are not using EHR's. The UK's NHS tried to implement a central, national, EHR system which failed
  13. 13. after several years at a cost of £12bn. One of the key reasons as stated by the UK Department of Health: “... we need to move on from a top down approach and instead provide information systems driven by local decision-making.” We see a trend for open-source systems to be adopted by large organisations or governments: The US Veterans Health Administration system, VistA_EHR, is open-source, and has been adopted by the Jordanian government. Parts of the UK's NHS are now considering adopting it, after the failure of the national system. OPEN-SOURCE ELECTRONIC HEALTH RECORDS We see are a lot of standards around EHR at ISO (ISO 18308 and many other standards under ISO/TC 215), CEN (EN 13606), and ANSI (many HL7 related standards). Some are concerned with how to capture and store data, some with what to capture, and others with how to transmit that data between organisations. There are, unsurprisingly, many interoperability problems: While many standards specify data formats and structures, the way different systems use those formats can vary widely and often mean that two systems which use the same format to store or exchange records can't meaningfully do it. One organisation's records are gibberish to another's systems. Open-source initiatives like the openEHR Foundation are attempting to solve these problems by providing specifications and implementations, intended to provide the building blocks of distributed and interoperable systems. This is still an organisation-centric view of the data though: Healthcare providers hold patient records for their own use. Patients themselves may well be unable to access most, or any, of the data held about them. PATIENT-CONTROLLED EHR'S The Australian government recently launched a system called The Personally Controlled eHealth Record System, a centrally-provided system that allows patients who opt-in to the system to control who can access, or even know about, their personal data. Currently it's only summary data, and full records are held by individual institutions still, but it's indicative of what we think the future will hold: Encryption used to secure personal data and to manage access to it Cryptographic signing used to ensure that people's data hasn't been tampered with, either by themselves, by institutions or by third parties Governments providing cloud-like storage and APIs for these records The UK's NHS central IT provisioning department N3 already provides an NHS- specific cloud compute platform, ideally placing them to offer a central API- oriented service.
  14. 14. Personally-controlled EHR's which allowed for patients to input data too opens the door to formalising and integration many of the Quantified Self practices we're seeing emerging elsewhere, as well as offering the possibility of better care delivered in part through better data: both richer and more frequently sampled. DEVELOPING SOFTWARE IN A REGULATED ENVIRONMENT There are several distinct areas of regulation that might affect software, and of course these will vary by jurisdiction. In the US, for example, software for keeping records related to clinical research trials of new drugs are subject to the FDA's Title 21 CFR Part 11, while software to run on a 'Medical device' must meet IEC 62304. EHR systems must comply with a host of standards under Title 45 CFR Part 170. These standards typically mandate that their various criteria must be demonstrably met. This usually means that you have to be able to document what you say the code will do and trace through from that to the relevant part of the code, along with proof that it does do what you say. While the standards don't generally explicitly mandate a particular way of working, most were written while traditional 'Waterfall' style software development methodologies were the norm, and often appear to endorse that. However, the basic requirements of feature, or point of compliance, tied to the code and proof of its function can also be thought of as akin to the 'outside-in' approach of Behaviour Driven Development, combined with a rigorous approach to documentation. One of this report's authors consulted on a project and developed documentation using this approach, and the initial results were very promising. Given the documentation and QA burden traditionally associated with regulatory compliance, an approach based on generating compliance documentation as a by-product of product design and development, rather than with a post-hoc scramble, has the potential to yield significant competitive advantage.
  15. 15. HARDWARE With the onset of cheaper microprocessors and increasingly pervasive connectivity, the health environment is seeing massive changes in terms of the capabilities of medical equipment. Following the trend line of consumer devices and utilizing advances in mobile computing and information display and capture, healthcare has so far only seen the tip of the iceberg when it comes to technological adoption. There are, however, promising steps to bring labour- intensive tasks in medical care up to date and adopt more automation. A lot of data can already be captured passively by connected devices, and increasingly, that data is relevant for the well-being of individuals, be it in preventative care or under clinical conditions. More data, captured passively, can also mean a better understanding for a patients baseline data and well-being, as well as freeing up capacities that would otherwise be devoted to manually capturing vital signs from patients. PERSONAL TRACKING When talking about connected devices in the healthcare environment, the same distinctions as touched upon earlier apply: there are big differences in speed of execution and exploration of potentially interesting avenues depending on whether companies or vendors target individual users in the unregulated fitness market, or whether they target medical professionals which operate under conditions of regulation, peer-review and cost-pressures by the health system. Participants in this segment of the market is much less likely to lend themselves to experimentation. Any company trying to enter this market needs to make a conscious decision as to which part of this spectrum which does not have a clearly defined boundary they want to fall on. Recent years have seen an onslaught of personal fitness devices which have developed to measure far more than just fitness performance. What started, in the consumer market, with specialised products for runners, such as the Polar Heartbeat monitor or the Nike+iPod has matured into an industry that far extends sports, but still notionally focuses on fitness and the well-being of individuals. Companies likes Withings have started manufacturing a whole range of devices that ease the consistent collection of vital signs, be it with their debut product, the Withings Smart Scale, which makes it easier to track the development of an individual’s weight, or more recently with a connected Blood Pressure monitor, that automatically uploads its measurements and makes it thus possible to easily have longitudinal data on a crucial health indicator. In conjunction with the rise of the “Quantified Self Movement”, a lot of pervasive activity trackers have come to market as well. What started with the FitBit as a means to regularly remind info workers to take breaks and exercise, and to make data about their actual exercise available to them, has blossomed into a varied product range, that capture all kinds of vital signs, with the most comprehensive probably being the Basis watch which sports sensors for heart rate, skin temperature, perspiration and a 3-axis accelerometer and thus claims to give insight into general activity, stress levels and quality of sleep.
  16. 16. SHAPING OUTCOMES Connected devices aren’t necessarily restricted to measuring and collecting data, however, but can drive behaviour by the actions that get triggered by them, and thus change outcomes. One of the best illustrations in this regard is the Vitality GlowCap – basically a connected cap for pill bottles and a companion base station. The GlowCap monitors whether medication is taken in adherence with the schedule, and will start to blink and make sounds if the patient has forgotten to take their medication. If, after two hours, the patient still hasn’t taken their pills, it will start a social feedback mechanism of first having a call made to the individual by Vitality’s service center and then alerting previously specified relatives. It also gives healthcare professionals much better insight into the compliance of their patients. The GlowCap is a good example which sits right at the intersection of privately managed, unregulated “gadgets” and regulated “medical equipment”. In the regulated health care industry, similar approaches will be fruitful. Automatically having a pulse or blood pressure reading committed to a patient’s medical history file without human intervention would free up time with medical staff and reduce the chance of mistakes in transcribing the measurements. In a scenario of hospitalized patients, this could go even so far as the patient, by means of a Bluetooth Low Energy-equipped bracelet, identifying himself to the measurement device and thus reducing the probability of mistakes even further. EASING THE BURDEN Making the life of diabetes patients easier and safer is another avenue which is being explored. The AgaMatrix Glucose Meter, for instance, is an iPhone peripheral with a companion app. The measurement of your blood glucose levels works just as usual with this device. It’s ability to utilize the capabilities of its host mobile computing platform, however, allows it to combine the measurement with all kinds of other data, and automatically log it for you. It’s not especially hard to imagine an insulin injection kit which automatically adjusts the dose to be administered on the last reading and the historical trendlines. It could also be possible to use additional information, such as workout data, or logged meals consumed. A more granular, and more accurate, view into longitudinal blood glucose data would also allow healthcare professionals to make much better determinations about the current status and future prognosis of an individual patient.
  17. 17. KEY CHALLENGES AND BENEFITS CHALLENGES More integrated systems of data capture and storage require different approaches to data management, disclosure and security. It is not sufficient anymore to just assume security as it could be in times of locked-away paper records. Platforms that actuate on specific parameters, like pacemakers or insulin pumps, need to be sufficiently secure as to avoid security exploits that could cause bodily harm. PRIVACY AND CONFIDENTIALITY One of the main concerns regarding Electronic Health Records is who gets access to these records. After all, information about a person’s health condition tends to be one of the most private and guarded sets of information, often protected by special patient-doctor privilege which prohibits medical practitioners to disclose even the faintest bits of information about the status of particular patients without court warrants. However, there are multiple third parties which have vested interests in getting access to data about patients, be that on individuals or in anonymized aggregates. Special care needs to be given to latitudinal data that tries to combine a breadth of data across large parts of the populace to look into potential early warning signs for the onset of specific diseases to not accidentally de-anonymize and make individuals identifiable. (As studies have shown, relatively few data points can be sufficient to identify individuals.) These considerations need to be accounted for even when developing applications that fall not strictly within the realm of regulated health care. As responsibilities and management of healthcare fall more and more inside the purview of individuals, the concerns that traditionally only applied to medical professionals do, too. SECURITY As with any technological development that relies on computerized equipment, safety and security concerns need to be taken serious. Even more so as a lot of medical equipment has the capability to cause actual bodily harm or death if malfunctioning or compromised. Examples exist of poorly secured pacemakers or insulin pumps that were almost trivial to exploit. Luckily, those exploits have not been visible in the wild. However, the question of security is one of the most salient in the discussion around a changing healthcare environment. It is important to note that the security of any particular technical device does not rely on the security features of that device alone. Accuracy of data must be
  18. 18. assured, if that data is relevant to the operation of a device, and it might be necessary to establish data provenance. Security is also important in terms of measuring devices. Professionals need to be able to rely on data collected by devices, and thus these devices themselves need to be secure and tamper-proof. The same is true for data transmission and storage. BENEFITS A MORE COMPLETE PICTURE As we gather more data, a more complete picture of our health emerges. This goes both for the individual and the societal level: Aggregate, longitudinal data paints a much clearer picture of the state of our health over time. This kind of data is priceless for research. To enable individuals to make most of this data, app developers and service providers need to make sure they capture, analyze and present the data back to the user in actionable form. In other words, it needs to be clear at any time not just what the data means – which story it tells – but also what the options for action are on the individual level. Intelligently designed feedback loops are essential. At the same time, ongoing data analysis might lead us to discover potential health problems before a routine check-up would have surfaced them. As such, health data gains additional importance in the context of prevention. EFFICIENCY & AUTONOMY Technologically enhanced prevention, treatment and rehabilitation can – by empowering patients and doctors alike – increase efficiency and lower costs of treatment. It can save further costs by enabling patients to take care of themselves better, or by supporting their families to care for them. The GlowCap is a powerful example of a relatively simple device – an internet-connected bottle pill – that increases compliance, thus increasing chances of successful treatments. As patients take more responsibility of their own health before, during and after treatment, costs are saved for the health system while at the same time allowing patients to lead a more independent life for longer. At the same time, automation of repeat tasks should lead to cost-saving across the whole sector, and enable a more purposeful despatch of medical staff. Electronic Health Records should, when rolled out properly, enable much quicker diagnosis on the basis of available medical history of a patient. Availability of data gathered from personal health tracking would enable practitioners access
  19. 19. to more data about the patient and thus make diagnosis even speedier and more accurate. BETTER RESEARCH DATA The breadth of data becoming available spells a boon for medical research, as it drastically increases the scope of what can be researched. The first effects of this can be seen in Genome analytics, but the whole field of medical research is bound to be changed by big data analytics.
  20. 20. OUTLOOK / WHAT TO EXPECT The trends we outlined in this report are clearly set to continue onward. More devices will reach the market that will track ever more of an ever larger number of people. The devices highlighted above are in many cases initial proof of concept versions that reach only very limited numbers of people but as this technology matures and the market becomes more accustomed to this type of technology it is due to reach higher and higher levels of market penetration as well as product maturity. These devices will also increase the data shadow that everybody casts with each device recording and transmitting data. All of this data will need to be stored somewhere and processed. The storage solutions that are currently most popular will probably be considered too insecure and too limited. At the point where just about everything is recorded, the architectures will need to change. These large volumes of data will yield better insights but will also prove to be immensely difficult to grasp as a whole. Combining disparate data streams in a way that does not lose the goodwill of the users is going to be the tightrope act that players in this industry will need to walk. Breaking down the data into feedback loops that present the users with concrete options for action is key. Overall spending on healthcare is rising. At the same time, it is highly doubtful whether these new technologies will be equally beneficial for all people: Financial aspects are just one dividing line that separates from those benefitting from those that don’t; Different speeds of adoption for new technologies (the so- called Digital Divide) is another. On the one hand automation and technology hold the promise to be able to do more for more people at lower cost. On the other hand technology increases the scope of what is possible so that its full application will probably be prohibitively expensive for all but a small elite. While we can expect improvements in production and development to increasingly become more accessible both within the richer industrialised states and and the global South, we do not currently see a rising tide insofar as it does not lift all ships equally quickly. Overcoming this barrier to entry and making emerging technologies count for a wider part of society will be a key challenge for the coming years. In the meantime, we will need to work towards positive change in both policies and cultural acceptance of data-intensive health applications to fully realize the potential of technology in the health space.
  21. 21. KANT KANT, the Kreuzberg Academy for Nerdery and Tinkering, is a Berlin based hub of creative technologists. KANT’s skill base covers everything in the fields of emerging technology and user centered design and members of KANT do work in all stages from conception to design and implementation and also in the fields of analysis, review and commentary. This report is a product of our analysis branch. Want us to take a crack at some of the challenges you are working on? Reach us at: Web: kantberlin.com Email: info@kantberlin.com Twitter: @kantberlin
  22. 22. ABOUT THE AUTHORS KANT consists of five collaborators: ALPER CUGUN Alper Çuğun M.Sc. is a designer, developer and publicist active on the focal point of technology, design and society. After graduating from Delft University of Technology, he has been active in the Amsterdam startup scene. Having built up an extensive network there, he moved to Berlin. Alper currently is partner at Hubbub, one of Europe’s leading studios for the design of games and playful systems. Hubbub helps organizations with understanding and inventing new games and playful systems, primarily in the space of social issues, creativity and collaboration. Alper is also on the board of the Open State Foundation, where he oversees the creation of open data and open government policy for the Netherlands and the rest of Europe. CHRIS EIDHOF Chris Eidhof is a software developer and entrepreneur originally from The Netherlands. He currently focuses on building iPhone and iPad products, from strategy down to implementation. Together with Peter and Matt, he organizes UIKonf. He is one of the founders of objc.io, a high-profile magazine about Objective-C. MARTIN SPINDLER Martin Spindler is interested in how digital technologies feed back into the world — how the bits start to shape the atoms. As such, he works as a strategy consultant, speaker and writer focussing on the Internet of Things and Smart Energy. Martin is Co-Founder of Internet of People, an international consultancy network centred on the Internet of Things, and the Cognitive Cities Conference, which looks into how the pulse of cities changes once they get equipped with “smarts.” Martin studied Political Sciences, Economics and Islamic Studies at the University of Heidelberg.
  23. 23. MATT PATTERSON Matt has been building for the web for over 10 years, doing everything from web design and front-end development all the way through to back-end development. He was one of the founding developers of the UK’s Government Digital Service, has worked on critically-acclaimed video games, is involved with the Rails Girls movement, coaching aspiring developers, and has been doing a lot of work with data and visualisation. PETER BIHR Peter explores emerging technologies, their implications, and the people driving them. This translates into a number of things: digital strategies, a great network of collaborators, curation services of sorts, and the occasional product or prototype. He is an independent digital strategist and consultant, serves as Program Director for the conference NEXT Berlin, and co-founded Makers Make. He also co-organizes events like UIKonf, Cognitive Cities Conference, TEDxKreuzberg and Ignite Berlin