KANT Report: HEALTH

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

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.

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.

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.)

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.

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.

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:

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.

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.

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

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

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.

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.

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.

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.

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

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

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.

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.

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

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.

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

KANT Report: HEALTH

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